Conference Paper

Formation Maintenance and Collision Avoidance in a Swarm of Drones

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Abstract

Distributed formation control and obstacle avoidance are two important challenges in autonomous navigation of a swarm of drones and can negatively affect each other due to possible competition that arises between them. In such a platform, a multi-priority control strategy is needed to be executed in each node to dynamically optimize the trade-offs between formation control and collision avoidance w.r.t. given system constraints, e.g. on energy and response time, by reordering priorities in run-time and choosing the appropriate formation and collision avoidance approach based on the state of the swarm, i.e., the kinematic parameters and geographical distribution of the nodes as well as the location of the observed obstacles. In this paper, we propose a method for formation/collision co-awareness with the goal of energy consumption and response time minimization. The algorithm is composed of two partial nested feedback-based control loops and based on three observations: 1) the relative location of the neighbor nodes for formation maintenance; 2) a boolean value indicating an observation of an obstacle by a local sensor, used for both formation control and collision avoidance; and 3) the distance of an obstacle to the node for collision avoidance in critical situations. The obtained comprehensive experimental results show that the proposed approach appropriately keeps the formation during the swarm’s travel in the presence of different obstacles.

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... Drones and swarms of drones, despite getting more sophisticated, still face several mission or design limitations and challenges, such as optimal navigation and obstacle avoidance, dynamic reformation, payload, flight time due to limited battery life, design of state-of-the-art stability controllers, and optimal resource allocation [4][5][6]. UAVs can encounter stationary or dynamic obstacles while navigating and therefore require a reliable collision avoidance system on-board for safe navigation [7,8]. Different types of collision avoidance methods can be generalized into four categories [9,10]: potential field based methods [11,12]; geometric methods [2,10,13]; sense and avoid methods [6,14,15]; optimization based methods [16,17]. ...
... UAVs can encounter stationary or dynamic obstacles while navigating and therefore require a reliable collision avoidance system on-board for safe navigation [7,8]. Different types of collision avoidance methods can be generalized into four categories [9,10]: potential field based methods [11,12]; geometric methods [2,10,13]; sense and avoid methods [6,14,15]; optimization based methods [16,17]. Furthermore, the methodologies for formation maintenance can be categorized into three approaches [18,19]: leader follower based [20,21]; virtual structure based [22,23]; behaviour based [24,25]. ...
... In this paper, for maintaining the formation, we utilize the leader-follower based approach due to its simplicity, non-complex implementation, reliability, and scalability [6]. Keeping the above mentioned limitations and time criticality, we propose our algorithm where a swarm formation comes across multiple obstacles and can go through the gap between them while dynamically changing the formation and reforming to original shape without slowing down. ...
Chapter
This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimizing the overall deviation of the drones and the overall time, i.e. without slowing down. To address the first problem, we introduce a set of routines for the drones/agents to follow while reshaping to a secondary formation shape. And the second problem is resolved by utilizing the temperature function reduction technique, originally used in the point set registration process. The goal is to be able to dynamically reform the shape of multi-agent based swarm in near-optimal manner while going through narrow openings between, for instance obstacles, and then bringing the agents back to their original shape after passing through the narrow passage using point set registration technique.
... When considering the navigation of a swarm of drones, collision avoidance and formation maintenance in the swarm are the two of the most prominent problems to solve [9][10][11]. With the exponential increase in the use of UAVs and their integration into many different commercial, military, and leisure applications, the need for an efficient onboard collision avoidance system increases exponentially. ...
... In this paper, we propose a strategy to reduce the processing power of individual agents in the swarm without losing the swarm's ability for autonomous operation. In order to achieve this, a leader-follower based approach is adopted for maintaining the formation, due to its relatively simple implementation, scalable nature, and reliability [9,38]. The global leader in the formation utilizes a given global collision avoidance algorithm, which is then used by the followers for the calculation of the relative coordinates, which are also known as translational coordinates, w.r.t. ...
... After successfully avoiding the collision or bypassing the obstacle, the follower agent switches back again to the low-conscious mode. Individual agents in the swarm utilize the collision avoidance technique developed and presented in [9] for avoiding collisions in the high-conscious mode. The main motivation behind this approach is not only the overall power cost reduction of the swarm, but also the capability of the agents to autonomously decide on switching between the low-conscious and high-conscious modes, enabling situation-aware optimization of power consumption, due to ranging sensors, at run-time. ...
Article
Full-text available
The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents.
... Drones and swarms of drones, despite getting more sophisticated, still face several mission or design limitations and challenges, such as optimal navigation and obstacle avoidance, dynamic reformation, payload, flight time due to limited battery life, design of state-of-the-art stability controllers, and optimal resource allocation [4][5][6]. UAVs can encounter stationary or dynamic obstacles while navigating and therefore require a reliable collision avoidance system on-board for safe navigation [7,8]. Different types of collision avoidance methods can be generalized into four categories [9,10]: potential field based methods [11,12]; geometric methods [2,10,13]; sense and avoid methods [6,14,15]; optimization based methods [16,17]. ...
... UAVs can encounter stationary or dynamic obstacles while navigating and therefore require a reliable collision avoidance system on-board for safe navigation [7,8]. Different types of collision avoidance methods can be generalized into four categories [9,10]: potential field based methods [11,12]; geometric methods [2,10,13]; sense and avoid methods [6,14,15]; optimization based methods [16,17]. Furthermore, the methodologies for formation maintenance can be categorized into three approaches [18,19]: leader follower based [20,21]; virtual structure based [22,23]; behaviour based [24,25]. ...
... In this paper, for maintaining the formation, we utilize the leader-follower based approach due to its simplicity, non-complex implementation, reliability, and scalability [6]. Keeping the above mentioned limitations and time criticality, we propose our algorithm where a swarm formation comes across multiple obstacles and can go through the gap between them while dynamically changing the formation and reforming to original shape without slowing down. ...
