Path Planning Improvement for Wheel-Legged Lunar Rovers Based on Fuzzy Control and Ant Colony Optimization

Path planning constitutes one of the fundamental technologies in robotic navigation.Focusing on the operational environment of wheel-legged lunar rovers on the actual lunar surface, this paper proposes and establishes a novel integrated planning methodology based on fuzzy control theory and ant colony optimization (ACO).First, based on terrain feature weights extracted from actual lunar surface scan data, we construct a three-dimensional lunar terrain model using PyCharm Matplotlib that accurately represents these characteristics.The performance parameters and constraints of the lunar rover are then systematically defined.Subsequently, we implement targeted improvements to the conventional ant colony optimization (ACO) algorithm based on actual operational conditions.A fuzzy logic controller is designed with an empirically initialized rule base, culminating in a novel hybrid control algorithm.Subsequently, the proposed algorithm was implemented in the 3D lunar terrain model.Simulation experiments were conducted under specified parameters and constraints, with comparative testing against both the A* algorithm and the genetic algorithm (GA).Results demonstrate that after 100 iterations, our method outperforms the benchmark algorithms in multiple performance metrics, including path optimality, response speed, and obstacle avoidance success rate, confirming its superior effectiveness and efficiency in lunar surface environments.