Simultaneous Task Allocation and Planning Under Uncertainty

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...

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Bibliographic Details
Main Authors: Faruq, Fatma, Parker, David, Laccrda, Bruno, Hawes, Nick
Format: Conference Proceeding
Language:English
Subjects:
Planning
Probabilistic logic
Resource management
Robot kinematics
Task analysis
Uncertainty
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS.2018.8594404