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... One of the main goals of MTS planners is to reduce the target detection time, which can be achieved by optimizing the expected time of target detection [5][6][7][8][9][10]. Other PTSP approaches optimize alternative criteria, such as maximizing the probability of target detection [11][12][13][14] or minimizing its counterpart probability of nondetection [15,16], maximizing the information gain [17], minimizing the system entropy [18], minimizing its uncertainty (areas with intermediate belief of target presence) [19], or optimizing normalized or discounted versions of the previous criteria [4,[20][21][22]. A common characteristic of the different approaches is that, in scenarios with bounded resources (e.g., limited flying time or fuel), they often obtain better results than predefined search patterns (e.g., spiral, lawnmower), as they adapt the UAV trajectories to the scenario specific target initial belief and motion [6,20]. ...
... The NP-hard complexity of PTSP [23] is tackled with suboptimal algorithms and heuristics, such as gradientbased approaches [13,[15][16][17]19], greedy methods [8,12,20], cross-entropy optimization [4,7], Bayesian optimization algorithms [5], ant colony optimization [6,9], or genetic algorithms [10]. Besides, streamlined formulations of the problem are typically accepted in order to further simplify the problem complexity. ...
... Besides, streamlined formulations of the problem are typically accepted in order to further simplify the problem complexity. They range from considering static targets [8,10,13,15,16,19,20] instead of dynamic ones [4-7, 9, 11, 12, 14, 17, 21, 22] to modeling the sensors ideally [4][5][6]8] instead of realistically (e.g., as radars [9][10][11][12] or downward-looking cameras [13,14,17,19]) or to assuming that the UAVs fly following straight-lines according to the eight cardinal directions [4][5][6][7][8] or optimized waypoints [17,21,22] instead of considering the physical maneuverability constraints induced by the dynamic models of the UAVs [9-16, 19, 20]. Additionally, in some cases (e.g., in [10,13,16,17,19,20]) the approach uses a receding horizon controller to divide the UAVs trajectory into sequentially optimized sections, narrowing the optimization search space at the expense of constructing suboptimal myopic solutions. ...
Article
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This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.
... With the rapid development of affordable robots with embedded sensing and computation capabilities, we are quickly approaching a point at which reallife applications will involve the deployment of hundreds, if not thousands, of robots [1,2]. Among these applications, significant research effort has been devoted to multi-agent search [3,4,5,6,7], where deploying numerous agents can greatly improve the time-efficiency and robustness of search. In fact, deploying robots with various motion or sensing modalities can further improve the search performance, by leveraging the natural synergies between these capabilities (see Fig. 1). ...
... The approach in this paper is based on ergodic search processes, which, similar to other information-theoretic coverage methods [3,4,8,11,12], rely on an a priori information distribution, representing the likelihood of finding a target at any point over the search domain, to guide the search. In practice, this information distribution can be obtained from scouting missions or from expert knowledge, and is updated during search if inaccurate. ...
... For example, in gradient-based, or "information surfing", methods [4,11,12], agents guide their movement in the direction of the derivative of the information map around their positions to greedily maximize the short-term information gain. That is, agents are always driven in the direction of the greatest information gain, which naturally leads them to areas where the likelihood of finding a target is maximized. ...
Chapter
This paper develops a multi-agent heterogeneous search approach that leverages the sensing and motion capabilities of different agents to improve search performance (i.e., decrease search time and increase coverage efficiency). To do so, we build upon recent results in ergodic coverage methods for homogeneous teams, where the search paths of the agents are optimized so they spend time in regions proportionate to the expected likelihood of finding targets, while still covering the whole domain, thus balancing exploration and exploitation. This paper introduces a new method to extend ergodic coverage to teams of heterogeneous agents with varied sensing and motion capabilities. Specifically, we investigate methods of leveraging the spectral decomposition of a target information distribution to efficiently assign available agents to different regions of the domain and best match the agents’ capabilities to the scale at which information needs to be searched for in these regions. Our numerical results show that distributing and assigning coverage responsibilities to agents based on their dynamic sensing capabilities leads to approximately \(40\%\) improvement with regard to a standard coverage metric (ergodicity) and a \(15\%\) improvement in time to search over a baseline approach that jointly plans search paths for all agents, averaged over 500 randomized experiments.
... The paths followed by the UAVs are dynamically computed and detection probabilities are directly related to the positions of the targets. For example, in Lanillos, Gan, Besada-Portas, Pajares, and Sukkarieh (2014) , the objective is to minimize time when real-time dynamic decisions are made. These decisions are actions (turn, accelerate, etc) that generate the trajectories followed by the UAVs. ...
... We will see also (see Section 3.1 ) how it can be relaxed. In Lanillos et al. (2014) , the authors have proposed a novel approach, based on a heuristic future expected reward to reduce the short-sighted aspect of the decision making. The coordination of UAVs for target search has also been based on biologically inspired metaheuristics that mimic swarm or flock behaviors, as shown in Senanayake et al. (2016) . ...
Article
This paper addresses the optimization problem of managing the research efforts of a set of sensors in order to minimize the probability of non-detection of a target. A novel formulation of the problem taking into account the traveling costs between the searched areas is proposed; it is more realistic and extends some previous problems addressed in the literature. A greedy heuristic algorithm is devised, it builds a solution gradually, using a linear approximation of the objective function refined at each step. The heuristic algorithm is complemented by a lower bound based on a piecewise linear approximation of the objective function with a parametric error, and extended to the case where the target is moving. Finally, a set of numerical experiments is performed to analyze and evaluate the proposed contributions.
... During the Great East Japan Earthquake, the number of deaths and missing people dramatically increased as time passed [6]. Surveillance using multiple UAVs has been receiving much attention for reasons such as increasing system reliability, robustness, and efficiency [7]- [11]. ...
... Previous studies [7]- [11] assumed that a user can obtain necessary information as soon as a UAV acquires data. However, the processing time of image data and data-transfer time should be taken into consideration because the time requirement for search and rescue in disaster-hit areas is strict. ...
Article
Using micro or small unmanned aerial vehicles (UAVs) is a promising solution for search and rescue of missing persons who have disappeared during emergencies such as natural disasters. In actual situations, the processing time of image data should be considered due to the wide variety of computing resources provided by UAVs. In addition, network connectivity and transmission speed could be unstable since communication infrastructure may have been damaged in disaster-hit areas. Thus, both the processing time of the acquired data and data transfer time are critical in search and rescue missions. Unlike solutions proposed in the past, we propose a scheduling method of multi-UAV search systems that takes into account both, the processing time of image data and data-transfer time. We present a utility-based problem formulation that ensures continuously updating information while obtaining as many pieces of information as possible for a certain period. Simulation results indicate that the proposed scheduling method maximizes user utility and performs better than a conventional scheduling method in terms of user-centric evaluation metrics.
... [12] noted in August 2017, right after Hurricane Harvey, that subsequent to power restoration, "delivering medical care to individuals in compromised living quarterswhether in a flooded home or in a shelterpresents unique challenges to first responders, as well as doctors and nurses who serve on the front lines". Right after Hurricane Irma, in September 2017, Aircraft Owners and Pilots Association (AOPA) volunteered to deliver medicine with piloted aircrafts to flooded areas in Florida and Virgin Islands due to Hurricane Irma [13]. Such observations motivate the development of a drone-based emergency medicine delivery systems, which this paper proposes. ...
... Note that constraint sets (13) and (14), as well as (15) and (16), (17) and (18), and (19) and (20), refer to the two days separately. Constraints (21)-(23) account for the relocations, where parameter Q < p specifies the maximum number of platform relocations that are allowed in this scenario. ...
