Multi-task closed-loop inverse kinematics stability through semidefinite programming

Conference Article


IEEE International Conference on Robotics and Automation (ICRA)





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Today’s complex robotic designs comprise in some cases a large number of degrees of freedom, enabling for multi-objective task resolution (e.g., humanoid robots or aerial manipulators). This paper tackles the stability problem of a hierarchical closed-loop inverse kinematics algorithm for such highly redundant robots. We present a method to guarantee system stability by performing an online tuning of the closedloop control gains. We define a semi-definite programming problem (SDP) with these gains as decision variables and a discrete-time Lyapunov stability condition as a linear matrix inequality, constraining the SDP optimization problem and guaranteeing the stability of the prioritized tasks. To the best of authors’ knowledge, this work represents the first mathematical development of an SDP formulation that introduces stability conditions for a multi-objective closed-loop inverse kinematic problem for highly redundant robots. The validity of the proposed approach is demonstrated through simulation case studies, including didactic examples and a Matlab toolbox for the benefit of the community.


robot kinematics.

Author keywords

Hierarchical inverse kinematics, Semidefinite Programming, closed-loop inverse kinematics

Scientific reference

J. Martí, A. Santamaria-Navarro, C. Ocampo-Martínez and J. Andrade-Cetto. Multi-task closed-loop inverse kinematics stability through semidefinite programming, 2020 IEEE International Conference on Robotics and Automation, 2020, Paris, France, pp. 7108-7114, IEEE.