Abstract
The paper proposes a method for the drone to catch the given target and avoid detected obstacles in its path based on the self-organizing migrating algorithm. In particular, a two-component fitness function is proposed based on the principle that the closer the target, the lower the fitness value, and the closer the obstacle, the higher the fitness value. Self-organizing migrating algorithm, a swarm intelligence algorithm, is used to predict the next positions that the drone will move to. These positions both satisfy the requirement to avoid obstacles and shorten the distance to the target. A map of two drones, two corresponding targets and four static obstacles was modeled on Matlab. The simulation results verify the correctness and effectiveness of the proposed method.
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Acknowledgment
The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2020/78, VSB-Technical University of Ostrava.
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Diep, Q.B., Truong, T.C., Zelinka, I. (2020). Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_35
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