Preprint
Full-text available
Motivated by issues dealing with delivery of emergency medical products during humanitarian disasters, this paper addresses the general problem of delivering perishable items to remote demands accessible only by helicopters or drones. Each drone operates out of platforms that may be move when not in use and each drone has a limited delivery range to service a demand points. Associated with each demand point is a disutility function, or a cost function, with respect to time that reflects preferred delivery clock time for the demanded item, as well as the item's perishability characteristic that models nonincreasing quality with time. The paper first addresses the problem of locating the platforms as well concurrently determining which platform serves which demand points and in what order to minimize total disutility for product delivery. The second scenario addresses the two-period problem where the platforms can be relocated, using useable road networks, after the first period. It can be easily proven that continuous time versions of these problems are NP-Hard. However, a practical "time-slot" version of the problem, where time is discretized into slots, can be solved by standard optimization software. Extensive computational experiments, using different drone delivery ranges as well as different drone fleet sizes, provide valuable insights on the performance of such drone delivery systems.
... To make the optimization tractable, we approximate the expectation by introducing a heuristic as proposed in (Lanillos et al. 2014b). Afterwards, we integrate over the target location variable τ , since it is an unknown variable. ...
Preprint
We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli provided by the visual sensors. Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available. In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment. The attention module couples both top-down and bottom-up attention models enabling the robot to search regions with higher importance first. The proposed algorithm is evaluated on a mobile robot platform in a 3D simulated environment. The results indicate that the use of visual attention significantly improves search, but the degree of improvement depends on the nature of the task and the complexity of the environment. In our experiments, we found that performance enhancements of up to 42\% in structured and 38\% in highly unstructured cluttered environments can be achieved using visual attention mechanisms.
... Although there have been studies [20][21][22][23][24] relating to probabilistic searching with UAVs, few studies have focused on the importance of search altitude. There are very few studies that analyze the search performance of drones according to the change of search altitude. ...
Article
Full-text available
When searching for targets using unmanned aerial vehicles, speed is important for many applications such as the discovery of patients in a medical emergency. The speed of operation of actual unmanned aerial vehicles is strongly related to the performance of the camera sensor used for target recognition, search altitude, and the search algorithm employed by the unmanned aerial vehicle. In this study, the major factors affecting the speed of a probabilistic unmanned aerial vehicle target search are analyzed. In particular, simulations are performed to analyze the influence of the search altitude, sensor false alarm rate, and sensor missed detection rate on the required travel distance and the time required for a search. Furthermore, the search performance of an unmanned aerial vehicle is analyzed by varying the search altitude with fixed false alarm and missed detection probabilities. The simulation results show that the search performance is significantly affected by changes in the false alarm and missed detection probabilities of the sensor, and it confirms that the effect of the missed detection probability is greater than that of the false alarm probability. The second simulation proves that the altitude of an unmanned aerial vehicle is a very important factor for the speed of a target search. In particular, the result shows that, for a real data set, the search distance and time at 10 and 5 m are about 2.8 times and 14.3 times larger, respectively, than those at 20 m.
... Most of the literature focuses on the problems of drones applied at a tactical or operational level such as routing (Bae and Rathinam 2015;Babel 2017;Coelho et al.,2017;Dorling et al. 2017), area coverage (Barrientos et al. 2011;Dille and Singh 2013;Avellar et al. 2015;Balampanis, Maza, and Ollero 2017), and search operations (Sujit and Ghose 2004;Lanillos et al. 2014;Oh et al. 2014;Ho and Ouaknine 2015). Drone delivery is a new technology, so it is important to analyse the barriers in drone logistics to provide critical insights to researchers and practitioners. ...
Article
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Companies are adopting innovative methods for responsiveness and efficiency in the logistics sector. The implementation of drones in logistics sector is a move in this direction. Potential obstacles in the smooth adoption of drones in the logistics sector must be thoroughly analysed. The present study identifies and prioritises the barriers of drone logistics implementation based on their criticality by using the fuzzy Delphi method (FDM) and the analytic hierarchy process (AHP). Initially, 34 barriers are identified through expert opinion and extensive literature review. Furthermore, relevant barriers are finalised among all the barriers by using the FDM. Finally, prioritisation of the barriers based on their criticality is done by AHP technique. The study concludes that regulations and threat to privacy & security are the most critical barriers to implement drones in logistics sector. Public perception & psychological, environmental, technical issues, and economic aspects are the other identified critical barriers. The managerial implications of the findings that could help practitioners and policymakers in the effective implementation of drones in the logistics sector are also discussed.
... First, due to the limited battery capacity, drones have to be recharged, which may result in more frequent search for cells (areas) close to a depot (Sujit and Ghose, 2004;Sujit and Ghose, 2010). In addition, drone sensors have limitations and have different characteristics than the ones used for traditional search operations that utilize piloted aircraft or ground vehicles (Ji et al., 2015;Lanillos et al., 2014;Nguyen et al., 2016;Tisdale et al., 2009). Furthermore, the communication concerns (e.g., between drones, between a drone and a depot, between a drone and a ground vehicle) may arise in certain applications (Ji et al., 2015;Sujit and Ghose, 2010;Yang et al., 2002). ...
Article
This paper surveys the state-of-the-art optimization approaches in the civil application of drone operations (DO) and drone-truck combined operations (DTCO) including construction/infrastructure, agriculture, transportation/logistics, security/disaster management, entertainment/media, etc. In particular, this paper reviews ongoing research on various optimization issues related to DO and DTCO including mathematical models, solution methods, synchronization between a drone and a truck, and barriers in implementing DO and DTCO. First, the paper introduces DO and DTCO and their applications, and explores some previous works including survey papers. In addition, this paper surveys the state of the art of DO and DTCO studies and discusses the research gaps in the literature. Furthermore, the detailed review of DTCO models and solution methods are reviewed. Finally, future research directions are discussed.
... Accordingly, the operations research community has been investigating approaches to improve the efficiency of UAV-powered applications [22,23]. In particular, decentralized optimization methods have fostered search problems [24,25,26,27,28,29], target assignment problems [30,31,32,33,34,35,36,37,38,39,40], node covering problems [41,42], scheduling problems [43], and other cases [44,45,46]. ...
Preprint
The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of those operations that are typically carried out by humans (e.g., building inspection, photo collection). The potential of drone applications can be pushed even further when they are operated in fleets and in a fully autonomous manner, acting de facto as a drone swarm. Besides automating field operations, a drone swarm can serve as an ad-hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other connected elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members themselves, as well as by the field agents operating within the area of interest. By considering the practical example of a swarm-powered 3D reconstruction application, we present a new optimization problem for the efficient generation and execution, on top of swarm-powered ad-hoc cloud infrastructure, of multi-node computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the inter-drone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones.
... It is assumed that the cooperative search is completed when the interested region is covered by the UAVs. The prior information of the environment can be obtained by some coarse estimate, which is described by a density function denoted as [33]: ...
Article
Full-text available
Unmanned Aerial Vehicle (UAV) has been widely used in a variety of application, and the target search is one of the hot issues in the UAV research fields. Compared with the single UAV, the multi-UAV system can be competent for more complex tasks, with higher execution efficiency and stronger robustness. However, there exist some new challenges in the multi-UAV cooperative search, such as collaborative control and search area covering problems. To complete these tasks efficiently, the cooperative search problem is modeled as a potential game, and a modified binary log linear learning (BLLL) algorithm is proposed in this paper, to solve the covering problem using multiple UAVs. Furthermore, to improve the cooperative control performance based on potential game theory, a novel action selection strategy for UAVs is proposed. This strategy can avoid a UAV wandering around at the zero utility area by exchanging the information with neighbors. Finally, various simulations are carried out. The experimental results show that the proposed method can effectively complete cooperative search tasks and has better performance than the original BLLL algorithm.
... Ergodic multi-agent coverage using RHC method, with a goal of achieving a more general, non-uniform, goal coverage density is considered in [28], [3]. Receding Horizon Control is utilized for uncertainty search in [9] and [20] ensuring reduction of time needed for target detection. Arbitrary target probability maps and sensor detection models, as well as multi-agent coordination are built-in features of the control method. ...
Preprint
Full-text available
Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm which is able to consider sensors characteristics, the uncertainty of target detection and complexity of needed maneuvers in order to make a multi-agent search autonomous. This paper provides a methodology for an autonomous two-dimensional search using multiple unmanned search agents. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via Heat Equation Driven Area Coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents which are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.
