Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms

Yang Wang; Wenjie Chen

doi:10.1109/ChiCC.2014.6896446

Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms

Yang Wang^*, Wenjie Chen

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

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

9 Citations (Scopus)

Abstract

Path planning is always an essential issue and complicated optimum problem for unmanned aerial vehicle (UAV). Genetic algorithms are well applied to solve such problems as a stochastic search method. In this paper, a new method of path planning for UAV based on genetic algorithms is introduced. Reasonable coding way and fitness function are used in this improved genetic algorithm, and prior knowledge is added to the genetic algorithm. By selecting essential points and moving strategy in advance, this new method can highly reduce the computation cost and find the optimal path more efficiently. The simulation result shows that this new approach is proved to improve the search efficiency.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control Conference, CCC 2014
Editors	Shengyuan Xu, Qianchuan Zhao
Publisher	IEEE Computer Society
Pages	8612-8616
Number of pages	5
ISBN (Electronic)	9789881563842
DOIs	https://doi.org/10.1109/ChiCC.2014.6896446
Publication status	Published - 11 Sept 2014
Event	Proceedings of the 33rd Chinese Control Conference, CCC 2014 - Nanjing, China Duration: 28 Jul 2014 → 30 Jul 2014

Publication series

Name	Proceedings of the 33rd Chinese Control Conference, CCC 2014
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	Proceedings of the 33rd Chinese Control Conference, CCC 2014
Country/Territory	China
City	Nanjing
Period	28/07/14 → 30/07/14

Keywords

Genetic Algorithms
Obstacle Avoidance
UAV

Access to Document

10.1109/ChiCC.2014.6896446

Cite this

Wang, Y., & Chen, W. (2014). Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms. In S. Xu, & Q. Zhao (Eds.), Proceedings of the 33rd Chinese Control Conference, CCC 2014 (pp. 8612-8616). Article 6896446 (Proceedings of the 33rd Chinese Control Conference, CCC 2014). IEEE Computer Society. https://doi.org/10.1109/ChiCC.2014.6896446

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title = "Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms",

abstract = "Path planning is always an essential issue and complicated optimum problem for unmanned aerial vehicle (UAV). Genetic algorithms are well applied to solve such problems as a stochastic search method. In this paper, a new method of path planning for UAV based on genetic algorithms is introduced. Reasonable coding way and fitness function are used in this improved genetic algorithm, and prior knowledge is added to the genetic algorithm. By selecting essential points and moving strategy in advance, this new method can highly reduce the computation cost and find the optimal path more efficiently. The simulation result shows that this new approach is proved to improve the search efficiency.",

keywords = "Genetic Algorithms, Obstacle Avoidance, UAV",

author = "Yang Wang and Wenjie Chen",

note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",

year = "2014",

month = sep,

day = "11",

doi = "10.1109/ChiCC.2014.6896446",

language = "English",

series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",

publisher = "IEEE Computer Society",

pages = "8612--8616",

editor = "Shengyuan Xu and Qianchuan Zhao",

booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",

address = "United States",

}

Wang, Y & Chen, W 2014, Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms. in S Xu & Q Zhao (eds), Proceedings of the 33rd Chinese Control Conference, CCC 2014., 6896446, Proceedings of the 33rd Chinese Control Conference, CCC 2014, IEEE Computer Society, pp. 8612-8616, Proceedings of the 33rd Chinese Control Conference, CCC 2014, Nanjing, China, 28/07/14. https://doi.org/10.1109/ChiCC.2014.6896446

Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms. / Wang, Yang; Chen, Wenjie.
Proceedings of the 33rd Chinese Control Conference, CCC 2014. ed. / Shengyuan Xu; Qianchuan Zhao. IEEE Computer Society, 2014. p. 8612-8616 6896446 (Proceedings of the 33rd Chinese Control Conference, CCC 2014).

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

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AU - Chen, Wenjie

PY - 2014/9/11

Y1 - 2014/9/11

N2 - Path planning is always an essential issue and complicated optimum problem for unmanned aerial vehicle (UAV). Genetic algorithms are well applied to solve such problems as a stochastic search method. In this paper, a new method of path planning for UAV based on genetic algorithms is introduced. Reasonable coding way and fitness function are used in this improved genetic algorithm, and prior knowledge is added to the genetic algorithm. By selecting essential points and moving strategy in advance, this new method can highly reduce the computation cost and find the optimal path more efficiently. The simulation result shows that this new approach is proved to improve the search efficiency.

AB - Path planning is always an essential issue and complicated optimum problem for unmanned aerial vehicle (UAV). Genetic algorithms are well applied to solve such problems as a stochastic search method. In this paper, a new method of path planning for UAV based on genetic algorithms is introduced. Reasonable coding way and fitness function are used in this improved genetic algorithm, and prior knowledge is added to the genetic algorithm. By selecting essential points and moving strategy in advance, this new method can highly reduce the computation cost and find the optimal path more efficiently. The simulation result shows that this new approach is proved to improve the search efficiency.

KW - Genetic Algorithms

KW - Obstacle Avoidance

KW - UAV

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M3 - Conference contribution

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EP - 8616

BT - Proceedings of the 33rd Chinese Control Conference, CCC 2014

A2 - Xu, Shengyuan

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PB - IEEE Computer Society

T2 - Proceedings of the 33rd Chinese Control Conference, CCC 2014

Y2 - 28 July 2014 through 30 July 2014

ER -

Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms

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Keywords

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