Skip to Main Content
High-performance robot-control algorithms often rely on system-dynamic models. For field robots, the dynamic parameters of these models may not be well known. The paper presents a mutual-information-based observability metric for the online dynamic parameter identification of a multibody system. The metric is used in an algorithm to optimally select the external excitation required by the dynamic system parameter identification process. The excitation is controlled so that the identification favors parameters that have the greatest uncertainty at any given time. This algorithm is applied to identify the vehicle and suspension parameters of a mobile-field manipulator, and is found to be computationally more efficient and robust to noise than conventional methods. Issues addressed include the development of appropriate vehicle models, compatible with the onboard sensors. Simulations and experimental results show the effectiveness of this algorithm.