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Path Planning for Minimizing the Expected Cost Until Success | IEEE Journals & Magazine | IEEE Xplore

Path Planning for Minimizing the Expected Cost Until Success


Abstract:

Consider a general path planning problem of a robot on a graph with edge costs, where each node has a Boolean value of success or failure (with respect to some task) with...Show More

Abstract:

Consider a general path planning problem of a robot on a graph with edge costs, where each node has a Boolean value of success or failure (with respect to some task) with a given probability. The objective is to plan a path for the robot on the graph that minimizes the expected cost until success. In this paper, it is our goal to bring a foundational understanding to this problem. We start by showing how this problem can be optimally solved by formulating it as an infinite-horizon Markov decision process, but with an exponential space complexity. We then formally prove its NP-hardness. To address the space complexity, we then propose a path planner, using a game-theoretic framework, that asymptotically gets arbitrarily close to the optimal solution. Moreover, we also propose two fast and nonmyopic path planners. To show the performance of our framework, we do extensive simulations for two scenarios: a rover on Mars searching for an object for scientific studies, and a robot looking for a connected spot to a remote station (with real data from downtown San Francisco). Our numerical results show a considerable performance improvement over existing state-of-the-art approaches.
Published in: IEEE Transactions on Robotics ( Volume: 35, Issue: 2, April 2019)
Page(s): 466 - 481
Date of Publication: 01 January 2019

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