... Despite problem diversity, most proposed problem-solving approaches often rely on common computational complexity reduction strategies to mitigate the curse of dimensionality, from ad hoc constraint relaxation to fast or global search methods utilization promoting low-cost computation. Some of these techniques include sampling (Lanillos et al. 2013), coarse-grained environment representation (Lanillos et al. 2012) and limiting action parameter specification (Lanillos et al. 2014). Problem complexity reduction strategies are coupled to traditional search theory methods such as branch and bound (Washburn 1998;Lau and Dissanayake 2005) or A* search techniques and related variants. ...
Article
Full-text available
Search path planning is critical to achieve efficient information-gathering tasks in dynamic uncertain environments. Given task complexity, most proposed approaches rely on various strategies to reduce computational complexity, from deliberate simplifications or ad hoc constraint relaxation to fast approximate global search methods utilization often focusing on a single objective. However, problem-solving search techniques designed to compute near-optimal solutions largely remain computationally prohibitive and are not scalable. In this paper, a new information-theoretic evolutionary anytime path planning algorithm is proposed to solve a dynamic search path planning problem in which a fleet of homogeneous unmanned aerial vehicles explores a search area to hierarchically minimize target zone occupancy uncertainty, lateness, and travel/discovery time respectively. Conditioned by new observation outcomes and request events, the evolutionary algorithm episodically solves an augmented static open-loop search path planning model over a receding time horizon incorporating any anticipated information feedback. The proposed approach has shown to outperform alternate myopic and greedy heuristics, significantly improving relative information gain at the expense of modest additional travel cost.
... To make the optimization tractable, we approximate the expectation by introducing a heuristic as proposed in (Lanillos et al. 2014b). Afterwards, we integrate over the target location variable τ , since it is an unknown variable. ...
Article
Full-text available
We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli provided by the visual sensors. Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available. In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment. The attention module couples both top-down and bottom-up attention models enabling the robot to search regions with higher importance first. The proposed algorithm is evaluated on a mobile robot platform in a 3D simulated environment. The results indicate that the use of visual attention significantly improves search, but the degree of improvement depends on the nature of the task and the complexity of the environment. In our experiments, we found that performance enhancements of up to 42% in structured and 38% in highly unstructured cluttered environments can be achieved using visual attention mechanisms.
... The migration procedure. 1 for j = 1 to n do 2 Select an emigrating solution X from the best half of population with a probability proportional to 1−λ(X ); 3 Let Aj = C(xj)\C(x j ); 4 Randomly remove a subset of subregions in Aj from xj; ...
Article
In recent years, there have been increasing reports of missing tourists around the world. The use of unmanned aerial vehicles (UAVs) can significantly improve the performance of search and rescue operations. However, planning the search paths of UAVs can be a highly complex optimization problem, and one of the most challenging tasks in the problem formulation is the estimation of target location probability distribution over time. This paper presents a problem of scheduling multiple UAVs to search for missing tourists, and proposes a method for estimating tourist location probabilities which change with topographic features, weather conditions, and time. To solve the problem efficiently, we propose a hybrid evolutionary algorithm which consists of a main algorithm and a sub-algorithm. The main algorithm uses specific migration and mutation operators to evolve a population of main solutions, and the sub-algorithm combines a problem-specific heuristic and tabu search method to optimize each UAV path. The experimental results on a wide variety of test instances (including five real-world instances) demonstrate the performance advantages of the proposed method.
... Aiming at the scenario of using a drone cluster to find targets in a hazardous environment, a collaborative search strategy for drones is proposed in [27], which instructs searchers to gradually move from one unit to the next to ensure that the search area is covered. e influence of the heuristic information on search agents was studied in [28]; Lanillos et al. compared the search strategy with heuristic information and the search strategy without heuristic information. e results show that the search strategy with the heuristic information can effectively avoid the search agent falling into the local optimal position. ...
Article
Full-text available
In this paper, a sequence decision framework based on the Bayesian search is proposed to solve the problem of using an autonomous system to search for the missing target in an unknown environment. In the task, search cost and search efficiency are two competing requirements because they are closely related to the search task. Especially in the actual search task, the sensor assembled by the searcher is not perfect, so an effective search strategy is needed to guide the search agent to perform the task. Meanwhile, the decision-making method is crucial for the search agent. If the search agent fully trusts the feedback information of the sensor, the search task will end when the target is “detected” for the first time, which means it must take the risk of founding a wrong target. Conversely, if the search agent does not trust the feedback information of the sensor, it will most likely miss the real target, which will waste a lot of search resources and time. Based on the existing work, this paper proposes two search strategies and an improved algorithm. Compared with other search methods, the proposed strategies greatly improve the efficiency of unmanned search. Finally, the numerical simulations are provided to demonstrate the effectiveness of the search strategies.
... However, UAVs have some challenges for autonomous flight, such as control strategy including parameter tuning, adaptive control, real-time path planning, and object recognition under uncertain environments. In previous studies, some approaches such as a negotiation approach, a heuristic approach, and a graph theory approach were proposed to solve it [24][25][26]. However, these approaches still had difficulties with sensors, system dynamics, qualities, and so on. ...
Article
Full-text available
In recent years, since researchers began to study on Unmanned Aerial Vehicles (UAVs), UAVs have been integrated into today's everyday life, including civilian area and military area. Many researchers have tried to make use of UAVs as an ideal platform for inspection, delivery, surveillance, and so on. In particular, machine learning has been applied to UAVs for autonomous flight that enables UAVs do designated task more efficiently. In this paper, we review the history and the classification of machine learning, and discuss the state-of-the-art machine learning that has been applied to UAVs for autonomous flight. We provide control strategies including parameter tuning, adaptive control for uncertain environment, and real-time path planning, and object recognition that have been described in the literature.
... They are highly suitable for the presence of a pilot being in dangers or other impossible situations. With the enhancement of high autonomy, it involves to a wide range of applications, including search and rescue (Lanillos et al. 2014;Varela et al. 2014), mobile three-dimensional (3D) mapping for surveying earthwork projects (Siebert and Teizer 2014), target tracking (Choi and Kim 2014), urban traffic analysis (Salvo et al. 2014), disaster recovery (Tuna et al. 2014) and two-dimensional (2D) indoor target searching (Shi et al. 2015). ...
Article
Full-text available
This paper puts forward a noninvasive electrooculography (EOG) and electroencephalogram (EEG)-based hybrid computer interface (HCI) system to implement the indoor target searching in three-dimensional (3D) space for a low-speed multi-rotor aircraft. The HCI system mainly consists of three subsystems, including the interface switching, decision and semi-autonomous navigation. The interface switching subsystem is accomplished by detecting the eyeblink EOG. The continuous wavelet transform is employed to indentify eyeblink features which are used to switch interfaces between horizontal and vertical motor imagery (MI) tasks. The average accuracy of the eyeblink feature detection reaches to 97.95%. The decision subsystem employs the joint regression (JR) model and spectral powers methods to extract the time and frequency domain features of MI tasks by analyzing the left- and right-hand MI EEG. Simultaneously, the support vector machine is applied to accomplish the MI tasks classification and final decision. The average classification accuracy of the HCI system reaches to 93.99%. The semi-autonomous navigation subsystem extracts the environmental features to avoid obstacles semi-automatically in 3D space and provide feasible directions for the decision subsystem. The actual indoor 3D space target searching experiments are put forward to verify the feasibility and adaptation performances of this proposed HCI system.
... Prior work has used belief networks for coordinated control of multi-robot teams in the context of Markov Random Fields [2,13,18]. Gradient-based navigation for multi-robot navigation using potential field has been proposed by other works [9,11]. However, these works do not incorporate human intentions and interaction. ...
Conference Paper
I introduce a novel multi-modal multi-sensor interaction method between humans and heterogeneous multi-robot systems. I have also developed a novel algorithm to control heterogeneous multi-robot systems. The proposed algorithm allows the human operator to provide intentional cues and information to a multi-robot system using a multimodal multi-sensor touchscreen interface. My proposed method can effectively convey complex human intention to multiple robots as well as represent robots' intentions over the spatiotemporal domain. The proposed method is scalable and robust to dynamic change in the deployment configuration. I describe the implementation of the control algorithm used to control multiple quad-rotor unmanned aerial vehicles in simulated and real environments. I will also present my initial work on human interaction with the robots running my algorithm using mobile phone touch screens and other potential multimodal interactions.
