Control Barrier Function-based Whole-body Control Framework for Humanoid Robots

Abstract

This paper addresses the challenge of deploying high-Degree-of-Freedom (DoF) humanoid robots in real-world scenarios, a critical issue in robotics. Previous approaches often relied on oversimplified assumptions, limiting robots’ adaptability. This work emphasizes the need for a flexible control framework for operational efficacy and safety. Significant advancements include floating-based kinematics for expanded robot workspace and stability, and a recursive algorithm for the Centroidal Momentum Matrix (CMM), enhancing motion planning. A novel self-collision avoidance algorithm employing boundary spheres and Control Barrier Functions (CBFs) ensures operational safety. A major contribution is the development of a control barrier function-based Quadratic Programming (QP)-based control framework integrating Proportional-Derivative (PD) control for stable walking. The paper simplifies trajectory design by categorizing motion tasks and enhances dynamic stability using a new angular momentum rate change compensation method with force-torque sensor feedback. These improvements mark a substantial leap in humanoid robotics, enhancing efficiency, functionality, and adaptability, and paving the way for their broader integration into human society.