Computationally Efficient HQP-based Whole-body Control Exploiting the Operational-space Formulation

This paper proposes a novel and practical approach to enhance the computational efficiency of the hierarchical quadratic programming (HQP)-based whole-body control. The HQP method is known to offer control solutions satisfying strict priority with various constraints for multiple-tasks execution. However, it inherently comes at the price of high computation time to solve QP optimization problems in each hierarchical level which limits practicability in a real-time control system with fast sampling time. To mitigate this issue, we propose that the operational space formulation is incorporated into the HQP method, where the decision variables are intuitively defined at the task level and possess smaller dimensions. Indeed, it serves faster whole-body control solution for multiple tasks under equality and inequality constraints yet strictly fulfilling the task priority. The performance of the proposed method is experimentally verified on the actual floating-based humanoid, named TOCABI with 33 degrees-of-freedom. In addition, computation time is analyzed by comparison with conventional HQP and other advanced implementation forms.