Preprint
This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimizing the overall deviation of the drones and the overall time, i.e., without slowing down. To address the first problem, we introduce a set of routines for the drones/agents to follow while reshaping to a secondary formation shape. And the second problem is resolved by utilizing the temperature function reduction technique, originally used in the point set registration process. The goal is to be able to dynamically reform the shape of multi-agent based swarm in near-optimal manner while going through narrow openings between, for instance obstacles, and then bringing the agents back to their original shape after passing through the narrow passage using point set registration technique.
... During the flight, they can encounter both stationary and moving obstacles and objects that need to be safely and reliably evaded using the collision avoidance system [23], [24]. Typically, algorithms for collision avoidance can be divided into three generic classes [25], [26]: 1) force-field methods that work on the principle of applying attractive/repulsive electric forces existing amongst charged objects; each drone in a swarm is considered a charged particle, and attractive or repulsive forces between drones and the obstacles are used to generate and choose the routes to be taken [27], [28]; 2) sense-andavoid based methods, where the process of collision avoidance is simplified into individual detection and avoidance of the objects and obstacles, resulting in short response times and reducing the computational power needed [29], [30]; and 3) optimization based methods which focus on providing the optimal or near-optimal solutions for path planning and motion characteristics of each drone with respect to the other drones and obstacles. In order to calculate efficient routes within a finite time horizon, these methods rely on static objects with known locations and dimensions [31], [32]. ...
... In formation flight, UAVs/nodes perform varying maneuvers like accelerating, decelerating, synchronized movements, and turning in different directions, that require each member of the formation to have a specific minimum distance from other members. To successfully perform those maneuvers and missions, nodes must have the ability to avoid collisions with other nodes in the swarm and with external obstacles [27], [33]. ...
... Whereas, the standard deviation of the distances calculated from the point the drones reach the formation until they arrive in the final destination is denoted by σ 2 . It is evident from the values shown in the table that the (a) Leader moves past the obstacle before its follower reaches it (b) Follower start navigating towards the destination and comes into ordered formation when the leader comes in its range For comparing our proposed technique with the stateof-the-art algorithms, we implemented the formation and collision avoidance algorithm presented in [23] and [27] and set it side by side with our proposed method. Figures 11, 12, and 13 show the simulation results for distance maintenance between the first three nodes. ...
Article
Full-text available
Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes’ states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.
... Drones and swarms of drones, despite getting more sophisticated, still face several mission or design limitations and challenges, such as optimal navigation and obstacle avoidance, dynamic reformation, payload, flight time due to limited battery life, design of state-of-the-art stability controllers, and optimal resource allocation [4][5][6]. UAVs can encounter stationary or dynamic obstacles while navigating and therefore require a reliable collision avoidance system on-board for safe navigation [7,8]. Different types of collision avoidance methods can be generalized into four categories [9,10]: potential field based methods [11,12]; geometric methods [2,10,13]; sense and avoid methods [6,14,15]; optimization based methods [16,17]. ...
... UAVs can encounter stationary or dynamic obstacles while navigating and therefore require a reliable collision avoidance system on-board for safe navigation [7,8]. Different types of collision avoidance methods can be generalized into four categories [9,10]: potential field based methods [11,12]; geometric methods [2,10,13]; sense and avoid methods [6,14,15]; optimization based methods [16,17]. Furthermore, the methodologies for formation maintenance can be categorized into three approaches [18,19]: leader follower based [20,21]; virtual structure based [22,23]; behaviour based [24,25]. ...
... In this paper, for maintaining the formation, we utilize the leader-follower based approach due to its simplicity, non-complex implementation, reliability, and scalability [6]. Keeping the above mentioned limitations and time criticality, we propose our algorithm where a swarm formation comes across multiple obstacles and can go through the gap between them while dynamically changing the formation and reforming to original shape without slowing down. ...
Conference Paper
This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimizing the overall deviation of the drones and the overall time, i.e., without slowing down. To address the first problem, we introduce a set of routines for the drones/agents to follow while reshaping to a secondary formation shape. And the second problem is resolved by utilizing the temperature function reduction technique, originally used in the point set registration process. The goal is to be able to dynamically reform the shape of multi-agent based swarm in near-optimal manner while going through narrow openings between, for instance obstacles, and then bringing the agents back to their original shape after passing through the narrow passage using point set registration technique.
... In swarm navigation and formation flight, collision avoidance and maintenance of the formation are the most important problems [7,8]. Formation control methodologies can be categorized into three main approaches: 1) the leader-follower based approach, where every node/drone works autonomously and individually by maintaining a given formation as perfectly as possible by adjusting its position with respect to its neighbours and the leader drone [4,9,10]; 2) the behaviour based approach, where, based on a pre-determined strategy, one behaviour is chosen out of the available ones [11,12]; and 3) the virtual structure based approach, where the swarm is considered a single entity, i.e. a single large drone effectively, and navigated through a trajectory accordingly [13,14]. ...
... generation of potential random solutions by defining different rules for each drone calculate the time and energy of each solution when reaching the highest disturbance formation is targeted, i.e., when all the drones pass the obstacle, regenerate new solutions by mutation of good obtained rules for each drone These routines are integrated with the collision avoidance developed and presented in [9], in such a way that the translated TShape destination of each node of the swarm is checked at each time interval. If there is an obstacle in the path of a node, the TShape destination may not be the same as the original destination. ...
