UAV mission scheduling with completion time, flight distance, and resource consumption constraints
Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint,...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849698843253276672 |
|---|---|
| author | Keqin Li |
| author_facet | Keqin Li |
| author_sort | Keqin Li |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. |
| format | Article |
| id | doaj-art-a7399e6a4fdc4ff9a5639967c587bdd3 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-a7399e6a4fdc4ff9a5639967c587bdd32025-08-20T03:18:46ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.2281250UAV mission scheduling with completion time, flight distance, and resource consumption constraintsKeqin Li0Department of Computer Science, State University of New York, New Paltz, NY, USAUnmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250Algorithmic frameworkcombinatorial optimisationheterogeneous UAVsheuristic algorithmmission scheduling |
| spellingShingle | Keqin Li UAV mission scheduling with completion time, flight distance, and resource consumption constraints Connection Science Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling |
| title | UAV mission scheduling with completion time, flight distance, and resource consumption constraints |
| title_full | UAV mission scheduling with completion time, flight distance, and resource consumption constraints |
| title_fullStr | UAV mission scheduling with completion time, flight distance, and resource consumption constraints |
| title_full_unstemmed | UAV mission scheduling with completion time, flight distance, and resource consumption constraints |
| title_short | UAV mission scheduling with completion time, flight distance, and resource consumption constraints |
| title_sort | uav mission scheduling with completion time flight distance and resource consumption constraints |
| topic | Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 |
| work_keys_str_mv | AT keqinli uavmissionschedulingwithcompletiontimeflightdistanceandresourceconsumptionconstraints |