Novel obstacle-avoiding path planning for crop protection UAV using optimized Dubins curve

Authors

  • Xihai Zhang College of Electronic and Information, Northeast Agricultural University, Harbin 150000, China
  • Chengguo Fan College of Electronic and Information, Northeast Agricultural University, Harbin 150000, China
  • Zhanyuan Cao College of Electronic and Information, Northeast Agricultural University, Harbin 150000, China
  • Junlong Fang College of Electronic and Information, Northeast Agricultural University, Harbin 150000, China
  • Yinjiang Jia College of Electronic and Information, Northeast Agricultural University, Harbin 150000, China

Keywords:

Dubins curve, path planning, genetic algorithm, overlap and skip spray, crop protection UAV

Abstract

In recent years, the crop protection unmanned aerial vehicle (UAV) has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport. However, there are few researches on obstacle-avoiding path planning for crop protection UAV. In this study, an improved Dubins curve algorithm was proposed for path planning with multiple obstacle constraints. First, according to the flight parameters of UAV and the types of obstacles in the field, the obstacle circle model and the small obstacle model were established. Second, after selecting the appropriate Dubins curve to generate the obstacle-avoiding path for multiple obstacles, the genetic algorithm (GA) was used to search the optimal obstacle-avoiding path. Third, for turning in the path planning, a strategy considering the size of the spray width and the UAV’s minimum turning radius was presented, which could decrease the speed change times. The results showed that the proposed algorithm can decrease the area of overlap and skip to 205.1%, while the path length increased by only 1.6% in comparison with the traditional Dubins obstacle-avoiding algorithm under the same conditions. With the increase of obstacle radius, the area of overlap and skip reduced effectively with no significant increase in path length. Therefore, the algorithm can efficiently improve the validity of path planning with multiple obstacle constraints and ensure the safety of flight. Keywords: Dubins curve, path planning, genetic algorithm, overlap and skip spray, crop protection UAV DOI: 10.25165/j.ijabe.20201304.3205 Citation: Zhang X H, Fan C G, Cao Z Y, Fang J L, Jia Y J. Novel obstacle-avoiding path planning for crop protection UAV using optimized Dubins curve. Int J Agric & Biol Eng, 2020; 13(4): 172–177.

References

Zhang X H, Zhang M M, Meng F F, Qiao Y, Xu S J, Hour S. A low-power wide-area network information monitoring system by combining NB-IoT and LoRa. IEEE Internet of Things Journal, 2019; 6(1): 590–598.

Xue Z J, Lin L X, Ma Y Z, Dong W B, Dou Z Y, Zhao J, et al. A shared bicycle intelligent lock control and management system based on multi-sensor. IEEE Internet of Things Journal, 2020. doi: 10.1109/JIOT.2020.2979899.

Zhang X H, Qiao Y, Meng F F, Fan C G, Zhang M M. Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 2018; 6: 30370–30377.

Xue X Y, Tu K, Qin W C, Lan Y B, Zhang H H. Drift and deposition of ultra-low altitude and low volume application in paddy field. Int J Agric & Biol Eng, 2014; 7(4): 23–28.

Xue X Y, Kang T, Lan Y B, Qin W C, Zhang L. Effects of pesticides aerial applications on rice quality. Transactions of the CSAM, 2013; 44(12): 94–98. (in Chinese)

Huang Y B, Thomson S J, Hoffmann W C, Lan Y B, Fritz B K. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int J Agric & Biol Eng, 2013; 6(3): 1–10.

Meng Y H, Lan Y B, Mei G Y, Guo Y W, Song J L, Wang Z G. Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle on wheat aphids control. Int J Agric & Biol Eng, 2018; 11(5): 46–53.

Li X, Xie J, Cai M Y, Xie M, Wang Z K. Path planning for UAV based on improved heuristic A* algorithm. 2009 9th International Conference on Electronic Measurement & Instruments. Beijing: IEEE, 2009; 3: pp.488–493.

Wang H J, Yu Y, Yuan Q B. Application of Dijkstra algorithm in robot path-planning. 2011 Second International Conference on Mechanic Automation and Control Engineering. Hohhot: IEEE, 2011; pp.1067–1069.

Moon J, Prasad J V R. Minimum-time approach to obstacle avoidance constrained by envelope protection for autonomous UAVs. Mechatronics, 2011; 21(5): 861–875.

Jeffril M A, Sariff N. The integration of fuzzy logic and artificial neural network methods for mobile robot obstacle avoidance in a static environment. 2013 IEEE 3rd International Conference on System Engineering and Technology. Sah Alam: IEEE, 2013; pp.325–330.

Zhu L H, Cheng X H, Yuan F G. A 3D collision avoidance strategy for

UAV with physical constraints. Measurement, 2016; 77: 40–49.

Hao W, Qin S. Multi-objective path planning for space exploration robot based on chaos immune particle swarm optimization algorithm. 2011 International Conference on Artificial Intelligence and Computational Intelligence. Berlin, Heidelberg: Springer, 2011; 7003: pp.42–52.

Ny J L, Feron E, Frazzoli E. On the Dubins traveling salesman problem. IEEE Transactions on Automatic Control, 2012; 57(1): 265–270.

Yeol J W, Ryu Y S, Montalvo M A. Shortest trajectory planning of wheeled mobile robots with constraints. 2005 IEEE Networking, Sensing and Control. Tucson: IEEE, 2005; pp.883–888.

Ozalp N, Ayan U, Oztop E. Cooperative multi-task assignment for heterogonous UAVs. 2015 International Conference on Advanced Robotics. Istanbul: IEEE, 2015; pp.599–604.

Zhu M O, Zhang X H, Luo H, Wang G Q, Zhang B B. Optimization Dubins path of multiple UAVs for post-earthquake rapid-assessment. Applied Sciences, 2020; 10(4): 1388. doi: 10.3390/app10041388.

Dobrokhodov V. Cooperative path planning of unmanned aerial vehicles. Journal of Guidance Control and Dynamics, 2010; 34(5): 1601–1602.

Yan Z P, Huang Y F, Li F. Research on the application of genetic algorithm in local path planning for AUV. Applied Science and Technology, 2009; 36(2): 47–51. (in Chinese)

Song B, Wang Z, Sheng L. A new genetic algorithm approach to smooth path planning for mobile robots. Assembly Automation, 2016; 36(2): 138–145.

Tu J P, Yang S X. Genetic algorithm based path planning for a mobile robot. 2003 IEEE International Conference on Robotics and Automation. Taipei: IEEE, 2003; 1: pp.1221–1226.

Cui L F, Mao H P, Xue X Y, Ding S M, Qiao B Y. Optimized design and test for a pendulum suspension of the crop spray boom in dynamic conditions based on a six DOF motion simulator. Int J Agric & Biol Eng, 2018; 11(3): 76–85.

Wang H Y, Cai X D, You B, Zhang L Y. Research on dynamic path planning of mobile robot based on genetic algorithm. Transducer & Microsystem Technologies, 2007; 26(8): 32–34. (in Chinese)

Downloads

Published

2020-08-07

How to Cite

Zhang, X., Fan, C., Cao, Z., Fang, J., & Jia, Y. (2020). Novel obstacle-avoiding path planning for crop protection UAV using optimized Dubins curve. International Journal of Agricultural and Biological Engineering, 13(4), 172–177. Retrieved from https://ijabe.migration.pkpps06.publicknowledgeproject.org/index.php/ijabe/article/view/3205

Issue

Vol. 13 No. 4 (2020): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed, open access journal, adopting Creative Commons Copyright Notices as follows.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).