Preprint
Full-text available
This work focuses on low-energy collision avoidance and formation maintenance in autonomous swarms of drones. Here, the two main problems are: 1) how to avoid collisions by temporarily breaking the formation, i.e., collision avoidance reformation, and 2) how do such reformation while minimizing the deviation resulting in minimization of the overall time and energy consumption of the drones. To address the first question, we use cellular automata based technique to find an efficient formation that avoids the obstacle while minimizing the time and energy. Concerning the second question, a near-optimal reformation of the swarm after successful collision avoidance is achieved by applying a temperature function reduction technique, originally used in the point set registration process. The goal of the reformation process is to remove the disturbance while minimizing the overall time it takes for the swarm to reach the destination and consequently reducing the energy consumption required by this operation. To measure the degree of formation disturbance due to collision avoidance, deviation of the centroid of the swarm formation is used, inspired by the concept of the center of mass in classical mechanics. Experimental results show the efficiency of the proposed technique, in terms of performance and energy.
... In the asynchronous adaptive collision avoidance module, we utilised and modified the collision avoidance algorithm proposed in [17] to tackle with the multi-priority obstacle switching. From the calculated values of the detected obstacles, priorities are assigned to the obstacles based on their respective poi, with highest priority given to the obstacle with closest poi (Line 3, Algorithm 1). ...
... If the calculated point of impact is same for more than one object, they can be treated as being parallel objects, as at the given time of potential collision t i , they both will be at the same distance from the vehicle (Line 4). Therefore, the distance or gap between the objects is calculated [17] and it is checked if the gap is sufficient enough to pass through, i.e., more than the defined collision radius R c . In which case, the vehicle is aligned to pass through the objects safely (Lines 5-7). ...
Preprint
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 dB. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.
... In the asynchronous adaptive collision avoidance module, we utilised and modified the collision avoidance algorithm proposed in [17] to tackle with the multi-priority obstacle switching. From the calculated values of the detected obstacles, priorities are assigned to the obstacles based on their respective poi, with highest priority given to the obstacle with closest poi (Line 3, Algorithm 1). ...
... If the calculated point of impact is same for more than one object, they can be treated as being parallel objects, as at the given time of potential collision t i , they both will be at the same distance from the vehicle (Line 4). Therefore, the distance or gap between the objects is calculated [17] and it is checked if the gap is sufficient enough to pass through, i.e., more than the defined collision radius R c . In which case, the vehicle is aligned to pass through the objects safely (Lines 5-7). ...
Conference Paper
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 dB. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.
... In the asynchronous adaptive collision avoidance module, we utilised and modified the collision avoidance algorithm proposed in [17] to tackle with the multi-priority obstacle switching. From the calculated values of the detected obstacles, priorities are assigned to the obstacles based on their respective poi, with highest priority given to the obstacle with closest poi (Line 3, Algorithm 1). ...
... If the calculated point of impact is same for more than one object, they can be treated as being parallel objects, as at the given time of potential collision t i , they both will be at the same distance from the vehicle (Line 4). Therefore, the distance or gap between the objects is calculated [17] and it is checked if the gap is sufficient enough to pass through, i.e., more than the defined collision radius R c . In which case, the vehicle is aligned to pass through the objects safely (Lines 5-7). ...
Preprint
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 dB. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.
... Collision and obstacle avoidance in a leaderfollowers swarm model is presented in [20][21][22]. However, these approaches focus on static obstacle avoidance and broadcast full states. ...
Preprint
Full-text available
A novel approach for achieving fast evasion in self-localized swarms of Unmanned Aerial Vehicles (UAVs) threatened by an intruding moving object is presented in this paper. Motivated by natural self-organizing systems, the presented approach of fast and collective evasion enables the UAV swarm to avoid dynamic objects (interferers) that are actively approaching the group. The main objective of the proposed technique is the fast and safe escape of the swarm from an interferer ~discovered in proximity. This method is inspired by the collective behavior of groups of certain animals, such as schools of fish or flocks of birds. These animals use the limited information of their sensing organs and decentralized control to achieve reliable and effective group motion. The system presented in this paper is intended to execute the safe coordination of UAV swarms with a large number of agents. Similar to natural swarms, this system propagates a fast shock of information about detected interferers throughout the group to achieve dynamic and collective evasion. The proposed system is fully decentralized using only onboard sensors to mutually localize swarm agents and interferers, similar to how animals accomplish this behavior. As a result, the communication structure between swarm agents is not overwhelmed by information about the state (position and velocity) of each individual and it is reliable to communication dropouts. The proposed system and theory were numerically evaluated and verified in real-world experiments.
... The increasing use of multiple UAVs has led to a significant focus on developing collision avoidance techniques and algorithms. Various studies, including those by Soria et al. [22], Yasin et al. [23,24], and Jenie et al. [25,26], have contributed to this field. Barfield et al. [27] outline technical requirements for an automatic collision avoidance system, introducing two zones: a de-confliction sphere and an avoidance sphere. ...
Article
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The complexities of decision-making in drone airlines prove to be pivotal and challenging as the dynamic environment introduces variability and many decisions are conventionally static. This paper introduces an advanced decision-making system designed for the multifaceted landscape of drone applications. Our proposed system addresses various aspects, including drone assignment, safety zone sizing, priority determination, and more. The scoring model enhances adaptability in real-time scenarios, particularly highlighted by the dynamic adjustment. Based on the scenario concerning the definition of the safety zone, we have successfully applied this method and evaluated all potential scores. The user-friendly and intuitive configuration further augments the system’s accessibility, facilitating efficient deployment. In essence, the proposed system stands as an innovative approach with decision-making paradigms in the dynamic landscape of drone operations.
... In swarm formation flight, UAVs perform a variety of maneuvers like accelerating, decelerating, coordinated motions, and turning in different directions. Other than that, each formation member must maintain a minimum distance from other members to avoid collisions with other nodes in the swarm and external barriers [92], [94]. Three general approaches can be utilized to classify UAVs formation control algorithms [92], [95], [96]: ...
Article
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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.
