期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 2, 页码 1194-1201出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2893691
关键词
Eye-in-hand visual-servo; learning-based control; local Gaussian process regression; soft robot control
类别
资金
- Croucher Foundation
- Research Grants Council of Hong Kong [17202317, 17227616, 27209515]
Soft robots, owing to their elastomeric material, ensure safe interaction with their surroundings. These robot compliance properties inevitably impose a tradeoff against precise motion control, as to which conventional model-based methods were proposed to approximate the robot kinematics. However, too many parameters, regarding robot deformation and external disturbance, are difficult to obtain, even if possible, which could be very nonlinear. Sensors self-contained in the robot are required to compensate modeling uncertainties and external disturbances. Camera (eye) integrated at the robot end-effector (hand) is a common setting. To this end, we propose an eye-in-hand visual servo that incorporates with learning-based controller to accomplish more precise robotic tasks. Local Gaussian process regression is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters. Experimental validation is also conducted to demonstrate the hyperelastic robot can compensate an external variable loading during trajectory tracking.
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