Multi-agent Collaborative Route Planning based on Ant Colony Optimization Algorithm

Aiming at the problem of multi-agent collaboration and optimal path planning in complex environments, an improved ant colony optimization algorithm is proposed. Firstly, an artificial potential field is constructed based on the wavefront algorithm to design prior information. Then the geometric path optimization method is used to improve the optimization efficiency. Mathematical morphology and pheromone interaction are used to achieve cluster collaboration. The ant colony optimization algorithm is run based on the prior information to complete optimal route planning. An experiment is designed to simulate a search task of six unmanned underwater vehicles. Through the simulation using the ant colony optimization algorithm, the proposed method is tested. Through algorithm visualization, the route planning results are intuitively displayed. The experimental results show that the optimization algorithm can complete the route planning of multiple agents in a given task. The planned path is safe for the unmanned underwater vehicles and the distance is optimal.