UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC)

Zhexuan Zhang; Teng Long; Zhu Wang; Guangtong Xu; Yan Cao

doi:10.1109/GNCC42960.2018.9019099

UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC)

Zhexuan Zhang, Teng Long, Zhu Wang, Guangtong Xu, Yan Cao

School of Aerospace Engineering

Beijing Institute of Technology

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

3 Citations (Scopus)

Abstract

This paper presents an effective method for unmanned aerial vehicle (UAV) dynamic path planning considering moving-target and obstacle-avoidance constraints. In the process of dynamic path planning, moving-target positions are predicted using Kalman filtering algorithm on the receding horizon. Then, anytime repairing sparse A algorithm (AR-SAS) is customized to generate feasible paths from the staring positions to the moving-target positions. By rolling planning, UAVs are able to track the moving targets efficiently. Simulation results demonstrate that the proposed method can generate feasible paths within limited time and has high robustness.

Original language	English
Title of host publication	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538611715
DOIs	https://doi.org/10.1109/GNCC42960.2018.9019099
Publication status	Published - Aug 2018
Event	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 - Xiamen, China Duration: 10 Aug 2018 → 12 Aug 2018

Publication series

Name	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Conference

Conference	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Country/Territory	China
City	Xiamen
Period	10/08/18 → 12/08/18

Access to Document

10.1109/GNCC42960.2018.9019099

Cite this

Zhang, Z., Long, T., Wang, Z., Xu, G., & Cao, Y. (2018). UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC). In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 Article 9019099 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GNCC42960.2018.9019099

Zhang, Zhexuan ; Long, Teng ; Wang, Zhu et al. / UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC). 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018).

@inproceedings{28954d74f5274848b42c1471e315b570,

title = "UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC)",

abstract = "This paper presents an effective method for unmanned aerial vehicle (UAV) dynamic path planning considering moving-target and obstacle-avoidance constraints. In the process of dynamic path planning, moving-target positions are predicted using Kalman filtering algorithm on the receding horizon. Then, anytime repairing sparse A algorithm (AR-SAS) is customized to generate feasible paths from the staring positions to the moving-target positions. By rolling planning, UAVs are able to track the moving targets efficiently. Simulation results demonstrate that the proposed method can generate feasible paths within limited time and has high robustness.",

author = "Zhexuan Zhang and Teng Long and Zhu Wang and Guangtong Xu and Yan Cao",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 ; Conference date: 10-08-2018 Through 12-08-2018",

year = "2018",

month = aug,

doi = "10.1109/GNCC42960.2018.9019099",

language = "English",

series = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

address = "United States",

}

Zhang, Z, Long, T, Wang, Z, Xu, G & Cao, Y 2018, UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC). in 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018., 9019099, 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Xiamen, China, 10/08/18. https://doi.org/10.1109/GNCC42960.2018.9019099

UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC). / Zhang, Zhexuan; Long, Teng; Wang, Zhu et al.
2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 9019099 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018).

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

TY - GEN

T1 - UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC)

AU - Zhang, Zhexuan

AU - Long, Teng

AU - Wang, Zhu

AU - Xu, Guangtong

AU - Cao, Yan

PY - 2018/8

Y1 - 2018/8

N2 - This paper presents an effective method for unmanned aerial vehicle (UAV) dynamic path planning considering moving-target and obstacle-avoidance constraints. In the process of dynamic path planning, moving-target positions are predicted using Kalman filtering algorithm on the receding horizon. Then, anytime repairing sparse A algorithm (AR-SAS) is customized to generate feasible paths from the staring positions to the moving-target positions. By rolling planning, UAVs are able to track the moving targets efficiently. Simulation results demonstrate that the proposed method can generate feasible paths within limited time and has high robustness.

AB - This paper presents an effective method for unmanned aerial vehicle (UAV) dynamic path planning considering moving-target and obstacle-avoidance constraints. In the process of dynamic path planning, moving-target positions are predicted using Kalman filtering algorithm on the receding horizon. Then, anytime repairing sparse A algorithm (AR-SAS) is customized to generate feasible paths from the staring positions to the moving-target positions. By rolling planning, UAVs are able to track the moving targets efficiently. Simulation results demonstrate that the proposed method can generate feasible paths within limited time and has high robustness.

UR - http://www.scopus.com/inward/record.url?scp=85082501243&partnerID=8YFLogxK

U2 - 10.1109/GNCC42960.2018.9019099

DO - 10.1109/GNCC42960.2018.9019099

M3 - Conference contribution

AN - SCOPUS:85082501243

T3 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

BT - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Y2 - 10 August 2018 through 12 August 2018

ER -

Zhang Z, Long T, Wang Z, Xu G, Cao Y. UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC). In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 9019099. (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). doi: 10.1109/GNCC42960.2018.9019099

UAV Dynamic Path Planning using Anytime Repairing Sparse A Algorithm and Targets Motion Estimation (IEEE/CSAA GNCC)

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