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A Cooperative-Control-Based Underwater Target Escorting Mechanism With Multiple Autonomous Underwater Vehicles for Underwater Internet of Things
Abstract
Escorting a moving object in a subsea environment with cooperative autonomous underwater vehicles (AUVs) is a typical subject in Underwater Internet of Things (UIoT) applications. It involves two issues that should be studied. First, a mobile task assignment method is required to lead the AUVs to the escorting positions; then, a formation control scheme should be utilized to safely escort the moving object to the destination. Accordingly, in this paper, a comprehensive target escorting mechanism called the cooperative-control-based underwater target estimating mechanism (CUTE) is proposed, which includes a belief function method-based self-organizing map algorithm for task assignment and an artificial potential field-based formation control method. The task assignment method aims to establish smooth routes from the AUVs to the escort position while flexibly avoiding obstacles, and the formation control method aims to improve the monitoring coverage on the escort route by rotating the formation structure while following the moving object. Simulations show that the proposed CUTE method may be very practical in underwater target escorting scenarios.
... With advances in communication, computation, and control technologies, it is now becoming possible to deploy intelligent systems in the form of a heterogeneous team of autonomous vehicles with different capabilities (sensors and actuators) to collectively accomplish complex missions and tasks, which are distributed in time and space and may not be possible to be achieved individually [1][2][3][4]. A cooperative control strategy not only can handle such complex scenarios, but also could significantly reduce the cost, enhance the resilience of the overall system, and improve the team functionality through sharing resources and distributing tasks and loads [5][6][7][8]. Nonetheless, multi-agents cooperation introduces challenges and complexities including but not limited to task decomposition, task assignment, communication, task execution, and task monitoring [9]. A common method for tasking multi-agent systems is to employ scheduling mechanisms [10]. ...
... , M, where t io = 1 during the time that R i is assigned to perform one of the actions A * , which takes R i for △t i * time units. (7) We define an operation R i | con which checks if the agent R i satisfies the condition con at its current state, where the condition con can be a condition for a task, i.e., C j , or a precondition for an action, c ikp . ...
... for c ikp in c ik do 7 T seq ij → Sequence(T seq ij , c ikp ) // sequence BT with the condition of action ...
... U NDERWATER acoustic (UWA) communication is considered one of the most complicated environments to deal with due to its doubly selective channel [1]. That nature requires inserting more overhead packets to track the existed effects leading to a dramatic deterioration in the spectral efficiency of UWA communication. ...
... , G, into two subgroups P via M r -PAM modulation resulting s g conveyed through the active subcarriers K o . Hence, we have P g = P (1) g + P (2) g such that ...
... The index selector maps P (1) g bits of each group g into an index set of active subcarriers A g , which can be written as follows: 1 , a g,2 , . . . . . . ., a g ...
This article presents orthogonal frequency division multiplexing (OFDM) that is based on discrete Hartley transform (DHT) as a modulation transform for underwater acoustic (UWA) communication instead of the conventional discrete Fourier transform (DFT). The proposed DHT-OFDM scheme has the advantages of reducing the pilot overheads required to track the UWA channel effects by 50%, increasing the robustness against the carrier frequency offset, and relaxing the system complexity. The receiver design of the proposed system is based on DFT instead of DHT to enable low-complexity estimation and equalization tasks and to overcome the issue of intercarrier coupling, which deteriorates up to half of the existed DHT-OFDM data rate. For more spectral efficiency harvesting, the proposed underwater DHT-OFDM has been extended to cooperate with the index modulation. Simulation and real experimental results have been conducted to demonstrate the outperformance of the proposed schemes against the benchmarks in terms of bit error rate, Doppler shift, spectral efficiency, and system complexity.
... In 2021, Zhang et al. 19 put forth a thorough target escorting system known as the CUTE framework, which incorporates a formation control technique on the basis of an artificial potential field and a task assignment heuristics on the basis of the belief-function-technique. Simulations demonstrate that the suggested CUTE approach might be particularly useful in cases of underwater target escorting. ...
... Zhang et al. 19 CUTE framework It is mostly helpful in underwater target escorting. ...
Underwater IoT is incredibly helpful in monitoring a variety of jobs, from instrument monitoring to climate recording, from pollution management to natural catastrophe forecasting. Nevertheless, there exist various issues that have an impact on a network's efficiency such as the formation of void holes, excessive EC, and low PDR. As a result, the IGOR protocol is suggested in this study to increase PDR by reducing the percentage of void hole occurrence. The developed routing protocols' scalability is also examined. Here, the parameter optimization for the EC minimization and PDR maximization is performed by a meta‐heuristic optimization algorithm referred to as TSA. In order to verify that the suggested protocol is EC‐optimal by calculating the viable areas. In addition, suggested protocols are evaluated against contemporaries' benchmark routing protocols. The outcomes of the simulations clearly demonstrate that the suggested routing protocols obtained greater PDR than the current techniques. Additionally, there exists a reduction in the ratio of void hole incidence. Comparative research reveals that suggested routing protocols outperformed benchmark routing protocols by 80–81% in PDR. Further, the suggested routing procedures reduced the frequency of void holes by around 30%.
... Accordingly, autonomous marine vehicles (AMVs) are rapidly developing and are widely used in practical tasks, typically the autonomous surface vehicle (ASV), autonomous underwater glider (AUG), autonomous underwater vehicle (AUV), etc., which are commonly used in seabed exploration to collect information on the distribution of underwater resources [4][5][6]. Accordingly, technical issues for controlling the AMVs, such as multi-vehicle collaboration, path planning, obstacle avoidance, etc., have been introduced in recent years to enable practical underwater exploration, and researchers have studied the innovation in terms of algorithm design and system frameworks to find optimal solutions for efficient, safe and energy-saving marine exploration tasks [7][8][9]. ...
... Determine the P G under T by sorting the frogs incrementally according to f (i); 6 Assign the frogs to M j by Eqaution (12); 7 Process I I : Local search 8 for j = 1 to k do 9 for t = 1 to t max do 10 Random selection of q frogs to form SM j ; ...
Ocean exploration is one of the fundamental issues for the sustainable development of human society, which is also the basis for realizing the concept of the Internet of Underwater Things (IoUT) applications, such as the smart ocean city. The collaboration of heterogeneous autonomous marine vehicles (AMVs) based on underwater wireless communication is known as a practical approach to ocean exploration, typically with the autonomous surface vehicle (ASV) and the autonomous underwater glider (AUG). However, the difference in their specifications and movements makes the following problems for collaborative work. First, when an AUG floats to a certain depth, and an ASV interacts via underwater wireless communication, the interaction has a certain time limit and their movements to an interaction position have to be synchronized; secondly, in the case where multiple AUGs are exploring underwater, the ASV needs to plan the sequence of surface interactions to ensure timely and efficient data collection. Accordingly, this paper proposes a heuristic surface path planning method for data collection with heterogeneous AMVs (HSPP-HA). The HSPP-HA optimizes the interaction schedule between ASV and multiple AUGs through a modified shuffled frog-leaping algorithm (SFLA). It applies a spatial-temporal k-means clustering in initializing the memeplex group of SFLA to adapt time-sensitive interactions by weighting their spatial and temporal proximities and adopts an adaptive convergence factor which varies by algorithm iterations to balance the local and global searches and to minimize the potential local optimum problem in each local search. Through simulations, the proposed HSPP-HA shows advantages in terms of access rate, path length and data collection rate compared to recent and classic path planning methods.
... Most of the existing works (e.g., [13], [45]) set f (l i,m ) as 1 if l i,m ≤ ν, and f (l i,m ) = 0 otherwise. The above design leads to the discontinuity of repulsive potential field, which causes the system dithering. ...
... Noting with (45) and (46), one can deduce the following result from (44), i.e., (47), the update weight vector can be updated by the following iteration procedure, i.e., ...
Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.
... In recent years, underwater Internet of Things (IoT) devices in sea regions have proliferated, driving a growing demand for high-speed and large-capacity communication technologies [1][2][3][4][5][6]. Against this backdrop, underwater wireless optical communication (UWOC) is a potential solution to the unique challenges of underwater environments, where electromagnetic waves rapidly attenuate while efficiently transmitting data [7][8][9][10][11][12]. ...
