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 suitable for path planning of lunar robot is 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. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

中文翻译：

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基于动态自适应蚁群算法和避障的月球机器人路径规划

月球机器人路径规划是月球机器人安全、准确完成任务的保障。针对蚁群算法收敛速度慢、易陷入局部最优的问题，以路径最短、能耗最小为目标，提出一种适用于月球机器人路径规划的自适应势场蚁群算法。该算法将人工势场法与蚁群算法相结合，引入诱导启发式因子，动态调整蚁群算法的状态转移规则，使算法具有更高的全局搜索能力和更快的收敛速度。得到规划路径后，根据可预测和不可预测的障碍物设计动态避障策略。特别是采用基于移动路径的几何方法检测不可预测的障碍物，实现动态障碍物的避让。实验结果表明，改进的自适应势场蚁群算法具有更高的全局搜索能力和更快的收敛速度。设计的避障策略可以有效判断是否会发生碰撞并采取避障措施。