文献类型：期刊文献

英文题名：Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

作者：Zhu, Shinan[1];Zhu, Weiyi[1];Zhang, Xueqin[1];Cao, Tao[2]

机构：[1]East China Univ Sci & Technol, Coll Informat Sci & Engn, Shanghai, Peoples R China;[2]Shanghai Aerosp Control Technol Inst, 3888 Yuanjiang Rd, Shanghai 201108, Peoples R China

年份：2020

卷号：17

期号：3

外文期刊名：INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS

收录：;EI(收录号：20202008672348);WOS:【SCI-EXPANDED(收录号:WOS:000536472900001)】；

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Shanghai Aerospace Science and Technology Innovation Fund (SAST2018-086).

语种：英文

外文关键词：Lunar robot; ant colony algorithm; artificial potential field; dynamic obstacle avoidance

摘要：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.