Path planning with hallucinated worlds

We describe an approach that integrates midrange sensing into a dynamic path planning algorithm. The algorithm is based on measuring the reduction in path cost that would be caused by taking a sensor reading from candidate locations. The planner uses this measure in order to decide where to take the next sensor reading. Ideally, one would like to evaluate a path based on a map that is as close as possible to the true underlying world. In practice, however, the map is only sparsely populated by data derived from sensor readings. A key component of the approach described in this paper is a mechanism to infer (or "hallucinate") more complete maps from sparse sensor readings. We show how this hallucination mechanism is integrated with the planner to produce better estimates of the gain in path cost occurred when taking sensor readings. We show results on a real robot as well as a statistical analysis on a large set of randomly generated path planning problems on elevation maps from real terrain.

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