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We propose a new minimum uncertainty planning technique for mobile robots localizing with beacons. We model the system as a partially-observable Markov decision process and use a sampling-based method in the belief space (the space of posterior probability density functions over the state space) to find a belief-feedback policy. This approach allows us to analyze the evolution of the belief more accurately,...
This paper discusses the research and development decision problem without rivalry. Specifically, the inter-arrival times and jump magnitudes are characterized as random variables with arbitrary probability distributions. Furthermore, the expected value model which maximizes the expected discounted net return from the project is developed. The stochastic simulation is designed to estimate the value...
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