Game Theory-Based Optimal Cooperative Path Planning for Multiple UAVs

This paper presents new cooperative path planning algorithms for multiple unmanned aerial vehicles (UAVs) using Game theory-based particle swarm optimization (GPSO). First, the formation path planning is formulated into the minimization of a cost function that incorporates multiple objectives and constraints for each UAV. A framework based on game theory is then developed to cast the minimization into the problem of finding a Stackelberg-Nash equilibrium. Next, hierarchical particle swarm optimization algorithms are developed to obtain the global optimal solution. Simulation results show that the GPSO algorithm can generate efficient and feasible flight paths for multiple UAVs, outperforming other path planning methods in terms of convergence rate and flexibility. The formation can adjust its geometrical shape to accommodate a working environment. Experimental tests on a group of three UAVs confirm the advantages of the proposed approach for a practical application.

Automated summary

This paper presents a cooperative path planning algorithm for multiple UAVs using Game theory-based particle swarm optimization. The algorithm achieves the global optimal solution by finding the Stackelberg-Nash equilibrium and outperforms other methods in terms of performance.