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Article: Learning Agile Flights Through Narrow Gaps with Varying Angles Using Onboard Sensing

TitleLearning Agile Flights Through Narrow Gaps with Varying Angles Using Onboard Sensing
Authors
KeywordsLearning agile flight
motion control
onboard sensing
Issue Date14-Jul-2023
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Robotics and Automation Letters, 2023, v. 8, n. 9, p. 5424-5431 How to Cite?
Abstract

This letter addresses the problem of traversing through unknown, tilted, and narrow gaps for quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods relied on accurate knowledge of the environment, including the gap's pose and size. In contrast, we integrate onboard sensing and detect the gap from a single onboard camera. The training problem is challenging for two reasons: a precise and robust whole-body planning and control policy is required for variable-tilted and narrow gaps, and an effective Sim2Real method is needed to successfully conduct real-world experiments. To this end, we propose a learning framework for agile gap traversal flight, which successfully trains the vehicle to traverse through the center of the gap at an approximate attitude to the gap with aggressive tilted angles. The policy trained only in a simulation environment can be transferred into different domains with fine-tuning while maintaining the success rate. Our proposed framework, which integrates onboard sensing and a neural network controller, achieves a success rate of 87.36% in real-world experiments, with gap orientations up to 60∘ . To the best of our knowledge, this is the first letter that performs the learning-based variable-tilted narrow gap traversal flight in the real world, without prior knowledge of the environment.


Persistent Identifierhttp://hdl.handle.net/10722/339577
ISSN
2023 Impact Factor: 4.6
2023 SCImago Journal Rankings: 2.119
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXie, Yuhan-
dc.contributor.authorLu, Minghao-
dc.contributor.authorPeng, Rui-
dc.contributor.authorLu, Peng-
dc.date.accessioned2024-03-11T10:37:45Z-
dc.date.available2024-03-11T10:37:45Z-
dc.date.issued2023-07-14-
dc.identifier.citationIEEE Robotics and Automation Letters, 2023, v. 8, n. 9, p. 5424-5431-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/339577-
dc.description.abstract<p>This letter addresses the problem of traversing through unknown, tilted, and narrow gaps for quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods relied on accurate knowledge of the environment, including the gap's pose and size. In contrast, we integrate onboard sensing and detect the gap from a single onboard camera. The training problem is challenging for two reasons: a precise and robust whole-body planning and control policy is required for variable-tilted and narrow gaps, and an effective Sim2Real method is needed to successfully conduct real-world experiments. To this end, we propose a learning framework for agile gap traversal flight, which successfully trains the vehicle to traverse through the center of the gap at an approximate attitude to the gap with aggressive tilted angles. The policy trained only in a simulation environment can be transferred into different domains with fine-tuning while maintaining the success rate. Our proposed framework, which integrates onboard sensing and a neural network controller, achieves a success rate of 87.36% in real-world experiments, with gap orientations up to 60∘ . To the best of our knowledge, this is the first letter that performs the learning-based variable-tilted narrow gap traversal flight in the real world, without prior knowledge of the environment.<br></p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Robotics and Automation Letters-
dc.subjectLearning agile flight-
dc.subjectmotion control-
dc.subjectonboard sensing-
dc.titleLearning Agile Flights Through Narrow Gaps with Varying Angles Using Onboard Sensing-
dc.typeArticle-
dc.identifier.doi10.1109/LRA.2023.3295655-
dc.identifier.scopuseid_2-s2.0-85164776385-
dc.identifier.volume8-
dc.identifier.issue9-
dc.identifier.spage5424-
dc.identifier.epage5431-
dc.identifier.eissn2377-3766-
dc.identifier.isiWOS:001036073300009-
dc.identifier.issnl2377-3766-

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