Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment
文献类型：期刊论文
| 作者 | Li, Haoran1,2 ; Zhang, Qichao1,2; Zhao, Dongbin1,2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2020-06-01 |
| 卷号 | 31期号:6页码:2064-2076 |
| 关键词 | Robot sensing systems Navigation Entropy Neural networks Task analysis Planning Automatic exploration deep reinforcement learning (DRL) optimal decision partial observation |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2019.2927869 |
| 通讯作者 | Zhao, Dongbin(dongbin.zhao@ia.ac.cn) |
| 英文摘要 | This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to this problem via stacking decision rules is impossible to cover various environments and sensor properties. Learning-based control methods are adaptive for these scenarios. However, these methods are damaged by low learning efficiency and awkward transferability from simulation to reality. In this paper, we construct a general exploration framework via decomposing the exploration process into the decision, planning, and mapping modules, which increases the modularity of the robotic system. Based on this framework, we propose a deep reinforcement learning-based decision algorithm that uses a deep neural network to learning exploration strategy from the partial map. The results show that this proposed algorithm has better learning efficiency and adaptability for unknown environments. In addition, we conduct the experiments on the physical robot, and the results suggest that the learned policy can be well transferred from simulation to the real robot. |
| 资助项目 | Beijing Science and Technology Plan[Z181100004618003] ; National Natural Science Foundation of China (NSFC)[61573353] ; National Natural Science Foundation of China (NSFC)[61803371] ; National Natural Science Foundation of China (NSFC)[61533017] ; National Natural Science Foundation of China (NSFC)[61603268] ; Huawei Technologies |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000542953000023 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Beijing Science and Technology Plan ; National Natural Science Foundation of China (NSFC) ; Huawei Technologies |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39927]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 通讯作者 | Zhao, Dongbin |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Haoran,Zhang, Qichao,Zhao, Dongbin. Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(6):2064-2076. |
| APA | Li, Haoran,Zhang, Qichao,&Zhao, Dongbin.(2020).Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(6),2064-2076. |
| MLA | Li, Haoran,et al."Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.6(2020):2064-2076. |
入库方式： OAI收割
来源：自动化研究所
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