UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

Abstract

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

Cooperative Target Search of UAV Swarm with Communication Distance Constraint

“…It can realize realtime obstacle avoidance but cannot resolve complex conflict problems. In [18][19][20], the speed obstacle method broadcasts automatic correlation monitoring to give each UAV the position and speed of the others; thus it solved the potential problem by detecting flight conflict and determining a relief flight path; however, the relief path can easily deviate the UAV from the search target point, thus compromising mission efficiency. In [21], the distributed model predictive control method is adopted, in which the collision avoidance management unit and the interactive graph updating mechanism address conflict resolution in multi-UAV route planning, but it requires a large amount of computation.…”

“…It can be seen from Figure 7(d) that when some members are damaged, which leads to a decline in swarm size, a UAV can still carry out search tasks through autonomous decision-making, which has good robustness. (9) end for (10) for k � 0: k max (11) Update the target probability distribution map according to equations ( 16)-( 21); (12) for U i � 1: N u (13) According to formula (26), judge whether to make an independent decision; (14) According to formula (11), a differential evolution algorithm is adopted to make a real-time decision; (15) for U j � 1: N u (16) Determine the interactive member set according to formula (27); (17) Complete information interaction and fusion according to formula (25); (18) end for (19) According to formula ( 14), the differential evolution algorithm is adopted to make real-time decisions; (20) Update the UAV position according to formula ( 5); (21) Update your own environmental cognition map according to formula ( 23); (22) end for (23) end for ALGORITHM 1: Algorithm pseudocode.…”

“…(1) It designed a cooperative search method suitable for dynamic communication distance (2) It analyzed the prior information of four typical moving targets to generate a mathematical model that defines them in a cooperative search (3) It provided an interface to apply to current UAV conflict resolution results [18][19][20][21] in swarm cooperative search missions…”

Distributed Adaptive Finite-Time Consensus for High-Order Multi-Agent Systems with Intermittent Communications under Switching Topologies

“…Consensus control means that the states of all agents converge to a same value. It has been widely applied in many applications, such as formation control, which includes spacecraft attitude control [2,3], multi-robot cooperative control [4] and unmanned aerial vehicle (UAV) consensus control [5,6]. Other fields like smart power grid systems [7], intelligent transportation systems [8] and target tracking [9] have also generated numerous new application scenarios.…”

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

“…Zhenhua Pan et al [18] combined the improved APF with PID algorithm, Daoyong Wang et al [19] combined the improved APF with collision prediction model, or realized the obstacle avoidance of UAV with bearings only measurement [20]. If the UAV companion is regarded as a dynamic obstacle [21,22], or a dynamic APF [23] is established, the collision avoidance between UAVs can be solved in the improved APF [24]. To sum up, the APF method has good effect in route planning and obstacle avoidance, and is less used in cluster control.…”