... Swarm flight can be modelled analytically which can produce digital representations which negate the need for a leader-follower system [16]. Application of the current position of drones in a swarm to remove chances of collision between drones by individually tracking each drone in a swarm and optimising routes while maintaining collision avoidance protocols [20]. Other methods base the swarm dynamics on living beings with swarm lifestyles such as bees [17] or other insects (i.e. ...
Experiment Findings
Full-text available
In this paper, authors discuss the development process of a drone swarm using DJI Tello. The system architecture is given to explain how the components interact together to deliver a human-interactive demonstration. The authors explore hardware reliability and compare multiple flight path controller approaches which apply fiducial marker based localization as well as human drone interaction based off an external camera feed. This paper demonstrates: that marker based localization is reliable; and that certain distance sensors may be vulnerable to measurement errors.
... It is essential to maintain the drone within the swarm during various maneuvers without colliding with its batch mates and also it should not go beyond the swarm boundary. Yasin et al. (2019) proposed an elaborated coordinate system that explains the actual position of the drone at any instant [12]. ...
... Often these approaches are used as a last resort. [21,22] Yasin et al. [10] presents a survey where they explain state-of-art techniques on collision avoidance. Their in-depth review provides a thorough understanding of the various approaches available. ...
Article
Full-text available
In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km x 2.5 km).
... Besides works on resilience, a lot of attention is going towards collision avoidance and swarm formations. In [10] the authors developed a novel algorithm that allows a swarm of UAVs to maintain a formation and avoid collisions. The formation is maintained by constantly assessing the distances between the UAVs, and then slowing down and speeding up individual UAVs whenever necessary. ...
Conference Paper
In recent years, the adoption of unmanned aerial vehicles (UAVs) has widely spread to different sectors worldwide. Technological advances in this field have made it possible to coordinate the flight of these aircraft so as to conform a swarm. A UAV swarm is defined as a group of UAVs working collaboratively to carry out more complex missions or perform tasks more efficiently. Common applications of these swarms include rescue missions, precision agriculture, and border control, among others. However, there are still certain problems that prevent us from ensuring the success of their mission, especially as the number of drones in a swarm increases. In this paper, we specifically address the problem of a swarm takeoff by optimizing the total time involved, while guaranteeing the safety of the UAVs during the takeoff stage. To this end, we propose a new approach that combines a collision detection algorithm based on trajectory analysis with a batch generation mechanism that we use in order to determine the takeoff sequence. Experiments show that our algorithm offers an efficient solution, managing to improve the performance of existing takeoff techniques.
... Collision and obstacle avoidance in a leader-followers swarm model is presented in [20][21][22]. However, these approaches focus on static obstacle avoidance and broadcast full states. ...
Article
Full-text available
A novel approach for achieving fast evasion in self-localized swarms of unmanned aerial vehicles (UAVs) threatened by an intruding moving object is presented in this paper. Motivated by natural self-organizing systems, the presented approach of fast and collective evasion enables the UAV swarm to avoid dynamic objects (interferers) that are actively approaching the group. The main objective of the proposed technique is the fast and safe escape of the swarm from an interferer discovered in proximity. This method is inspired by the collective behavior of groups of certain animals, such as schools of fish or flocks of birds. These animals use the limited information of their sensing organs and decentralized control to achieve reliable and effective group motion. The system presented in this paper is intended to execute the safe coordination of UAV swarms with a large number of agents. Similar to natural swarms, this system propagates a fast shock of information about detected interferers throughout the group to achieve dynamic and collective evasion. The proposed system is fully decentralized using only onboard sensors to mutually localize swarm agents and interferers, similar to how animals accomplish this behavior. As a result, the communication structure between swarm agents is not overwhelmed by information about the state (position and velocity) of each individual and it is reliable to communication dropouts. The proposed system and theory were numerically evaluated and verified in real-world experiments.
... Additional problems regarding collisions usually arise when several robots operate in a shared environment. In particular, swarm robot scenarios are prone to mutual collisions due to the larger number of robots, the frequently considered operations in unknown or changing environments, or because of the often limited sensorial information due to simple and inexpensive robot concepts [13] - [18]. On the other hand, communication between robots allow for better self-organized coordination and decision making which may help avoiding collisions [19] - [23]. ...
Article
Full-text available
Collision avoidance in the area of swarm robotics is very important. The lacking ability of such collision avoidance is mentioned as one important reason for the sparse distribution of the small test robots named Kilobots. In this research paper, two new algorithms providing a collision avoidance strategy are presented and compared with previous research results. The first algorithm uses randomness to decide which one of several approaching Kilobots are stopped for a defined time before starting to move again. The second algorithm tries to determine the assumed position of approaching Kilobots based on its radio signal strength and then to move away in the opposite direction by rotation. The results, especially of the second algorithm, are promising as the number of collisions can be significantly reduced.
... In case only one obstacle was detected initially, path planning is performed, for a single obstacle, to bypass the obstacle (8)(9)(10). For aligning the agent to navigate through the gap between the obstacles and path planning, we utilized and implemented the technique presented in [40]. If the distance to the obstacle is no longer in the detection range, the control is returned to the overall routine by resetting the Detection flag to False. ...
Article
Full-text available
The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize a thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods.
... Additional problems regarding collisions usually arise when several robots operate in a shared environment. In particular, swarm robot scenarios are prone to mutual collisions due to the larger number of robots, the frequently considered operations in unknown or changing environments, or because of the often limited sensorial information due to simple and inexpensive robot concepts [13] - [18]. On the other hand, communication between robots allow for better self-organized coordination and decision making which may help avoiding collisions [19] - [23]. ...
