Enhancing Joint Dynamics Modeling for Underwater Robotics Through Stochastic Extension

Accurate joint dynamics models are essential for the compliance and robustness of robot control, especially for robots operating in complex underwater environments. To improve the precision of joint dynamics models, much research focuses on refining specific parameters or incorporating previously overlooked parameters through theoretical deductions and simulations. However, the effectiveness of these advancements can only be determined through empirical validation using the new model. This letter delineates a methodology that facilitates the assessment of potential avenues for enhancing the model, without necessitating prior theoretical derivation. Specifically, a methodology based on stochastic extension is proposed for evaluating directions of model improvement, applied to enhancing the LuGre model for underwater sealed joints. This approach employs the coefficient of variation in LuGre model parameters to assess the direction of model enhancement, with the comparison of coefficients of variation before and after improvement elucidating the superiority of the enhancements. Experimental outcomes corroborate that the LuGre model, refined using this evaluative technique, can precisely estimate friction forces across diverse typical conditions in underwater joint applications. The sealed joints utilizing the improved model demonstrated enhanced response times and precision in underwater environments.