Abstract
In this study, a two-layer algorithm is proposed to solve the mission planning problem of heterogeneous unmanned aerial vehicles (UAVs) in the presence of obstacles. In the upper layer, an improved genetic algorithm (IGA) with specially designed unlocking mechanism is developed to address the task assignment problem. As for the lower layer, a geometric-based method is developed for flight range estimation in the obstacle-dense environment. Three kinds of UAVs with different capabilities, i.e., reconnaissance UAV, combat UAV, and ammunition UAV can be deployed in combination. The maximum flight range among the UAVs, which is proportional to the makespan when different UAVs share the same flight speed, is taken as the objective function to be minimized. Meanwhile, constraints such as precedence, ammunition load, and mission requirements are taken into consideration. Two mission scenarios are carried out to validate the effectiveness and scalability of the proposed algorithm. And sensitivity analysis against different algorithm parameters is explored.
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Yu, X., Wang, L., Gao, X., Wang, X., Lu, C. (2023). Mission Planning for Heterogeneous UAVs in Obstacle-Dense Environment. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_75
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DOI: https://doi.org/10.1007/978-981-99-0479-2_75
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