UR-EARL: A framework for designing underwater robots using evolutionary algorithm-driven reinforcement learning
Designing an autonomous underwater vehicle (AUV) is a significant challenge, which involves two primary tasks: shaping the body and developing the control system. Both elements are essential for the AUV’s effective performance. This study introduces a novel framework that integrates evolutionary alg...
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| Vydané v: | Ocean engineering Ročník 321; s. 120402 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
30.03.2025
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| ISSN: | 0029-8018 |
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| Abstract | Designing an autonomous underwater vehicle (AUV) is a significant challenge, which involves two primary tasks: shaping the body and developing the control system. Both elements are essential for the AUV’s effective performance. This study introduces a novel framework that integrates evolutionary algorithms with reinforcement learning to simultaneously optimize both the shape and behavior of AUVs toward specific objectives. Our framework is designed to be highly expandable, making it easy to incorporate and test new design algorithms alongside existing methods. Additionally, we provide an underwater simulation platform based on the Pybullet engine to realistically evaluate the performance of evolved/trained AUVs. To demonstrate the capabilities of our methodology, we present a case study focused on navigating and maintaining positions within diverse underwater environments with different flow dynamics. We use a Lindenmayer-System-based evolutionary algorithm for optimizing the AUV’s morphology and utilize Stable Baselines 3 algorithms to train the controllers within our proposed framework. Being entirely open-source, we expect this framework to provide researchers with a convenient platform for efficiently testing their innovative methods and accelerating progress in the field.
•Concurrent design of the shape and control system of an AUV.•An evolutionary algorithm is used for morphological optimization.•Reinforcement learning is applied for controller training.•A simulator is implemented for realistic simulation of the designed AUVs. |
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| AbstractList | Designing an autonomous underwater vehicle (AUV) is a significant challenge, which involves two primary tasks: shaping the body and developing the control system. Both elements are essential for the AUV’s effective performance. This study introduces a novel framework that integrates evolutionary algorithms with reinforcement learning to simultaneously optimize both the shape and behavior of AUVs toward specific objectives. Our framework is designed to be highly expandable, making it easy to incorporate and test new design algorithms alongside existing methods. Additionally, we provide an underwater simulation platform based on the Pybullet engine to realistically evaluate the performance of evolved/trained AUVs. To demonstrate the capabilities of our methodology, we present a case study focused on navigating and maintaining positions within diverse underwater environments with different flow dynamics. We use a Lindenmayer-System-based evolutionary algorithm for optimizing the AUV’s morphology and utilize Stable Baselines 3 algorithms to train the controllers within our proposed framework. Being entirely open-source, we expect this framework to provide researchers with a convenient platform for efficiently testing their innovative methods and accelerating progress in the field.
•Concurrent design of the shape and control system of an AUV.•An evolutionary algorithm is used for morphological optimization.•Reinforcement learning is applied for controller training.•A simulator is implemented for realistic simulation of the designed AUVs. |
| ArticleNumber | 120402 |
| Author | Schmeink, Anke Hallawa, Ahmed Sariman, Cagatay |
| Author_xml | – sequence: 1 givenname: Cagatay orcidid: 0000-0001-7023-8813 surname: Sariman fullname: Sariman, Cagatay email: cagatay.sariman@rwth-aachen.de organization: RWTH Aachen University - Chair of Information Theory and Data Analytics, Kopernikusstraße 16, Aachen, 52074, NRW, Germany – sequence: 2 givenname: Ahmed orcidid: 0000-0003-3932-4873 surname: Hallawa fullname: Hallawa, Ahmed email: hallawa@ice.rwth-aachen.de organization: RWTH Aachen University - Institute for Communication Technologies and Embedded Systems, Kopernikusstraße 16, Aachen, 52074, NRW, Germany – sequence: 3 givenname: Anke orcidid: 0000-0002-9929-2925 surname: Schmeink fullname: Schmeink, Anke email: anke.schmeink@inda.rwth-aachen.de organization: RWTH Aachen University - Chair of Information Theory and Data Analytics, Kopernikusstraße 16, Aachen, 52074, NRW, Germany |
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| Cites_doi | 10.1007/s40815-017-0401-3 10.1162/artl.1994.1.4.353 10.1145/2661735.2661737 10.1016/j.oceaneng.2017.04.030 10.1007/s00773-015-0312-7 10.1109/TEVC.2022.3199045 10.1016/j.robot.2012.11.009 10.1016/S1474-6670(17)51653-5 10.1109/JOE.2007.893685 10.1109/TRA.2003.814502 10.3390/jmse9091020 10.1049/cth2.12044 10.1016/j.robot.2018.05.016 10.1146/annurev-control-072220-093055 10.1007/s10846-019-01004-2 10.1016/j.oceaneng.2019.04.011 10.1007/s10710-007-9028-8 10.1016/j.robot.2016.05.016 10.1016/j.margeo.2014.03.012 10.1145/3520304.3528991 10.1007/s13748-018-0155-7 10.1137/090761811 10.1080/10426914.2020.1746335 10.1109/TEVC.2013.2281535 10.1109/6.981854 10.1038/s41467-021-25874-z 10.1007/s11370-015-0179-9 10.1007/s00773-015-0343-0 10.1177/1687814016659597 10.1109/IROS51168.2021.9635857 10.1016/j.matcom.2015.08.021 10.1038/35023115 10.1007/s11633-014-0792-7 10.1007/978-1-4613-1419-6_7 10.1145/3449726.3459475 10.1007/978-3-319-32552-1_22 10.1016/j.engappai.2013.09.009 |
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| Keywords | Autonomous underwater vehicles Morphological evolution Robotics Reinforcement learning |
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| SubjectTerms | Autonomous underwater vehicles Morphological evolution Reinforcement learning Robotics |
| Title | UR-EARL: A framework for designing underwater robots using evolutionary algorithm-driven reinforcement learning |
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