Simultaneous Task Allocation and Planning Under Uncertainty
This addresses coordination challenges for multi-robot systems in uncertain settings, though it appears incremental by building on formal verification techniques.
The paper tackles the problem of task allocation and planning for multi-robot systems in uncertain environments by simultaneously performing both processes, using Markov decision processes and linear temporal logic to model behavior and specify tasks. The result is an iterative approach that generates multi-robot policies with probabilistic guarantees on performance and safety, evaluated on a benchmark example.
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.