摘要
多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space,DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。
In view of the application scenarios that multiple UAVs cooperate with each other to complete the field sensor data collection task,it is particularly important to establish a path planning problem model for multiple UAVs with accurate energy consumption model.This paper presented the MUPP-AEC problem.The MUPP-AEC toke into account the differences in energy consumption under UAV acceleration,deceleration,cruising in constant speed and turning.For solving the MUPP-AEC,this paper proposed the DBSO-OS.In DBSO-OS,this paper proposed individual space integer encoding and the phased greedy decoding strategy with 2-opt,and defined the perturbation operator and individual update operator discretely.The individual update operators adopted the new individual generation strategy utilizing the random inversion transformation and the partial matching transformation.The experimental results show that the proposed algorithm can effectively solve the MUPP-AEC.The proposed discrete brainstorm operator is superior to the traditional brainstorm operators in terms of global convergence ability,convergence precision,and stability.In the small and medium-sized test cases and the large test cases,the proposed algorithm is better than the compared algorithms.
作者
戚远航
黄子峻
曾楚祥
黄戈文
王福杰
Qi Yuanhang;Huang Zijun;Zeng Chuxiang;Huang Gewen;Wang Fujie(School of Computer Science,University of Electronic Science&Technology of China,Zhongshan Institute,Zhongshan Guangdong 528402,China;School of Computer Science&Engineering,University of Electronic Science&Technology of China,Chengdu 611731,China;Information&Network Center,Jiaying University,Meizhou Guangdong 514015,China;School of Electrical Engineering&Intelligentization,Dongguan University of Technology,Dongguan Guangdong 523808,China)
出处
《计算机应用研究》
CSCD
北大核心
2022年第1期177-182,共6页
Application Research of Computers
基金
广东省自然科学基金资助项目(2019A1515010493)
广东省普通高校青年创新人才项目(2018KQNCX333,2018KQNCX252)
广东省普通高校重点领域专项(2019KZDZX1052,2020ZDZX3030)。
