The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Sensory systems, such as computer vision, can be used to measure relative robot end-effector positions to derive feedback signals for control of end-effector positioning. The role of vision as the feedback transducer affects closed-loop dynamics, and a visual feedback control strategy is required. Vision-based robot control research has focused on vision processing issues, while control system design has been limited to ad-hoc strategies. We formalize an analytical approach to dynamic robot visual servo control systems by first casting position-based and image-based strategies into classical feedback control structures. The image-based structure represents a new approach to visual servo control, which uses image features (e.g., image areas, and centroids) as feedback control signals, thus eliminating a complex interpretation step (i.e., interpretation of image features to derive world-space coordinates). Image-based control presents formidable engineering problems for controller design, including coupled and nonlinear dynamics, kinematics, and feedback gains, unknown parameters, and measurement noise and delays. A model reference adaptive controller (MRAC) is designed to satisfy these requirements.

Notes

Number of pages: 14

Text Reference

Lee Weiss, Arthur C. Sanderson, and C. P. Neuman, "Dynamic Sensor-Based Control of Robots with Visual Feedback," IEEE Journal on Robotics and Automation, Vol. RA-3, No. 5, October, 1987

BibTeX Reference

@article{Weiss_1987_495,
   author = "Lee Weiss and Arthur C Sanderson and C. P. Neuman",
   title = "Dynamic Sensor-Based Control of Robots with Visual Feedback",
   journal = "IEEE Journal on Robotics and Automation",
   month = "October",
   year = "1987",
   volume = "RA-3",
   number = "5",
}

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