[LPH17] Bruno Lacerda, David Parker and Nick Hawes. Multi-objective Policy Generation for Mobile Robots under Probabilistic Time-Bounded Guarantees. In Proc. 27th International Conference on Automated Planning and Scheduling (ICAPS'17), pages 504-512, AAAI Press. June 2017. [pdf] [bib] [Proposes techniques for generating mobile robot controllers with probabilistic time-bounded guarantees using multi-objective probabilistic model checking and an extension of PRISM.]
Abstract. We present a methodology for the generation of mobile robot controllers which offer probabilistic time-bounded guarantees on successful task completion, whilst also trying to satisfy soft goals. The approach is based on a stochastic model of the robot’s environment and action execution times, a set of soft goals, and a formal task specification in co-safe linear temporal logic, which are analysed using multi-objective model checking techniques for Markov decision processes. For efficiency, we propose a novel two-step approach. First, we explore policies on the Pareto front for minimising expected task execution time whilst optimising the achievement of soft goals. Then, we use this to prune a model with more detailed timing information, yielding a time-dependent policy for which more fine-grained probabilistic guarantees can be provided. We illustrate and evaluate the generation of policies on a delivery task in a care home scenario, where the robot also tries to engage in entertainment activities with the patients.