Path planning of plant protection UAV based on improved A* algorithm under wind conditions

Guanghui Sun, Xianfa Fang, Licheng Zhu, Yanwei Yuan, Bo Zhao, Zhenhao Han

Abstract


Abstract: In order to improve safety and reduce energy consumption, a path planning method for the transition of plant protection unmanned aerial vehicles (UAVs) in mountainous and hilly terrain between different plots based on improved A* algorithm was proposed.  According to the height of three-dimensional terrain information, a raster voxel map was built.  The search space was determined by assigning weights to each grid.  The wind cost was introduced into the actual cost function to improve the algorithm.  Different weight parameters were set for distance cost and wind cost.  Simulation experiments were carried out in the fixed wind field and the variable wind field respectively to obtain the track length and flight time of path planning.  The simulation results showed that compared with the classic A* algorithm, the improved A* algorithm could save 9.76%, 7.22% and 11.4% in trajectory, energy and time at the maximum under the condition of fixed wind.  Under the condition of variable wind field, the maximum trajectory, energy and time saving percentage was 27.6%, 33.1% and 26.2% respectively compared with the classic A* algorithm.  The proposed algorithm could effectively plan a safe and reliable three-dimensional flight path, which provided an effective method for better application of plant protection UAVs in complex hilly terrain.

Keywords: A* algorithm; Path-planning; wind vector

DOI: 10.33440/j.ijpaa.20200304.125

 

Citation:Sun G H, Fang X F, Zhu L C, Yuan Y W, Zhao B, Han Z H.  Path planning of plant protection UAV based on improved A* algorithm under wind conditions.  Int J Precis Agric Aviat, 2020; 3(4): 31–38.

 


Full Text:

PDF

References


Lan Y B, Chen S D. Current status and trends of plant protection UAV and its spraying technology in China. Int J Precis Agric Aviat, 2018; 1(1): 1–9. doi: 10.33440/j.ijpaa.20180101.0002

Hu J, Yang J C. Application of distributed auction to multi-UAV task assignment in agriculture. Int J Precis Agric Aviat, 2018; 1(1): 44–50. doi: 10.33440/j.ijpaa.20180101.0008

Gu Q, Dou F Q, Ma F. Energy Optimal Path Planning of Electric Vehicle Based on Improved A* Algorithm. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(12): 316–322. doi: 10.6041/j.issn.1000-1298.2015.12.043 (in Chinese)

Li Q, Xie S J, Tong X H, et al. A self-adaptive genetic algorithm for the shortest path planning of vehicles and its comparison with Dijkstra and A*algorithms. Journal of University of Science and Technology Beijing, 2006, 28(11): 1082–1086. doi: 10.3321/j.issn:1001-053X.2006.11.017 (in Chinese)

Zhang J Y, Zhao Z P, Liu D.A path planning method for mobile robot based on artificial potential field. Journal of Harbin Institute of Technology, 2006, 38(8): 1306–1309. doi: 10.3321/j.issn:0367-6234. 2006.08.026 (in Chinese)

Shi E X, Chen M M, Li J, et al. Research on method of global path-planning for mobile robot based on ant-colony algorithm. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(6): 53–57. doi: 10.6041/j.issn.1000-1298.2014.06.009 (in Chinese)

He L Y, Yang T W, Wu C Y, et al. Optimization of Replugging Tour Planning Based on Greedy Genetic Algorithm. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(05): 36–43. doi: 10.6041/j.issn.1000-1298.2017.05.004 (in Chinese)

Yuan J Q, Li S, Wu Y F, et al. Robot Path Planning Method Based on Simulated Annealing Ant Colony Algorithm. Computer Integrated Manufacturing Systems, 2019, 36(10): 329–333. doi: 10.3969/ j.issn.1006-9348.2019.10.068 (in Chinese)

Yuan Y W, Zhang X C, Hu X A. Algorithm for optimization of apple harvesting path and simulation[J]. Transactions of the SAE, 2009, 25(04): 141–144. (in Chinese)

Zhang S, Li X R, Zhang P, et al. UAV path planning based on improved A* algorithm. Flight Dynamics, 2016, 34(03): 39–43. doi: 10.13645/ j.cnki.f.d.20160110.007 (in Chinese)

Ma Y H, Zhang H, Qi L R, et al. A 3D UAV Path Planning Method Based on Improved A* Algorithm. Electronics Optics & Control, 2019, 26(10): 22–25. doi: 10.3969/j.issn.1671-637X.2019.10.005 (in Chinese)

