{"title":"An efficient RRT-based motion planning algorithm for autonomous underwater vehicles under cylindrical sampling constraints","authors":"Fujie Yu, Huaqing Shang, Qilong Zhu, Hansheng Zhang, Yuan Chen","doi":"10.1007/s10514-023-10083-y","DOIUrl":null,"url":null,"abstract":"<div><p>Quickly finding high-quality paths is of great significance for autonomous underwater vehicles (AUVs) in path planning problems. In this paper, we present a cylinder-based heuristic rapidly exploring random tree (Cyl-HRRT*) algorithm, which is the extension version of the path planner presented in our previous publication. Cyl-HRRT* increases the likelihood of sampling states that can improve the current solution by biasing the states into a cylindrical subset, thus providing better paths for AUVs. A direct greedy sampling method is proposed to explore the space more efficiently and accelerate convergence to the optimum. To reasonably balance the optimization accuracy and the number of iterations, a beacon-based adaptive optimization strategy is presented, which adaptively establishes a cylindrical subset for the next focused sampling according to the current path. Furthermore, the Cyl-HRRT* algorithm is shown to be probabilistically complete and asymptotically optimal. Finally, the Cyl-HRRT* algorithm is comprehensively tested in both simulations and real-world experiments. The results reveal that the path generated by the Cyl-HRRT* algorithm greatly improves the power savings and mobility of the AUV.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10083-y.pdf","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Robots","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10514-023-10083-y","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 3
Abstract
Quickly finding high-quality paths is of great significance for autonomous underwater vehicles (AUVs) in path planning problems. In this paper, we present a cylinder-based heuristic rapidly exploring random tree (Cyl-HRRT*) algorithm, which is the extension version of the path planner presented in our previous publication. Cyl-HRRT* increases the likelihood of sampling states that can improve the current solution by biasing the states into a cylindrical subset, thus providing better paths for AUVs. A direct greedy sampling method is proposed to explore the space more efficiently and accelerate convergence to the optimum. To reasonably balance the optimization accuracy and the number of iterations, a beacon-based adaptive optimization strategy is presented, which adaptively establishes a cylindrical subset for the next focused sampling according to the current path. Furthermore, the Cyl-HRRT* algorithm is shown to be probabilistically complete and asymptotically optimal. Finally, the Cyl-HRRT* algorithm is comprehensively tested in both simulations and real-world experiments. The results reveal that the path generated by the Cyl-HRRT* algorithm greatly improves the power savings and mobility of the AUV.
期刊介绍:
Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development.
The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.