Self-calibration is a desirable feature for an intelligent machine such as a robot that must function outside of controlled laboratory conditions. This is because it is inevitable that variations in the kinematic model arise from imperfections in the manufacturing process and changes of environment conditions.; Self-calibration has the potential of (a) removing the dependence on external pose sensing, (b) producing high accuracy measurement data over the entire workspace of the system with an extremely fast measurement rate, (c) being automated and completely non invasive, (d) facilitating on-line accuracy compensation, and (e) being cost effective.; This dissertation concentrates on the study of self-calibrating parallel-link mechanisms. A framework of self-calibration of a parallel-link mechanism is created, which is based on kinematic analysis and the construction of measurement residuals utilizing the information provided by redundant sensors embedded in the system. Forward and inverse kinematic measurement residuals of the mechanisms are proposed. To avoid the estimation of redundant kinematic parameters of the mechanism, the concept of relative residuals is introduced. Guidelines for placement of sensors for self-calibration are presented. An approach to determining the number of independent kinematic parameters of the mechanism is introduced. Extensive simulation and experimental studies conducted on a parallel-link mechanism, the Stewart platform built in the Robotics Center at Florida Atlantic University, confirm the effectiveness of the proposed approach.
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