Zhao D Q, Duan J Y, Chen P Y, et al. Optimal Path Planning for 3D Map Based on A? Algorithm. Computer Systems & Applications, 2017, 26(07): 146–152. doi: 10.15888/j.cnki.csa.005859 (in Chinese)

Li J Y, Wu H, Hu X D, Fan G A, Li Y F, Long B, Wei X, Lan Y B. Method for establishing the UAV-rice vortex 3D model and extracting spatial parameters. Int J Precis Agric Aviat, 2020; 3(2): 56–64. doi: 10.33440/j.ijpaa.20200302.84

Shiga K, Kumon M. Online path optimization for unmanned aerial vehicles under steady wind. IEEE/SICE International Symposium on System Integration. IEEE, 2012. doi: 10.1109/sii.2012.6427369

Rucco A, Aguiar A P, Pereira F L, et al. 2015. A Predictive Path-Following Approach for Fixed-Wing Unmanned Aerial Vehicles in Presence of Wind Disturbances. Robot 2015: Second Iberian Robotics Conference, 623–634. doi: 10.1007/978-3-319-27146-0_48

Ailliot P, Monbet V, Prevosto M. An autoregressive model with time-varying coefficients for wind fields. Environmetrics, 2006, 17(2): p.107–117. doi: 10.1002/env.753

Ceccarelli N, Enright J J, Frazzoli E, et al. Micro UAV Path Planning for Reconnaissance in Wind. 2007 American Control Conference. doi: 10.1109/acc.2007.4282479

Qu Y H, Xiao Z B, Yuan D L. An Effective Method of UAV Flight Path Planning On-Line in Wind Field Using Improved A* Searching Algorithm. Journal of Northwestern Polytechnical University, 2012, 30(04): 576–581. doi: 10.3969/j.issn.1000-2758.2012.04.018 (in Chinese)

Wu J W, Xue X Y, Zhang S C, Qin W C, Chen C, Sun T. Plant 3D reconstruction based on LiDAR and multi-view sequence images. Int J Precis Agric Aviat, 2018; 1(1): 37–43. doi: 10.33440/j.ijpaa.20180101. 0007

Pan S Y, Xu X R. 2D and 3D Robot Path Planning Based on the A* Algorithm. Journal of Jing gangshan University (Natural Sciences Edition), 2015, 36(05): 84–88. doi: 10.3969/j.issn.1674-8085.2015.05. 016 (in Chinese)

Lu L, Wang J Q, Zong C X, et al. Simulation of 3D path planning approach for quad-rotor helicopter based on A* algorithm. Journal of Hefei University of Technology(Natural Science), 2017, 40(03): 304–309.

doi: 10.3969/j.issn.1003-5060.2017.03.004 (in Chinese)

Zhao F, Yang W, Yang C X, et al. UAV three-dimensional dynamic route planning and guidance control research. Computer Engineering and Applications, 2014, 50(2): 58–64. doi: 10.3778/j.issn.1002-8331.1308- 0305 (in Chinese)

Zhang D S, Huang C Q, Ding D L, et al. Attacking Track Calculation of UAVs Based on A* Algorithm[J]. Electronics Optics & Control, 2011, 18(03): 18–20+65. doi: 10.3969/j.issn.1671-637X.2011.03.005 (in Chinese)

Wang LL, Xiao WW, Qi Y, Gao Q C,Li L, YanKT, Zhang Y L, Lan Y B. Farmland human-shape obstacles identification based on Viola-Jones Algorithm. Int J Precis Agric Aviat, 2020; 3(3): 35–40. doi: 10.33440/ j.ijpaa.20200303.99

Pei S Y, Wang S Z, Zhang H H, Zhu H. Methods for monitoring and controlling multi-rotor micro-UAVs position and orientation based on LabVIEW. Int J Precis Agric Aviat, 2018; 1(1): 51–58. doi: 10.33440/ j.ijpaa.20180101.0009

Goss K, Musmeci R, Silvestri S. Realistic Models for Characterizing the Performance of Unmanned Aerial Vehicles. 2017 26th International Conference on Computer Communication and Networks (ICCCN). doi: 10.1109/icccn.2017.8038444

Zhang Q Q, Xu W W, Zhang H H, et al. Path planning for logistics UAV in complex low-altitude airspace. Journal of Beijing University of Aeronautics and Astronautics: 1-15[2020-06-09]. doi: 10.13700/ j.bh.1001-5965.2019.0455 (in Chinese)

Christian B. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2005, 2(4): 353–373. doi: 10.1016/j.plrev.2005. 10.001


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.