Algorithms for Routing an Unmanned Aerial Vehicle in the presence of Refueling Depots
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2013.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 3.
4 papers.
- 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomycited by 16
- 2024iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learningcited by 4
- 2022Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robotcited by 1
- 2024Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraintscited by 0
- 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy23 Jun 2024
- 2024iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning1 May 2024
- 2024Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints9 Apr 2024
- 2022Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot1 Oct 2022