This study proposes a novel optical wireless communication system for high-speed, large-capacity data transmission, supporting underwater IoT devices in shallow seas. The system employs a mirror-equipped aerial drone as a relay between underwater drones and a terrestrial station, using 850 nm optical signals for low atmospheric loss and enhanced confidentiality. Adaptive modulation optimizes transmission capacity based on SNR, accounting for air and underwater channel characteristics. Experiments confirmed an exponential SNR decrease with distance (0.6–1.8 m) and demonstrated successful 4K UHD video streaming in shallow seawater (turbidity: 2.2 NTU) without quality loss. The design ensures cost-effectiveness and stable optical alignment using advanced posture control.
... Note that the forwarding node selection may lead to frequent retransmission due to the existence of underwater obstacles. Followed by this, a cooperative-control-based target estimating mechanism was proposed in [90]. With consideration of cable length, seafloor classification, angle of change of course, bathymetry, and steepness of slope, an A-star algorithm was adopted [91] to determine appropriate cable routes. ...
Underwater acoustic sensor networks (UASNs), which are formed by a number of interconnected mobile vehicles and static sensors, have emerged as a promising solution to explore and utilize the ocean resources. Typically, the localization, communication and control are the fundamental services for the applications of UASNs. Although they are closely related, the localization, communication and control issues are usually separately tackled. The separate design directly affects the localization accuracy, transmission reliability and control efficiency, especially for resource-constrained UASNs. In this regard, it is essential and necessary to co-design the localization, communication and control systems for UASNs. At present, the theoretical framework of the above integration design is still in the construction phase, and some key problems remain unresolved. Therefore, this article aims to give a survey on the integration design of localization, communication and control for UASNs. We first present the communication architecture, through which the main challenges aiming at the integration design are analyzed. After that, a holistic survey on the underwater localization, communication and control basics is provided. Followed by this, the recent advances on the integration design are given. At last, we make an outlook to the future research directions on the integration design of localization, communication and control for UASNs.
... The artificial potential field method simulates each AUV's movement in a virtual potential field to achieve coordinated formation behavior. To establish smooth routes from multiple AUVs to escort positions while flexibly avoiding obstacles, Zhang et al. [9] proposed a cooperative underwater target estimation mechanism (CUTE) that includes a self-organizing map algorithm based on the trust function method, then applied it for task allocation and formation control based on the artificial potential field method. In [10], the authors designed a distributed leader control method combining consensus theory with the artificial potential field method for multi-AUV systems with a leader AUV. ...
To address the control challenges posed by increasingly complex mission scenarios, this paper aims to develop an advanced formation control and obstacle avoidance strategy for autonomous underwater vehicles (AUVs) in SE(3). This study establishes a dynamic model for fully actuated AUVs and designs a consensus-based formation control strategy to achieve coordinated movement. Motivated by limitations of existing obstacle avoidance strategies such as local minima issues and mutual interference between formation members in high-density environments, this paper introduces a novel gyroscopic force-based obstacle avoidance method. The proposed approach leverages the principles of rotation and angular momentum conservation to enable effective obstacle avoidance while maintaining formation integrity. Simulation results demonstrate the effectiveness of the proposed methodology in achieving robust formation control and collision avoidance under challenging conditions.
... For instance, a consensus formation controller was developed in [14] for the trajectory tracking of UUV, UAV and USV networks within obstacle environment, where the block kronecker product was adopted to describe the communication topology. Followed by this, an artificial potential field-based underwater tracking controller was presented in [15] to achieve target monitoring via the cooperation of USVs and UUVs. In [16], a collaborative tracking system with UAV, USV and UUV was constructed to achieve multidomain sensing on partially submerged target. ...
Detection and tracking of underwater target play an important role in enhancing the marine sensing ability. Currently, the above mission is usually conducted in a single platform, and there is a lack of necessary cooperation between different platforms. This paper employs unmanned aerial/surface/underwater vehicles (UAV-USV-UUV) to develop a cooperation detection and tracking solution for underwater target. We first use the measure theory to construct a heterogeneous detection mode, such that the target detection probability can be maximized by adjusting the formation shape between USV and UUV. After the target is detected by the UAV-USV-UUV networks, a deep learning algorithm called
d
epth
d
eterministic
p
olicy
g
radient (DDPG) is designed for UUV to track the trajectory of target. In order to guarantee the communication connectivity among UAV, USV and UUV, a multi-step location prediction strategy is incorporated into the tracking procedure. Note that the advantages of our solution are highlighted as: 1) the cooperation of UAV, USV and UUV in this paper can improve the detection probability over the single platform system; 2) the DDPG-based tracking algorithm in this paper is more efficient for handling continuous complex underwater environment as compared with the deep Q-network. Finally, simulation and experimental results are both presented to verify the effectiveness of our solution. As such, our solution is more useful for marine engineer to remotely sense the ocean from the communication and control viewpoints.
... In the underwater network, this secure technique defends against possible threats. These approaches [19][20][21][22][23] address some security concerns and counteract some assaults, but our improved scheme validates every security requirement. ...
Water covers about 71% of the Earth’s surface, which is crucial for transportation, climate regulation, and the production of pharmaceuticals. The Internet of Underwater Things (IoUT) can detect valuable items such as minerals, metals, corals, and coral reefs. One of the crucial purposes is preventing damage from natural disasters. IoT principles helped advance plans for a new underwater network. Underwater networks suffer from some issues, including a lack of dependability, constrained capacity, long propagation delays, high processing requirements, high energy costs, and node detection with encrypted communication. Along with node identification and dynamic network setup, real-time, secure data exchange is one of our main research interests. These problems provide significant challenges for IoUT. Our proposed scheme involves a dynamic graph for network design, an Artificial fish swarm algorithm (AFSA) based on AI for node recognition, and Elliptic Curve Cryptography (ECC) for a secure communication mechanism. For underwater objects, this proposed approach is more trustworthy, safe, and resilient, preventing damage from natural disasters and being helpful for design and secure communication in maritime engineering.
... The underwater environment covers approximately 71% of the earth surface and supports approximately 90% of all life forms, making it a strategic and resource-based environment with great potential benefits for human life [1]. However, accessing the deeper floors of the ocean presents significant challenges, including poor visibility, extreme pressure, low temperature, and unstructured topography, which induce safety risks to humans and conventional manned vehicles (submarines) [2]. To overcome these challenges, researchers have proposed using unmanned underwater vehicles/robots (UUVs) equipped with intelligent capabilities to explore and exploit underwater environments. ...
This paper deals with the tracking control problem of small autonomous tethered underwater vehicles. It proposes a new extended robust integral of the sign of the error (RISE) feedback control. The proposed RISEbased extension benefits from a fuzzy inference system to automatically and online tune the parameters of the RISE controller. The resulting intelligent control scheme is named Fuzzy RISE (FRISE) feedback control. Several real-time experimental scenarios, in different operating conditions, were conducted on Leonard underwater vehicle to demonstrate the efficiency and robustness of the proposed control scheme. It was also compared with some existing controllers from the literature to show its performances.
... Research on the integration of autonomous underwater vehicles (AUVs) into the underwater Internet of Things (UIoT) has grown significantly. The UIoT has focused on underwater communication protocols, data routing mechanisms, and energy-efficient networking solutions tailored to the unique challenges of underwater environments [38]. The comprehensive architecture for the Internet of Underwater Things (IoUT) using multiple wave gliders for acoustical observation and target localization, aligns with the potential of wave gliders and acoustic techniques in underwater sensor networks. ...
... In the underwater network, this secure technique defends against possible threats. These approaches [6], [9][10] [12][14] [15][16][17][18] address some security concerns and counteract some assaults, but our improved scheme validates every security requirement. ...
Transportation, temperature control, and the production of pharmaceuticals all rely on 71 percent of the Earth's surface, which is covered by water. Valuable things, including minerals, metals, corals, and coral reefs, can be identified with the help of the IoUT, among other applications. One of the crucial uses is in preventing damage caused by natural disasters. The IoT boosted concepts for a new undersea network (IoUT). Underwater networks have many drawbacks, including a lack of dependability, limited bandwidth, long propagation delays, high processing demands, high energy costs, and node detection with secure communication. Real-time, secure data communication is a significant area of research for us, along with node identification and dynamic network configuration. IoUT faces severe obstacles in the form of these issues. Our strategy involves a dynamic graph for network design, an AFA algorithm based on AI for node recognition, and ECC for a secure communication mechanism. In order to increase discretion and provide the undersea network with adequate robust security.
