Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty

TL;DR

This paper presents a multi-robot planning framework that integrates graph neural networks with model-based planning to efficiently identify and prioritize urgent locations in time-critical search and inspection tasks under uncertainty.

Contribution

It introduces a novel learning-augmented multi-robot planning approach combining GNN-based likelihood estimation with model-based planning for improved efficiency.

Findings

Planner improves performance by at least 16.3% with 1 robot.

Performance gains of 26.7% and 26.2% with 3 and 5 robots.

Validated on real-world quad-copter platforms.

Abstract

In disaster response or surveillance operations, quickly identifying areas needing urgent attention is critical, but deploying response teams to every location is inefficient or often impossible. Effective performance in this domain requires coordinating a multi-robot inspection team to prioritize inspecting locations more likely to need immediate response, while also minimizing travel time. This is particularly challenging because robots must directly observe the locations to determine which ones require additional attention. This work introduces a multi-robot planning framework for coordinated time-critical multi-robot search under uncertainty. Our approach uses a graph neural network to estimate the likelihood of PoIs needing attention from noisy sensor data and then uses those predictions to guide a multi-robot model-based planner to determine the cost-effective plan. Simulated experiments demonstrate that our planner improves performance at least by 16.3\%, 26.7\%, and 26.2\% for 1, 3, and 5 robots, respectively, compared to non-learned and learned baselines. We also validate our approach on real-world platforms using quad-copters.