Dynamic Load Adaptive Control for Legged Robot with Manipulator Based on Whole-Body MPC
IEEE International Conference on Robotics and Biomimetics（2024）
Southern University of Science and Technology
Abstract
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.
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Key words
Adaptive Control,Model Predictive Control,Whole-body Model Predictive Control,Root Mean Square Error,Mean Square Error,Dynamic Model,Rigid Body,Manipulation Tasks,Objective Parameters,Robot State,Rotation Error,Unknown Objects,Online Identification,Rigid Body Dynamics,Whole-body Model,Dynamic Process,Actuator,Cost Function,Equations Of Motion,Kalman Filter,Position Error,Robot Dynamics,Asteraceae,Contact Force,Inequality Constraints,World Frame,Stage Cost,Model Predictive Control Framework,Orientation Error,Model Predictive Control Problem
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