... Information sharing consistently suffers high delays in the hostile underwater communication environment. 89,90 This makes it hard to establish time-sensitive cooperation between AUVs while they are drifting in ocean currents. 91 The irregularity in the seafloor topography as well as unpredictable movement of obstacles results in variations of the task execution abilities of the AUVs. ...
The 6G wireless network with its superior capabilities must be considered to accommodate the much more stringent quality of service (QoS) requirements of the numerous emerging applications and services in the future. Attempting to adhere these constraints raise various challenges that are required to be addressed and solved in order to realize the vision of a fully intelligent and automated future society. This article provides the key enabling technologies and future applications for 6G networks followed by a discussion on QoS. It also presents a state‐of‐the‐art survey on the challenges faced while trying to meet the QoS requirements regarding the time latency, throughput, connectivity, packet loss, and bandwidth, together with possible solutions to handle various constraints. A discussion on user satisfaction estimation from QoS analysis is also included. Finally, the article concludes with future research opportunities in meeting the QoS constraints of the 6G network. This paper presents a state‐of‐the‐art survey on the challenges faced in meeting stringent quality of service (QoS) requirements while realizing the 6G wireless networks, to enable new applications by means of innovative key technologies. It also presents the probable solutions to meet the criteria such as time latency, throughput, connectivity, packet loss and bandwidth. Besides, it discusses the estimation of user satisfaction from QoS analysis and the scope of future researches to meet the QoS constrains of the 6G wireless network.
... But there are still many difficulties in establishing underwater Internet of ings for marine target recognition. For example, a mobile task assignment method is designed in [5] to lead the AUVs to the specified positions under rough ocean environment and propose a formation control scheme to escort the target to the destination safely. To extend the life of UIoT and overcome challenges of ocean network building project, such as high path loss, limited battery power, and available bandwidth, a new balanced energy adaptive routing protocol is designed in [6]. ...
In order to solve the problems of poor data processing ability of underwater hardware equipment and low accuracy of classification algorithms in the existing marine target recognition and detection methods based on sensors and transducers, by combining perception technology, underwater Internet of Things technology, and artificial intelligence, multiple devices could communicate with each other to achieve automatic and intelligent high-precision marine target recognition. Compared with existing methods, not only the accuracy rate is improved but also the hardware requirements are lower, and it is easier to deploy in engineering.
... UIoT (Domingo, 2012;Qiu et al., 2019;Zhang et al., 2020) is a promising technology emerging for developing the smart oceans. This is an extended version of IoT in terrestrial networks, designed for ocean environments. ...
Underwater Mobile Sensor Network (UWMSN) has marked a new era in ocean observation systems involving large scale ocean phenomena monitoring applications. These spatial and temporally varying applications demand multiple mobile entities for the collection of large amounts of data. This paper gives a concise view of the current state of the art of such networks comprising multiple Autonomous Underwater Vehicles (AUVs) and their challenges. It provides a literature review of the channel model and networking protocols of the physical layer, data link layer and network layer. Important algorithms and techniques for localisation and time synchronisation have also been reviewed. These algorithms play a huge role in a cooperative mission involving multiple AUVs, to achieve a common notion of time and to share the location information among the vehicles. Moreover, this paper includes a survey of various software platforms that support UWMSN, testbeds/real-time deployments of UWMSN developed by various research institutes/organizations and briefly discusses the recent advancement in the field of UWMSN.
... Recently, benefit from the rapid development of networks communication technology and computer science, information processing based on multi-agent systems(MASs) has been attracted extensive attention. In particular, the study of cooperative control problems for MASs have attracted tremendous attention of the researchers on account of its wide applications in numerous cases, such as wheeled robots [1], [2], autonomous surface vehicles/autonomous underwater vehicles [3], [4], spacecrafts [5], [6], and so on. ...
... Cooperative target enclosing and tracking control is one of the most actively studied topics within the coordination control of multi-robot system since with such cooperative missions the robots can benefit from moving in a desired formation with certain geometric shapes [13][14][15][16]. In such patterns, circular formation has huge merits to successfully complete the tasks and improve their performance due to its flexible, simple and easily implement properties. ...
This paper investigates the cooperative control problem for a group of autonomous nonholonomic mobile robots, in which the robots are required to collaboratively enclose and track a stationary or moving target in a circular formation. In order to solve the challenging problem that the robots with speed constraints move uniformly to the exact position on the circles centered on the target while avoiding obstacles encountered, a distributed coupling controller scheme consisting of target encircling, phase positioning and spacing assignment, and the avoidance of obstacles is proposed. First, a novel circular motion control law based on the feedback control idea of trajectory tracking is proposed, which guides all robots move to the target-centered circles and maintains the expected distances between the robots and the target. Second, a phase positioning and spacing assignment control law by introducing a nonlinear function is proposed, which can be coupled into the circular motion controller to implement the robots converge to the specified position on the circles. Finally, the obstacles avoidance control law based on artificial potential field only with repulsive force is adopted to ensure each robot effectively avoids obstacles. The rigorous theoretical analysis of the convergence of the proposed controller is given, and then the simulations and experiments are provided to validate the effectiveness and applicability of the proposed control scheme.
... Some recent articles are listed below. For task assignment, Zhang et al., 16 proposed a cooperative control based underwater target estimating mechanism (CUTE) also known as target escorting mechanism. This method contains a belief function method based self-organizing map technique. ...
The underwater wireless sensor network (UWSN) faces many problems such as limited bandwidth, large path loss, high attenuation, and limited battery power. In recent times, different routing protocols have been proposed in the Internet of Things (IoT) enabled UWSN (IoT-UWSNs) to explore the underwater environment for different purposes, that is, scientific and military purposes. However, high energy consumption (EC), end to end (E2E) delay, low packet delivery ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in UWSN. The high E2E delay, EC, and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. For effective achievement of EC, proposed a new routing algorithm called bald eagle search (BES). It is a nature-inspired optimization algorithm that copied bald eagle hunting behavior. The proposed routing protocol includes three stages: initialization stage, construction stage, and the data transmission phase. The nodes identify the location and the sector during the initialization process. The construction phase includes two steps, neighbor node selection, and identification of energy efficient path. Then the last phase includes the transmission, in this the broadcasting node chooses the nodes with high residual energy (RE) in comparison to the average RE of the entire network. The proposed method is implemented in MATLAB. The outcomes of simulation confirm that the BES algorithm has better performance on EC, average RE, and network lifetime than the three existing algorithm in UWSN.
... The common objective of escorting missions in the literature is to circulate around the boundary of a given target, using multiple robots, in order to create a virtual fence allowing to avoid internal or external agents crossing through the delimited area [11]. This problem has been dealt with from a control point of view in the literature, for example, in [12], [13] and [14], many cooperative control approaches has been applied. Furthermore, in [11] a distributed planning method with dynamics boundaries has been presented. ...
This paper investigates the trajectory generation problem for a fleet of Unmanned Aerial Vehicles (UAVs) performing escort missions with respect to a convoy with a time-varying velocity.Using camera-equipped UAVs, we propose a framework allowing to adapt the drone altitudes to achieve a trade-off between the size of the covered surface and the image resolution by solving an optimization problem. Furthermore, we perform polynomial refinement based on the generated planar way-points to yield a minimum-jerk time-parameterized trajectory with dynamical constraints on the virtual leader to follow. The numerical simulations presented in this paper show the effectiveness of the optimization algorithm in adapting the drones altitudes to satisfy a compromise between the size of the covered area and its corresponding image resolution.
This article presents a concurrent learning-based adaptive critic formation for multi-robots under safety constraints, which comprises of an initial formation consensus item and a collision-free adaptive critic policy. Firstly, based on directed graph communication, an initial formation consensus item is designed to maintain the velocity agreement under a leader-follower setting. Particularly, a collision-free adaptive critic poli-cy is developed that enables robots to preserve formation config-uration with the minimum cost while excluding collisions caused by inter-robots and static/moving obstacles, wherein safety con-straints encoded by an elegantly devised penalty function are enforced by converting constrained optimal control into uncon-strained optimal control issue. Furthermore, by revisiting real-time and historical information, a concurrent weight learning rule is elaborated under a critic-only adaptive dynamic pro-gramming, improving the weight convergence without demand-ing the persistence excitation conditions. The remarkable benefits outperforming existing outcomes are safety-critical coordination with energy-saving performances is assured under a computa-tionally efficient optimal learning paradigm. Involved errors are theoretically proved to be convergent. Finally, the values and superiorities are verified through extensive simulations on two-dimensional and three-dimensional multi-robots.
