Evolutionary algorithms (EA) have been successfully used to compute near-optimal paths through obstructed, dynamically changing environments. The locations of the obstacles that form the obstructions in these environments may only be known with limited accuracy. Explicitly accounting for this uncertainty can result in the survival of "best" paths which differ from those that would be favored in a purely deterministic environment. In this paper, we consider the application of evolutionbased path planning to the motion of an unmanned air vehicle (UAV) through a field of obstacles at uncertain locations. Specifically, we focus on the "cost function" utilized by the evolutionary algorithm to judge the likelihood of a given path successfully traversing the uncertain environment. We first show a method for computing a cost function based on the exact probability of intersection of the vehicle with an obstacle. A more computationally tractable approximation technique for this cost function is then derived. Both cost functions are compared to the weighted graph search technique found in much of the literature on path planning.
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