Multi-objective Policy Generation for Mobile Robots under Probabilistic Time-Bounded Guarantees

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Colleges, School and Institutes


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.


Original languageEnglish
Title of host publicationProceedings of the twenty seventh International Conference on Automated Planning and Scheduling (ICAPS 2017)
Publication statusAccepted/In press - 26 Jan 2017
Event27th International Conference on Automated Planning and Scheduling (ICAPS 2017) - Pittsburgh, United States
Duration: 18 Jun 201723 Jun 2017


Conference27th International Conference on Automated Planning and Scheduling (ICAPS 2017)
CountryUnited States