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

arXiv: https://arxiv.org/abs/2507.06129 · HTML: https://mlsanity.com/paper/2507.06129/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2025.

## Built on by (most cited first, 4 of 4)

- [DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving](https://mlsanity.com/paper/2508.04066/) (2025)
- [POrTAL: Plan-Orchestrated Tree Assembly for Lookahead](https://mlsanity.com/paper/2512.06002/) (2025)
- [Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection](https://mlsanity.com/paper/2603.23800/) (2026)
- [D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems](https://mlsanity.com/paper/2609.07350/) (2026)

## Abstract (arXiv)

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.