Enhancing Humanoid Robot Dynamics: An Optimization Framework for Shoulder Base Angle Adjustment

Abstract

Optimizing the initial angle of the shoulder’s base frame is crucial for defining the workspace and enhancing the manipulation performance of humanoid robotic arms. Previous studies primarily emphasized geometric analyses, neglecting dynamic factors, which limits their practical applicability. This study presents a multi-metric optimization framework to enhance the dynamic performance of humanoid robotic arms by optimizing the shoulder’s initial angle. We formulate a cost function that incorporates torque efficiency, energy consumption, and overload ratio, utilizing the differential evolution (DE) algorithm for optimization. Furthermore, to address the limitations of conventional geometric workspace analysis, we introduce the concept of effective workspace, integrating dynamic constraints to quantitatively evaluate the effects of optimized shoulder angles. We validate the proposed framework in a hybrid simulation environment combining MuJoCo and RBDL, using the KIST humanoid and Unitree G1 robotic arms. Experimental results confirm that the optimized shoulder angles enhance torque distribution, expanding the effective workspace by 18.4% and 3.78% for the KIST and Unitree G1 robotic arms, respectively. These findings demonstrate that the proposed optimization framework enhances manipulation and dynamic performance as well as energy efficiency and system reliability, contributing to advancements in humanoid robotic arm design.