Article
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Collision avoidance in the area of swarm robotics is very important. The lacking ability of such collision avoidance is mentioned as one important reason for the sparse distribution of the small test robots named Kilobots. In this research paper, two new algorithms providing a collision avoidance strategy are presented and compared with previous research results. The first algorithm uses randomness to decide which one of several approaching Kilobots are stopped for a defined time before starting to move again. The second algorithm tries to determine the assumed position of approaching Kilobots based on its radio signal strength and then to move away in the opposite direction by rotation. The results, especially of the second algorithm, are promising as the number of collisions can be significantly reduced.
... The main strategy here is to perform near-optimal algorithms on each drone to save the battery energy as much as possible and also remove the drone vibration in a way that object detection, i.e., bird, and tracking is done appropriately. To guarantee a suitable collision avoidance we used the technique explained in [33]. To cover all the areas in a land, simultaneous localization, and mapping (SLAM) technique is applied [34]. ...
Chapter
In Namibia, agricultural production depends on small-scale farms. One of the main threats to these farms is the birds’ attack. There are various traditional methods that have been used to control these pest birds such as making use of chemicals, fires, traps, hiring people to scare the birds, as well as using different aspects of agricultural modifications. The main problem of using such methods is that they are expensive, many are harmful to the environment or demand extra-human resources. In this paper, we investigate the potential and challenges of using a swarm of drones as an Intelligent surveillance and reconnaissance (ISR) system in a bird scaring system targeting a specific type of bird called Quelea quelea, i.e., weaver bird, and on Pearl millet crop. The idea is to have a co-design methodology of the swarm control system, involving technology developers and end-users. To scare away the birds from the field, a disruption signal predator-like sound will be produced by the drone. This sound is extremely threatening and terrifying for most bird species. The empirical results show that using the aforementioned technology has a great potential to increases food security and sustainability in Africa.
... Or in case, only one obstacle was detected initially, path planning is performed, for a single obstacle, to bypass the obstacle (8)(9)(10). For aligning the agent to navigate through the gap between the obstacles and path planning, we utilized and implemented the technique presented in [18]. If the distance to the obstacle is no longer in the detection range, the control is returned to the global routine by resetting the Detection flag to False. ...
Preprint
The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize a thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the traditional method based on the shortest path results in 14.7% higher energy consumption as compared to our proposed approach.
... Bearing in mind the considerably low risk to human life, as well as improved durability for longer missions and accessibility in difficult terrains, the demand for such unmanned vehicles is increasing rapidly and their path planning in dynamic environments remains one of the most challenging issues to solve [6]. Due to their autonomy and ability to travel far from the base stations or their operators (the range naturally depends on the type and size of the vehicle), the need for having an onboard mechanism to avoid collisions with objects and other vehicles is obvious [7][8][9][10]. ...
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This work focuses on the development of an effective collision avoidance algorithm that detects and avoids obstacles autonomously in the vicinity of a potential collision by using a single ultrasonic sensor and controlling the movement of the vehicle. The objectives are to minimise the deviation from the vehicle's original path and also the development of an algorithm utilising one of the cheapest sensors available for very lost cost systems. For instance, in a scenario where the main ranging sensor malfunctions, a backup low cost sensor is required for safe navigation of the vehicle while keeping the deviation to a minimum. The developed algorithm utilises only one ultrasonic sensor and approximates the front shape of the detected object by sweeping the sensor mounted on top of the unmanned vehicle. In this proposed approach, the sensor is rotated for shape approximation and edge detection instead of moving the robot around the encountered obstacle. It has been tested in various indoor situations using different shapes of objects, stationary objects, moving objects, and soft or irregularly shaped objects. The results show that the algorithm provides satisfactory outcomes by entirely avoiding obstacles and rerouting the vehicle with a minimal deviation.
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Anti-collision is a critical issue that needs to be addressed when operating UAVs in a cluster formation. This paper proposes a centralized multi-UAV swarm formation avoidance collision system that comprises two layers of collision avoidance strategies. The first layer utilizes an artificial potential field method with small-amplitude angular perturbations to overcome local minimum problems and achieve simple avoidance using position information. The second layer uses a velocity barrier approach to complement the output of the first layer. Mutual wind disturbances between UAVs are handled by applying model-compensated control in real-world experiments. The proposed system is verified to be stable and feasible in both simulation and real-world experiments. The structure is simple and easy to implement, making it a practical solution for ensuring anti-collision of UAVs in a cluster formation.
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This paper presented an exhaustive survey on the security and privacy issues of drones. These security concerns were thoroughly dissected, particularly the aspect of cybersecurity, which was classified into nine levels. These levels include emerging issues, communication-based attacks, sensors, hardware, hardware-based attacks, software attacks, and physical attacks on the drone itself. Furthermore, we discussed the other non-cybersecurity challenges of drones, such as terrorism, mid-air collisions, illegal surveillance, smuggling, electronic snooping, and reconnaissance, alongside proffering possible solutions. Many of the discovered aspects of drone cybersecurity issues were then quantitatively analyzed using a multi-criteria decision-making problem-solving technique. The questionnaire responses from the general public, experts, and stakeholders in the aviation industry were analyzed. The findings revealed variations in cyber-attack techniques such as distributed denial-of-service (DDoS), denial-of-service (DoS), hacking, jamming, spoofing, electronic snooping, eavesdropping, advanced persistent threat (APT), reconnaissance, hijacking, man-in-the-middle attack, and so on. However, the majority of the participants in the survey, which constitute 70%, were unaware of the existing drone cybersecurity challenges. The remaining 30% were aware of the current drone security issues. Meanwhile, both parties are looking for an immediate solution that will fully provide an atmosphere of prospects in the drone industry. Following that, we presented our experience with drone security and privacy, as well as potential future research directions. This paper is unique in that it discusses the various types of drone cyber-attacks and non-cyber-attack scenarios that threaten the socio-economic system, aviation industry, national security, as well as public security and privacy concerns. It also offers solutions to the cyber-attack and non-cyber-attack cases that have been investigated. As a result, the findings of this study could be used to create, develop, and implement more secure cloud systems to safeguard drones from cyber and non-cyber-attacks.
