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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Published in:IEEE/ASME transactions on mechatronics pp. 1 - 12
Main Authors: Shen, Han, Wen, Guanghui
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Accuracy
Autonomous surface vehicle (ASV)
Collaboration
Estimation
Heuristic algorithms
Multi-robot systems
multirobot systems
Noise measurement
Parameter estimation
Robots
Robustness
variational inference
Vehicle dynamics
ISSN:1083-4435, 1941-014X
Online Access:Get full text
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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.
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
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  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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