| Summary: | 博士 === 國立交通大學 === 電機與控制工程系 === 89 === When robot manipulators are used to perform compliance tasks,
exquisite planning and control are necessary. It is quite difficult to achieve accurate task planning due to insufficient information about the tasks and the uncertainties occurring during task execution. The complexity in control of compliance tasks also arises from simultaneous control of both position and force and inevitable contact with environments. And, the modeling of the dynamic interactions between the robot and the
environment is complicated. In this dissertation, we propose a
control structure for robot compliance tasks, in which intelligence and control load are distributed between the operator and remote sites in performing task planning and control. We focus on the issues of planning, control, and coordination between the operator and remote sites. A reinforcement learning mechanism for task planning and several compliance control schemes for task execution are then developed. Moreover, for further investigation on the cooperative relation between the operator and remote sites,
we developed a telerobotic system. We are interested in how the
intelligence in control to be distributed between the human
operator and the remote intelligence controller according to the
specific characteristics of a given compliance task. The telerobotic system for compliance tasks is developed using the VR(Virtual Reality) technique. Simulations and experiments based on various types of compliance tasks are performed to demonstrate the effectiveness of the proposed system, and to analyze the issues of planning, control, and coordination between the operator and remote sites in facing different tasks.
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