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面向深海资源探测的多AUV任务规划研究
Alternative TitleMulti-AUV mission planing for deep sea resource exploration
赵旭浩
Department水下机器人研究室
Thesis Advisor刘健
Keyword多AUV集群 任务规划 多样性任务 离散粒子群算法 局部路径规划
Pages81页
Degree Discipline模式识别与智能系统
Degree Name硕士
2020-05-26
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract自主水下机器人(Autonomous Underwater Vehicle,AUV)是进行深海资源探测的重要工具,随着对深海资源的迫切需求,多AUV集群探测逐渐成为了深海资源探测领域的重要发展方向。而多AUV任务规划是多AUV集群探测领域的一个研究重点。本文将多AUV任务规划分成上层的任务分配和底层的局部路径规划两个部分展开。在大规模海洋环境下多AUV集群的上层任务分配问题上,针对现有任务分配模型只考虑同构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分配模型,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、真实路径代价、海流干扰等关键因素。为提高问题模型的求解效率,本文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,改进了粒子更新过程,优化了下潜次数和任务执行时间,实现了高效的任务分配。多AUV集群在获得任务序列之后,需要底层的局部路径规划算法保障AUV在执行任务期间的安全。本文提出了适用于深海环境下多AUV局部路径规划的改进流石算法。设计了在安全距离、路径段长度约束之下的快速自适应参数调整策略,满足了多AUV协同对局部路径规划算法计算复杂度的需求。设计了AUV通过障碍物重叠区域的算法。在原始流石算法模型中加入了运动AUV对初始流场的扰动,解决了复杂环境中AUV之间的相互影响问题。实现了多 AUV集群在深海多复杂障碍物环境下的局部路径规划功能。最后,为了在更加真实的环境之下验证本文所提出的任务分配算法,本文基于ROS和现有水下机器人仿真环境搭建了多AUV任务分配仿真环境,为下一步任务规划算法的工程应用打下了坚实的基础。
Other AbstractAutonomous Underwater Vehicle (AUV) is an important tool for deep sea resource exploration. With the urgent demand for deep sea resource, multi-AUV cluster exploration has gradually become the research focus in the field of deep sea resource exploration. Multi-AUV task planning is a research focus of multi-AUV cluster detection. In this paper, multi AUV task planning is divided into two parts: task allocation in the upper layer and local path planning in the lower layer. In the problem of multi-AUV upper layer task allocation in large-scale marine environment, For the existing task allocation model, only the problem of homogeneous AUV cluster and single dive task allocation is considered, this paper proposes a multi dive task allocation model suitable for AUV heterogeneous cluster, 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, the diversity of tasks, real path cost and current disturbance. In order to improve the efficiency of solving the problem model, an optimization algorithm based on discrete particle swarm optimization is proposed. The algorithm introduces matrix coding to describe the speed and position of particles and task loss model to evaluate the quality of particles, improves the process of updating particles, optimizes the number of dives and task execution time, and realizes efficient task allocation. After multi-AUV cluster obtains the task sequence, the lower layer local path planning algorithm is needed to ensure the safety of AUV during the task execution. In this paper, an improved algorithm of flow avoiding obstacles for multi-AUV local path planning in deep sea environment is proposed. A fast parameter self-adaptive adjustment strategy is designed under the constraints of safe distance and path length, which can meet the needs of multi-AUV cooperation for the calculation complexity of local path planning algorithm. The algorithm of AUV passing through the overlapping area of obstacles is designed. The disturbance of moving AUV to the initial flow field is added to the original model, which solves the problem of interaction between AUVs in complex environment. The local path planning function of multi-AUV cluster in the environment of deep sea multi complex obstacles is realized. Finally, in order to verify the proposed task allocation algorithm in a more realistic environment, a multi-AUV task allocation simulation environment is built based on ROS and the existing underwater robot simulation environment, which lays a solid foundation for the future task planning algorithm engineering application.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/27118
Collection水下机器人研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
赵旭浩. 面向深海资源探测的多AUV任务规划研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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