Realization of a Real-Time Optimal Control Strategy to Stabilize a Falling Humanoid Robot with Hand Contact

Abstract

In this paper, we present a real-time falling robot stabilization system for a humanoid robot in which the robot can prevent falling using hand contact with walls and other surfaces in the environment. Instead of ignoring or avoiding interaction with environmental obstacles, our system uses obstacle geometry to determine a contact point that reduces impact and necessary friction. It uses a planar dynamic model that is appropriate for falling stabilization in the robot's sagittal plane and frontal plane. The hand contact is determined with an optimal control approach, and to make the algorithm run in realtime, a simplified three-link robot model and a pre-computed database of subproblems for the hand contact optimization are adopted. Moreover, if the robot is not leaning too far after stabilization, we employ a heuristic push-up strategy to recover the robot to a standing posture. System integration is performed on the Darwin-Mini robot and validation is conducted in several environments and falling scenarios.