Simultaneous Task Allocation and Planning Under Uncertainty

TL;DR

This paper introduces a new integrated approach for task allocation and planning in multi-robot systems under uncertainty, using formal methods to ensure safety and performance guarantees.

Contribution

It presents a novel method combining task allocation with planning using Markov decision processes and linear temporal logic, enabling efficient, safe, and adaptive multi-robot coordination.

Findings

Successfully generates multi-robot policies with probabilistic guarantees.

Refines task allocation iteratively to handle robot failures.

Demonstrates effectiveness on a benchmark multi-robot scenario.

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.