Path Planning of Lunar Robot Based on an Adaptive Ant Colony Algorithm

Abstract

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm (A-APFACO) suitable for path planning of lunar robot was proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. The simulation experiment results show that the convergence speed of the improved adaptive ant colony algorithm is significantly faster, and the success rate of global searching for the shortest path can reach 96%.