# Algorithms for Routing an Unmanned Aerial Vehicle in the presence of Refueling Depots

arXiv: https://arxiv.org/abs/1304.0494 · HTML: https://mlsanity.com/paper/1304.0494/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2013.

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

- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning](https://mlsanity.com/paper/2405.00285/) (2024)
- [Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot](https://mlsanity.com/paper/2210.00327/) (2022)
- [Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints](https://mlsanity.com/paper/2404.06423/) (2024)

## Abstract (arXiv)

We consider a single Unmanned Aerial Vehicle (UAV) routing problem where there are multiple depots and the vehicle is allowed to refuel at any depot. The objective of the problem is to find a path for the UAV such that each target is visited at least once by the vehicle, the fuel constraint is never violated along the path for the UAV, and the total fuel required by the UAV is a minimum. We develop an approximation algorithm for the problem, and propose fast construction and improvement heuristics to solve the same. Computational results show that solutions whose costs are on an average within 1.4% of the optimum can be obtained relatively fast for the problem involving 5 depots and 25 targets.