The development of unmanned surface vehicles (USVs) technologies considerably promote the application of Internet of Things (IoT) in marine activities. However, frequently changed geometrical configurations and potential network congestion severely threaten system performance. Regarding this, this paper proposes an anti-competition communication mechanism based affine formation maneuver control (AFMC) scheme for the internet of USVs. Firstly, an auxiliary system is introduced to construct two-layer based formation maneuver structure, i.e., localization layer and tracking layer. In the localization layer, an intermittent interleaved period event-triggered mechanism (IIPETM) is proposed such that each individual could enjoy interleaved communication periods. Then, by establishing a backstepping-based controller in the tracking layer, formation members will sail along a trajectory generated by the localization layer, thus forming the desired geometrical configuration. The proposed method can significantly reduce network throughput and improve communication efficiency even under intermittent network connectivity. Finally, theoretical analysis and simulations are performed to validate the feasibility of the proposed method.
Background: Modern maritime navigation, especially in uncharted waters, faces major challenges that require innovative solutions. Geospatial technologies play a key role in providing effective solutions for mapping and navigation. This study aims to explore the role of geospatial technologies in improving the safety and efficiency of maritime navigation, as well as supporting sustainable management of marine resources. Methods: This study used both qualitative and quantitative approaches. Data was obtained through secondary collection from journals, books and other documents. Results: Data analysis revealed that geospatial technology plays an important role in identifying safe navigation routes, monitoring sea conditions, and sustainably managing marine resources. The integration of geospatial data from various sources enables more effective decision-making in maritime spatial planning and safe navigation. Conclusion: This research concludes that geospatial technology is a critical aspect of modern maritime navigation. With an integrated and collaborative approach, these technologies can improve navigation efficiency and safety, and support sustainable management of marine resources. Awareness and education on geospatial technology in the maritime industry is considered essential to maximize its potential in maintaining the balance of marine ecosystems and the sustainability of the maritime industry.
Underwater fine-grained classification technology is crucial for discerning subtle differences among marine life classes, playing a pivotal role in marine resource exploration and the discovery of new species. Autonomous underwater vehicles equipped with this technology can enhance their environmental interaction and perception, providing critical data for Internet of Underwater Things (IoUT) systems. However, popular vision transformer-based methods encounter challenges in complex marine environments, particularly due to limited computational resources. In this paper, we introduce an Efficient Vision Transformer with Token-selective and Merging Strategies (TSMVT), which significantly improves underwater fine-grained classification performance while reducing the number of processed tokens. TSMVT can be flexibly integrated into various IoUT systems, promoting the discovery of new species and the sustainable development of marine ecology. Firstly, we propose a dynamic token filtering mechanism that effectively retains important tokens, merges low-information tokens, and discards irrelevant background tokens, significantly reducing computational demands. Secondly, we propose the Multi-head Attention Weighting Token-Selective (MAWTS) module, which dynamically adjusts attention weights. MAWTS enables the network to focus on key features such as fin shape, head structure, and body proportions, thereby improving classification accuracy. With a 30% reduction in tokens, TSMVT achieves superior precision in classifying marine species, enhancing its applicability on various underwater mobile platforms. Extensive experiments conducted on four marine and three terrestrial datasets demonstrate the outstanding accuracy and efficiency of the proposed TSMVT.
We have addressed several secure coordination control design issues for networked robotic systems under synchronous and asynchronous, known and unknown DoS attacks and adversarial node attacks from two distinct perspectives: time-triggered control and event-triggered control. Secure coordination control design for networked robotic systems plays a crucial role in ensuring the resilience, efficiency, and robustness of these systems. It not only fosters collaboration among robots but also addresses security concerns, making it a key enabler for applications in diverse domains, from industrial automation (Bou-Harb et al (2017) IEEE Commun Mag 55(5):198–204; Olowononi et al (2021) IEEE Commun Surv Tutor 23(1):524–552) to autonomous intelligent transportation (Ganin et al (2019) Transp Res Part C Emerg Technol 100:318–329). In this chapter, we present several research directions that depict future investigation on networked robotic systems including even-based secure coordination of networked robotic systems under multisource cyber attacks, secure coordination of underwater distributed cyber-physical systems, and distributed source-seeking control of networked robotic systems with unreliable communication.
In this paper, the formation obstacle avoidance problem of autonomous underwater vehicles (AUVs) under the disturbances of ocean currents is studied. A variable formation reconfiguration and obstacle avoidance control scheme based on affine transform and the improved artificial potential field (AT-IAPF) is designed, which enable AUVs to avoid both static and dynamic obstacles under external interference, and maintain the desired time-varying formation. Because of the robustness and strong effectiveness of the time-varying control of AT and the obstacle avoidance control law of IAPF. The AT-IAPF algorithm improves the multi-AUV systems’ environmental adaptability and obstacle avoidance performance. Using the Lyapunov function’s stability constraint guarantees stability of a multi-AUV system. A series of simulation results based on MATLAB verify that AUVs can effectively avoid obstacles with different formation shapes. Obstacle avoidance experiments on bionic robotic fish demonstrate the proposed method’s feasibility.
Note to Practitioners
—This paper was motivated by the problem of formation reconfiguration and obstacle avoidance for AUVs. Still, it also applies to unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The existing formation control methods usually solve the problems of formation acquisition and time-invariant maneuvering, and rarely consider the problem of formation obstacle avoidance. This paper presents a new formation obstacle avoidance method using affine transformation (AT) and improved artificial potential field (IAPF) techniques. We use the IAPF method to plan a possible path for the formation in the obstacle environment. At the same time, the appropriate formation shape is selected according to the obstacle information to better adapt to the environment. The preliminary experiments of two bionic robot fish in near-surface positions show that this method is feasible. During the experiment, UWB is used for positioning, and a Zigbee module is used to communicate and transmit data. But it still needs to solve the problem of underwater communication, and it has yet to be tested on multiple bionic robot fish. In future studies, we will conduct multiple actual AUV formation obstacle avoidance experiments or do 3D formation control experiments underwater.
This paper delves into the intricacies of adaptive fuzzy event-triggered formation tracking control for nonholonomic multirobot systems characterized by infinite actuator faults and range constraints. To address these issues, we leverage the power of fuzzy logic systems (FLSs) and employ adaptive methods to approximate unknown nonlinear functions and uncertain parameters present in robotic dynamics. In the course of information exploration, the problems of collision avoidance and connectivity maintenance are ever-present due to limitations of distance and visual fields. In this regard, we introduce a general barrier function and prescribed performance methodology to tackle constrained range impediments effectively. Furthermore, to reduce the number of controller executions and compensate for any effect arising from infinite actuator failures, robots engage with their leader at the moment of actuator faults using fewer network communication resources yet maintain uninterrupted tracking of the desired trajectory generated by the leader. With the aid of dynamic surface technology, we propose a decentralized adaptive event-triggering fault-tolerant (ETFT) formation control strategy. We guarantee that all signals are semi-global uniformly ultimately bounded (SGUUB). Ultimately, we demonstrate the practical feasibility of the ETFT control strategy for nonholonomic multirobot systems.
As an emerging multi-submersible system, Human Occupied Vehicle (HOV) under a convoy of a set of Autonomous Underwater Vehicles (AUVs) is regarded as the future framework for underwater exploration. In this work, to improve the interoperability and communication efficiency of the multi-submersible formations, we treat the multi-submersible system as a paradigm of the underwater Internet of Vehicle (IoV) and show how to utilize the Software-Defined Networking (SDN) technique to optimize the system architecture. With the assistance of SDN, we consider the ocean current factors and propose an artificial flow potential field algorithm that combines the artificial potential field algorithm and the gradient descent algorithm, to plan the path for the multi-submersible system. In particular, to improve the safety and efficiency of path planning, we propose a dual leader-follower algorithm-based escort formation obstacle avoidance mechanism for dealing with all categories of obstacle avoidance situations. Simulation tests show that the proposed scheme performs better in data delivery among the multi-submersible system, at a lower energy cost. And it shows high stability and strong practicability in multi-submersible formation control and path planning, respectively.
