Target Search in Unknown Environment Based on Temporal Differential Learning

Yiming Li; Jinwen Hu; Congzhe Zhang; Zhao Xu; Caijuan Jia

doi:10.1007/978-981-15-8155-7_196

Target Search in Unknown Environment Based on Temporal Differential Learning

Yiming Li, Jinwen Hu, Congzhe Zhang, Zhao Xu, Caijuan Jia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper provides a path planning algorithm to solve the problem of target search in the unknown environment. By updating the value function after each action in turn, this paper overcomes the problem that traditional reinforcement learning algorithms require a large number of training processes. Besides, this paper further expands and optimizes the algorithm based on the hardware characteristics of UAV (Unmanned Aerial Vehicle). When the detection range of the sensor is different, the efficiency of the algorithm can be improved by taking it into consideration. To solve the problem of high steering time cost, it increase the number of possible non-existing paths based on the value function. The improvement and optimization for practical problems in this paper makes the algorithm can be applied to UAV better. Finally, the paper tests the algorithm in a simulation environment to ensure that the algorithm can effectively complete the path planning task of the search target.

Original language	English
Title of host publication	Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
Editors	Liang Yan, Haibin Duan, Xiang Yu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	2333-2343
Number of pages	11
ISBN (Print)	9789811581540
DOIs	https://doi.org/10.1007/978-981-15-8155-7_196
State	Published - 2022
Event	International Conference on Guidance, Navigation and Control, ICGNC 2020 - Tianjin, China Duration: 23 Oct 2020 → 25 Oct 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	644 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Guidance, Navigation and Control, ICGNC 2020
Country/Territory	China
City	Tianjin
Period	23/10/20 → 25/10/20

Keywords

Path planning
Target search
Temporal differential learning
UAV (Unmanned Aerial Vehicle)

Access to Document

10.1007/978-981-15-8155-7_196

Cite this

Li, Y., Hu, J., Zhang, C., Xu, Z., & Jia, C. (2022). Target Search in Unknown Environment Based on Temporal Differential Learning. In L. Yan, H. Duan, & X. Yu (Eds.), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020 (pp. 2333-2343). (Lecture Notes in Electrical Engineering; Vol. 644 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8155-7_196

Li, Yiming ; Hu, Jinwen ; Zhang, Congzhe et al. / Target Search in Unknown Environment Based on Temporal Differential Learning. Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. editor / Liang Yan ; Haibin Duan ; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 2333-2343 (Lecture Notes in Electrical Engineering).

@inproceedings{6c81736d300748ec8fb73793621d6867,

title = "Target Search in Unknown Environment Based on Temporal Differential Learning",

abstract = "This paper provides a path planning algorithm to solve the problem of target search in the unknown environment. By updating the value function after each action in turn, this paper overcomes the problem that traditional reinforcement learning algorithms require a large number of training processes. Besides, this paper further expands and optimizes the algorithm based on the hardware characteristics of UAV (Unmanned Aerial Vehicle). When the detection range of the sensor is different, the efficiency of the algorithm can be improved by taking it into consideration. To solve the problem of high steering time cost, it increase the number of possible non-existing paths based on the value function. The improvement and optimization for practical problems in this paper makes the algorithm can be applied to UAV better. Finally, the paper tests the algorithm in a simulation environment to ensure that the algorithm can effectively complete the path planning task of the search target.",

keywords = "Path planning, Target search, Temporal differential learning, UAV (Unmanned Aerial Vehicle)",

author = "Yiming Li and Jinwen Hu and Congzhe Zhang and Zhao Xu and Caijuan Jia",

note = "Publisher Copyright: {\textcopyright} 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2022",

doi = "10.1007/978-981-15-8155-7_196",

language = "英语",

isbn = "9789811581540",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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}

Li, Y, Hu, J, Zhang, C, Xu, Z & Jia, C 2022, Target Search in Unknown Environment Based on Temporal Differential Learning. in L Yan, H Duan & X Yu (eds), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Lecture Notes in Electrical Engineering, vol. 644 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 2333-2343, International Conference on Guidance, Navigation and Control, ICGNC 2020, Tianjin, China, 23/10/20. https://doi.org/10.1007/978-981-15-8155-7_196

Target Search in Unknown Environment Based on Temporal Differential Learning. / Li, Yiming; Hu, Jinwen; Zhang, Congzhe et al.
Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. ed. / Liang Yan; Haibin Duan; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. p. 2333-2343 (Lecture Notes in Electrical Engineering; Vol. 644 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Target Search in Unknown Environment Based on Temporal Differential Learning

AU - Li, Yiming

AU - Hu, Jinwen

AU - Zhang, Congzhe

AU - Xu, Zhao

AU - Jia, Caijuan

PY - 2022

Y1 - 2022

N2 - This paper provides a path planning algorithm to solve the problem of target search in the unknown environment. By updating the value function after each action in turn, this paper overcomes the problem that traditional reinforcement learning algorithms require a large number of training processes. Besides, this paper further expands and optimizes the algorithm based on the hardware characteristics of UAV (Unmanned Aerial Vehicle). When the detection range of the sensor is different, the efficiency of the algorithm can be improved by taking it into consideration. To solve the problem of high steering time cost, it increase the number of possible non-existing paths based on the value function. The improvement and optimization for practical problems in this paper makes the algorithm can be applied to UAV better. Finally, the paper tests the algorithm in a simulation environment to ensure that the algorithm can effectively complete the path planning task of the search target.

AB - This paper provides a path planning algorithm to solve the problem of target search in the unknown environment. By updating the value function after each action in turn, this paper overcomes the problem that traditional reinforcement learning algorithms require a large number of training processes. Besides, this paper further expands and optimizes the algorithm based on the hardware characteristics of UAV (Unmanned Aerial Vehicle). When the detection range of the sensor is different, the efficiency of the algorithm can be improved by taking it into consideration. To solve the problem of high steering time cost, it increase the number of possible non-existing paths based on the value function. The improvement and optimization for practical problems in this paper makes the algorithm can be applied to UAV better. Finally, the paper tests the algorithm in a simulation environment to ensure that the algorithm can effectively complete the path planning task of the search target.

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KW - Target search

KW - Temporal differential learning

KW - UAV (Unmanned Aerial Vehicle)

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SN - 9789811581540

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BT - Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020

A2 - Yan, Liang

A2 - Duan, Haibin

A2 - Yu, Xiang

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 23 October 2020 through 25 October 2020

ER -

Li Y, Hu J, Zhang C, Xu Z, Jia C. Target Search in Unknown Environment Based on Temporal Differential Learning. In Yan L, Duan H, Yu X, editors, Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 2333-2343. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8155-7_196

Target Search in Unknown Environment Based on Temporal Differential Learning

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