... • Búsqueda y rescate (Lanillos et al., 2014;Varela et al., 2014;Erdos et al., 2013;Sun and Duan, 2012;Pitre et al., 2012;Tomic et al., 2012;Riehl et al., 2011;Jin et al., 2005). • Patrullaje en la frontera, seguridad nacional, monitoreo y seguimiento de droga, control y monitoreo de manifestaciones civiles (Khaleghi et al., 2013b,a;Sun et al., 2011;Matveev et al., 2010;Bolkcom, 2004;Girard et al., 2004). ...
... [24] designs an algo- rithm combining a consensus protocol and a sliding mode control for cooperative formations with incomplete target information. [25] executes the team negotiation using a de- centralized gradient-based optimization in the multi-UAV- based target searching. [26] attempts to locate the multi- ple sources in two steps, which are the region of interest (ROI) selection and the source localization. ...
... According to previous studies on various types of sensors, the ability of sensors to collect feature information decays with increasing distance [44,45]. Therefore, the HAUV's ability to collect information about the surrounding workspace at point ρ j can be expressed in the following form: ...
Preprint
This paper presents a novel Rapidly-exploring Adaptive Sampling Tree (RAST) algorithm for the adaptive sampling mission of a hybrid aerial underwater vehicle (HAUV) in an air-sea 3D environment. This algorithm innovatively combines the tournament-based point selection sampling strategy, the information heuristic search process and the framework of Rapidly-exploring Random Tree (RRT) algorithm. Hence can guide the vehicle to the region of interest to scientists for sampling and generate a collision-free path for maximizing information collection by the HAUV under the constraints of environmental effects of currents or wind and limited budget. The simulation results show that the fast search adaptive sampling tree algorithm has higher optimization performance, faster solution speed and better stability than the Rapidly-exploring Information Gathering Tree (RIGT) algorithm and the particle swarm optimization (PSO) algorithm.
... Ergodic multiagent coverage using the RHC method, with a goal of achieving a more general, nonuniform, goal coverage density is considered in [12] and [13]. RHC is utilized for an uncertainty search in [14] and [15], ensuring reduction of time needed for target detection. Arbitrary target probability maps and sensor detection models, as well as multiagent coordination are built-in features of the control method. ...
Article
Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm that is able to consider sensor characteristics, the uncertainty of target detection, and complexity of needed maneuvers in order to make a multiagent search autonomous. This article provides a methodology for an autonomous 2-D search using multiple unmanned (aerial or possibly other) vehicles. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via a heat equation-driven area coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents that are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides a near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve the proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of an HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.
... Although target detection algorithms based on deep learning can improve performance by increasing the types and numbers of small target samples in the training set, the adaptability and accuracy of many algorithms are difficult to guarantee when dealing with small target detection. In vision tasks such as target searching [3] and target positioning [4], traditional vision systems have significant limitations in the efficiency and accuracy of image processing . However, the thorny issues are simple for the creatures [5]. ...
Preprint
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Small target detection is known to be a challenging problem. Inspired by the structural characteristics and physiological mechanism of eagle-eye, a miniature vision system is designed for small target detection in this paper. First, a hardware platform is established, which consists of a pan-tilt, a short-focus camera and a long-focus camera. Then, based on the visual attention mechanism of eagle-eye, the cameras with different focal lengths are controlled cooperatively to achieve small target detection. Experimental results show that the designed biological eagle-eye vision system can accurately detect small targets, which has a strong adaptive ability.
... Searching for targets when there is incomplete information about their location is a generalization of the MTS problem, where one or more targets are in unknown locations and need to be found as soon as possible. There are many approaches for handling the MTS problem under the assumption of a static target [17,8,12,24]; however, we are interested in moving targets. ...
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This paper considers the complex problem of a team of UAVs searching targets under uncertainty. The goal of the UAV team is to find all of the moving targets as quickly as possible before they arrive at their selected goal. The uncertainty considered is threefold: First, the UAVs do not know the targets' locations and destinations. Second, the sensing capabilities of the UAVs are not perfect. Third, the targets' movement model is unknown. We suggest a real-time algorithmic framework for the UAVs, combining entropy and stochastic-temporal belief, that aims at optimizing the probability of a quick and successful detection of all of the targets. We have empirically evaluated the algorithmic framework, and have shown its efficiency and significant performance improvement compared to other solutions. Furthermore, we have evaluated our framework using Peer Designed Agents (PDAs), which are computer agents that simulate targets and show that our algorithmic framework outperforms other solutions in this scenario.
... Likewise, the swarm robotics has drawn the attention of the operations research community as an attractive opportunity to improve the efficiency of swarm-powered missions [23,24]. For instance, decentralized optimization methods have been leveraging search problems [25,26,27,28,29,30], target assignment problems [31,32,33,34,35,36,37,38,39,40,41], node covering problems [42,43], scheduling problems [44], etc. ...
Preprint
Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors -- also known as swarming behaviors -- within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members.We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones.
... Many targeted heuristic algorithms have been applied to solve multi-target optimization problems in recent years [19][20][21], but there is currently no general cooperative task assignment model that can simultaneously meet complex battlefield environments with coupled task relationships and strict requirements for the task timing sequence. Although existing algorithms cannot guarantee optimal results, they do provide satisfactory solutions within an acceptable time period [22,23]. ...
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The multi-unmanned aerial vehicle (UAV) must autonomously perform reconnaissance-attack-evaluation tasks under multiple constraints in the battlefield environment. This paper proposes a nearest neighbor method designed with the shortest neighboring distance as an indicator which quickly solves the optimal sequence of multiple tasks for cooperative execution. Each target to be destroyed requires a different quantity of ammunition; a cooperative task assignment model for heterogeneous UAVs is established accordingly. Based on the nearest neighbor method, and with reference to fruit-picking techniques currently in use, a novel “orchard picking algorithm (OPA)” is investigated as well. This algorithm proposed in this paper is a heuristic algorithm, which has a broad application prospect in complex task assignment. A cooperative attack task assignment is simulated to test the performance of the algorithm. In essence, it balances the assignment of tasks, works within a brief execution time, and exhibits high flexibility, strong robustness, and scalability.
... In turn, the functionality of an information fusion strategy greatly depends on robots networking capabilities. Data aggregation can be achieved through different communication protocols, namely, centralized [37], [40], [68], decentralized [24], [71], [194], or distributed [36], [195]- [197]. ...
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Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.
... When the UAV visits the cell, its time is reset to zero [47]. If the detection model is probabilistic, the cell value can be a measure related to the probability of a target existing in the cell instead of the time last visited [37]. ...
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Groups of battery powered unmanned aerial vehicles (UAVs) are effective in a variety of scenarios that require autonomous cooperation to achieve a goal. However, the complexity of modeling and analyzing UAV cooperation in situations with stochastic elements leads to unique challenges. This paper introduces a framework for one such problem domain, the multi-UAV persistent search and retrieval task with stochastic target appearances (PSR-STA), in which UAVs continuously search an area for stochastically appearing targets to retrieve and deliver them to collector locations. Design decisions are introduced for understanding how to successfully simulate multi-UAV PSR-STA. Common tools for analyzing search algorithm effectiveness through statistical and graphical methods are presented. A case study of multi-UAV park cleanup is implemented to demonstrate the framework, where algorithms for choosing the locations of collectors and charging stations based on stochastic target appearance models are proposed, methods for continuous multi-UAV operation over a long period time are demonstrated, and the differences in effectiveness between four coverage search patterns are analyzed.
... Sin embargo, al igual que otros métodos basados en optimización, el coste computacional crece rápidamente con el número de UAVs, amplitud de los pasos temporales (longitud de la trayectoria planificada) y tamaño delárea. Un trabajo similar puede encontrarse en Lanillos et al. (2014), también basado en ventanas temporales deslizantes y optimización descentralizada. ...
