Probabilistic Search for a Moving Target in an Indoor Environment

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Conference Proceeding
2006 IEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 3393 - 3398
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We consider a search for a target moving within a known indoor environment partitioned into interconnected regions of varying sizes. The knowledge of target location is described as a probability distribution over the regions, and the searcher can only move from one region to another as the structure allows. The objective is to find a feasible path through the regions that maximizes the probability of locating the target within fixed time. This problem generalizes the existing optimal searcher path problem (OSP) by additionally stipulating a minimum amount of time that a finite-speed searcher must spend to travel through a region before reaching the next. We propose a technique to obtain the upper bound of detection for solving the problem in a branch and bound framework. Comparisons show that the technique is also superior to known bounding methods for the original optimal searcher path problem
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