Abstract
Rate of revenue (ROR) is significant for unmanned aerial vehicle (UAV) to search targets located in probabilistic positions. To improve search efficiency in a situation of multiple static targets, this paper first transfers a continuous area to a discrete space by grid division and proposes some related indexes in the UAV search issue. Then, cooperative strategies of multiple UAVs are studied in the searching process: clustering partition of search area based on minimum spanning tree (MST) theory is put forward as well as path optimization using spiral flying model. Finally, a series of simulation experiments are carried out through the method in this paper and two compared algorithms. Results show that: optimized cooperative strategies can achieve greater total revenue and more stable performance than the other two.
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Zhu, T., He, W., Ling, H., Zhang, Z. (2018). Cooperative Search Strategies of Multiple UAVs Based on Clustering Using Minimum Spanning Tree. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_11
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DOI: https://doi.org/10.1007/978-3-319-93818-9_11
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