With the rapid development of AUVs and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.
Internet of Things (IoT) has extended its coverage to various spatial domains and has established interconnection to serve widespread applications of a larger spatial scale. Such IoT is called SAGOI-Net, which consists of multiple battery-powered heterogeneous devices. Hence, energy efficiency is the key point of SAGOI-Net to be stably operated for a long time without manual maintenance. This paper proposes a novel scheme of energy efficient autonomous and decentralized SAGOI-Net establishment using intelligent Autonomous Underwater Glider (AUG) to serve marine applications. The proposed SAGOI-Net is energy efficient because the energy consumption is minimized by: 1) Employing non-propeller-driven AUG; 2) Navigating AUG under water without acoustic sensor or extra energy-consuming vision sensors; 3) Equipping the self-navigation (SN) system based on lightweight neural network model to save the energy consumption of onboard computing resource. Moreover, assuming the AUG navigation problem as time series regression, the proposed scheme designs SAGOI-Net to be autonomous and decentralized with the aid of lightweight Long Short-Term Memory (LSTM) network-based self-navigation (SN-LSTM) system of AUG. The lightweight SN-LSTM model is trained end-to-end on dynamically modeled AUG motion information along with numerically modeled ocean environment data to quantitively analyze the impact of the ocean environment on AUG. The simulation results demonstrate superior performance of the AUG self-navigation along with energy efficiency of the proposed SAGOI-Net.
In the leader-follower-based multiple underwater unmanned vehicles (UUVs) cooperative positioning system, the precision of the compass and doppler velocity log equipped with the follower UUV is low, which will inevitably lead to yaw error and velocity error (YEaVE) in the output data. These errors will seriously affect the positioning accuracy of follower UUV. In addition, the observation information often has many outliers due to the influence of underwater multipath, which will cause the increase of the positioning error. Herein, we propose a novel adaptive sliding observation-based cooperative positioning algorithm under factor graph framework. Firstly, the influence mechanism of YEaVE on positioning accuracy of the follower UUV is analyzed. Thereafter, under the factor graph framework, a cooperative positioning algorithm based on maximum correntropy criterion is designed, in which the estimation and compensation of YEaVE are innovatively added. At last, an adaptive sliding observation method is designed to deal with outliers in observation information. The proposed algorithm is verified by simulations and shipboard experiment, and the results show that it can accurately estimate the YEaVE. At the same time, compared with the ordinary factor graph algorithm, the positioning accuracy of the proposed algorithm has been improved by 88.31% and 81.63% in simulation and shipboard experiment.
This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV, several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey.
Acoustic ranging is required to obtain the location of underwater mobile vehicles in the Internet of Underwater Things (IoUT). As seawater is an inhomogeneous medium, the sound velocity in the ocean is not constant, thereby causing sound waves to deviate from a straight line of propagation and bend. Thus, the travel time of the sound wave from the transmitter to receiver cannot be directly converted to a range value using a linear relationship as is done in the case of wireless radio ranging in the air. Therefore, the concept of effective sound velocity was introduced to account for the differences between the sound velocities at a transmitter and receiver. This enables conversion of the travel time to slant distance via a linear relationship. However, the existing methodologies for computing effective sound velocities are computationally intensive, which hinders the use of acoustic ranging in small underwater mobile vehicles operating in the deep sea. This study aimed to resolve this problem by developing an effective, computationally-less demanding algorithm that could improve the precision of acoustic ranging on such platforms. The proposed algorithm converts global integrals into local integrals that correspond to depth variation ranges, thereby reducing the amount of integral calculation. The performance of the proposed algorithm is evaluated using extensive simulations and real deep-sea experimental datasets obtained at a depth of 3000 m. The results verify the efficacy of the algorithm in realizing real-time underwater acoustic ranging in deep sea. The proposed algorithm can improve the accuracy of real-time effective sound velocity measurement by small underwater mobile vehicles, and subsequently realize low-complexity underwater acoustic ranging in the deep-sea IoUT networks.
The coming 6G communication technology introduces the possibility of practice underwater Internet of Things (UIoT) applications with high-speed and reliable underwater communications. Among them, the cooperative coverage path planning (CPP) with autonomous underwater vehicles (AUVs) is a promising approach for enabling deep ocean exploration. The cooperative CPP in underwater is challenged by several marine factors, typically the harsh underwater communication environment, which brings difficulties in sharing the coverage progresses of AUVs and the environmental information such as the seafloor bathymetry, obstacles, etc. Accordingly, this paper proposes a novel CPP method based on 6G enabled cooperative AUVs, named the Pac-AUV. As the name suggests, the method is based on the mechanism of the Ms. Pac-Man game, where AUV-assisted subsea exploration is considered as cooperative Pac-Men sharing the Pac-Dots distributed on the seafloor. The Pac-AUV involves two steps in cooperative CPP. One is a dot-spreading-based mission assignment (DMA), which is discretely performed by each AUV and requires support from reliable underwater communication in sharing the Pac-Dots. The other step is virtual attraction-based coverage path planning (V-CPP), which adopts the virtual attraction force from the Pac-Dots, results in low computational complexities in generating the coverage paths and avoids obstacles. Simulations are performed to demonstrate the performance of the Pac-AUV, and the results prove the advantages of cooperative and balanced CPP executions.
Pair-wise ranging between nodes plays a crucial role in Internet of Underwater Things (IoUT) networks, and it typically impacts the overall performance of such networks. As the sound speed depends on several parameters, time-of-flight-based techniques cannot work well under varying sound speeds in actual underwater conditions. Pair-wise ranging algorithms that consider stratification should be studied to improve ranging accuracy. However, there is a tradeoff between underwater acoustic ranging accuracy and number of calculations. This makes it challenging to implement underwater acoustic ranging algorithms in IoUT networks. In this paper, we propose an underwater acoustic horizontal ranging algorithm that rapidly and accurately estimates the horizontal range from a sender to a receiver under an arbitrary sound-speed profile in the deep sea. Simulation results show that the proposed algorithm accurately calculates the horizontal range with a low computational complexity. We validate the performance of the proposed algorithm using the data collected during an ultrashort baseline precision test in the South China Sea.
In the research of multi-robot systems, multi-AUV (multiple autonomous underwater vehicles) cooperative target hunting is a hot issue. In order to improve the target hunting efficiency of multi-AUV, a multi-AUV hunting algorithm based on dynamic prediction for the trajectory of the moving target is proposed in this article. Firstly, with moving of the target, sample points are updated dynamically to predict the possible position of a target in a short period time by using the fitting of a polynomial, and the safe domain of the moving target, which is a denied area for the hunting AUVs, is built to avoid the target’s escape when it detects AUVs. Secondly, the method of negotiation is adopted to allocate appropriate desired hunting points for each AUV. Finally, the AUVs arrive at desired hunting points rapidly through deep reinforcement learning (DRL) algorithm to achieve hunting the moving target. The simulations show that hunting AUVs can surround the moving target of which the trajectory is unknown rapidly and accurately by the algorithm in the 3D environment with complex obstacles and results obtained is satisfactory.
This manuscript considers a team of Unmanned Underwater Vehicles (UUVs) such that each UUV measures the arrival time of sound signal generated from the evader. The purpose of the UUV team is to intercept the underwater evader based on the arrival time measurements. We present both the motion control for the UUV team and the algorithm to estimate the evader position in real time. In this manuscript, the UUVs chase the evader while preserving a 3D spherical formation. Moreover, the formation size is controlled adaptively to assure the convergence of the evader estimation. Our tracking approach doesn’t require global localization of every UUV. The proposed approach only requires that the central UUV measures the relative position of any other UUV using proximity sensors. Simulations are utilized to verify the effectiveness of the proposed method.
