Task Assignment Algorithm for Road Patrol by Multiple UAVs With Multiple Bases and Rechargeable Endurance
| Autor: | Jiaxu Xing, Luo Zhong, Shasha Tian, Linhui Cheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Sequence
General Computer Science Computer science time priority Ant colony optimization algorithms UAV General Engineering Particle swarm optimization Scale (descriptive set theory) Field (computer science) Task (project management) Dynamic programming optimization-revision General Materials Science Immune clonal selection algorithm lcsh:Electrical engineering. Electronics. Nuclear engineering rechargeable endurance Algorithm lcsh:TK1-9971 road patrol |
| Zdroj: | IEEE Access, Vol 7, Pp 144381-144397 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Increasing attention has been paid to the application of Unmanned Aerial Vehicles (UAVs) in traffic field. Existing studies on UAVs in road patrol task assignment rarely integrate the actual requirements of multiple bases, charging problems during inspection and multiple types of UAVs into the patrol model, and rarely consider the fastest time to complete the patrol of all task points as the optimization target. In addition, most existing solving algorithms are difficult to use to directly solve problem models meeting the above requirements and objectives, and some existing algorithms need to be improved in terms of solving quality. In order to solve those problems, a task assignment model for road patrol by multiple UAVs with multiple bases and rechargeable endurance is established, and a time-priority immune clonal selection algorithm with optimization-revision is proposed. The optimal sequence of task points is obtained by the immune clonal selection algorithm, and the time-priority method is adopted to divide the sequence of task points. The optimal UAV paths are further optimized and modified. With 20 task points, the experimental results show that the earliest completion time obtained by the proposed algorithm decreased by 1.071 and 2.1209 compared with the earliest reachable time particle swarm optimization algorithm and the improved dynamic programming ant colony algorithm, respectively. The results indicate that proposed algorithm achieves better performance in terms of solution quality. In addition, experiments with 50 and 100 task points show that the algorithm is also suitable for medium and large scale problems. |
| Databáze: | OpenAIRE |
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