# Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment

arXiv: https://arxiv.org/abs/2201.10150 · HTML: https://mlsanity.com/paper/2201.10150/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics](https://mlsanity.com/paper/2306.17609/) (2023)
- [Large-Scale Multi-Robot Coverage Path Planning via Local Search](https://mlsanity.com/paper/2312.10797/) (2023)
- [Multi-Robot Connected Fermat Spiral Coverage](https://mlsanity.com/paper/2403.13311/) (2024)
- [Priority-Aware Multi-Robot Coverage Path Planning](https://mlsanity.com/paper/2601.00580/) (2026)
- [See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones](https://mlsanity.com/paper/2604.13292/) (2026)

## Abstract (arXiv)

For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the disaster area, which requires the exploration of more valuable areas under limited energy. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem, by ranking the possible disaster areas according to their importance through satellite or remote aerial images and completing path planning according to the importance level. Experimental results show that our proposed algorithm is at least twice as effective as the existing method in terms of search efficiency.