Searching for a one-dimensional random walker: Deterministic strategies with a time budget when crossing is allowed

IROS(2013)

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摘要
We present deterministic strategies for capturing a target performing a discrete random walk on a discretized line segment. The searcher has a limited time budget. Its goal is to maximize the probability of capturing the target within the budget. A challenging aspect of our model is that the target can cross the searcher without being captured when they take the same edge at the same time in opposite directions. We present a Partially Observable Markov Decision Process (POMDP) approach for finding the optimal search strategy. We also present an efficient approximate solution to the POMDP. The strategies found by this approach reveal structural properties of the efficient search strategies which we exploit to solve the problem efficiently without running the POMDP.
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关键词
one-dimensional random walker,partially observable markov decision process,discrete random walk,random processes,decision making,deterministic strategies,mobile robots,structural properties,game theory,time budget,path planning,optimal search strategy,markov processes,probability,probability maximization,pomdp approach
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