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Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment | |
Li, Haoran1,2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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ISSN | 2162-237X |
2020-06-01 | |
卷号 | 31期号:6页码:2064-2076 |
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. |
关键词 | Robot sensing systems Navigation Entropy Neural networks Task analysis Planning Automatic exploration deep reinforcement learning (DRL) optimal decision partial observation |
DOI | 10.1109/TNNLS.2019.2927869 |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | Beijing Science and Technology Plan ; National Natural Science Foundation of China (NSFC) ; Huawei Technologies |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000542953000023 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 强化与进化学习 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39928 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Zhao, Dongbin |
作者单位 | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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