A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem
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Abstract (arXiv)
A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Sep 2020.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 3.
5 papers.
- 2023TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problemcited by 3
- 2023Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaningcited by 0
- 2026Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quotacited by 0
- 2026NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problemcited by 0
- 2026Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encodingcited by 0
- 2026Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding21 Jul 2026
- 2026NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem18 Apr 2026
- 2026Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota27 Feb 2026
- 2023TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem30 Nov 2023
- 2023Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning12 Mar 2023