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span class="fontstyle0">A día de hoy, existen en el mercado una gran cantidad de aeronaves sin piloto que pueden ser comandadas con ordenes de alto nivel para realizar tareas complejas de forma casi automatica, como por ejemplo el mapeo de explotaciones agrícolas. De forma natural, nos podemos preguntar si sería posible coordinar a un grupo de estos robots para realizar esas mismas tareas de forma más rápida, flexible y robusta. En este trabajo se repasan las tareas que se han planteado resolver con sistemas compuestos por grupos de aeronaves no tripuladas y los algoritmos empleados, así como los metodos y estrategias en los que están basados. Aunque el futuro de estos sistemas es prometedor, existen ciertos obstaculos legislativos y técnicos que frenan su implantación de forma generalizada.</span
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The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of operations that are typically carried out by humans. Besides automating field operations, drones acting de facto as a swarm can serve as an ad hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members and the field agents. By considering the practical example of a swarm-powered 3-D reconstruction application on top of such cloud infrastructure, we present a new optimization problem for the efficient generation and execution of multinode computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the interdrone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones.
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Multiple autonomous underwater vehicles (Multi-AUV) target search is the important element to realize underwater rescue, underwater detection. To improve efficiency of multi-AUV target search in three dimensional (3-D) underwater environments, a potential field hierarchical reinforcement learning (PHRL) approach is proposed in this paper. Unlike other algorithms that need repeated training in the choice of parameters, the proposed approach obtains all the required parameters automatically through learning. By integrating segmental options with the traditional hierarchy reinforcement learning (HRL) algorithm, the potential field hierarchy is built. The potential field is implemented in the parameters of the HRL, which provides with reasonable paths of the target search for the unexplored environments. In search tasks, the designed method can control the multi-AUV system to find the target effectively. The simulation results show that the proposed approach is capable of controlling multi-AUV to achieve search task of multiple targets with higher efficiency and adaptability compared with the HRL algorithm and the lawn-mowing algorithm.
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Motivated by issues dealing with delivery of emergency medical products during humanitarian disasters, this paper addresses the general problem of delivering perishable items to remote demands accessible only by helicopters or drones. Each drone operates out of platforms that may be moved when not in use and each drone has a limited delivery range to service a demand point. Associated with each demand point is a disutility function, or a cost function, with respect to time that reflects preferred delivery clock time for the demanded item, as well as the item’s perishability characteristic that models nonincreasing quality with time. The paper first addresses the problem of locating the platforms as well concurrently determining which platform serves which demand points and in what order – to minimize total disutility for product delivery. The second scenario addresses the two-period problem where the platforms can be relocated, using useable road network, after the first period. It can be easily proven that continuous time versions of these problems are NP-Hard. However, a practical “time-slot” version of the problem, where time is discretized into slots, can be solved by standard optimization software. Extensive computational experiments, using different drone delivery ranges as well as different drone fleet sizes, provide valuable insights on the performance of such drone delivery systems.
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A novel distributed cooperative search strategy for multiple underwater robots is proposed based on the initial target information, so as to reduce the average time of searching target and improve the search target probability in statistical sense. The cooperative search strategy divides the target search process into two phases. In the first search stage, the underwater robot predicts the possible existence range of the target based on the speed and the elapsed time of the target. When the underwater robot is not in the target prediction range, it directly moves towards the initial position of the target to reduce the time of the blind search. In the second search stage, the underwater robot enters the target prediction range, then the target existence probability is updated in real time according to the sensor detection results, and the predictive control thought is used to make optimization decisions according to the target existence probability. Finally, the Monte Carlo simulation experiments are carried out for 1000 times. The proposed search strategy in this paper is compared with the cooperative search strategy with no target initial information. The comparison results show the effectiveness and feasibility of searching a moving target using the proposed search strategy.
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In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time.
Article
Purpose This paper aims to present a distributed Bayesian approach with connectivity maintenance to manage a multi-agent network search for a target on a two-dimensional plane. Design/methodology/approach The Bayesian framework is used to compute the local probability density functions (PDFs) of the target and obtain the global PDF with the consensus algorithm. An inverse power iteration algorithm is introduced to estimate the algebraic connectivity λ2 of the network. Based on the estimated λ2, the authors design a potential field for the connectivity maintenance. Then, based on the detection probability function, the authors design a potential field for the search target. The authors combine the two potential fields and design a distributed gradient-based control for the agents. Findings The inverse power iteration algorithm can distributed estimate the algebraic connectivity by the agents. The agents can efficient search the target with connectivity maintenance with the designed distributed gradient-based search algorithm. Originality/value Previous study has paid little attention to the multi-agent search problem with connectivity maintenance. Our algorithm guarantees that the strongly connected graph of the multi-agent communication topology is always established while performing the distributed target search problem.
Chapter
This chapter presents the proposed multi-criteria MTS algorithm with a continuous UAV dynamical model and a realistic sensor model. Contrary to the MTS algorithms for discrete UAV dynamic models presented in Chap. 4, the algorithms presented in this chapter consider continuous UAV dynamical models that fulfill fixed-wing dynamic restrictions. Besides, the algorithm considers a realistic sensor model and optimizes multiple criteria and constraints. Due to the successful performance of ACO techniques in the discrete approach, in this chapter we select a continuous ant colony based algorithm and propose a continuous MTS heuristic to test if it allows to reduce the computational time (now higher due to the complexity added by the UAV models and by the evaluation criteria).
Chapter
This chapter discusses the state of the art of Minimum Time Search (MTS) problem, analysing with greater detail several works that have motivated this thesis. The chapter is divided into two sections. The first one discusses, from a general point of view, related probabilistic search problems such as coverage or the Travelling Salesman Problem (TSP), stressing their common characteristics and differences with MTS. The second section analyzes in more detail the state of the art of Probabilistic Search (PS), which aims to find the best Unmanned Vehicles (UV) search trajectories in uncertain environments and which encompasses the MTS problem.
Book
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Vibration Testing and System Dynamics is an interdisciplinary journal serving as the forum for promoting dialogues among engineering practitioners and research scholars. As the platform for facilitating the synergy of system dynamics, testing, design, modeling, and education, the journal publishes high-quality, original articles in the theory and applications of dynamical system testing. The aim of the journal is to stimulate more research interest in and attention for the interaction of theory, design, and application in dynamic testing. Manuscripts reporting novel methodology design for modelling and testing complex dynamical systems with nonlinearity are solicited. Papers on applying modern theory of dynamics to real-world issues in all areas of physical science and description of numerical investigation are equally encouraged.
Book
Vibration Testing and System Dynamics is an interdisciplinary journal serving as the forum for promoting dialogues among engineering practitioners and research scholars. As the platform for facilitating the synergy of system dynamics, testing, design, modeling, and education, the journal publishes high-quality, original articles in the theory and applications of dynamical system testing. The aim of the journal is to stimulate more research interest in and attention for the interaction of theory, design, and application in dynamic testing. Manuscripts reporting novel methodology design for modelling and testing complex dynamical systems with nonlinearity are solicited. Papers on applying modern theory of dynamics to real-world issues in all areas of physical science and description of numerical investigation are equally encouraged.
Chapter
In this chapter we introduce the mathematical formulation of Minimum Time Search (MTS). First, we state the MTS optimization problem and explain how the uncertain information of the elements involved is probabilistically modelled. Then, we introduce how the information about the target location is updated and how the search trajectories are evaluated. Next, we describe, from a general point of view, how PS algorithms solve the search problem. And finally, we introduce the ant colony optimization techniques that are chosen in this thesis to solve the MTS problem.
Article
The search for multiple escaping targets is a significant issue of cooperative control in multi-agent systems since targets consciously seek to avoid being captured. Moreover, the assumption of continuous observations in existing works is not always suitable due to the limit of measuring equipment and uncertain movement of targets. Therefore, the problem with searching for escaping targets, which can be more aptly labeled "multiple escaping-targets search with random observation conditions" (MESROC), is difficult to address by conventional methods. Inspired by machine learning and the immune response mechanism of human bodies, a self-learning immune co-evolutionary network (SLICEN) is proposed. The SLICEN consists mainly of an immune cellular network (ICN) and an immune learning algorithm (ILA). The ICN provides feasible solutions to MESROC. Different kinds of network models are introduced to work as an ICN, such as convolutional neural networks, extreme learning machines, and support vector machines. The ILA evaluates the performance of feasible solutions and selects the optimal ones to further strengthen ICN reversely. Solutions are repeatedly improved through the co-evolution of ICN and ILA. An essential distinction to conventional machine learning approaches is that SLICEN works well without training samples. Simulations and comparisons demonstrate that patterns of advanced cooperative behavior among searchers function properly. SLICEN is an efficient method for solving MESROC.
