Xiang Dan, Lin Hanxi, Ouyang Jian, Huang Dan
School of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.
School of Computer Science and Information Engineering, Guangzhou Maritime University, Guangzhou, 510725, Guangdong, China.
Sci Rep. 2022 Aug 2;12(1):13273. doi: 10.1038/s41598-022-17684-0.
With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.
随着人工智能的发展，自主移动机器人（AMR）的路径规划近年来一直是研究热点。本文提出了一种结合贪心算法的改进A算法用于多目标路径规划策略。首先，对评估函数进行改进以使A算法收敛更快。其次，去除A算法中不必要的节点，同时仅保留路径规划所需的必要拐点。第三，将改进的A算法与贪心算法相结合应用于多目标点规划。最后，在仓库环境中对五个目标节点进行路径规划，以比较路径长度、转弯角度等参数。仿真结果表明，所提算法路径更平滑，路径长度减少了约5%。结果表明，所提方法能够减少一定的路径长度。
