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This work focuses on the implementation of a vision-based motion guidance method, called virtual fixtures, on admittance-controlled human-machine cooperative robots with compliance. The robot compliance here refers to the structural elastic deformation of the device. The system uses computer vision as a sensor for providing a reference trajectory, and the virtual fixture control algorithm then provides haptic feedback to implemented direct, shared manipulation. It then discusses experiments to evaluate both speed and accuracy of the proposed constraints on human speed and accuracy versus free motion in a steady-hand paradigm. The result indicates improvements in the performance of human in the desired task execution.

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W e present the design and implementation of a visionbased system for micron-scale, cooperative manipulation of a surgical tool. The system is based o n a control algorithm that implements a broad class of guidance modes called virtual fixtures. A virtual fixture, like a real fixture, limits the motion of a tool to a prescribed class or range. The implemented system uses vision as a sensor for providing a reference trajectory, and the control algorithm then provides haptic feedback involving direct, shared manipulation of Q surgical tool. W e have tested this system o n the JHU Steady Hand robot and provide experimental results f o r path following and positioning o n structures at both macroscopic and microscopic scales.