Unmanned Aerial-Ground Vehicles Path Planning Using Modified Grey Wolf Optimization Algorithm

This paper proposes a modified grey wolf optimization (GWO) algorithm for the collaborative path planning problem of heterogeneous unmanned aerial vehicles (UAVs) and an unmanned ground vehicle (UGV) in complex urban underground pipeline gallery environments. The objective is to navigate each vehicle from a starting position to a target position while avoiding both obstacles and inter-vehicle collisions. By combining the objective function of UAVs and the UGV with step constraint, dynamics constraints, and collision avoidance constraints, the vehicles' path planning is formulated as a multi-objective optimization problem. To address this problem, we enhance the GWO algorithm by introducing a pigeon flock bifurcation (PFB) mechanism to improve its convergence speed. Finally, a simulation case demonstrates the effectiveness of the proposed path planning algorithm.