Simultaneous task allocation and planning under uncertainty

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


Colleges, School and Institutes

External organisations

  • University of Oxford


We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments. Task allocation is performed simultaneously with planning, which provides more detailed information about individual robot behaviour, but also exploits independence between tasks to do so efficiently. We use Markov decision processes to model robot behaviour and linear temporal logic to specify tasks and safety constraints. Building upon techniques and tools from formal verification, we show how to generate a sequence of multi-robot policies, iteratively refining them to reallocate tasks if individual robots fail, and providing probabilistic guarantees on the performance (and safe operation) of the team of robots under the resulting policy. We implement our approach and evaluate it on a benchmark multi-robot example.


Original languageEnglish
Title of host publicationProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'18)
Publication statusAccepted/In press - 29 Jul 2018
EventIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'18) - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'18)