Force-and-moment-based Model Predictive Control for Achieving Highly Dynamic Locomotion on Bipedal Robots
This work addresses the challenge of dynamic and versatile locomotion for bipedal robots, which is incremental as it builds on existing MPC methods with a novel formulation for force and moment inputs.
The paper tackled the problem of achieving highly dynamic locomotion on bipedal robots by proposing a force-and-moment-based Model Predictive Control framework, resulting in the robot achieving fast walking speeds up to 1.6 m/s on rough terrain and enabling a wide range of dynamic motions like walking, hopping, and running with accurate velocity tracking.
In this paper, we propose a novel framework on force-and-moment-based Model Predictive Control (MPC) for dynamic legged robots. Specifically, we present a formulation of MPC designed for 10 degree-of-freedom (DoF) bipedal robots using simplified rigid body dynamics with input forces and moments. This MPC controller will calculate the optimal inputs applied to the robot, including 3-D forces and 2-D moments at each foot. These desired inputs will then be generated by mapping these forces and moments to motor torques of 5 actuators on each leg. We evaluate our proposed control design on physical simulation of a 10 degree-of-freedom (DoF) bipedal robot. The robot can achieve fast walking speed up to 1.6 m/s on rough terrain, with accurate velocity tracking. With the same control framework, our proposed approach can achieve a wide range of dynamic motions including walking, hopping, and running using the same set of control parameters.