{"title":"Robot path planning in narrow passages based on improved PRM method","authors":"","doi":"10.1007/s11370-024-00527-4","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Probabilistic roadmap (PRM) method has been shown to perform well in robot path planning. However, its performance degrades when the robot needs to pass through narrow passages. To solve this problem, an improved PRM method with hybrid uniform sampling and Gaussian sampling is proposed in this paper. With the proposed method, the robot can improve the success rate and efficiency of path planning in narrow passages. Firstly, the narrow-passage-aware Gaussian sampling method is developed for narrow passages. Combining uniform sampling globally, the new sampling strategy can increase the sampling density at the narrow passages and reduce the redundancy of the samples in the wide-open regions. Then, we propose to use density-based clustering method to achieve accurate identification of narrow channels by removing the noise points. Next, graph search algorithm is used to search the shortest path from the start point to the goal point. Finally, simulations are carried out to evaluate the validity of the proposed method. Results show that the improved PRM method is more effective for path planning with narrow passages.</p>","PeriodicalId":48813,"journal":{"name":"Intelligent Service Robotics","volume":"3 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Service Robotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11370-024-00527-4","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Probabilistic roadmap (PRM) method has been shown to perform well in robot path planning. However, its performance degrades when the robot needs to pass through narrow passages. To solve this problem, an improved PRM method with hybrid uniform sampling and Gaussian sampling is proposed in this paper. With the proposed method, the robot can improve the success rate and efficiency of path planning in narrow passages. Firstly, the narrow-passage-aware Gaussian sampling method is developed for narrow passages. Combining uniform sampling globally, the new sampling strategy can increase the sampling density at the narrow passages and reduce the redundancy of the samples in the wide-open regions. Then, we propose to use density-based clustering method to achieve accurate identification of narrow channels by removing the noise points. Next, graph search algorithm is used to search the shortest path from the start point to the goal point. Finally, simulations are carried out to evaluate the validity of the proposed method. Results show that the improved PRM method is more effective for path planning with narrow passages.
期刊介绍：
The journal directs special attention to the emerging significance of integrating robotics with information technology and cognitive science (such as ubiquitous and adaptive computing,information integration in a distributed environment, and cognitive modelling for human-robot interaction), which spurs innovation toward a new multi-dimensional robotic service to humans. The journal intends to capture and archive this emerging yet significant advancement in the field of intelligent service robotics. The journal will publish original papers of innovative ideas and concepts, new discoveries and improvements, as well as novel applications and business models which are related to the field of intelligent service robotics described above and are proven to be of high quality. The areas that the Journal will cover include, but are not limited to: Intelligent robots serving humans in daily life or in a hazardous environment, such as home or personal service robots, entertainment robots, education robots, medical robots, healthcare and rehabilitation robots, and rescue robots (Service Robotics); Intelligent robotic functions in the form of embedded systems for applications to, for example, intelligent space, intelligent vehicles and transportation systems, intelligent manufacturing systems, and intelligent medical facilities (Embedded Robotics); The integration of robotics with network technologies, generating such services and solutions as distributed robots, distance robotic education-aides, and virtual laboratories or museums (Networked Robotics).
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