Effective Multi-Robot Spatial Task Allocation using Model Approximations

TL;DR

This paper introduces an approximation-based approach for multi-robot spatial task allocation that reduces computational complexity while maintaining near-optimal performance, demonstrated through rescue simulation scenarios.

Contribution

It proposes novel approximation techniques for multi-agent Markov decision processes applied to spatial tasks, enabling faster and effective multi-robot planning.

Findings

Faster planning with negligible performance loss

Comparable to DCOP methods in rescue scenarios

Effective in complex spatial task environments

Abstract

Real-world multi-agent planning problems cannot be solved using decision-theoretic planning methods due to the exponential complexity. We approximate firefighting in rescue simulation as a spatially distributed task and model with multi-agent Markov decision process. We use recent approximation methods for spatial task problems to reduce the model complexity. Our approximations are single-agent, static task, shortest path pruning, dynamic planning horizon, and task clustering. We create scenarios from RoboCup Rescue Simulation maps and evaluate our methods on these graph worlds. The results show that our approach is faster and better than comparable methods and has negligible performance loss compared to the optimal policy. We also show that our method has a similar performance as DCOP methods on example RCRS scenarios.