Trajectory planning for an unmanned ground vehicle group using augmented particle swarm optimization in a dynamic environment

Optimal path planning is a key problem for the control of autonomous unmanned ground vehicles. Particle swarm optimization has been used to solve the optimal problem in the static environment; however, optimal path planning for UGV groups in a dynamical environment has not been fully discussed. Accordingly, a dynamic obstacle-avoidance path planning for an unmanned ground vehicle group was considered as optimal problem for shortest path with formation constraints. The problem was formulated in Cartesian space with detectable velocity of both the vehicles and obstacles. The fitness function was defined by minimizing the trajectory of the group while keeping the V-shape formation of the group. Stable region of the parameters are determined by analyzing the convergence of the PSO algorithm. The simulation results demonstrated that the augmented particle swarm optimization could get the shortest path while keeping the V-formation and converged very fast.