Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithms

Yeonju Eun; Hyochoong Bang

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Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithms

机译：遗传算法的多无人机协同任务分配/路径规划

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A grouping and assignment problem of a SEAD mission using multiple UAVs is formulated as a new combinatorial optimization problem, and the GA is used to solve. According to the scenario in this study, the multiple fixed targets are located on the ground, with target locations initially given. To avoid threats and obstacles, a Voronoi diagram is used and multiple candidate paths are generated for the optimization with the GA. For various conditions of the mission, timing constraints and path constraints are formulated. The modified GA operations are purposed to maintain the feasibility of populations. By using the proposed algorithm, one can obtain a task assignment with proper path planning and obstacle avoidance concurrently. Moreover, the simulation results indicate that the proposed genetic algorithm can be used for the complicated group mission with timing constraints. In addition, because the proposed chromosome consists of two levels, a task and a method to accomplish the task, it can be applied to various types of assignment problems of group missions using multiple agents.

机译：将使用多个无人机的SEAD任务的分组和分配问题表述为新的组合优化问题，并使用遗传算法进行求解。根据本研究中的场景，多个固定目标位于地面上，最初给出了目标位置。为了避免威胁和障碍，使用了Voronoi图，并生成了多个候选路径以使用GA进行优化。对于任务的各种条件，制定了时间约束和路径约束。修改后的通用航空运营旨在维持总体可行性。通过使用所提出的算法，可以同时获得具有适当路径规划和避障功能的任务分配。仿真结果表明，所提出的遗传算法可以用于具有时间约束的复杂群体任务。另外，由于建议的染色体由两个层次组成，一个任务和一个完成任务的方法，因此它可以应用于使用多个代理的各种类型的小组任务分配问题。

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《Journal of Aircraft》 |2009年第1期|338-343|共6页
作者
Yeonju Eun; Hyochoong Bang;
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作者单位

Korea Advanced Institute of Science and Technology, Daejon 305-701, Republic of Korea;

Korea Advanced Institute of Science and Technology, Daejon 305-701, Republic of Korea;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
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2. Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes [J] . Deng Qibo, Yu Jianqiao, Wang Ningfei 中国航空学报（英文版） . 2013,第005期

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3. COOPERATIVE TASK PLANNING FOR MULTIPLE UNMANNED AERIAL VEHICLES USING A GENETIC ALGORITHM [J] . L. Geng, Y.F. Zhang, J.J. Wang, Control and Intelligent Systems . 2014,第2期

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6. Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind [O] . He Luo, Zhengzheng Liang, Moning Zhu, -1

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7. Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes [O] . Deng Qibo, Yu Jianqiao, Wang Ningfei 2013

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8. UAV Cooperative Multiple Task Assignments using Genetic Algorithms [R] . Shima, T. , Rasmussen, S. J. , Sparks, A. G. 2005

机译：基于遗传算法的无人机协同多任务分配

Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithms

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