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面向海洋观测的水下机器人规划与控制方法研究
Alternative TitleResearch on planning and control methods for underwater vehicles in ocean observation
刘世杰
Department海洋机器人卓越创新中心
Thesis Advisor张艾群 ; 俞建成
Keyword水下机器人 海洋观测 覆盖优化 路径规划 编队控制
Pages114页
Degree Discipline机械电子工程
Degree Name博士
2019-11-20
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract本文依托国家自然科学基金重点项目 “水下机器人海洋环境自主观测理论 与技术” 和国家自然科学基金联合基金项目 “基于水下移动平台的近海海洋环境 观测技术研究”,结合海洋学对于高精度数据的观测需求,针对上述问题,深入研究了面向海洋观测的水下机器人规划与控制方法。重点研究了面向海洋特征 场重构的观测任务优化方法;研究任务执行时海流影响下的水下机器人路径规划方法;研究了面向协同观测的多水下机器人控制方法。主要包括以下内容: 1. 开展基于现场观测数据的高分辨率海洋特征场重构方法研究。针对当前海洋数值模式难以生成高分辨率特征场数据的问题,提出了基于模式数据背景场结构的水下机器人观测数据驱动的海洋特征场重构方法,实现了满足物理海洋约束的高分辨率特征场重构。2. 开展基于降低特征场重构不确定性的多水下机器人覆盖观测优化方法。 针对当前采样路径会导致局部区域特征场重构误差明显高于其他区域的问题,提出了基于规则观测模式的覆盖观测优化方法,降低区域内特征场重构的总体不确定性。进一步针对给定规则观测模式对最优解的限制问题,提出了一种观测数据驱动的自主覆盖观测优化方法,进一步降低重构不确定性。3. 开展时变强流场中水下机器人路径规划方法研究。针对强流场制约水下机器人运动进而影响观测行为的问题,提出了改进的基于水平集的最短时间路径规划方法,显著提高最优路径精度,保证所得路径在强流场中的可行性。4. 开展基于水声通信和流场共享的水下机器人分布式路径规划方法研究。针对海洋环境通信受限从而影响水下机器人之间信息交互的问题,提出了基于实测通信链路质量的单数据包传输策略和流场数据动态压缩方法,实现多水下机器人在共享流场下的分布式路径规划。5. 开展多水下机器人编队协同控制方法研究。针对当前海洋环境中不能对多水下机器人进行实时控制从而影响协同编队性能的问题,建立了基于自身感知流场的水下机器人状态估计模型,提出了基于状态估计和协同路径跟踪算法的多水下机器人协同速度控制和协同航点生成方法,实现多水下机器人协同编队观测。
Other AbstractThis dissertation is supported by the Key Program of National Natural Science Foundation of China, titled “Autonomous ocean observation theory and technology for underwater vehicles”, and the Joint Funds of the National Natural Science Foundation of China, titled “Offshore Marine Environment Observation Technology Based on Underwater Mobile Platforms”. Combined with oceanographic observation requirements for high-precision data, this dissertation deeply studies the underwater vehicle planning and control methods for ocean observation, focusing on the following researches: the observation task optimization method for ocean feature field reconstruction; the underwater vehicle path planning method under the influence of ocean current; the multi-underwater vehicle control method for collaborative observation. This dissertation mainly includes the following contents. 1. Research on high-resolution ocean feature field reconstruction based on field observation data. Aiming at the problem that the current ocean model cannot make full use of the intensive observation data sampled by underwater vehicles to generate fine feature fields, a high-resolution ocean feature field reconstruction method based on the best linear unbiased estimation theory is proposed. This method is mainly driven by underwater vehicle observation data combining with the background field structure extracted from the ocean model prediction data. 2. To reduce the uncertainty of field reconstruction, this dissertation studies the optimization method of multi-underwater vehicle coverage observation. Aiming at the problem that the current sampling path will cause the local characteristic field reconstruction error to be significantly higher than other areas, a coverage observation optimization method based on regular observation mode is proposed to reduce the overall uncertainty of the characteristic field reconstruction in the region. Furthermore, without specifying the regular observation mode, an autonomous coverage observation optimization method driven by observation data is proposed, which further reduces the uncertainty of the field reconstruction. 3. Research on a path planning method of underwater vehicles in a time-varying strong flow field. Aiming at the problem that strong flow fields restrict the motion of underwater vehicles and affect the observation behavior, an improved minimum time path planning method based on level set is proposed to significantly improve the optimal path accuracy and ensure the feasibility of the obtained path in the strong flow field. 4. Research on a distributed path planning method of underwater vehicles based on underwater acoustic communication and flow field sharing. To solve the limited communication conditions in the marine environment affecting the information interaction between underwater vehicles, a single packet transmission strategy and a dynamic compression method based on the measured communication link quality are proposed to realize the distributed path planning of multiple underwater vehicles under the shared flow field. 5. Research on a cooperative formation control method for multiple underwater vehicles. Aiming at the problem that the multi-underwater vehicles cannot be controlled in real-time in the marine environment, which affects the performance of cooperative formation, an underwater vehicle state estimation model based on the self-perceived flow field is established. Based on the state estimation and a cooperative path tracking algorithm, a multi-underwater vehicle cooperative speed control and collaborative waypoint generation method is proposed to realize multi-underwater vehicle cooperative formation observation.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/25948
Collection海洋机器人卓越创新中心
Recommended Citation
GB/T 7714
刘世杰. 面向海洋观测的水下机器人规划与控制方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2019.
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