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 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.