A Hybrid Bio-Inspired Optimization Algorithm for UAV-UGV Cooperative Path Planning

This paper investigates a path planning approach for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) systems. Considering the position error corrections (PEC) of UAV, threats, and other complex constraints, a UAV-UGV cooperative path planning model is developed to minimize the total cost. Then, a Hybrid Ant-Pigeon Cooperative Optimization (HAPCO) algorithm is presented to address issues in existing bio-inspired optimization algorithms. A new pheromone update strategy and adaptive inertia weight are designed to optimise the performance. the performance of the Ant Colony Optimization (ACO) algorithm and Pigeon-inspired Optimization (PIO) algorithm. Moreover, the two algorithms are integrated to perform a full-cycle search. Simulation results reveal that the HAPCO algorithm outperforms ACO and PIO regarding path quality, cooperation cost, convergence, and computational efficiency, further demonstrated the validity and superiority of our algorithm.