# A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams

arXiv: https://arxiv.org/abs/2309.12589 · HTML: https://mlsanity.com/paper/2309.12589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints](https://mlsanity.com/paper/2606.06877/) (2026)

## Abstract (arXiv)

In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects but overlook crucial elements like information gathering, task assignment, and planning. Furthermore, previous methods considering robot capabilities and victim requirements suffer from time complexity due to repetitive planning steps. To overcome these challenges, we introduce a comprehensive framework__the Multi-Stage Multi-Robot Task Assignment. This framework integrates scouting, task assignment, and path-planning stages, optimizing task allocation based on robot capabilities, victim requirements, and past robot performance. Our iterative approach ensures objective fulfillment within problem constraints. Evaluation across four maps, comparing with a state-of-the-art baseline, demonstrates our algorithm's superiority with a remarkable 97 percent performance increase. Our code is open-sourced to enable result replication.