Planning Hand-Arm Grasping Motions with Human-Like Appearance

This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing grasping actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to both obtain human-like movements and to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning following the synergies and considering different types of grasps. The introduced approach is tested in an application example and thoroughly compared with a state-of-the-art planning algorithm, obtaining better results.