Article
This paper focuses on solving the multi-agent cooperative target search problem with the demand for obtaining the maximal cumulative detection reward, given the prior target probability map and the sensor detection ability under various constraints. First, a topologically organized model of Glasius bio-inspired neural network (GBNN) is constructed individually for each agent in order to represent the searching environment. The neural activities are determined not only by the activity propagation among neurons, but also by the external input containing the single detection reward and various constraints synthetically. Then, the agent’s searching motion can be selected greedily based on the dynamic activity landscape of GBNN. With the disadvantages of propagation time delay and activity attenuation, however, the relatively global mechanism in GBNN may lead to unsatisfactory performance or even fail to avoid the local optimal problem. Hence the Gaussian mixture model (GMM) is utilized to extract the high-value subregions and compute the future detection reward quantitatively, which can be introduced into the neuron’s external excitatory input of GBNN directly. The simulation results verify the high efficiency and strong robustness of GBNN-GMM in the searching scenarios.
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In this research, a cooperative search for multiple dynamic targets in an unknown marine environment by multiple unmanned aerial vehicles is studied based on a novel multi‐bee‐colony (MBC) elite learning algorithm. First, a specialized searching model is established which includes the UAV dynamics, the sensor model, the target probability and environmental certainty at different flight altitudes. Then, a new search strategy, which consists of rough search and accurate search, is proposed by maximizing the multiobjective utility function with the dynamic changing of the flight altitude. In order to solve the optimization problem, an improved MBC algorithm based on elite learning is designed, which can improve the adaptability and computation speed of the standard artificial bee colony (ABC) algorithm under different search missions. Finally, extensive simulations are conducted to show the effectiveness and superiority of the proposed search strategy.
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This paper proposes a Bayesian approach for minimizing the time of finding an object of uncertain location and dynamics using several moving sensing agents with constrained dynamics. The approach exploits twice the Bayesian theory: on one hand, it uses a Bayesian formulation of the objective functions that compare the constrained paths of the agents and on the other hand, a Bayesian optimization algorithm to solve the problem. By combining both elements, our approach handles successfully this complex problem, as illustrated by the results over different scenarios presented and statistically analyzed in the paper. Finally, the paper also discusses other formulations of the problem and compares the properties of our approach with others closely related.
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This paper formulates and proposes a discrete solution for the problem of finding a lost target under uncertainty in minimum time (Minimum Time Search). Given a searching region where some information about the target is known but uncertain (i.e. location and dynamics), and a searching agent with constrained dynamics, we provide two decision making algorithms that optimizes the agent actions to find the target in a minimum time. The problem is faced as a discrete optimization: the actions and the sensor are discrete, and the target probabilistic model is described over a graph, where each vertex contains the target's location probability information and each edge defines the agent possible actions. We revisit the mathematical model of the optimal search problem and we propose a novel approach to include the time into the decision making by reinterpreting it as a maximum discounted time reward problem. The optimal decision plan for the agent is obtained by solving this non convex discrete problem using the cross entropy method. By performing statistical simulations we show how the target is found in minimum time.
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This article is based on a talk given by the author at the 1988 TIMS/ORSA meeting in Washington, D.C. about developments in search theory since the Lanchester Prize for 1975 was awarded to him for Theory of Optimal Search. The answer to the question in the title is that a lot has happened, particularly in the area of the search for moving targets and in the use of computers in search theory and applications. The development of search theory began during World War II and has evolved through four eras: classical (1942-1965); Mathematical (1965-1975); algorithmic (1975-1985); dynamic (1985-Present). Each of these eras is discussed.
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This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints. Our case study is a POMDP model of an online multi-target detection and recognition mission by an autonomous UAV. The POMDP model of the multitarget detection and recognition problem is generated online from a list of areas of interest, which are automatically extracted at the beginning of the flight from a coarse-grained high altitude observation of the scene. The POMDP observation model relies on a statistical abstraction of an image processing algorithm's output used to detect targets. As the POMDP problem cannot be known and thus optimized before the beginning of the flight, our main contribution is an "optimize-while- execute" algorithmic framework: it drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints. We present new results from real outdoor flights and SAIL simulations, which highlight both the benefits of using POMDPs in multi-target detection and recognition missions, and of our "optimize-while-execute" paradigm. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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We present a decentralized optimization method for solving the coordination problem of interconnected nonlinear discrete-time dynamic systems with multiple decision makers. The optimization framework embeds the inherent structure in which each decision maker has a mathematical model that captures only the local dynamics and the associated interconnecting global constraints. A globally convergent algorithm based on sequential local optimizations is presented. Under assumptions of differentiability and linear independence constraint qualification, we show that the method results in global convergence to ε-feasible Nash solutions that satisfy the Karush-Kuhn-Tucker necessary conditions for Pareto-optimality. We apply this methodology to a multiple unmanned air vehicle system, with kinematic aircraft models, coordinating in a common airspace with separation requirements between the aircraft.
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This paper extends a recently developed statistical framework for UAV search with uncertain probability maps to the case of dynamic targets. The probabilities used to encode the information about the environment are typically assumed to be exactly known in the search theory literature, but they are often the result of prior information that is both erroneous and delayed, and will likely be poorly known to mission designers. Our previous work developed a new framework that accounted for the uncertainty in the probability maps for stationary targets, and this paper extends the approach to more realistic dynamic environments. The dynamic case considers probabilistic target motion, creating uncertain probability maps (UPMs) that take into account both poor knowledge of the probabilities and the propagation of their uncertainty through the environment. A key result of this paper is a new algorithm for implementing UPM's in real-time, and it is shown in various simulations that this algorithm leads to more cautious information updates that are less susceptible to false alarms. The paper also provides insights on the impact of the design parameters on the responsiveness of the new algorithm. Several numerical examples are presented to demonstrate the effectiveness of the new framework
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This paper develops a set of methods enabling an information-theoretic distributed control architecture to facilitate search by a mobile sensor network. Given a particular configuration of sensors, this technique exploits the structure of the probability distributions of the target state and of the sensor measurements to control the mobile sensors such that future observations minimize the expected future uncertainty of the target state. The mutual information between the sensors and the target state is computed using a particle filter representation of the posterior probability distribution, making it possible to directly use nonlinear and non-Gaussian target state and sensor models. To make the approach scalable to increasing network sizes, single-node and pairwise-node approximations to the mutual information are derived for general probability density models, with analytically bounded error. The pairwise-node approximation is proven to be a more accurate objective function than the single-node approximation. The mobile sensors are cooperatively controlled using a distributed optimization, yielding coordinated motion of the network. These methods are explored for various sensing modalities, including bearings-only sensing, range-only sensing, and magnetic field sensing, all with potential for search and rescue applications. For each sensing modality, the behavior of this non-parametric method is compared and contrasted with the results of linearized methods, and simulations are performed of a target search using the dynamics of actual vehicles. Monte Carlo results demonstrate that as network size increases, the sensors more quickly localize the target, and the pairwise-node approximation provides superior performance to the single-node approximation. The proposed methods are shown to produce similar results to linearized methods in particular scenarios, yet they capture effects in more general scenarios that are not possible with linearized methods.
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In this paper we address the problem of finding time-optimal search paths in known environments. In particular, we address the problem of searching a known environment for an object whose unknown location is characterized by a known probability density function (PDF). With this formulation, the time required to find the object is a random variable induced by the choice of search path together with the PDF for the object's location. The optimization problem we consider is that of finding the path that minimizes the expected value of the time required to find the object. As the complexity of the problem precludes finding an exact optimal solution, we propose a two-level, heuristic approach to finding the optimal search path. At the top level, we use a decomposition of the workspace based on critical curves to impose a qualitative structure on the solution trajectory. At the lower level, individual segments of this trajectory are refined using local numerical optimization methods. We have implemented the algorithm and present simulation results for the particular case when the object's location is specified by the uniform PDF.
