Abstract
With the continuing increase in use of UAVs (Unmanned Aerial Vehicles) in various applications, much effort is directed towards creating fully autonomous UAV systems to handle tasks independently of human operators. One such task is the monitoring of an area, e.g. by deploying sensors in this area utilizing a system of multiple UAVs to autonomously create an efficient dynamic WSN (Wireless Sensor Network). The locations, order and which UAV to deal with deployment of individual sensors is a complex problem which in any real life problem is deemed to be hard to solve using brute force methods. A method is proposed for multi-UAV cooperative path planning by allocation of sensor placement tasks between UAVs, using a cooperative coevolving genetic algorithm as a basis for the solution to the described challenge. Algorithms have been implemented and preliminary tested in order to show proof of concept.
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Sørli, JV., Graven, O.H., Bjerknes, J.D. (2017). Multi-UAV Cooperative Path Planning for Sensor Placement Using Cooperative Coevolving Genetic Strategy. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_46
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DOI: https://doi.org/10.1007/978-3-319-61833-3_46
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