NSSC OpenIR
UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy
Alternative TitleWOS:000587193000001;20204109309171
Xie, Jingyi1; Peng, Xiaodong1; Wang, Haijiao2; Niu, Wenlong1; Zheng, Xiao1
Source PublicationSENSORS
2020
Volume20Issue:19Pages:5630
DOI10.3390/s20195630
Language英语
Keywordquadrotor unmanned aerial vehicle deep reinforcement learning autonomous tracking and landing
AbstractUnmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher than that of the Proportional-Integral-Derivative (PID) method. As an indirect result, a state-of-the-art deep reinforcement learning-based UAV control method is validated, where the UAV can learn the optimal strategy of a continuously autonomous landing and perform properly in a simulation environment.
Indexed BySCI ; EI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7732
Collection中国科学院国家空间科学中心
Affiliation1.Chinese Acad Sci, Natl Space Sci Ctr, Key Lab Elect & Informat Technol Space Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Alibaba Damo Acad, Hangzhou 311121, Peoples R China
Recommended Citation
GB/T 7714
Xie, Jingyi,Peng, Xiaodong,Wang, Haijiao,et al. UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy[J]. SENSORS,2020,20(19):5630.
APA Xie, Jingyi,Peng, Xiaodong,Wang, Haijiao,Niu, Wenlong,&Zheng, Xiao.(2020).UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy.SENSORS,20(19),5630.
MLA Xie, Jingyi,et al."UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy".SENSORS 20.19(2020):5630.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xie, Jingyi]'s Articles
[Peng, Xiaodong]'s Articles
[Wang, Haijiao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xie, Jingyi]'s Articles
[Peng, Xiaodong]'s Articles
[Wang, Haijiao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xie, Jingyi]'s Articles
[Peng, Xiaodong]'s Articles
[Wang, Haijiao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.