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Unmanned aerial vehicles (UAV) can be used to cover large areas searching for targets. However, sensors on UAVs are typically limited in their accuracy of localization of targets on the ground. On the other hand, unmanned ground vehicles (UGV) can be deployed to accurately locate ground targets, but they have the disadvantage of not being able to move rapidly or see through such obstacles as buildings or fences. In this paper, we describe how we can exploit this synergy by creating a seamless network of UAVs and UGVs. The keys to this are our framework and algorithms for search and localization, which are easily scalable to large numbers of UAVs and UGVs and are transparent to the specificity of individual platforms. We describe our experimental testbed, the framework and algorithms, and some results
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This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as nonGaussian target state densities. The JMPD technique simultaneously estimates both the target states and the number of targets in the surveillance region based on the set of measurements made. We give an implementation of the JMPD method based on particle filtering techniques and provide an adaptive sampling scheme which explicitly models the multitarget nature of the problem. We show that this implementation of the JMPD technique provides a natural way to track a collection of targets, is computationally tractable, and performs well under difficult conditions such as target crossing, convoy movement, and low measurement signal-to-noise ratio (SNR).
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We consider decentralized control of Markov decision processes and give complexity bounds on the worst-case running time for algorithms that find optimal solutions. Generalizations of both the fullyobservable case and the partially-observable case that allow for decentralized control are described. For even two agents, the finite-horizon problems corresponding to both of these models are hard for nondeterministic exponential time. These complexity results illustrate a fundamental difference between centralized and decentralized control of Markov decision processes. In contrast to the problems involving centralized control, the problems we consider provably do not admit polynomial-time algorithms. Furthermore, assuming EXP NEXP, the problems require super-exponential time to solve in the worst case.
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This paper addresses the problem of coordinating a team of mobile autonomous sensor agents performing a cooperative mission while explicitly avoiding inter-agent collisions in a team negotiation process. Many multi-agent cooperative approaches disregard the potential hazards between agents, which are an important aspect to many systems and especially for airborne systems. In this work, team negotiation is performed using a decentralized gradient-based optimization approach whereas safety distance constraints are specifically designed and handled using Lagrangian multiplier methods. The novelty of our work is the demonstration of a decentralized form of inter-agent collision avoidance in the loop of the agents' real-time group mission optimization process, where the algorithm inherits the properties of performing its original mission while minimizing the probability of inter-agent collisions. Explicit constraint gradient formulation is derived and used to enhance computational advantage and solution accuracy. The effectiveness and robustness of our algorithm has been verified in a simulated environment by coordinating a team of UAVs searching for targets in a large-scale environment.
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The paper formulates the optimal control problem for a class of mathematical models in which the system to be controlled is characterized by a finite-state discrete-time Markov process. The formulation is illustrated by a simple machine-maintenance example, and other specific application areas are also discussed. The paper demonstrates that, if there are only a finite number of control intervals remaining, then the optimal payoff function is a piecewise-linear, convex function of the current state probabilities of the internal Markov process.
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While several time-optimal trajectory planning techniques have been developed for continuous non-linear systems, there has been little discussion on the subject for discrete non-linear systems. This paper, therefore, presents a technique to search for the time sub-optimal trajectory for general discrete non-linear systems. In this technique, the control inputs with respect to time are partitioned into piecewise constant functions. The piecewise constant functions and the time step interval, which are used in the discretisation of the system, are then searched by a general-purpose non-linear programming optimization method. The example of a time sub-optimal trajectory planning of a SCARA-type manipulator presented in this paper indicates that the proposed technique has the same ability as the existing time sub-optimal trajectory planning techniques for continuous systems. The second numerical example of a non-differentiable car backing-up system shows that the proposed technique also works well for general discrete systems.
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This note discusses the complexity of optimally searching for a stationary target in discrete time and space. In this problem, a target's position is fixed within a gridwork of cells. A searcher moves through this gridwork in an attempt to locate the target. At each time step, the searcher must either remain at its current location or move to a neighboring cell. For the discussion of complexity, it is convenient to view the search problem in terms of a finite, connected graph. In this (equivalent) formulation, cells correspond to vertices of a graph and edges of the graph connect neighboring cells. We consider two versions of the optimal searcher path problem. In the first version (denoted by PD), the time horizon is finite and the measure of search effectiveness is probability of detection. In the second version (denoted by ET), the time horizon is infinite and the measure of search effectiveness is expected time to detection. Our main results are that PD is NP-complete and that ET is NP-hard. Refs.
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Ben Grocholsky Doctor of Philosophy The University of Sydney March 2002 Information-Theoretic Control of This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue, mining or intelligent manufacturing. However, the e#ective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis.
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A search is conducted for a target moving in discrete time among a finite number of cells according to a known Markov process. The searcher must choose one cell in which to search in each time period. The set of cells available for search depends upon the cell chosen in the last time period. The problem is to find a searcher path, i.e., a sequence of search cells, that maximizes the probability of detecting the target in a fixed number of time periods. We formulate the problem as a partially observable Markov decision process and present a finite time horizon POMDP solution technique which is simpler than the standard linear programming methods.
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From the Publisher:This timely book presents such a consistent framework for addressing data fusion and sensor management. While the framework and the methods presented are applicable to a wide variety of multi-sensor systems, the book focuses on decentralized systems. The book also describes an actual to robot navigation and presents real data and results. The vehicle makes use of sonar sensors with focus of attention capability.
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This paper presents an efficient planning and execution algorithm for the navigation of an autonomous rotary wing UAV (RUAV) manoeuvering in an unknown and cluttered environment. A Rapidly-exploring Random Tree (RRT) variant is used for the generation of a collision free path and linear Model Predictive Control(MPC) is applied to follow this path. The guidance errors are mapped to the states of the linear MPC structure by using the nonlinear kinematic equations. The proposed path planning algorithm considers the run time of the planning stage explicitly and generates a continuous curvature path whenever replanning occurs. Simulation results show that the RUAV with the proposed methodology successfully achieves autonomous navigation regardless of its lack of prior information about the environment.
Conference Paper
This paper presents a coordinated control technique that allows heterogeneous vehicles to autonomously search for and track multiple targets using recursive Bayesian filtering. A unified sensor model and a unified objective function are proposed to enable search-and-tracking (SAT) within the recursive Bayesian filter framework. The strength of the proposed technique is that a vehicle can switch its task mode between search and tracking while maintaining and using information collected during the operation. Numerical results first show the effectiveness of the proposed technique when a found target becomes lost and must be searched for again. The proposed technique was then applied to a practical marine search-and-rescue (SAR) scenario where heterogeneous vehicles coordinated to search for and track multiple targets. The result demonstrates the applicability of the technique to real search world scenarios
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Unmanned aerial vehicles (UAVs) have shown promise in recent years for autonomous sensing. UAVs systems have been proposed for a wide range of applications such as mapping, surveillance, search, and tracking operations. The recent availability of low-cost UAVs suggests the use of teams of vehicles to perform sensing tasks. To leverage the capabilities of a team of vehicles, efficient methods of decentralized sensing and cooperative path planning are necessary. The goal of this work is to examine practical control strategies for a team of fixed-wing vehicles performing cooperative sensing. We seek to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions. Sensing goals are posed from an information theoretic standpoint to design strategies that explicitly minimize uncertainty. This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning.
Conference Paper
This paper describes a decentralised asynchronous algorithm for negotiation in team decision and control problems, allowing multiple decision makers to propose and refine future decisions to optimise a common non-linear objective or cost function. A convergence requirement provides an intuitive relationship between the communication frequency, transmission delays and the degree of inter-agent coupling inherent in the system. The coupling is defined by the cross derivative of the objective function. The algorithm is applied to the control of multiple vehicles performing a search task with simulation results given.
