Planning and Inverse Kinematics of Hyper-Redundant Manipulators with VO-FABRIK

Cristian Morasso, Daniele Meli, Yann Divet, Salvatore Sessa,Alessandro Farinelli

arxiv(2024)

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摘要
Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and flexibility of operation, but solving Inverse Kinematics (IK) is challenging. In this work, we introduce VO-FABRIK, an algorithm combining Forward and Backward Reaching Inverse Kinematics (FABRIK) for repeatable deterministic IK computation, and an approach inspired from velocity obstacles to perform path planning under collision and joint limits constraints. We show preliminary results on an industrial HRM with 19 actuated joints. Our algorithm achieves good performance where a state-of-the-art IK solver fails.
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