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基于能量约束的多AUV任务规划算法
Alternative TitleTask planning algorithm of multi-AUV based Energy constraint
赵旭浩1,2,3,4; 王轶群1,2,3,4; 刘健1,2,3; 徐春晖1,2,3
Department水下机器人研究室
Source Publication计算机应用
ISSN1001-9081
2019
Pages1-8
Contribution Rank1
Funding Organization国家重点研发计划(2017YFC0306800)
Keyword自主水下航行器(Autonomous Uderwater Vehicle AUV) 多AUV集群 任务规划 离散粒子群算法 多样性任务
Abstract多水下自主航行器(Autonomous Uderwater Vehicle,AUV)任务规划是影响集群智能水平的关键技术,针对现有任务规划模型只考虑同构AUV集群和单潜次任务规划的问题,本文提出了适用于AUV异构集群的多潜次任务规划模型。首先该模型考虑了AUV的能量约束、AUV多次往返母船充电的工程代价、异构集群个体间的效能差异、任务多样性等关键因素;然后为提高问题模型的求解效率,提出了一种基于离散粒子群的优化算法,该算法引入用于描述粒子速度、位置的矩阵编码和用于评估粒子质量的任务损耗模型,改进粒子更新过程,实现了高效的目标寻优。仿真实验表明,该算法不但解决了异构AUV集群的多潜次任务规划问题,而且与采用遗传算法的任务规划模型相比较,任务损耗降低了11%。
Other AbstractAutonomous Underwater Vehicle (AUV) mission planning is the key technology that affects the level of cluster intelligence. For the existing mission planning model, only the problem of homogeneous AUV cluster and single dive mission planning is considered. So aa multi-dive mission planning model for AUV heterogeneous clusters is proposed. Firstly the model considered the energy constraints of AUV, the engineering cost of AUV multiple round-trip mother ship charging, the efficiency difference between heterogeneous cluster individuals, and the diversity of tasks. Then in order to improve the efficiency of solving the problem model, an optimization algorithm based on discrete particle swarm optimization was proposed. The algorithm introduced matrix coding for describing particle velocity and position, and the task loss model for evaluating particle quality, and improves the particle update process, achieving efficient target optimization. Simulation experiments show that the algorithm not only solves the multi-dive mission planning problem of heterogeneous AUV clusters, but also reduces the task loss by 11% compared with the mission planning model using genetic algorithm.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24705
Collection水下机器人研究室
Corresponding Author王轶群
Affiliation1.中国科学院沈阳自动化研究所
2.机器人学国家重点实验室(中国科学院沈阳自动化研究所)
3.中国科学院机器人与智能制造创新研究院
4.中国科学院大学
Recommended Citation
GB/T 7714
赵旭浩,王轶群,刘健,等. 基于能量约束的多AUV任务规划算法[J]. 计算机应用,2019:1-8.
APA 赵旭浩,王轶群,刘健,&徐春晖.(2019).基于能量约束的多AUV任务规划算法.计算机应用,1-8.
MLA 赵旭浩,et al."基于能量约束的多AUV任务规划算法".计算机应用 (2019):1-8.
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