The airdropping of multi-parafoil systems is of great significance to earthquake relief and military material transportation. In order to achieve the coordinated motion of multiple parafoils, a formation guidance strategy based on a virtual structure is proposed, which enables the formation of multiple parafoils to follow the planned trajectory and land at the target precisely. Firstly, since the main movement mode of a parafoil is turning and gliding, a multiphase homing trajectory for the reference point is planned, which mainly consists of a turning and gliding phase. Then, the trajectories of all the points on the virtual structure are generated by superimposing the relative positions of the virtual structure on the planned trajectory. Based on Lyapunov stability theory, a guidance strategy is designed to guide all parafoils to track the corresponding points on the virtual structure and complete the desired formation task. The simulation results show that the guidance strategy based on a virtual structure can effectively guide multiple parafoils to achieve coordinated formation movement. Parafoils dropped from different positions and heading angles can gradually gather together and form a formation, track the planned trajectories, land at the target point precisely and align up against the wind.
This paper deals with the problem of formation collision avoidance for unmanned surface vehicles (USVs). Compared with the general ship formation, the formation collision avoidance system (FCAS) need better responsiveness and stability because of faster speed and smaller volume for USVs. A method based on finite control set model predictive control (FCS-MPC) is proposed to solve this problem. The novelty of the method is that it can control formation quickly to avoid obstacles and reach the destination in accordance with the dynamics of each vessel in the formation, without the prior knowledge of the environment and reference trajectory. The thruster speed and propulsion angle of the USV form a finite control set, which is more practical. The FCAS adopts the leader-follower structure and distributed control strategy to ensure that the Followers have a certain autonomy. The first two simulation tests verify that the system has the formation stability, formation forming ability and the applicability in restricted water. The last simulation test shows that the system can control the USV formation to sail quickly and safely in complex sea scenarios with formation transformation task and multiple dynamic obstacles.
This paper addresses a local minima problem for multiple unmanned aerial vehicles (UAVs) in the process of the collision avoidance by using the artificial potential field method, thereby enabling UAV avoid the obstacle effectively in three-dimensional space. The main contribution is to propose a collision avoidance control algorithm based on the virtual structure and the “leader-follower” control strategy in three-dimensional space that can avoid the obstacle effectively and then track the motion target. The three UAVs constitute the regular triangular formation as the control object, the virtual leader flight trajectory as the expected path, the obstacles as simplified cylinders, and the artificial potential fields around them as approximately spherical surfaces. The attractive force of the artificial potential field can guide the virtual leader to track the target. At the same time, the follower tracks the leader to maintain the formation flight. The effect of the repulsive force can avoid the collision between the UAVs and arrange the followers such that they are evenly distributed on the spherical surface. Moreover, the follower’s specific order and position are not required. The collision path of the UAV formation depends on the artificial potential field with the two composite vectors, and every UAV may choose the optimal path to avoid the obstacle and reconfigure the regular triangle formation flight after passing the obstacle. The effectiveness of the proposed the collision avoidance control algorithm is fully proved by simulation tests. Meanwhile, we also provide a new concept for multi-UAV formation avoidance of an obstacle.
This paper investigates the cooperative control problem of multiple nonholonomic robots for an escorting and patrolling mission with respect to a target with a time-varying velocity. The goal of the multi-robot system is to orbit a moving target in a common circle with prescribed radius and circular velocity, while maintaining even spacing along the perimeter of the circle. This study proposes a distributed control strategy based on the vector field that does not require each robot to know the full state of the target. A distributed estimation law is developed to enable each robot to estimate the position of the target and a tracking differentiator is used to estimate the velocity of the target. Based on these estimates of the target, the distributed control law is designed to ensure asymptotic convergence of the multi-robot system to achieve the desired motion. Under some mild assumptions about the interaction graph among the target and the robots, and the velocity of the target, explicit stability and convergence analyses are presented using Lyapunov tools. Simulation results from test cases of a group of nonholonomic mobile robots verify the effectiveness of the proposed distributed control algorithms.
Many problems of interest for cyber-physical network systems can be formulated as Mixed-Integer Linear Programs in which the constraints are distributed among the agents. In this paper we propose a distributed algorithmic framework to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. At each communication round, agents locally solve a small linear program, generate suitable cutting planes and communicate a fixed number of active constraints. Within the distributed framework, we first propose an algorithm that, under the assumption of integer-valued optimal cost, guarantees finite-time convergence to an optimal solution. Second, we propose an algorithm for general problems that provides a suboptimal solution up to a given tolerance in a finite number of communication rounds. Both algorithms work under asynchronous, directed, unreliable networks. Finally, through numerical computations, we analyze the algorithm scalability in terms of the network size. Moreover, for a multi-agent multi-task assignment problem, we show, consistently with the theory, its robustness to packet loss.
An adaptive self-organizing map (SOM) neural network method is proposed for distributed formation control of a group of autonomous underwater vehicles (AUVs). This method controls the AUVs holding their positions in the formation when the formation moves as a whole. The group of AUVs can reach the desired locations in an expected formation shape along pre-planned trajectories. The proposed control law is distributed in the sense that the controller of each AUV only uses its own information and the information of its neighboring AUVs. Formation control strategies based on self-organizing competitive calculations are carried out with workload balance taken into consideration, so that a group of AUVs can reach the desired locations on the premise of workload balance and energy sufficiency. Moreover, the formation can avoid obstacles and change its shape as needed. The formation is in a distributed leader-follower like structure, but there is no need to designate the leader and the followers explicitly. All the AUVs in the formation are treated equal to be the leader and the followers, so that important characteristics such as adaption and fault tolerance are achieved. Comparison results with traditional method and experiments demonstrate the effectiveness of the proposed method.
Cooperative hunting of multi-AUV is an important research topic. Current studies concentrate on AUVs with the same speed abilities, and mostly do not consider their speed differences. In fact, AUVs in a hunting group are often of different type and possess different maximum sailing speed. For inhomogeneous multi-AUV, a novel time competition mechanism is proposed to construct an efficient dynamic hunting alliance. Hunting team with AUVs possessing higher speed abilities is more suitable for the vast underwater environment. In the pursuing stage, AUV needs to act fast enough to avoid the escape of evader. To achieve a quick and accurate pursuit, a combined path planning approach is presented, which combines GBNN (Glasius Bio-inspired Neural Network model) and belief function. Simulation experiments demonstrate the feasibility and efficiency of the proposed algorithm in the cooperative hunting of inhomogeneous multi-AUV under dynamic underwater environment with intelligent evaders and multi-obstacle.
Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present a graph search based method to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithm lies in the use of an adaptive discretization scheme to construct the search graph. We demonstrate the proposed algorithm by computing optimal energy paths using an analytical time-varying flow model and using time-varying ocean flow data provided by the Regional Ocean Model System. We compare the output paths with those computed via an optimal control formulation and numerically demonstrate that the proposed method can overcome problems inherent in existing fixed discretization schemes.
An integrated biologically inspired self-organizing map (BISOM) algorithm is proposed for task assignment and path planning of an autonomous underwater vehicle (AUV) system in three-dimensional underwater environments with obstacle avoidance. The algorithm embeds the biologically inspired neural network (BINN) into the self-organizing map (SOM) neural networks. The task assignment and path planning aim to arrange a team of AUVs to visit all appointed target locations, while assuring obstacle avoidance without speed jump. The SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then, in order to avoid obstacles and speed jump for each AUV that visits the corresponding target location, the BINN is utilized to update weights of the winner of SOM, and achieve AUVs path planning and effective navigation. The effectiveness of the proposed hybrid model is validated by simulation studies.
This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.
The complexity of heterogeneous wireless networks in synergy with battery powered mobile devices is driving new stringent requirements in terms of power efficiency to ensure that battery life, environmental and thermal criteria can be met. Modern mobile devices are equipped with multiple interfaces, which allow them to exploit the benefits offered by heterogeneous networking environments, but on the other hand, drain battery swiftly. In this paper, architecture for a context-based node and a testbed platform for the analysis of energy consumption of heterogeneous cooperative communications are presented. The demonstrative testbed comprises a WiFi Access Point, which provides WiFi coverage in the infrastructure mode, as well as nodes capable of communicating through short-range ultra-wideband WiMedia. The testbed includes a context aware module that provides and stores information related to different nodes in the system. The paper shows how context information can be used to save the energy of mobile devices and extend their battery lifetime using short-range communications. The testbed is used as a proof-of-concept for the practical implementation of the cooperative communications concept. The obtained results show that significant amount of energy can be saved using context information along cooperation among multiple interfaces, in comparison to direct communications.
For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.
