Regularized Hierarchical Quadratic Program for Real-Time Whole-Body Motion Generation

• Published in: IEEE/ASME transactions on mechatronics Vol. 26; no. 4; pp. 2115 - 2126
• Main Authors: Hong, Seongil, Jang, Keunwoo, Kim, Sanghyun, Park, Jaeheung
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Constraints; IEEE transactions; Mechatronics; Optimization; Quadratic program (QP); Quadratic programming; Real time; Real-time systems; Regularization; rescue robot; Robots; Robustness; Robustness (mathematics); Singular value decomposition; singularity robustness; Task analysis; task priority
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2020.3032522

The goal of this article is to find an optimal and robust solution for on-line hierarchical least-squares optimization subject to both equality and inequality constraints. We focus on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face a singularity. The mixed problem of a regularization and inequality-constrained hierarchical optimization is not fully discussed due to the mathematical complexity. We address this problem by formulating a regularized hierarchical quadratic programming. The solution is obtained in a unified and computationally efficient manner by leveraging a singular value decomposition and an active set method. At the same time, we concentrate on the realization of the proposed algorithm as a practical means of real-time whole-body motion generation. The effectiveness of the algorithm is validated through extensive numerical simulations and experimental tests of a rescue robot successfully executing manipulation missions in a highly unstructured environment.