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Technical Report

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Technical Report Number



Self-reconfiguring robots are modular systems that can change shape, or "reconfigure," to match structure to task. They comprise many small, discrete, often identical modules that connect together and that are minimally actuated. Global shape transformation is achieved by composing local motions. Systems with a single module type, known as "homogeneous" systems, gain fault tolerance, robustness and low production cost from module interchangeability. However, we are interested in "heterogeneous" systems, which include multiple types of modules such as those with sensors, batteries or wheels. We believe that heterogeneous systems offer the same benefits as homogeneous systems with the added ability to match not only structure to task, but also capability to task. Although significant results have been achieved in understanding homogeneous systems, research in heterogeneous systems is challenging as key algorithmic issues remain unexplored. We propose in this thesis to investigate questions in four main areas: 1) how to classify heterogeneous systems, 2) how to develop efficient heterogeneous reconfiguration algorithms with desired characteristics, 3) how to characterize the complexity of key algorithmic problems, and 4) how to apply these heterogeneous algorithms to perform useful new tasks in simulation and in the physical world. Our goal is to develop an algorithmic basis for heterogeneous systems. This has theoretical significance in that it addresses a major open problem in the field, and practical significance in providing self-reconfiguring robots with increased capabilities.