Global Path Planning Algorithm for Unmanned Vehicles Based on Multi-objective Particle Swarm Optimization Strategy

To overcome the limitations of unmanned vehicle global path planning with the sole objective of distance, a multi-objective particle swarm optimization strategy is proposed for indoor unmanned vehicle path planning. This strategy integrates objectives related to travel distance and cumulative turning angle. The traditional distance function is enhanced to accelerate algorithm convergence, and cumulative turning angle is introduced to construct a comprehensive function, meeting the demands of multi-objective navigation. The Pareto solution set concept is incorporated, and through the multi-objective particle swarm optimization algorithm, optimal paths for different travel objectives are identified, enhancing solution comprehensiveness. Experimental results validate the feasibility and effectiveness of the improved algorithm.