{"title":"A Compound Path Planning Algorithm for Mobile Robots","authors":"Huailin Zhao, Zhen Nie, Fangbo Zhou, Shengyang Lu","doi":"10.1109/ICPECA51329.2021.9362724","DOIUrl":null,"url":null,"abstract":"In this paper, we designed a compound algorithm which combined the advantages of Dijkstra and ant colony optimization to complete the robot path planning. The compound algorithm is one that uses the Dijkstra algorithm for initial path planning under a viewable environment model, and then optimizes the initial path with an improved ACO. Pointing at the problem that the ACO is slow to converge and easy to fall into the local optimal solution, the performance of ACO is improved by constructing a new heuristic function and improving the pheromone update principle. Through the simulation on MATLAB, the designed algorithm shows higher path search efficiency and path optimization rate than the traditional algorithms.","PeriodicalId":119798,"journal":{"name":"2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPECA51329.2021.9362724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we designed a compound algorithm which combined the advantages of Dijkstra and ant colony optimization to complete the robot path planning. The compound algorithm is one that uses the Dijkstra algorithm for initial path planning under a viewable environment model, and then optimizes the initial path with an improved ACO. Pointing at the problem that the ACO is slow to converge and easy to fall into the local optimal solution, the performance of ACO is improved by constructing a new heuristic function and improving the pheromone update principle. Through the simulation on MATLAB, the designed algorithm shows higher path search efficiency and path optimization rate than the traditional algorithms.