A Hybrid Motion Optimization Framework for the Humanoid Upper-Body Robot: Safe and Dexterous Object Carrying

In this letter, a novel hybrid motion optimization framework is proposed for a humanoid upper-body robot with two 7-DOF arms and a 2-DOF waist, to flexibly and safely carry objects in constrained and dynamic environments. The framework consists of trilayer interconnected optimization, which is dedicated to planning the optimal carrying configuration, waist-arm motion trajectory, and dual-arm nullspace trajectory, respectively. The top layer finds the most dexterous waist-arm carrying configuration that satisfies environmental constraints, which is achieved by the effective integration of the multi-objective evolutionary algorithm (EA), the proposed optimal manipulation index, and the parameterization method. The middle layer applies a non-linear optimization method to plan collision-free trajectory that connects the initial and optimal carrying configuration. The bottom layer is achieved by equipping the top layer with elite reproduction operator strategy and adaptive evolution space strategy, generating dual-arm nullspace trajectory in real-time for dynamic obstacle avoidance. The various carrying experiments demonstrate the effectiveness of the proposed framework.