Push Recovery of a Humanoid Robot Based on Model Predictive Control and Capture Point

In this paper, a push recovery controller based on the Model Predictive Control (MPC) and Capture Point (CP) is developed. The three bio-inspired strategies that have been used for balance recovery are the ankle, hip and stepping Strategies. There are several cases for a biped robot where stepping is not possible. 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 MPC scheme is employed for guiding the CP to a desired position by modulating both the ZMP and the Centroidal Moment Pivot (CMP). Therefore, 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 successfully on an abstract model of the SURENA III humanoid robot in the presence of severe pushes, while the support polygon is shrunk to a point or a line.