{"title":"An Optimal Frontier Enhanced “Next Best View” Planner For Autonomous Exploration","authors":"Liang Lu, A. Luca, L. Muratore, N. Tsagarakis","doi":"10.1109/Humanoids53995.2022.10000175","DOIUrl":null,"url":null,"abstract":"This work introduces a novel Optimal Frontier Enhanced “Next Best View” Planner (OFENBVP), which can enable autonomous exploration capabilities in mobile robots. The proposed planner combines a local “Next Best View” (NBV) planner and an optimal frontier-based global enhancement algorithm. The local NBV planner takes charge of exploring a small area surrounding the robot while the optimal frontier-based global enhancement algorithm is in charge of moving the robot to a new unexplored area after a certain number of iterations of the local NBV planner. The proposed method is implemented and validated on the hybrid mobility platform CENTAURO performing an exploration task, demonstrating much less time to complete the exploration of the environment.","PeriodicalId":180816,"journal":{"name":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Humanoids53995.2022.10000175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This work introduces a novel Optimal Frontier Enhanced “Next Best View” Planner (OFENBVP), which can enable autonomous exploration capabilities in mobile robots. The proposed planner combines a local “Next Best View” (NBV) planner and an optimal frontier-based global enhancement algorithm. The local NBV planner takes charge of exploring a small area surrounding the robot while the optimal frontier-based global enhancement algorithm is in charge of moving the robot to a new unexplored area after a certain number of iterations of the local NBV planner. The proposed method is implemented and validated on the hybrid mobility platform CENTAURO performing an exploration task, demonstrating much less time to complete the exploration of the environment.
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