This paper addresses the H∞ robust control problem for robot manipulators using unit dual quaternion representation, which allows an utter description of the end-effector transformation without decoupling rotational and translational dynamics. We propose three different H∞ control criteria that ensure asymptotic convergence, whereas reducing the influence of disturbances upon the system stability. Also, with a new metric of dual quaternion error in SE(3) we prove independence from robot coordinate changes. Simulation results highlight the importance and effectiveness of the proposed approach in terms of performance, robustness, and energy efficiency.
In this paper, we address the rigid body pose stabilization problem using dual quaternion formalism. We propose a hybrid control strategy to design a switching control law with hysteresis in such a way that the global asymptotic stability of the closed-loop system is guaranteed and such that the global attractivity of the stabilization pose does not exhibit chattering, a problem that is present in all discontinuous-based feedback controllers. Using numerical simulations, we illustrate the problems that arise from existing results in the literature-as unwinding and chattering-and verify the effectiveness of the proposed controller to solve the robust global pose stability problem.
This work has been accepted and will appear in the 2018 IEEE-RAS International Conference on Humanoid Robots (HUMANOID 2018).This paper presents a planning algorithm designed to improve cooperative robot behaviour concerning human comfort during forceful human-robot physical interaction. Particularly, we are interested in planning for object grasping and positioning ensuring not only stability against the exerted human force but also empowering the robot with capabilities to address and improve human experience and comfort. Herein, comfort is addressed as both the muscular activation level required to exert the cooperative task, and the human spatial perception during the interaction, namely, the peripersonal space. By maximizing both comfort criteria, the robotic system can plan for the task (ensuring grasp stability) and for the human (improving human comfort). We believe this to be a key element to achieve intuitive and fluid human-robot interaction in real applications. Real HRI drilling and cutting experiments illustrated the efficiency of the proposed planner in improving overall comfort and HRI experience without compromising grasp stability. * First two authors contributed equally to this work.
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