Mobile robots are increasingly used, but navigation in complex dynamic environments suffers from issues like local optima, poor obstacle avoidance, and difficulty in coordination. This paper proposes a field swarm cooperative algorithm that integrates improved potential field and Particle Swarm Optimization. It uses simulated annealing to escape local optima, leverages the potential field force to enhance obstacle avoidance, and establishes a three-objective cooperative framework. Simulation outcomes indicate that in simple obstacle environments, the proposed algorithm successfully overcomes the local minima problem in U-shaped traps using the traditional Artificial Potential Field method. In the complex static library scenario, the path length of the algorithm proposed by the study is 218.7 m, the average safe distance is 0.56 m, and the smoothness is 3.89 rad. All the indicators have achieved balanced and superior results. Compared with the single path length optimization mode, the multi-objective collaborative optimization mode, at the cost of a slight increase in path length (p>0.05), has achieved significant improvements in the average safe distance and smoothness (p<0.001).The simulation results show that the algorithm proposed in this paper is significantly superior to the comparison methods, and multi-objective equilibrium, and has important practical value for improving the intelligence level and task execution efficiency of robots.