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This work focuses on low-energy collision avoidance and formation maintenance in autonomous swarms of drones. Here, the two main problems are: 1) how to avoid collisions by temporarily breaking the formation, i.e., collision avoidance reformation, and 2) how do such reformation while minimizing the deviation resulting in minimization of the overall time and energy consumption of the drones. To address the first question, we use cellular automata based technique to find an efficient formation that avoids the obstacle while minimizing the time and energy. Concerning the second question, a near-optimal reformation of the swarm after successful collision avoidance is achieved by applying a temperature function reduction technique, originally used in the point set registration process. The goal of the reformation process is to remove the disturbance while minimizing the overall time it takes for the swarm to reach the destination and consequently reducing the energy consumption required by this operation. To measure the degree of formation disturbance due to collision avoidance, deviation of the centroid of the swarm formation is used, inspired by the concept of the center of mass in classical mechanics. Experimental results show the efficiency of the proposed technique, in terms of performance and energy.
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This work focuses on an autonomous swarm of drones, a multi-agent system, where the leader agent has the capability of intelligent decision making while the other agents in the swarm follow the leader blindly. The proposed algorithm helps with cost cutting especially in the multi-drone systems, i.e., swarms, by reducing the power consumption and processing requirements of each individual agent. It is shown that by applying a pre-specified formation design with feedback cross-referencing between the agents, the swarm as a whole can not only maintain the desired formation and navigate but also avoid collisions with obstacles and other drones. Furthermore, the power consumed by the nodes in the considered test scenario, is reduced by 50% by utilising the proposed methodology.
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Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs.
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This paper proposes a novel formation control strategy for nonholonomic intelligent vehicles based on virtual structure and consensus approach. The formation model is obtained based on the coordinate transformation and the virtual structure technique. The controllers are designed by using nonholonomic target tracking technique and leader-following consensus protocol. Depending on virtual structure approach and coordinate transformation, the formation control problem of multiple nonholonomic intelligent vehicles is converted into the target tracking and state consensus stabilization problem. Simulation and real-world experimental results show the correctness and effectiveness of the strategy.
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Collision avoidance is a key factor in enabling the integration of unmanned aerial vehicle into real life use, whether it is in military or civil application. For a long time there have been a large number of works to address this problem; therefore a comparative summary of them would be desirable. This paper presents a survey on the major collision avoidance systems developed in up to date publications. Each collision avoidance system contains two main parts: sensing and detection, and collision avoidance. Based on their characteristics each part is divided into different categories; and those categories are explained, compared and discussed about advantages and disadvantages in this paper.
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The ability to integrate unmanned and manned aircraft into airspace is a critical capability that will enable growth in wide varieties of applications. Collision avoidance is a key enabler for the integration of manned and unmanned missions in civil and military operation theaters. Large efforts have been done to address collision avoidance problem to both manned and unmanned aircraft. However, there has been little comparative discussion of the proposed approaches. This paper presents a survey of the collision avoidance approaches those deployed for aircraft, especially for unmanned aerial vehicles. The collision avoidance concept is introduced together with proposing generic functions carried by collision avoidance systems. The design factors of the sense and avoid system, which are used to categorize methods, are explained deeply. Based on the design factors, several typical approaches are categorized.
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Using tools from algebraic graph theory and nonsmooth analysis in combination with ideas of collective potential functions, velocity consensus and navigation feedback, a distributed leader–follower flocking algorithm for multi-agent dynamical systems with time-varying velocities is developed where each agent is governed by second-order dynamics. The distributed leader–follower algorithm considers the case in which the group has one virtual leader with time-varying velocity. For each agent i, this algorithm consists of four terms: the first term is the self nonlinear dynamics which determines the final time-varying velocity, the second term is determined by the gradient of the collective potential between agent i and all of its neighbors, the third term is the velocity consensus term, and the fourth term is the navigation feedback from the leader. To avoid an impractical assumption that the informed agents sense all the states of the leader, the new designed distributed algorithm is developed by making use of observer-based pinning navigation feedback. In this case, each informed agent only has partial information about the leader, yet the velocity of the whole group can still converge to that of the leader and the centroid of those informed agents, having the leader's position information, follows the trajectory of the leader asymptotically. Finally, simulation results are presented to demonstrate the validity and effectiveness of the theoretical analysis. Surprisingly, it is found that the local minimum of the potential function may not form a commonly believed α lattice.
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Navigation problems of unmanned air vehicles (UAVs) flying in a formation in a free and an obstacle-laden environment are investigated in this brief. When static obstacles popup during the flight, the UAVs are required to steer around them and also avoid collisions between each other. In order to achieve these goals, a new dual-mode control strategy is proposed: a "safe mode" is defined as an operation in an obstacle-free environment and a "danger mode" is activated when there is a chance of collision or when there are obstacles in the path. Safe mode achieves global optimization because the dynamics of all the UAVs participating in the formation are taken into account in the controller formulation. In the danger mode, a novel algorithm using a modified Grossberg neural network (GNN) is proposed for obstacle/collision avoidance. This decentralized algorithm in 2-D uses the geometry of the flight space to generate optimal/suboptimal trajectories. Extension of the proposed scheme for obstacle avoidance in a 3-D environment is shown. In order to handle practical vehicle constraints, a model predictive control-based tracking controller is used to track the references generated. Numerical results are provided to motivate this approach and to demonstrate its potential.
