Velocity Obstacle Method for Non-cooperative Autonomous Collision Avoidance System for UAVs

Jenie, Yazdi I.; Kampen, E. van; Visser, Coen C. de; Chu, Q.P.

doi:10.2514/6.2014-1472

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…For example, obstacles can be described in the velocity space, allowing for the computation of the speed that will best avoid collision [1]. Such technique has been applied to fixed-wing aircraft [2]. Another approach is to optimize a trajectory for a user-defined function.…”

Section: Introductionmentioning

confidence: 99%

Distributed deconfliction algorithm for Unmanned Aerial Vehicles with limited range and field of view sensors

Roelofsen

Martinoli²,

Gillet

2015

2015 American Control Conference (ACC)

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Abstract-This paper proposes a novel approach for collision avoidance between Unmanned Aerial Vehicles (UAVs) with limited range and field of view sensors. The algorithm is designed for unicycle vehicles that need to fly above a specific minimal speed to maintain flight. It uses a navigation function approach when the UAV is clear from conflict and smoothly switches to a collision avoidance maneuver when other UAVs are encountered. We show that the proposed avoidance algorithm can ensure a collision-free path. We also carry out a throughout quantitative analysis of the algorithm singularities and propose heuristic recipes for avoiding deadlock situations. Simulations are performed to show the effectiveness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Distributed deconfliction algorithm for Unmanned Aerial Vehicles with limited range and field of view sensors

Roelofsen

Martinoli²,

Gillet

2015

2015 American Control Conference (ACC)

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“…The more information available about the obstacle (e.g. speed, relative position, detection uncertainty), the higher the likelihood of successful planning and executing collision avoidance maneuvers [28]. This makes the proposed solution advantageous with respect to monocular detection methods, which typically provide information about the obstacle only in the image plane.…”

Section: Target Drone Localizationmentioning

confidence: 99%

Onboard Detection and Localization of Drones Using Depth Maps

et al. 2020

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Obstacle avoidance is a key feature for safe drone navigation. While solutions are already commercially available for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are much harder to develop due to the efficient perception, planning and control capabilities required, particularly in small drones with constrained takeoff weights. For reasonable performance, obstacle detection systems should be capable of running in real-time, with sufficient field-of-view (FOV) and detection range, and ideally providing relative position estimates of potential obstacles. In this work, we achieve all of these requirements by proposing a novel strategy to perform onboard drone detection and localization using depth maps. We integrate it on a small quadrotor, thoroughly evaluate its performance through several flight experiments, and demonstrate its capability to simultaneously detect and localize drones of different sizes and shapes. In particular, our stereo-based approach runs onboard a small drone at 16 Hz, detecting drones at a maximum distance of 8 meters, with a maximum error of 10% of the distance and at relative speeds up to 2.3 m/s. The approach is directly applicable to other 3D sensing technologies with higher range and accuracy, such as 3D LIDAR.

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“…In [12], the authors define another velocity obstacle cone in a velocity space by moving the collision cone by the obstacle's velocity. In [21], the authors introduce a non-cooperative concept for avoiding collision by assuming that the distance, direction, and range of speed of the obstacle are known. In this work, the feasible velocity set is obtained by checking speeds in the obstacle speed range.…”

Section: Related Workmentioning

confidence: 99%

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

2018

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It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method. IntroductionCurrently, the cooperative control of a multiple unmanned aerial vehicle (UAV) system is attracting growing interest. This is motivated by the constantly growing number of civil and commercial UAV applications [1]. One of the core problems in multi-UAV systems is motion planning, where each UAV navigates a path to the target by sharing each other's information. This should result in a collision-free path derived from UAV motion control. This has driven the development of various UAV collision avoidance algorithms.UAV collision avoidance necessitates a way to predict UAV motion. There are different methods to predict UAV motion and the future positions of the UAV. Early collision avoidance strategies focus on the static obstacles [2,3], using decision trees to avoid various troublesome situations [4], and using path planning to avoid the obstacles [5]. As moving obstacles are more often found in a real environment, many techniques have been proposed for dealing with this situation. For example, in [6], the authors developed a vision-based collision avoidance method by using minimum effort guidance. In [7], the authors presented a reactive avoidance method by using nonlinear differential geometric guidance; in [8,9], the authors developed a local path planning method for UAV navigation; in [10], the authors presented a collision avoidance algorithm by using dynamic programming; in [11], the authors proposed a collision avoidance algorithm based on potential fields; in [12][13][14][15][16][17], the authors presented the velocity obstacle method for deconflicting the UAV paths. Among these methods, the velocity obstacle has been the most actively studied in multi-UAV control over the past ten years. These include methods from avoidance maneuver based on conflict geometry [12]; optimal reciprocal collision avoidance [13]; reciprocal collision avoidance using on-board decentralized sensing without communication [16]; the recursive probabilistic velocity obstacles algorithm [14]; the hybrid reciprocal 1 2 of 25 velocity obstacle algorithm [15]; and the artificial bee colony optimized reciprocal velocity obstacle alg...

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Velocity Obstacle Method for Non-cooperative Autonomous Collision Avoidance System for UAVs

Cited by 11 publications

References 9 publications

Distributed deconfliction algorithm for Unmanned Aerial Vehicles with limited range and field of view sensors

Distributed deconfliction algorithm for Unmanned Aerial Vehicles with limited range and field of view sensors

Onboard Detection and Localization of Drones Using Depth Maps

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

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