Gaogao Shang1 , Gang Liu1 , Peng Zhu1 , and Jiangyi Han This email address is being protected from spambots. You need JavaScript enabled to view it.1

1College of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, P.R. China


Received: November 14, 2020
Accepted: January 14, 2021
Publication Date: June 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||  


Complete coverage path planning is one of the key technologies to realize the autonomous action of intelligent agricultural machinery in complex environments. In this paper, a spatial model of a 2.5D working environment is established by a grid method, and an algorithm of complete coverage path planning for electric tractors is also studied based on an improved genetic algorithm. To reduce the driving energy consumption of tractors, a multi-objective fitness function of the flat driving path length, the number of turns and the total elevation difference of the genetic algorithm is established by analyzing the influencing factors of energy consumption. Crossover and mutation operators are also improved to enhance the execution efficiency of the algorithm and reduce the driving repetition rate. Simulation and comparison experiments are carried out in parallel as well. The test results demonstrate that, in contrast with the traditional genetic algorithm and other intelligent optimization methods, the complete coverage path planning based upon the improved genetic algorithm proposed in this paper can prominently reduce the energy consumption and driving repetition rate of the electric tractor and improve the convergence speed and optimization ability.

Keywords: Grid Method; Optimation; Multi-objective; Genetic Algorithm


  1. [1] Xiaohua Wang, Tengteng Nie, and Daixian Zhu. Indoor robot path planning assisted by wireless network. Eurasip Journal on Wireless Communications and Networking, 2019(1), dec 2019.
  2. [2] Seung Yoon Choi, Seung Gwan Lee, Hoang Huu Viet, and Tae Choong Chung. B-Theta*: an Efficient Online Coverage Algorithm for Autonomous Cleaning Robots. Journal of Intelligent and Robotic Systems: Theory and Applications, 87(2):265–290, aug 2017.
  3. [3] Y.T. Ren. The research and design of obstacle avoidance complete coverage path planning algorithm for robot vacuums. Huazhong University of Science & Technology, 2019.
  4. [4] Dario Calogero Guastella, Luciano Cantelli, Giuseppe Giammello, Carmelo Donato Melita, Gianluca Spatino, and Giovanni Muscato. Complete coverage path planning for aerial vehicle flocks deployed in outdoor environments. Computers and Electrical Engineering, 75:189– 201, 2019.
  5. [5] Qing Gu, Fengqian Dou, and Fei Ma. Energy optimal path planning of electric vehicle based on improved A* algorithm. Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 46(12):316–322, 2015.
  6. [6] Rongchuan Sun, Chunhua Tang, Jianying Zheng, Yongzheng Zhou, and Shumei Yu. Multi-robot Path Planning for Complete Coverage with Genetic Algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 11744 LNAI, pages 349–361. Springer Verlag, 2019.
  7. [7] Daqi Zhu, Tingting Zhu, and Mingzhong Yan. MultiAUV Complete Coverage Path Planning Based on Improved Neural Network. Xitong Fangzhen Xuebao / Journal of System Simulation, 32(8):1505–1514, 2020.
  8. [8] K. Li, Y.F. Chen, Z.Y. Jin, T. Liu, Z.T. Liu, and J.Z. Zheng. A full coverage path planning algorithm based on backtracking method. Computer Engineering & Science, 41(7):1227–1235, 2019.
  9. [9] M. S. Ganeshmurthy and G. R. Suresh. Path planning algorithm for autonomous mobile robot in dynamic environment. In 2015 3rd International Conference on Signal Processing, Communication and Networking, ICSCN 2015, 2015.
  10. [10] Andrew R. Leach and Keith Prout. Automated conformational analysis: Directed conformational search using the A* algorithm. Journal of Computational Chemistry, 11(10):1193–1205, 1990.
  11. [11] B. Sun, P. Jiang, G.R. Zhou, and Y.T. Lu. Application of improved genetic algorithm in path planning of mobile robots. Computer Engineering and Application, 55(17):162– 168, 2019.
  12. [12] D.Q. Zhao, J.Y. Duan, P.Y. Chen, and J.H. Su. Optimal path planning for 3D map based on A* algorithm. Computer Systems & Application, 26(7):146–152, 2017.
  13. [13] Jiajun Gu and Qixin Cao. Path planning for mobile robot in a 2.5-dimensional grid-based map. Industrial Robot, 38(3):315–321, 2011.
  14. [14] P. Chen. Research on path planing algorithm for horticultural electric tractor autonomous operation. Jiangsu: Jiangsu University, 2019.
  15. [15] Tobias Rainer Schäfle, Shuaiby Mohamed, Naoki Uchiyama, and Oliver Sawodny. Coverage path planning for mobile robots using genetic algorithm with energy optimization. In Proceedings - 2016 International Electronics Symposium, IES 2016, pages 99–104, 2017.
  16. [16] Adem Tuncer and Mehmet Yildirim. Dynamic path planning of mobile robots with improved genetic algorithm. Computers and Electrical Engineering, 38(6):1564– 1572, 2012.
  17. [17] T. Wei and C. Long. Path Planning of Mobile Robots based on Improved Genetic Algorithm. In Journal of Beijing University of Aeronautics and Astronautics, pages 703–711. Association for Computing Machinery (ACM), oct 2019.
  18. [18] H.J. Zhang, Z.B. Su, D.E. Hernandez, and B. Su. Energy Optimal Path Planning for Mobile Robots Based on Improved AD* Algorithm. Transactions of the Chinese Society for Agriculture Machinery, 49(9):19–26, 2018.
  19. [19] J.J. Yin, W.L. Dong, and L.H. Liang. Optimization Method of Agricultural Robot Path Planning in Complex Environment. Transactions of the Chinese Society for Agricultural Machinery, 50(5):17–22, 2019.
  20. [20] Z.J. Men, H. Liu, and H. Wang. Optimal Path Planning for Agricultural Machinery. Transactions of the Chinese Society for Agricultural Machinery, 43(6):147–152, 2012.
  21. [21] Kamran H. Sedighi, Kaveh Ashenayi, Theodore W. Manikas, Roger L. Wainwright, and Heng Ming Tai. Autonomous local path planning for a mobile robot using a genetic algorithm. In Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, volume 2, pages 1338–1345, 2004.
  22. [22] Muzaffer Kapanoglu, Mete Alikalfa, Metin Ozkan, Ahmet Yazıcı, and Osman Parlaktuna. A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time. Journal of Intelligent Manufacturing, 23(4):1035–1045, 2012.
  23. [23] Q.S. Song, S.B. Li, L.X. Zhe, Z.A. Li, and X.X. Zhang. Path planning for automatic guided vehicle based on improved genetic algorithm. Modular Machine Tool & Automatic Manufacturing Technique, 2020(7):88–92, 2020.
  24. [24] W.K. Xue. Application of genetic algorithm in path planning of agriculture mobile robot—based on matrix binary code. Journal of Agriculture Mechanization Research, 41(12):69–73, 2019.
  25. [25] X.W. Yang and L.J. Yang. An improved genetic algorithm based on crossover model. Control and Decision, 31(10):1837–1844, 2016.
  26. [26] Hong Qu, Ke Xing, and Takacs Alexander. An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing, 120:509–517, 2013.
  27. [27] Q. Zhang, C.R. Li, and X. Lu. Research on complete coverage path planning for unmanned surface vessel. In International Conference on Components, Packaging and Manufacturing Technology. Institute of Physics Publishing, 2019.


27th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.