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New reactive behaviors that implement formations in multirobot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards and simultaneously remain in formation. The behaviors are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based unmanned ground vehicles. The technique has been integrated with the autonomous robot architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous, human-led and communications-restricted applications, and their appropriateness in different types of task environments
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Collision avoidance strategies for multiple UAVs (Unmanned Aerial Vehicles) based on geometry are investigated in this study. The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of UAVs. The geometric approach uses line-of-sight vectors and relative velocity vectors where dynamic constraints are included in the formation. Each UAV can determine which plane and direction are available for collision avoidance. An analysis is performed to define an envelope for collision avoidance, where angular rate limits and obstacle detection range limits are considered. Based on the collision avoidance envelope, each UAV in a formation determines whether the formation can be maintained or not while avoiding obstacles. Numerical simulations are performed to demonstrate the performance of the proposed strategies.
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Robot path planning in uncertain dynamic environment is a hot issue in the field of Unmanned ground vehicle (UGV). Starting from the practical demands of UGV, we propose a novel dynamic obstacle avoidance algorithm based on Collision time histogram (CTH). Given current steering angle, an effective collision check model, which is called Collision check circles (CCC), is firstly calculated. The local environment information is then combined with CCC to generate the proposed CTH. The nonholonomic nature of the vehicle is embedded in this process. Finally, the proposed algorithm calculates the executing steering angle by considering both the CTH and the target point. Extensive experiments and comparisons are conducted to evaluate the performance of the proposed algorithm. Simulation experiments are firstly conducted to verify its feasibility. Furthermore, real-world experiment is conducted to verify its effectiveness. Experimental results demonstrate the practical value of the proposed algorithm.
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This paper deals with leader-following formation control and heading synchronization for a group of quadcopters. Unlike most of existing works that involve a complex design of nonlinear formation and heading control laws, this work proposes an almost linear control law for quadcopters in leader-following formation. In fact, the quadcopter dynamics involves state variables being coupled nonlinearly. However, it is shown that the dynamics can be treated as a linear system via a static feedback linearization scheme. Consequently, a linear formation and heading control law can be designed for the linear system such that (1) the group of quadcopters achieves a desired formation (desired relative altitude/position and hovering) through local information exchange; (2) follower quadcopters synchronize their heading angles with the leader's; and (3) the leader quadcopter moves along a prescribed path to guide the formation. Although the present work considers quadcopter dynamics only, the proposed methodology can also be applied to various attitude synchronization problems arising in space and robotic applications. (C) 2015 Elsevier Masson SAS. All rights reserved
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In this paper we propose Hoare style proof systems called PR D 0 and PRKW D 0 for plan generation and plan verification under 0-approximation semantics of the action language A K . In PR D 0 (resp. PRKW D 0 ), a Hoare triple of the form {X}c{Y} (resp. {X}c{KW p }) means that all literals in Y become true (resp. p becomes known) after executing plan c in a state satisfying all literals in X. The proof systems are shown to be sound and complete, and more importantly, they give a way to efficiently generate and verify longer plans from existing verified shorter plans by applying so-called composition rule, provided that an enough number of shorter plans have been properly stored. The idea behind is a tradeoff between space and time, we refer it to off-line planning and point out that it could be applied to general planning problems.
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In this paper, we consider the mobile robots formation control problem without direct measurement of the leader robot's linear velocity. Two decentralized nonlinear algorithms are proposed, respectively, based on adaptive dynamic feedback and immersion Rz invariance estimation based second order sliding mode control methodologies. The main idea is to solve formation problem by estimating the leader robots's linear velocity, while maintaining the given predefined separation distance and bearing angle between the leader robot and the follower robot. The stability of the closed-loop system is proven by means of the Lyapunov method. The proposed controllers are smooth, continuous and robust against unknown bounded uncertainties such as sensor inaccuracy between the outputs of sensors and the true values in collision free environments. Simulation examples and physical vehicles experiments are presented to verify the effectiveness of the proposed design approaches, and the proposed designed methodologies are carefully compared to illustrate the pros and cons of the approaches.
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The sense and avoid capability is one of the greatest challenges that has to be addressed to safely integrate unmanned aircraft systems into civil and nonsegregated airspace. This paper gives a review of existing regulations, recommended practices, and standards in sense and avoid for unmanned aircraft systems. Gaps and issues are identified, as are the different factors that are likely to affect actual sense and avoid requirements. It is found that the operational environment (flight altitude, meteorological conditions, and class of airspace) plays an important role when determining the type of flying hazards that the unmanned aircraft system might encounter. In addition, the automation level and the data-link architecture of the unmanned aircraft system are key factors that will definitely determine the sense and avoid system requirements. Tactical unmanned aircraft, performing similar missions to general aviation, are found to be the most challenging systems from an sense and avoid point of view, and further research and development efforts are still needed before their seamless integration into nonsegregated airspace.
Conference Paper
The airborne Sense and Avoid (SAA) problem is a challenging and complex optimal control problem to solve. This paper builds on the previous work by the authors,1 which focused on the methodology for formulating the airborne SAA problem as an optimal control problem. The focus of this current work is on how to realistically formulate the constraints associated with solving the airborne SAA problem as a nonlinear optimal control problem. Based on user defined cost functions, the objective of this optimization problem is to determine the optimal trajectory for an aircraft to y in order to avoid a collision. A fundamental necessity in solving the airborne collision avoidance problem is modeling and estimating the current and future trajectories of all potential intruder air- craft. To this end, this paper implements a 3D particle filter to model and estimate the intruder's nonlinear dynamic and measurement equations. As a result, the "point cloud" outputs of the particle filter realistically define 3D probability regions associated with the intruder's position at some (ti) time initial to some (tf) time final. The ability to accurately capture and model these probability regions as system constraints is essential in the formulation of the optimal control problem. The previous work by the authors assumed an underlying Gaussian distribution to model these probability regions; however, based on the nonlinearities associated with the system dynamics, the underlying distribution for these regions are not necessarily Gaussian. Therefore, this paper presents an alternate approach that does not assume any underlying distribution. This proposed approach efficiently captures the intruder's 3D probability regions as a convex optimization problem utilizing Khachiyan's Algorithm2, 3 and then smoothly interpolates between these probability regions to accurately identify "collision avoidance corridors" for the airborne SAA optimal control problem. This approach is demonstrated on a representative SAA scenario.
