电子测量与仪器学报

姜媛媛,李林,朱文昌.基于 ISMA 的多点遍历路径规划方法[J].电子测量与仪器学报,2023,37(3):202-210

基于 ISMA 的多点遍历路径规划方法

Mobile robot multi-goal path planning using improved slime mould algorithm

DOI：

英文关键词:path planning reverse learning triangular grid map improved SMA B spline function

基金项目:安徽省重点研究与开发计划(202104g01020012)、安徽理工大学环境友好材料与职业健康研究院研发专项基金(ALW2020YF18)项目资助

作者	单位
姜媛媛	1. 安徽理工大学电气与信息工程学院，2. 安徽理工大学环境友好材料与职业健康研究院(芜湖)
李林	3. 安徽理工大学人工智能学院
朱文昌	1. 安徽理工大学电气与信息工程学院

Author	Institution
Jiang Yuanyuan	1. School of Electrical and Information Engineering, Anhui University of Science and Technology,2. Institute of Environment-friendly Materials and Occupational Health, Anhui University of Science and Technology
Li Lin	3. School of Institute of Artificial Intelligence, Anhui University of Science and Technology
Zhu Wenchang	1. School of Electrical and Information Engineering, Anhui University of Science and Technology

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中文摘要:

针对移动机器人在遍历多目标点的路径规划中存在路径较长且不平滑等问题,本文提出一种基于 ISMA 的多点遍历路径规划方法。首先,结合 Singer 映射和小孔成像反向学习策略改进标准黏菌算法( SMA);然后初步构建地图,使用 ISMA 规划路径,以确定三角网格最大边长的最优值;最后,基于三角网格最大边长的最优值重新构建三角网格地图,使用 ISMA 生成路径,并通过 B 样条函数对路径进行光滑处理,提升路径平滑度。基准函数测试结果表明,ISMA 收敛速度更快,寻优精度更高。三角网格地图上的路径规划实验表明:ISMA 规划的路径长度和平滑度明显优于 SMA、SSA 和 WOA,与 SMA、SSA 和 WOA 相比, 在较复杂的场景中 ISMA 生成路径的长度依次减少了 6. 31%、18. 76%和 19. 74%,验证了 ISMA 方法的有效性。

英文摘要:

Aiming at the problems of long and unsmooth paths in the path planning of mobile robots traversing multiple target points, this paper proposes a multi-point traversal path planning method based on improved SMA. Firstly, the standard slime mold algorithm (SMA) is improved by combining Singer mapping and small hole imaging reverse learning strategy. Then, the map is preliminarily constructed, and the improved SMA is used to plan the path to determine the optimal value of the maximum side length of the triangular mesh. Finally, the triangular grid map is reconstructed based on the optimal value of the maximum edge length of the triangular mesh, the improved SMA is used to generate the path, and the path is smoothed by the B spline function to improve the smoothness of the path. The benchmark function test results show that the improved SMA converges faster and has higher optimization accuracy. Path planning experiments on triangular grid maps show that the path length and smoothness of improved SMA planning are significantly better than those of SMA, SSA and WOA, and compared with SMA, SSA and WOA, the length of the improved SMA generated path in complex scene is reduced by 6. 31%, 18. 76% and 19. 74%, which verifies the effectiveness of the improved SMA method.

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