期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 1240-1247出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3057024
关键词
Robots; Humanoid robots; Manipulators; Legged locomotion; Collision avoidance; Support vector machines; Torso; Collision avoidance; humanoid robot systems; machine learning for robot control
类别
资金
- European Research Council (SAHR) [741945]
- European Research Council (ERC) [741945] Funding Source: European Research Council (ERC)
This letter introduces a real-time self-collision avoidance approach for whole-body humanoid robot control, which generates collision-free motions by learning boundary functions and employs machine learning techniques to improve computational efficiency and accuracy.
In this letter, we propose a real-time self-collision avoidance approach for whole-body humanoid robot control. To achieve this, we learn the feasible regions of control in the humanoid's joint space as smooth self-collision boundary functions. Collision-free motions are generated online by treating the learned boundary functions as constraints in a Quadratic Program based Inverse Kinematic solver. As the geometrical complexity of a humanoid robot joint space grows with the number of degrees-of-freedom (DoF), learning computationally efficient and accurate boundary functions is challenging. We address this by partitioning the robot model into multiple lower-dimensional submodels. We compare performance of several state-of-the-art machine learning techniques to learn such boundary functions. Our approach is validated on the 29-DoF iCub humanoid robot, demonstrating highly accurate real-time self-collision avoidance.
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