Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1177/0278364914521306 | I. J. Robotic Res. |
Keywords | Field | DocType |
Inverse kinematics,redundancy,task hierarchy,humanoid robot | Mathematical optimization,Inverse kinematics,Quadratic equation,Redundancy (engineering),Artificial intelligence,Quadratic programming,Solver,Hierarchy,Mathematics,Robotics,Humanoid robot | Journal |
Volume | Issue | ISSN |
33 | 7 | 0278-3649 |
Citations | PageRank | References |
66 | 2.42 | 30 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Escande | 1 | 273 | 22.91 |
| Nicolas Mansard | 2 | 490 | 39.67 |
| Pierre-Brice Wieber | 3 | 302 | 22.93 |