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Optimal Probabilistic Motion Planning with Partially Infeasible LTL Constraints

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Document pages: 11 pages

Abstract: This paper studies optimal probabilistic motion planning of a mobile agentwith potentially infeasible task specifications subject to motion andenvironment uncertainties. Instead of the traditional Rabin automata,limit-deterministic Büchi automata are applied and a relaxed product MDPbetween PL-MDP (i.e., probabilistic labeled Markov decision process) and LDBAis developed, which allows the agent to revise its motion plan whenever thetask is not fully feasible. A multi-objective optimization problem is thenformulated to jointly consider the probability of the task satisfaction , theviolation cost, and the implementation costs, which is solved via coupledlinear programs. To the best of our knowledge, it is the first work thatbridges the gap between planning revision and optimal control synthesis of bothprefix and suffix of the agent trajectory. Simulation results are provided todemonstrate the effectiveness of the proposed framework.

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