Paper Info
Title
Multi-Objective Policy Generation for Mobile Robots under Probabilistic Time-Bounded Guarantees.
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.
Year
Venue
Field
2017
Proceedings of the International Conference on Automated Planning and Scheduling
Mathematical optimization,Computer science,Real-time computing,Probabilistic logic,Mobile robot,Distributed computing,Bounded function
DocType
ISSN
Citations 
Conference
2334-0835
4
PageRank 
References 
Authors
0.38
11
3
Name
Order
Citations
PageRank
Bruno Lacerda18512.96
David Parker24018184.00
Nick Hawes332134.18