Swarm Cooperative Strategy for Multi-UAV Based on Adaptive Boundary Discretization and Distributed Particle Swarm Optimization

In traditional multi-unmanned aerial vehicles (UAVs) cooperative strategies within complex irregular regions, the low coverage efficiency, boundary constraint failure, and insufficient robustness bring the challenges. This paper proposes a cooperative search-andattack algorithm, which integrates adaptive mission area discretization and distributed intelligent particle swarm optimization (DIPSO). An adaptive grid model is constructed with ray casting algorithm to discretize arbitrary polygonal mission areas precisely. It can effectively reduce the invalid searches in non-mission zones by abandoning the traditional rectangular assumption. Moreover, a multi-objective fitness function (MOFF) is designed by incorporating coverage ratio, path length, collision penalty, and load balancing. With the discrete path manipulation operators (PMOs) and dynamic learning factors, the proposed method achieves distributed trajectory planning in convex polygonal spaces. Simulation results demonstrate that compared with counterparts, the proposed method performs closer boundaries to actual ones. Furthermore, it achieves 100% coverage while shorten the mission time significantly, validating its adaptability in various convex polygonal scenarios.