Research on Multi-Unmanned Vehicle Formation Strategy Based on Improved Chaotic Particle Swarm Optimization Algorithm and Leader-Following Method

Aiming at the existing problem that the multi unmanned vehicle control strategies of formation based on the leader-following method are prone to getting trapped in local optimal value, a multi-unmanned vehicle control strategy of formation which combining the improved chaotic particle swarm optimization algorithm with the leader-following method is proposed in this paper. This strategy is based on the traditional particle swarm optimization algorithm and chaotic sequences are used to initialize the population, then, the chaotic optimization is adopted during the population updating to help escape from local optimal solutions. Meanwhile, an adaptive inertia weight is introduced to accelerate the convergence speed in the PSO. During the formation process, the improved chaotic particle swarm optimization algorithm is first used to seek an optimal path for the leader. Then, the leader-following method is used to control each follower to follow the leader at a set distance and angle, thereby it can achieve the formation control of multiple unmanned vehicles. The simulation results show that the multi - unmanned vehicle formation control method combining the improved chaotic particle swarm optimization algorithm with the leader-following method can escape from local optimal solutions and approach the global optimal solution. It has a fast convergence speed, feasibility and reliability have been confirmed through many experiments. Moreover, this algorithm provides a new way for multi-unmanned vehicle control strategies of formation.