Hierarchical quadratic programming: Fast online humanoid-robot motion generation

作者：Adrien Escande, Nicolas Mansard, Pierre-Brice Wieber · 发表于：The International Journal of Robotics Research · 年份：2014 · DOI：10.1177/0278364914521306 · 被引用次数：553 · 研究领域：Robotic Locomotion and Control、Robotic Mechanisms and Dynamics、Robot Manipulation and Learning

Hierarchical least-square optimization is often used in robotics to inverse a direct function when multiple incompatible objectives are involved. Typical examples are inverse kinematics or dynamics. The objectives can be given as equalities to be satisfied (e.g. point-to-point task) or as areas of satisfaction (e.g. the joint range). This paper proposes a complete solution to solve multiple least-square quadratic problems of both equality and inequality constraints ordered into a strict hierarchy. Our method is able to solve a hierarchy of only equalities 10 times faster than the iterative-projection hierarchical solvers and can consider inequalities at any level while running at the typical control frequency on whole-body size problems. This generic solver is used to resolve the redundancy of humanoid robots while generating complex movements in constrained environments.