A path planning strategy for marine vehicles based on deep reinforcement learning and data-driven dynamic flow fields prediction
文献类型：会议论文
| 作者 | Song SM(桑启明)1,2,3; Tian Y(田宇)1,3 ; Jin QL(金乾隆)1,2,3; Yu JC(俞建成)1,3
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| 出版日期 | 2021 |
| 会议日期 | July 15-17, 2021 |
| 会议地点 | Dalian, China |
| 关键词 | marine vehicle path planning deep reinforcement learning dynamic mode decomposition sensing optimization |
| 页码 | 466-471 |
| 英文摘要 | This paper presents a strategy for planning a path of a marine vehicle in dynamic flow fields. This strategy composes of two modules: deep reinforcement learning based path planning and dynamic mode decomposition (DMD) based flow fields prediction. The path planning module employs the deep reinforcement learning algorithm of proximal policy optimization (PPO) to implement the time-optimal path planning of a marine vehicle in predicted spatially-temporally dynamic flow fields, where the long short-term memory (LSTM) is introduced to address the partially observable issue. The objective of the flow prediction module is to provide the path planning module with predicted dynamic flow fields. In the flow prediction module, the data-driven method of DMD is used to learn the low-dimensional model of flow dynamics and make future predictions. And a network of marine vehicles with flow sensing capability are adopted to generate data of flow fields for the on-line DMD learning and prediction, where their flow sensing locations are optimized by the deep reinforcement learning algorithm of deep-Q learning with the aim at minimizing the reconstruction error of the flow field with the sparse in-situ point flow observations by the swarm of marine vehicles. The strategy is implemented in computer simulations, where the flow data outputted by a numerical ocean model is utilized to test the strategy. The simulation results demonstrate the performance of the proposed strategy. |
| 产权排序 | 1 |
| 会议录 | 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-6654-3576-5 |
| 源URL | [http://ir.sia.cn/handle/173321/29901]  |
| 专题 | 海洋机器人卓越创新中心 |
| 通讯作者 | Song SM(桑启明) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.University of Chinese Academy of Sciences, Beijing 100049, China Shenyang, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China |
| 推荐引用方式 GB/T 7714 | Song SM,Tian Y,Jin QL,et al. A path planning strategy for marine vehicles based on deep reinforcement learning and data-driven dynamic flow fields prediction[C]. 见:. Dalian, China. July 15-17, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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