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Robust Precision Manipulation With Simple Process Models Using Visual Servoing Techniques With Disturbance Rejection
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DOI：10.1109/TASE.2018.2819661.png)
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Abstract
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This paper presents a high-performance vision-based precision manipulation technique that does not rely on an object, contact, or gripper model, which are challenging and often times impractical to acquire. Instead, we utilize a simple process model that roughly maps object velocities to actuator velocities, and we maintain system efficiency and robustness via advanced vision-based control techniques with disturbance rejection mechanisms. For obtaining simple models, we derive a set of actuator coordination rules for achieving common task space motions. The performance degradation due to modeling inaccuracies is then minimized via the model predictive control framework and a correction matrix method. Our experimental results show that the proposed strategy results in high-performance precision manipulation with minimal modeling effort.
Keywords:
Dexterous manipulation
in-hand manipulation
model predictive control (MPC)
visual servoing
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