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We present a survey of formation control of multi-agent systems. Focusing on the sensing capability and the interaction topology of agents, we categorize the existing results into position-, displacement-, and distance-based control. We then summarize problem formulations, discuss distinctions, and review recent results of the formation control schemes. Further we review some other results that do not fit into the categorization.
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Recently, a model for flocking was introduced by Cucker and Smale together with a proof of convergence. This proof established unconditional convergence to flocking (i.e., to a common velocity), provided the interaction between agents was strong enough and conditional convergence otherwise. The strength of the interaction is measured by a parameter β, and the critical value at which unconditional convergence stops holding is β = 1 ∕ 2. This model was extended by Shen to allow for a hierarchical leadership structure among the agents, and similar convergence results were proved. But, for discrete time, convergence result was only for the flock with an overall leader moving with a constant velocity. In this note, we establish convergence result for the flock with a free-will leader. Copyright © 2012 John Wiley & Sons, Ltd.
Conference Paper
In this paper we first introduce a fundamental consensus algorithm for systems modeled by second-order dynamics. We then apply variants of the consensus algorithm to tackle formation control problems by appropriately choosing information states on which consensus is reached. Even in the absence of centralized leadership, the consensus based formation control strategies can guarantee accurate formation maintenance in the general case that information flow is unidirectional. We also show that existing leader-follower, behavioral, and virtual structure/virtual leader formation control approaches can be unified in the general framework of consensus building. A multi-vehicle formation control example is shown in simulation to illustrate our strategies
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All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.
Conference Paper
In this paper, the synchronized position tracking controller is incorporated in formation flight control for multiple flying wings. With this technology, the performance and effectiveness of the formation controller are improved when the virtual structure approach is utilized to maintain formation geometry. Simulations are conducted on the nonlinear model of two flying wings to verify the proposed controller.
Conference Paper
The utilization of unmanned aerial vehicles requires the ability to navigate in urban or unknown terrain where many moving and/or stationary obstacles of different types and sizes may endanger the safety of the mission. Large efforts have been addressed to resolve conflicts to unmanned aircraft. This paper explores the fundamental concept and presents an up-to-date survey of the collision sensing, detection and resolution methods those deployed for aircraft, especially for unmanned aerial vehicles. The collision avoidance concept is demonstrated through proposing generic functions carried by collision avoidance systems with special emphasis on the context aware implementation. These functions are then presented together with design factors that are used then to classify major collision avoidance methods.
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This paper is concerned with planning the motion of mobile robots in formation, which means certain geometrical constraints are imposed on the relative positions and orientations of the robots throughout their travel. Specifically, a method of planning motion for formations of mobile robots with non-holonomic constraints is presented. The kinematic equations developed allow a certain class of formations to be maintained while the group as a whole exhibits motion. The work was validated using the Stanford Micro-Autonomous RoverS Testbed.
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Using tools from algebraic graph theory and nonsmooth analysis in combination with ideas of collective potential functions, velocity consensus and navigation feedback, a distributed leader–follower flocking algorithm for multi-agent dynamical systems with time-varying velocities is developed where each agent is governed by second-order dynamics. The distributed leader–follower algorithm considers the case in which the group has one virtual leader with time-varying velocity. For each agent i, this algorithm consists of four terms: the first term is the self nonlinear dynamics which determines the final time-varying velocity, the second term is determined by the gradient of the collective potential between agent i and all of its neighbors, the third term is the velocity consensus term, and the fourth term is the navigation feedback from the leader. To avoid an impractical assumption that the informed agents sense all the states of the leader, the new designed distributed algorithm is developed by making use of observer-based pinning navigation feedback. In this case, each informed agent only has partial information about the leader, yet the velocity of the whole group can still converge to that of the leader and the centroid of those informed agents, having the leader’s position information, follows the trajectory of the leader asymptotically. Finally, simulation results are presented to demonstrate the validity and effectiveness of the theoretical analysis. Surprisingly, it is found that the local minimum of the potential function may not form a commonly believed α lattice.
Conference Paper
This paper presents a flexible virtual structure (V-S) formation control schemes for nonholonomic fixed-wing UAVs. In contrast to geometric rigid V-S formation control schemes, flexible V-S formation scheme enables a formation to turn smoothly, and hence allows the UAVs in the formation to follow the desired heading profile of the planned formation trajectory. In this work, trajectory generation algorithms are developed to compute the coordinated formation reference trajectories where each of these reference trajectories is executed by our recently proposed trajectory tracking control scheme. Results are also developed to assess the feasibility of the formation trajectory based on operating envelopes of a given UAV. These results provide basic tools for understanding and implementing the formation keeping control scheme according to the fixed-wing UAV's capability. The effectiveness of the V-S formation control scheme is validated using nonlinear six degree-of-freedom (6DOF) fixed-wing UAV models and simulation results are presented.
Article
This paper presents a behavior-based approach to formation maneuvers for groups of mobile robots. Complex formation maneuvers are decomposed into a sequence of maneuvers between formation patterns. The paper presents three formation control strategies. The first strategy uses relative position information configured in a bidirectional ring topology to maintain the formation. The second strategy injects interrobot damping via passivity techniques. The third strategy accounts for actuator saturation. Hardware results demonstrate the effectiveness of the proposed control strategies.
Xiaojing Zhang Alexander Liniger and Francesco Bor 2017. Optimization-based collision avoidance
  • Xiaojing Zhang
  • Alexander Liniger
  • Francesco Bor