Multi-Conflict-Based Optimal Algorithm for Multi-UAV Cooperative Path Planning

Abstract

Multi-UAV cooperative path planning can improve the efficiency of task completion. To deal with the space and time conflicts of multi-UAVs in complex environments, a multi-collision-based multi-UAV cooperative path planning algorithm, multi-conflict-based search (MCBS), is proposed. First, the flight and cooperative constraints of UAV are analyzed, and a three-dimensional environment model is established that incorporates geographical information. Then, hierarchical optimization is used to design collaborative algorithms. In the low-level path design, UAV flight constraints are combined with a sparse A* algorithm, and by improving the cost function, the search space is reduced, and the search time is shortened. In high-level cooperation, the priorities of different conflicts are set, heuristic information is introduced to guide the constraint tree to grow in the direction of satisfying the constraints, and the optimal path set is searched by the best priority search algorithm to reduce the convergence time. Finally, the planning results of the proposed algorithm, the traditional CBS algorithm, and the sparse A* algorithm for different UAV tasks are compared, and the influence of the optimization parameters on the calculation results is discussed. The simulation results show that the proposed algorithm can solve cooperative conflict between UAVs, improve the efficiency of path searches, and quickly find the optimal safe cooperative path that satisfies flight and cooperative constraints.

Hierarchical Maneuver Decision Method Based on PG-Option for UAV Pursuit-Evasion Game

“…Unmanned aerial vehicles (UAVs) [1][2][3][4][5][6][7] are used in many fields, such as intelligent confrontation [8], target rounding [9] and intelligent transportation [10], because of their characteristics of being unmanned, having good concealment and having no casualties. UAV pursuit-evasion [11] involves a game between two UAVs with competing interests.…”

Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm

“…This approach aims to overcome the limitations of traditional multi-UAV path-planning methods in terms of computational efficiency and scalability. In a similar vein, Liu et al [ 9 ] analyzed the flight and cooperative constraints of UAVs and established a three-dimensional environmental model incorporating geographical information. Chen et al [ 10 ], on the other hand, achieved kdiff multi-UAV cooperative autonomous path planning in unknown environments, considering the dynamic and partially observable nature of the environmental state.…”