Task Assignment Algorithm for Road Patrol by Multiple UAVs With Multiple Bases and Rechargeable Endurance

• Main Authors: Linhui Cheng, Luo Zhong, Shasha Tian, Jiaxu Xing
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Immune clonal selection algorithm; optimization-revision; rechargeable endurance; road patrol; time priority; UAV
• Online Access: https://ieeexplore.ieee.org/document/8854089/

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.