Robust Collaborative Dynamic Parameter Estimation for Multirobot Systems: A Distributed Variational Inference-Based Approach
Accurate system identification is crucial for model-based control, planning, and algorithm training. Although numerous robotic model structures have been established, the specific parameter values within these models still require further estimation in practical applications. Meanwhile, with the rap...
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| Vydáno v: | IEEE/ASME transactions on mechatronics s. 1 - 12 |
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2025
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| ISSN: | 1083-4435, 1941-014X |
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| Abstract | Accurate system identification is crucial for model-based control, planning, and algorithm training. Although numerous robotic model structures have been established, the specific parameter values within these models still require further estimation in practical applications. Meanwhile, with the rapid development of multirobot systems, leveraging collaboration among robots to enhance parameter estimation accuracy and accelerate convergence becomes a viable approach. To this end, a new collaborative parameter estimation strategy is proposed in this article, allowing decentralized fusion estimation and distributed computations. Meanwhile, this decentralized and distributed framework is able to achieve comparable results to centralized estimation that collects measurements from all robots and directly estimate the posterior in a single computing unit. To enhance the robustness against environmental disturbance containing outliers, the robust local estimator is designed based on mean-field variational inference. Finally, we employ autonomous surface vehicles as research subject and conduct a series of experiments to demonstrate the effectiveness of proposed approach. |
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| AbstractList | Accurate system identification is crucial for model-based control, planning, and algorithm training. Although numerous robotic model structures have been established, the specific parameter values within these models still require further estimation in practical applications. Meanwhile, with the rapid development of multirobot systems, leveraging collaboration among robots to enhance parameter estimation accuracy and accelerate convergence becomes a viable approach. To this end, a new collaborative parameter estimation strategy is proposed in this article, allowing decentralized fusion estimation and distributed computations. Meanwhile, this decentralized and distributed framework is able to achieve comparable results to centralized estimation that collects measurements from all robots and directly estimate the posterior in a single computing unit. To enhance the robustness against environmental disturbance containing outliers, the robust local estimator is designed based on mean-field variational inference. Finally, we employ autonomous surface vehicles as research subject and conduct a series of experiments to demonstrate the effectiveness of proposed approach. |
| Author | Wen, Guanghui Shen, Han |
| Author_xml | – sequence: 1 givenname: Han orcidid: 0000-0003-3461-1603 surname: Shen fullname: Shen, Han email: shenhan@seu.edu.cn organization: Department of Systems Science, Southeast University, Nanjing, China – sequence: 2 givenname: Guanghui orcidid: 0000-0003-0070-8597 surname: Wen fullname: Wen, Guanghui email: wenguanghui@gmail.com organization: School of Automation, Southeast University, Nanjing, China |
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| Snippet | Accurate system identification is crucial for model-based control, planning, and algorithm training. Although numerous robotic model structures have been... |
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| SubjectTerms | Accuracy Autonomous surface vehicle (ASV) Collaboration Estimation Heuristic algorithms Multi-robot systems multirobot systems Noise measurement Parameter estimation Robots Robustness variational inference Vehicle dynamics |
| Title | Robust Collaborative Dynamic Parameter Estimation for Multirobot Systems: A Distributed Variational Inference-Based Approach |
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