Many applications require teams of robots to cooperatively execute tasks. Among these domains are those in which successful coordination must respect intra-path constraints, which are constraints that occur on the paths of agents and affect route planning. This work focuses on multi-agent coordination for disaster response with intra-path precedence constraints, a compelling application that is not well addressed by current coordination methods. In this domain a group of fire truck agents attempt to address fires spread throughout a city in the wake of a large-scale disaster. The disaster has also caused many city roads to be blocked by impassable debris, which can be cleared by bulldozer robots. A high-quality coordination solution must determine not only a task allocation but also what routes the fire trucks should take given the intra-path precedence constraints and which bulldozers should be assigned to clear debris along those routes.
This work presents two methods for generating time-extended coordination solutions—solutions where more than one task is assigned to each agent—for domains with intra-path constraints. Our first approach uses tiered auctions and two heuristic techniques, clustering and opportunistic path planning, to perform a bounded search of possible time-extended schedules and allocations. Our second method uses a centralized, non-heuristic, genetic algorithm-based approach that provides higher quality solutions but at substantially greater computational cost. We compare our time-extended approaches with a range of single task allocation approaches in a simulated disaster response domain.
In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies
New reactive behaviors that implement formations in multirobot
teams are presented and evaluated. The formation behaviors are
integrated with other navigational behaviors to enable a robotic team to
reach navigational goals, avoid hazards and simultaneously remain in
formation. The behaviors are implemented in simulation, on robots in the
laboratory and aboard DARPA's HMMWV-based unmanned ground vehicles. The
technique has been integrated with the autonomous robot architecture
(AuRA) and the UGV Demo II architecture. The results demonstrate the
value of various types of formations in autonomous, human-led and
communications-restricted applications, and their appropriateness in
different types of task environments
With the boost of ocean development, the study of UASNs has been gaining much attention from researchers. Nevertheless, UASNs are particularly vulnerable to malicious attacks because of their open and unattended nature. Hence, efficient and reliable security mechanisms are required to maintain the normal operation of UASNs. To defend against sudden network faults and malicious attacks, a novel trust model is proposed. The model is divided into three phases. First, a quantified environment model is developed to reflect the impact of the underwater environment on trust evaluation. Then, a trust update model is constructed based on reinforcement learning to confront hybrid attacks. Finally, a trust redemption model is given to reinstate low trust sensor nodes for improving resource utilization of the network. The experimental results prove that this trust model is effective in achieving highly efficient trust updating in the face of changing attack modes.
Underwater gliders are being increasingly used for data collection, and the development of methods for optimizing their routes has become a topic of active research. With this aim in mind, in this paper, a complete-coverage path-planning obstacle-avoidance (CCPP-OA) algorithm that ensures avoidance for underwater gliders in sea areas with thermoclines is proposed. First, the entire sea area with the thermocline layer is stratified based on the underwater communication radii of the gliders. Next, the glide angles and initial navigation points of the gliders are determined based on their communication radii at each level to construct the complete-coverage path. Finally, by combining the ant colony algorithm and the determined initial navigation points, the complete-coverage path with obstacle avoidance is planned for the gliders. Simulation results show that the proposed CCPP-OA algorithm enables complete coverage of the entire sea area. Furthermore, the length of the planned path is shorter and the amount of energy consumed is less than that of other algorithms.
In this paper, we investigate formation tracking control of autonomous underwater vehicles (AUVs) with model parameter uncertainties and external disturbances. The external disturbances due to the wind, waves, and ocean currents are combined with the model parameter uncertainties as a compound disturbance. Then a disturbance observer (DO) is introduced to estimate the compound disturbance, which can be achieved within a finite time independent of the initial estimation error. Based on a DO, a novel fixed-time sliding control scheme is developed, by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time. The effectiveness and performance of the method are demonstrated by numerical simulations.
With the emergence of new underwater ICTs, UWNs based on AUV have become the mainstream technology for underwater search tasks. These advanced underwater searching technologies are leading to smart perceptual ocean technologies. However, to support precise multi-AUVs cooperative underwater search and provide intelligent data collection and data transfer among the AUVs, one of the challenging issues is to design a scalable network architecture capable of fine-grained control and smart underwater data routing. In this article, we employ the paradigm of SDN technology and propose an SDN-based underwater cooperative searching framework for AUV-based UWNs. In particular, we propose a software-defined beaconing framework integrating two categories of defined beacons to synchronize network information and execute network operations. Based on the software-defined beaconing framework, we introduce the USBL positioning system and propose a hierarchical localization framework to localize/track each AUV in the network. Then, we utilize the potential field theory to model the multi-AUV cooperative operation, leading to a cooperative control framework. Finally, to guarantee the potential data transfer among the AUVs, we also propose a software-defined hybrid data transfer scheduling framework. Simulation results demonstrate that our proposed scheme performs more efficiently than some existing schemes especially the distributed control policy.
The formation control of mobile underwater wireless sensor networks (MUWSNs) is difficult due to the severe errors in distance and motion measurements. To address this problem, we propose a distributed formation control scheme, TRiForm (Triangle Formation). TRiForm constructs a virtual structure that is a rigid graph formed by triangles of nodes in an MUWSN for high reliability and efficiency in formation control. Furthermore, it utilizes the anchor information to detect and to compensate for measurement errors. We have proven the correctness of the rigidity graph construction by TRiForm. We have also evaluated TRiForm in various scenarios using simulation with the measured parameters from real underwater sensor nodes. The results show that TRiForm can successfully maintain the formations of an MUWSN and control the MUWSN to reach the predefined destination under distance and motion measurement errors.
The non-cooperative target tracking is an important issue for the internet of underwater things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this paper, we investigate the problem of the non-cooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and non-cooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce the communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. Simulation results validate the proposed algorithms by comparing with the state-of-the-art methods.
As an emergent Internet of Underwater Things (IoUT) system, the Underwater Wireless Networks (UWNs), especially the Autonomous Underwater Vehicle (AUV)-based UWNs are considered to be future of deep-sea exploration. Instead of underwater exploring or data collection based on an independent AUV, the multi-AUVs cooperative system or the AUV flock-based UWNs perform more efficiently and accurately in some particular underwater exploring tasks. In this paper, we focus on improving the scalability or controllability of the AUV flock-based UWNs, and utilize the paradigm of Software Defined Networking (SDN) to improve the flexibility and controllability of the AUV flock-based UWNs. With the proposed SDN-enabled architecture for the AUV flock-based UWNs, the UWNs are divided into three layers, and the data transmission, synchronization, collection among the AUVs are implemented by the proposed software-defined beacon and control frameworks. By the centralized management feature of SDN, we define the concept of AUV flock and the united control model based on artificial potential field theory. Then, we propose an exact path planning scheme for the AUV flock, especially when potential underwater obstacles or ’no-go’ areas are taken into account. We will show how an SDN controller can be a director/leader for the AUV flock-based UWNs to perform exact underwater path planning mission. Simulation results show that our proposal is efficient in managing the operation of the AUV flock, especially the proposal SDN controller-guided path planning scheme performs more efficiently than the normal distributed path planning scheme.
The oceans cover more than 71% of the Earth’s sur-face and have a surging amount of data. It is of great significance to seek energy-effective and ultra-reliable communication and transmis-sion mechanism for effectively gathering the abundant maritime data. In this paper, we propose an Autonomous-underwater-vehicle (AUV) assisted data gathering scheme based on Clustering and Matrix Com-pletion (ACMC) to improve the data gathering efficiency in underwa-ter wireless sensor network (UWSN). Specifically, we first improve the K-means algorithm by adopting the Elbow method to determine the optimal K and setting a distance threshold to select the separate initial cluster centers. Then we introduce a two-phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary noodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we pre-sent an in-cluster data collection mechanism based on matrix comple-tion. Extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.
Source location privacy (SLP) protection is an important means of security protection in wireless sensor networks (WSNs). With the development of underwater acoustic sensor networks (UASNs), security and privacy have attracted increasing attention. In this study, we incorporated SLP into UASNs as the basis for a novel stratification-based source location privacy (SSLP) scheme. In the SSLP scheme, SLP is protected through the cooperation of autonomous underwater vehicles (AUVs) in each network layer. Because fake source nodes and fake data streams are commonly used in WSNs, methods that have similar functions in UASNs are required. Hence, we incorporate a fake source node into the underwater cluster structure to add randomness to the underwater network. Furthermore, fake data streams have been included within the AUV data collection and transmission for each cluster. Simulation results confirm that the SSLP scheme offers improved security in comparison with existing underwater data transmission schemes.
Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this paper, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.
Underwater acoustic sensor networks (UASNs) are widely used in a variety of ocean applications, such as exploring ocean resources or monitoring abnormal ocean environments. However, data collection schemes in UASNs are significantly different than those in wireless sensor networks due to high power consumption, high propagation delay, and so on. What's more, previous research has overlooked practical conditions, such as water delamination characteristics and autonomous underwater vehicles (AUV) limited energy. To solve these problems, we propose a data collection scheme based on a stratification model (DCSM) for 3D UASNs. In our scheme, the network is divided into two layers. The top layer, called the Ekman layer, is dynamic; we employ a forwarding set-based multi-hop forwarding algorithm in data collection. Then, a neighbor density clustering-based AUV data gathering algorithm is adopted in the second layer, which is relatively static. By employing different data collection algorithms in different layers, we can integrate the advantages of a multi-hop transmission scheme and AUV-aided data collection scheme to reduce network consumption and improve network lifetime. The simulation results also confirm the proposed method has good performance. IEEE
For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.
This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A∗-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A∗ approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.
As an extension of wireless sensor network in underwater environment, Underwater Acoustic Sensor Networks (UASNs) have caused widespread concern of academia. In UASNs, the efficiency and reliability of data transmission are very challenging due to the complex underwater environment in variety of ocean applications, such as monitoring abnormal submarine oil pipelines. Motivated by the importance of energy consumption in many deployments of UASNs, we therefore propose an energy-efficient data transmission scheme in this paper, called EGRC (Energy-efficiency Grid Routing based on 3D Cubes) in Underwater Acoustic Sensor Networks, taking complex properties of underwater medium into consideration, such as three dimensional changing topology, high propagation delay, node mobility and density, as well as rotation mechanism of cluster-head nodes. First, the whole network model is regarded as a 3D cube from the grid point of view, and this 3D cube is divided into many small cubes, where a cube is seen as a cluster. In the 3D cube, all the sensor nodes are duty-cycled in the MAC layer. Second, in order to make energy efficient and extend network lifetime, EGRC shapes an energy consumption model taking residual energy and location of sensor nodes into account to select the optimal cluster-heads. Moreover, EGRC utilizes residual energy, locations and end-to-end delay for searching for the next-hop node to maintain the reliability of data transmission. Simulation validations of the proposed algorithm are carried out to show the effectiveness of EGRC, which performs better than the representative algorithms in terms of energy efficiency, reliability and end-to-end delay.
This paper proposes a novel cooperative coevolutionary algorithm (CCEA)-based distributed model predictive control (MPC) that guarantees asymptotic stability of multiagent systems whose state vectors are coupled and nonseparable in a cost function. While conventional evolutionary algorithm-based MPC approaches cannot guarantee stability, the proposed CCEA-based MPC approach guarantees asymptotic stability regardless of the optimality of the solution that the CCEA-based algorithm generates with a small number of individuals. To guarantee stability, a terminal state constraint is found, and then a repair algorithm is applied to all candidate solutions to meet the constraint. Furthermore, as the proposed CCEA-based algorithm finds the Nash-equilibrium state in a distributed way, robots can quickly move into a desired formation from their locations. A novel dynamic cooperatively coevolving particle swarm optimization (CCPSO), dynamic CCPSO (dCCPSO) in short, is proposed to deal with the formation control problem based on the conventional CCPSO, which was the most recently developed algorithm among CCEAs. Numerical simulations and experimental results demonstrate that the CCEA-based MPC greatly improves the performance of multirobot formation control compared with conventional particle swarm optimization-based MPC.
Potential-function-based control strategy for path planning and formation control of Quadrotors is proposed in this work. The potential function is used to attract the Quadrotor to the goal location as well as avoiding the obstacle. The algorithm to solve the so called local minima problem by utilizing the wall-following behavior is also explained. The resulted path planning via potential function strategy is then used to design formation control algorithm. Using the hybrid virtual leader and behavioral approach schema, the formation control strategy by means of potential function is proposed. The overall strategy has been successfully applied to the Quadrotor's model of Parrot AR Drone 2.0 in Gazebo simulator programmed using Robot Operating System.
The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper. In the virtual structure, each mobile robot is modeled by an electric charge. The mobile robots move toward a circle, and due to repulsive forces between the identical charges, regular polygon formations of the mobile robots will be realized. For swarm formation, a virtual mobile robot is located at the center of the circle, and other mobile robots follow it. In the introduced approach, each mobile robot finds its position in the formation autonomously, and the formation can change automatically in the case of change in the number of the mobile robots. This paper also proposes a technique for avoiding obstacles based on the behavioral structure. In this technique, when a mobile robot gets close to an obstacle, while moving toward its target, a rotational potential field is applied to lead the mobile robot to avoid the obstacle, without locating in local minimum positions.
This paper addresses the formation control problem for fleets of autonomous underwater vehicles (AUVs). The solution is based on the virtual leader approach, with the main goal of designing a control system to cope with the inter-vehicle communication problems, especially significant in underwater environments. The use of kinematic relations allows the linearization of the AUV dynamics maintaining its turning capacities. The control strategy consists of a feedback controller in combination with a feedforward controller, which makes it possible to deal with delays and packets dropouts while ensuring a good formation control performance.
This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an upper speed limit. The artificial potential field ap proach has been extended to collision avoidance for all ma nipulator links. In addition, a joint space artificial potential field is used to satisfy the manipulator internal joint con straints. This method has been implemented in the COSMOS system for a PUMA 560 robot. Real-time collision avoidance demonstrations on moving obstacles have been performed by using visual sensing.
Formation control of mobile multi-agent system is a question with practical significance in the complex dynamics system. In this paper, a multi-agent system moving in free-space and moving with obstacle avoidance is studied. Applying potential function, the multi-agent algorithms are proposed. The first algorithm is an algorithm for multi-agent system moving in free-space. Then we discuss flocking with Obstacle avoidance in multi-agent system, the second algorithm has obstacle avoidance capabilities. We present the control algorithm and make stability analysis. Finally, many computer simulations are used to show the validity of the results.
A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments.We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems: an environment recognition system using link angles and an adaptive control system. In the environment recognition system, we introduce a Self-Organizing Map (SOM) for clustering link angles. In the adaptive controllers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot.The environment recognition system can recognize the environment in which the robot travels, and the adjustable controllers are tuned by experimental results for each environment. The dual sub-system switching controller system is experimentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.
The problem of escorting a moving target with a team of mobile robots was solved in this article by resorting to a formation control algorithm that can be cast in the framework of the NSB control approach. The overall mission, therefore, is decomposed into properly defined elementary tasks that are hierarchically arranged, so that the higher-priority tasks are not influenced by the lower-priority ones. The validity of the proposed approach has been proved by both simulation case studies and experimental results with a team of six Khepera II mobile robots. Stability analysis concerning effective conditions needed to verify that the behaviors of specific missions are properly defined and merged is under investigation. Future improvements might regard decentralization of the algorithm, consideration of the vehicles' nonholonomicity in the definition of the task functions, and the introduction of a piecewise-constant constraint for the linear velocity to allow application of the method to teams of cruise vehicles (e.g., a fleet of vessels or a flight of planes).
In this paper, we present a silent positioning scheme termed UPS for underwater acoustic sensor networks. UPS relies on the time difference of arrivals locally measured at a sensor to detect range differences from the sensor to four anchor nodes. These range differences are averaged over multiple beacon intervals before they are combined to estimate the 3-D sensor location through trilateration. UPS requires no time synchronization and provides location privacy at underwater vehicles/sensors whose locations need to be determined. To study the performance of UPS, we model the underwater acoustic channel as a modified ultrawideband Saleh-Valenzuela model: The arrival of each path cluster and the paths within each cluster follow double Poisson distributions, and the multipath channel gain follows a Rician distribution. Based on this channel model, we perform both theoretical analysis and simulation study on the position error of UPS under acoustic fading channels. The obtained results indicate that UPS is an effective scheme for underwater vehicle/sensor self-positioning.