Dynamic Load Adaptive Control for Legged Robot with Manipulator Based on Whole-Body MPC

Legged robots equipped with manipulators exhibit significant benefits in unstructured manipulation tasks. How-ever, ensuring that these robots successfully perform locomotion and manipulation tasks remains challenging, especially when both the center-of-mass (CoM) state and object parameters are unknown and time-varying. Based on the whole-body model predictive control (MPC), a novel online payload identification method is proposed to compensate for modeling errors and tackle loco-manipulation tasks. The whole-body MPC integrates the robot state and user inputs in the robot centroidal dynamics. Moreover, the online payload identification method facilitates evaluating the time-varying CoM state and the parameters of unknown objects. Additionally, the MPC efficacy of the centroidal dynamics model is evaluated by a single rigid body dynamics model, considering both scenarios: with and without the object dynamics. The robot can manipulate an unknown and time-varying load of up to 20kg, equivalent to 50% of the manipulator's mass. After online identification, the linear and rotational root mean square errors of the manipulator are reduced by approximately 70%. The results indicate that the proposed strategy can effectively coordinate the interaction force/moment between the robot and the manipulated object while maintaining stability.