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    ABSTRACT: This paper presents an efficient approach for asymptotically-optimal path planning on implicitly-defined configuration spaces. Recently, several asymptotically-optimal path planners have been introduced, but they typically exhibit slow convergence rates. Moreover, these planners cannot operate on the configuration spaces that appear in the presence of kinematic or contact constraints, such as when manipulating an object with two arms or with a multifingered hand. In these cases, the configuration space usually becomes an implicit manifold embedded in a higher-dimensional joint ambient space. Existing sampling-based path planners on manifolds focus on finding a feasible solution, but they do not optimize the quality of the path in any sense and, thus, the returned solution is usually not adequate for direct execution. In this paper, we adapt several techniques to accelerate the convergence of the asymptotically-optimal planners and we use higher-dimensional continuation tools to deal with the case of implicitly-defined configuration spaces. The performance of the proposed approach is evaluated through various experiments.
    Robotics and Autonomous Systems 08/2013; 61(8):797-807. · 1.16 Impact Factor
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    ABSTRACT: This paper presents robust control algorithm for motion control of humanoid robot upper-body. Upper-body consists of multi-segment lumbar spine with six degrees of freedom (DOFs) and two arms, each having seven DOFs. Movable spine enables motion of the trunk which increases workspace of robot arms and contributes to anthropomorphic appearance of the robot movements. Problem of simultaneous motion control of robot spine and arms in presence of parameter uncertainties and external disturbance has been considered. Nonlinearity of robot dynamical model and coupling between robot segments has been taken into account during control design. Mechanical design of biologically inspired robot spine and arms is described in this paper and kinematic and dynamic models of robot upper-body are given. Efficiency of the proposed control algorithm is verified through a numerical simulation and results are presented.
    ETRAN (57; Zlatibor; 2013 ); 06/2013
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    ABSTRACT: Marker-based multi-camera optical tracking systems are being used in the robotics field to track robots for validation, verification, and calibration of their kinematic and dynamic models. These tracking systems estimate the pose of tracking bodies attached to objects within a tracking volume. In this work, we explore the case of tracking the origins of joints of articulated robots when the tracking bodies are mounted on limbs or structures relative to the joints. This configuration leads to an unknown relative pose between the tracking body and the joint origin. The identification of this relative pose is essential for an accurate representation of the kinematic model. We propose an approach for the identification of the origin of joints relative to tracking bodies by using state-of-the-art center of rotation (CoR) and axis of rotation (AoR) estimation methods. The applicability and effectiveness of our approach is demonstrated in two successful case studies: (i) the verification of the upper body kinematics of DLR’s humanoid Rollin’ Justin and (ii) the identification of the kinematic parameters of an ST Robot arm relative to its environment for the embodiment of a situated conversational assistant.
    Robotics and Autonomous Systems 06/2013; 61(6):580–592. · 1.16 Impact Factor

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May 20, 2014