Simultaneous task allocation and planning under uncertainty
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Authors
Colleges, School and Institutes
External organisations
- University of Oxford
Abstract
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.
Details
Original language | English |
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Title of host publication | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Publication status | Published - 7 Jan 2019 |
Event | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'18) - Madrid, Spain Duration: 1 Oct 2018 → 5 Oct 2018 |
Publication series
Name | IEEE International Workshop on Intelligent Robots and Systems (IROS) |
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Publisher | IEEE |
ISSN (Print) | 2153-0858 |
ISSN (Electronic) | 2153-0866 |
Conference
Conference | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'18) |
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Country | Spain |
City | Madrid |
Period | 1/10/18 → 5/10/18 |