Push Recovery of a Humanoid Robot Based on Model Predictive Control and Capture Point
This work addresses a critical challenge in robotics for humanoid robots operating in constrained environments, though it is incremental as it builds on existing MPC and capture point methods.
The paper tackles the problem of push recovery for humanoid robots when stepping is not possible, by using a single Model Predictive Control scheme to control the Capture Point via modulation of both the Zero Moment Point and Centroidal Moment Pivot, achieving effective balance recovery even with severely shrunken support polygons.
The three bio-inspired strategies that have been used for balance recovery of biped robots are the ankle, hip and stepping Strategies. However, there are several cases for a biped robot where stepping is not possible, e. g. when the available contact surfaces are limited. In this situation, the balance recovery by modulating the angular momentum of the upper body (Hip-strategy) or the Zero Moment Point (ZMP) (Ankle strategy) is essential. In this paper, a single Model Predictive Control (MPC) scheme is employed for controlling the Capture Point (CP) to a desired position by modulating both the ZMP and the Centroidal Moment Pivot (CMP). The goal of the proposed controller is to control the CP, employing the CMP when the CP is out of the support polygon, and/or the ZMP when the CP is inside the support polygon. The proposed algorithm is implemented on an abstract model of the SURENA III humanoid robot. Obtained results show the effectiveness of the proposed approach in the presence of severe pushes, even when the support polygon is shrunken to a point or a line.