Kinematic Control of Redundant Manipulators: Generalizing the Task-Priority Framework to Inequality Task

Redundant mechanical systems like humanoid robots are designed to fulfill multiple tasks at a time. A task, in velocity-resolved inverse kinematics, is a desired value for a function of the robot configuration that can be regulated with an ordinary differential equation (ODE). When facing simultaneo...

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Vydáno v:	IEEE transactions on robotics Ročník 27; číslo 4; s. 785 - 792
Hlavní autoři:	Kanoun, O., Lamiraux, F., Wieber, P-B
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.08.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Computer science; control theory; systems Control Control theory. Systems Exact sciences and technology Fundamental areas of phenomenology (including applications) hierarchy Humanoid humanoid robot Humanoid robots Inequalities inequality constraints inverse kinematics Joints Kinematics Linear matrix inequalities Manipulators Mathematical analysis Ordinary differential equations Physics Priorities Quadratic programming redundancy Redundant Robotics Robots Silicon Solid dynamics (ballistics, collision, multibody system, stabilization...) Solid mechanics task priority Tasks Redundant system inequality constraints Differential equation Redundancy Least squares problem hierarchy Solid dynamic Constraint satisfaction inverse kinematics task priority Robotics Hierarchized structure Manipulator Inverse problem Humanoid robot Control Kinematics Inequality constraint Equality constraint Mechanical system
ISSN:	1552-3098, 1941-0468
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Popis
Shrnutí:	Redundant mechanical systems like humanoid robots are designed to fulfill multiple tasks at a time. A task, in velocity-resolved inverse kinematics, is a desired value for a function of the robot configuration that can be regulated with an ordinary differential equation (ODE). When facing simultaneous tasks, the corresponding equations can be grouped in a single system or, better, sorted in priority and solved each in the solutions set of higher priority tasks. This elegant framework for hierarchical task regulation has been implemented as a sequence of least-squares problems. Its limitation lies in the handling of inequality constraints, which are usually transformed into more restrictive equality constraints through potential fields. In this paper, we propose a new prioritized task-regulation framework based on a sequence of quadratic programs (QP) that removes the limitation. At the basis of the proposed algorithm, there is a study of the optimal sets resulting from the sequence of QPs. The algorithm is implemented and illustrated in simulation on the humanoid robot HRP-2.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	1552-3098 1941-0468
DOI:	10.1109/TRO.2011.2142450