Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds
This work addresses the problem of stable and dynamic motion control for quadrupedal robots, particularly for maneuvers involving singularities, which is a significant challenge for robotics researchers and practitioners.
This paper introduces a Representation-Free Model Predictive Control (RF-MPC) framework for quadrupedal robots, directly using rotation matrices for rotational dynamics. The controller operates at 250 Hz and successfully stabilizes dynamic motions, including a controlled backflip, which involve singularities in 3D maneuvers.
This paper presents a novel Representation-Free Model Predictive Control (RF-MPC) framework for controlling various dynamic motions of a quadrupedal robot in three dimensional (3D) space. Our formulation directly represents the rotational dynamics using the rotation matrix, which liberates us from the issues associated with the use of Euler angles and quaternion as the orientation representations. With a variation-based linearization scheme and a carefully constructed cost function, the MPC control law is transcribed to the standard Quadratic Program (QP) form. The MPC controller can operate at real-time rates of 250 Hz on a quadruped robot. Experimental results including periodic quadrupedal gaits and a controlled backflip validate that our control strategy could stabilize dynamic motions that involve singularity in 3D maneuvers.