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自动化研究所 [2]
长春光学精密机械与物... [1]
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会议论文 [1]
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Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 6, 页码: 2064-2076
作者:
Li, Haoran
;
Zhang, Qichao
;
Zhao, Dongbin
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浏览/下载:13/0
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提交时间:2020/08/03
Robot sensing systems
Navigation
Entropy
Neural networks
Task analysis
Planning
Automatic exploration
deep reinforcement learning (DRL)
optimal decision
partial observation
Deep reinforcement learning based automatic exploration for navigation in unknown environment
期刊论文
IEEE Transactions on Neural Network and Learning Systems, 2019, 期号: early acess, 页码: 1-13
作者:
Li Haoran
;
Zhang Qichao
;
Zhao Dongbin
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浏览/下载:87/0
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提交时间:2019/10/09
Automatic Exploration
Deep Reinforcement Learning
Optimal Decision
Partial Observation
Error analysis and test study of fiber optic gyroscope north-finder (EI CONFERENCE)
会议论文
Advanced Sensor Systems and Applications II, November 8, 2004 - November 12, 2004, Beijing, China
Zhang Z.
;
Sun J.
;
Wu K.
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浏览/下载:21/0
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提交时间:2013/03/25
North-finder system is an important application of the inertial technology. It provides true north azimuth information to f all kinds of ground-based equipment. This paper designed a dynamic automatic north-finder based on the modulation output of fiber optic gyroscope (FOG). In this scheme
the sine output signal of the FOG was sampled and disposed
and then the real north of surveyed point on the surface of earth was calculated. The deviation caused by the base tilt was compensated by an accelerator. The output signals of FOG and accelerator were computed combined with the position sampled from increasing encoder by digital signal processor. Successive rotating technique was adopted to modulate the FOG signal periodically. Most of the low frequency part of random gyro drift errors was compensated. The north-finder accuracy was improved compared with the traditional static measuring method. The test result shows the FOG automatic north-finder obtains 2' true north angle accuracies within not more than three minutes
can meet the orientation need of artillery
terrain measurement vehicle
armored car
unmanned drive vehicle
geodetic survey and tunnel exploration.
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