Hierarchical quadratic programming

Pubblicato il: 23 aprile 2015
sul canale di: LAAS GEPETTO

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Authors: A Escande, N Mansard and P-B. Wieber

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 ten 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 environment.

Paper published in International Journal of Robotic Research (IJRR), 33(7):1006-1028, June 2014.

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