Global Path Planning for Mobile Robots Based on NSGA-II and Entropy-Weighted TOPSIS Method

This paper addresses the multi-objective optimization challenges (e.g., conflicts between path length, safety, and real-time performance) faced by mobile robots in complex environments, proposing a path planning method that integrates the NSGA-II multi-objective optimization algorithm with the TOPSIS entropy weight method. The NSGA-II algorithm generates a Pareto optimal solution set considering path length and obstacle distance through fast non-dominated sorting and crowding distance calculations. Its elitist preservation strategy and crossover/mutation mechanisms effectively prevent the loss of promising solutions. The entropy-weighted TOPSIS method objectively assigns weights to optimization objectives based on information entropy theory. It selects the optimal path with comprehensive performance from non-inferior solutions through ideal solution approximation ranking, avoiding subjective weight bias. Simulation experiments demonstrate that the optimal trajectory achieves a relative closeness of 0.9509, verifying the algorithm's effectiveness in multi-objective trade-offs under complex scenarios. This research provides an efficiency-safety balanced solution for mobile robot autonomous navigation in dynamic environments, with future work focusing on dynamic parameter adaptation mechanisms to enhance practical deployment capabilities.