On Motion Planning with Uncertainty. Revised.
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
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Robots must successfully plan and execute tasks in the presence of uncertainty. Uncertainty arises from errors in modelling, sensing, and control. Planning in the presence of uncertainty constitutes one facet of the general motion planning problem in robotics. This problem is concerned with the automatic synthesis of motion strategies from high level task specifications and geometric models of environments. In order to develop successful motion strategies, it is necessary to understand the effect of uncertainty on the geometry of object interactions. Object interactions, both static and dynamic, may be represented in geometrical terms. This thesis investigates geometrical tools for modelling and overcoming uncertainty. The thesis describes an algorithm for computing backprojections of desired task configurations. Task goals and motion states are specified in terms of a moving objects configuration space. Backprojections specify regions in configuration space from which particular motions are guaranteed to accomplish a desired task. The backprojection algorithm considers surfaces in configuration space that facilitate halt.
- Theoretical Mathematics