Abstract
The forest fire is an extremely dangerous natural disaster. The traditional fire-fighting equipment have great difficulty in performing firefighting in mountain terrain. Unmanned aerial vehicles (UAVs) are coming into a popular form in forest firefighting. In view of the suddenness of forest fires, the adaptive and dynamic firefighting task assignment for UAV is of great significance, and the current firefighting task assignment cannot address this issue. This paper proposed an adaptive and dynamic multiple task assignment method for UAVs. Firstly, the adaptive and dynamic firefighting task assignment is formulated as an optimization problem. Secondly, an assignment algorithm is proposed to solve the problem by extending the particle swarm optimization (PSO) algorithm. Finally, the experiment results verify the effectiveness of the proposed algorithm.
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Acknowledgment
This research is supported in part by NSFC (61571066, 61602054), and Beijing Natural Science Foundation under Grant No. 4174100 (BNSF, 4174100).
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Chen, K., Sun, Q., Zhou, A., Wang, S. (2018). Adaptive Multiple Task Assignments for UAVs Using Discrete Particle Swarm Optimization. In: Skulimowski, A., Sheng, Z., Khemiri-Kallel, S., Cérin, C., Hsu, CH. (eds) Internet of Vehicles. Technologies and Services Towards Smart City. IOV 2018. Lecture Notes in Computer Science(), vol 11253. Springer, Cham. https://doi.org/10.1007/978-3-030-05081-8_16
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DOI: https://doi.org/10.1007/978-3-030-05081-8_16
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