Conference Paper
This paper presents a novel contribution to the problem of coordinating a team of autonomous sensor agents searching for targets in a large scale environment. Team negotiation is performed using a decentralized gradient-based optimization algorithm. Conventional approaches use finite differencing to approximate the gradient information that is computationally less efficient and exposes the gradient-based optimizer to potential numerical errors and instability. The novelty of our work is the explicit formulation of the gradient for the target search problem that significantly enhances the efficiency in gradient evaluation and robustness for the gradient-based optimization algorithm. We present results by firstly showing the computational advantage and robustness of this explicit gradient model against the finite differencing approach and further demonstrate its application in simulation by coordinating multiple UAVs searching a large scale environment in a decentralized network.
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The brain is first and foremost a control system that is capable of building an internal representation of the external world, and using this representation to make decisions, set goals and priorities, formulate plans, and control behavior with intent to achieve its goals. The internal representation is distributed throughout the brain in two forms: (1) firmware embedded in synaptic connections and axon-dendrite circuitry, and (2) dynamic state-variables encoded in the firing rates of neurons in computational loops in the spinal cord, midbrain, subcortical nuclei, and arrays of cortical columns. It assumes that clusters and arrays of neurons are capable of computing logical predicates, smooth arithmetic functions, and matrix transformations over a space defined by large input vectors and arrays. Feedback from output to input of these neural computational units enable them to function as finite-state-automata (fsa), Markov decision processes (MDP), or delay lines in processing signals and generating strings and grammars. Thus, clusters of neurons are capable of parsing and generating language, decomposing tasks, generating plans, and executing scripts. In the cortex, neurons are arranged in arrays of cortical columns that interact in tight loops with their underlying subcortical nuclei. It is hypothesized that these circuits compute sophisticated mathematical and logical functions that maintain and use complex abstract data structures. It is proposed that cortical hypercolumns together with their underlying thalamic nuclei can be modeled as a cortical computational unit (CCU) consisting of a frame-like data structure (containing attributes and pointers) plus the computational processes and mechanisms required to maintain it and use it for perception cognition, and sensory-motor behavior. In sensory processing areas of the brain, CCU processes enable focus of attention, segmentation, grouping, and classification. Pointers stored in CCU frames define relationships that link pixels and signals to objects and events in situations and episodes. CCU frame pointers also link objects and events to class prototypes and overlay them with meaning and emotional values. In behavior generating areas of the brain, CCU processes make decisions, set goals and priorities, generate plans, and control behavior. In general, CCU pointers are used to define rules, grammars, procedures, plans, and behaviors. CCU pointers also define abstract data structures analogous to lists, frames, objects, classes, rules, plans, and semantic nets. It is suggested that it may be possible to reverse engineer the human brain at the CCU level of fidelity using next-generation massively parallel computer hardware and software.
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This paper addresses planning of continuous paths for mobile sensors to reduce uncertainty in some quantities of interest in the future. The mutual information between the continuous measurement path and the future verification variables defines the information reward. Two expressions for computing this mutual information are presented: the filter form extended from the state-of-the-art and the smoother form inspired by the conditional independence structure. The key properties of the approach using the filter and smoother strategies are presented and compared. The smoother form is shown to be preferable because it provides better computational efficiency, facilitates easy integration with existing path synthesis tools, and most importantly, enables correct quantification of the rate of information accumulation. A spatial interpolation technique is used to relate the motion of the sensor to the evolution of the measurement matrix, which leads to the formulation of the optimal path planning problem. A gradient-ascent steering law based on the concept of information potential field is also presented as a computationally efficient suboptimal strategy. A simplified weather forecasting example is used to compare several planning methodologies and to illustrate the potential performance benefits of using the proposed planning approach.
Conference Paper
This paper presents a Bayesian approach to the problem of searching for a single lost target by a single autonomous sensor platform. The target may be static or mobile but not evading. Two candidate utility functions for the control solution are highlighted, namely the Mean Time to Detection, and the Cumulative Probability of Detection. The framework is implemented for an airborne vehicle looking for both a stationary and a drifting target at sea. Simulation results for different control solutions are investigated and compared to demonstrate the effectiveness of the method.
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This paper presents a technique for dynamically reconfiguring search spaces in order to enable Bayesian au- tonomous search and tracking missions with moving targets. In particular, marine search and rescue scenarios are con- sidered, highlighting the need for space reconfiguration in situations where moving targets are involved. The proposed technique improves the search space configuration by main- taining the validity of the recursive Bayesian estimation. The advantage of the technique is that autonomous search and tracking can be performed indefinitely, without loss of in- formation. Numerical results first show the effectiveness of the technique with a single search vehicle and a single mov- ing target. The efficacy of the approach for coordinated au- tonomous search and tracking is shown through simulation, incorporating multiple search vehicles and multiple targets. The examples also highlight the added benefit to human mis- sion planners resulting from the technique's simplification of the search space allocation task.
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Decision field theory (DFT), widely known in the field of mathematical psychology, provides a mathematical model for the evolution of the preferences among options of a human decision-maker. The evolution is based on the subjective evaluation for the options and his/her attention on an attribute (interest). In this paper, we extend DFT to cope with the dynamically changing environment. The proposed extended DFT (EDFT) updates the subjective evaluation for the options and the attention on the attribute, where Bayesian belief network (BBN) is employed to infer these updates under the dynamic environment. Four important theorems are derived regarding the extension, which enhance the usability of EDFT by providing the minimum time steps required to obtain the stabilized results before running the simulation (under certain assumptions). A human-in-the-loop experiment is conducted for the virtual stock market to illustrate and validate the proposed EDFT. The preliminary result is quite promising.
Conference Paper
This paper addresses the problem of coordinating a team of multiple heterogeneous sensing platforms searching for a single lost target. In this approach, the utility of a control sequence is a function of the probability density function (PDF) of the target state. Each decision maker builds an equivalent estimate of this PDF by communicating and fusing the information from the other sensor nodes. Coupled utilities incite the agents to collaborate and to agree on the next best set of actions. Decentralized cooperative planning is achieved via anonymous negotiation based on communication of expected observed information. Simulation results demonstrate the efficiency of the cooperative trajectories for a team of autonomous airborne search vehicles.
Conference Paper
This paper addresses the problem of cooperative search in a given environment by a team of unmanned aerial vehicles (UAVs). We present a decentralized control model for cooperative search and develop a real-time approach for online cooperation among vehicles, which is based on treating the possible paths of other vehicles as "soft obstacles" to be avoided. Using the approach of "rivaling force" between vehicles to enhance cooperation, each UAV takes into account the possible actions of other UAVs such that the overall information about the environment is increased. The simulation results illustrate the effectiveness of the proposed strategy.
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We introduce the concept of a Rapidly-exploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the beneficial properties of existing randomized planning techniques, RRTs are specifically designed to handle nonholonomic constraints (including dynamics) and high degrees of freedom. An RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected points, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. Several desirable properties and a basic implementation of RRTs are discussed. To date, we have successfully applied RRTs to holonomic, nonholonomic, and kinodynamic planning problems of up to twelve degrees of freedom.
Asynchronous Decision Making for Decentralised Autonomous Systems
  • G Mathews
G. Mathews, Asynchronous Decision Making for Decentralised Autonomous Systems, Ph.D. Thesis, The University of Sydney, 2008.
A bayesian approach for constrained multi-agent minimum time search in uncertain dynamic domains
  • P Lanillos
  • J Zuluaga
  • J J Ruz
  • E Besada-Portas
P. Lanillos, J. Yañez Zuluaga, J.J. Ruz, E. Besada-Portas, A bayesian approach for constrained multi-agent minimum time search in uncertain dynamic domains, in: Proc. of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO 2013, ACM, New York, NY, USA, 2013, pp. 391-398.
Minimum time search for lost targets using cross entropy optimization
  • P Lanillos
  • E Besada-Portas
  • G Pajares
  • J J Ruz
P. Lanillos, E. Besada-Portas, G. Pajares, J.J. Ruz, Minimum time search for lost targets using cross entropy optimization, in: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, pp. 602 -609.