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Biyun Xie's PhD Thesis Abstract

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Kinematic Design and Motion Planning of Fault Tolerant Robots with Locked Joint Failures

Ph.D., Colorado State University, August 2019

Major Professor: Anthony A. Maciejewski

The problem of kinematic design and motion planning of fault tolerant robots with locked joint failure is studied in this work. In kinematic design, the problem of designing optimally fault tolerant robots for equal joint failure probabilities is first explored. A family of optimally fault tolerant 7R robots has previously been designed to optimize a measure of local fault tolerance. In this work, the structure and global pre- and post-failure dexterity performance of this family of robots are further explored. The characteristics of the kinematic properties, described by Denavit and Hartenberg parameters, of these robots are analyzed, and used to illustrate the structural correlations between these robots. In addition, the global pre- and post- failure dexterity performance of these robots are studied, and the optimal robot designs are obtained. Then, the problem of designing optimally fault tolerant robots for different joint failure probabilities is considered. A measure of fault tolerance for different joint failure probabilities is defined based on the properties of the singular values of the Jacobian after failures. Using this measure, methods to design optimally fault tolerant robots for an arbitrary set of joint failure probabilities and multiple cases of joint failure probabilities are introduced separately. After the optimally fault tolerant robots are designed, the problem of planning the optimal trajectory with minimum probability of task failure for a set of point-to-point tasks, after experiencing locked joint failures, is studied. The proposed approach first develops a method to calculate the probability of task failure for an arbitrary trajectory, where the trajectory is divided into small segments, and the probability of task failure of each segment is calculated based on its failure scenarios. Then, a motion planning algorithm is proposed to find the optimal trajectory with minimum probability of task failure.