Electrical Engineering

Rai Bijay 1, Matsa Amarendra2, and Datta Asim3  

1Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Rangpo, Sikkim- 737136, India
2Department of Electrical Engineering, Mizoram University, Mizoram, Aizawl- 796004, India
3Department of Electrical Engineering, India, Tezpur University, Tezpur, Assam-784028, India

 

Received: March 24, 2022
Accepted: November 5, 2022
Publication Date: January 4, 2023

 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: ||https://doi.org/10.6180/jase.202310_26(10).0002  


The application of autonomous robots has been increasing in agriculture sector to substitute human labor and to improve the production yields. A self-sufficient robot is intended to accomplish specific jobs in different locations of the working field area, thereby an economical and effective navigation system for differential wheeled mobile robots is a paramount importance. In this paper, an autonomous navigation system of an agricultural mobile robot is proposed using pure pursuit algorithm (PPA) which is also assisted by vector field histogram (VFH). PPA autonomously guides towards waypoints, whereas the VFH algorithm helps the vehicle steer away for obstacles. The 2-dimensional light detection and ranging (LiDAR) sensors are used to monitor through the VFH algorithm. Minimum number of waypoints are set in PPA for convenience on map setup. Several indicators such as distance covered by robot, number of iterations required for completion of travel, etc., are investigated with the variable settings in PPA algorithm. Result analysis shows that mobile robot can travel at speed range of 2.5-25 km/hr with obstacle evasion which is adequate for agricultural mobile robots.


Keywords: agricultural autonomous robot, vector field histogram (VFH), pure pursuit 33 algorithm, light detection and ranging (LiDAR), obstacle avoidance


  1. [1] V. B. Hans, (2016) “Water management in agriculture: Issues and strategies in India" SSRN Electron:
  2. [2] X. Kan, T. C. Thayer, S. Carpin, and K. Karydis, (2021) “Task Planning on Stochastic Aisle Graphs for Precision Agriculture" IEEE Robotics and Automation Letters 6(2): 3287–3294. DOI: 10.1109/LRA.2021.3062337.
  3. [3] India’s Rural Farmers Struggle to Read and Write. Here’s How ‘AgriApps’ Might Change That. - GOOD. https://www.good.is/articles/agricultural-apps-bridgeliteracy-gaps-in-india. accessed: Jul. 17, 2022.
  4. [4] We still can’t agree how to regulate self-driving cars - The Verge. https://www.theverge.com/2020/2/11/21133389/house- energy-commerce- self- drivingcar-hearing-bill-2020. accessed: Jul. 23, 2022.
  5. [5] Autonomous vehicles: How 7 countries are handling the regulatory landscape | TechRepublic. https://www.techrepublic .com/article/autonomous- vehicles -how- 7 - countries - are - handling - the - regulatory -landscape/.accessed: Jul. 23, 2022.
  6. [6] F. Zhang, W. Zhang, X. Luo, Z. Zhang, Y. Lu, and B. Wang, (2022) “Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems" Agriculture (Switzerland) 12(2): DOI: 10.3390/agriculture12020310.
  7. [7] T. S. Hong, D. Nakhaeinia, and B. Karasfi, (2012) “Application of fuzzy logic in mobile robot navigation" Fuzzy Logic-Controls, Concepts, Theories and Applications: 21–36.
  8. [8] I. Susnea, A. Filipescu, G. Vasiliu, G. Coman, and A. Radaschin. “The bubble rebound obstacle avoidance algorithm for mobile robots”. In: Cited by: 12. 2010, 540–545. DOI: 10.1109/ICCA.2010.5524302.
  9. [9] T. S. Abhishek, D. Schilberg, and A. S. A. Doss. “Obstacle avoidance algorithms: A review”. In: IOP Conference Series: Materials Science and Engineering. 1012.1. IOP Publishing. 2021, 012052.
  10. [10] A. Ohya, A. Kosaka, and A. Kak, (1998) “Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing" IEEE Transactions on Robotics and Automation 14(6): 969–978. DOI: 10.1109/70.736780.
  11. [11] S. A. Hiremath, G. W. van der Heijden, F. K. van Evert, A. Stein, and C. J. Ter Braak, (2014) “Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter" Computers and Electronics in Agriculture 100: 41–50. DOI: 10.1016/j.compag.2013.10.005.
  12. [12] J.-K. Yoo and J.-H. Kim, (2015) “Gaze Control-Based Navigation Architecture with a Situation-Specific Preference Approach for Humanoid Robots" IEEE/ASME Transactions on Mechatronics 20(5): 2425–2436. DOI: 10.1109/TMECH.2014.2382633.
  13. [13] S. Sukkarieh, E. M. Nebot, and H. F. Durrant-Whyte, (1999) “A high integrity IMU/GPS navigation loop for autonomous land vehicle applications" IEEE Transactions on Robotics and Automation 15(3): 572–578. DOI: 10.1109/70.768189.
  14. [14] M. Gilmartin, (2005) “INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS, by Roland Siegwart and Illah R. Nourbakhsh, MIT Press, 2004, xiii+ 321pp., ISBN 0-262-19502-X.(Hardback,£ 27.95)" Robotica 23(2): 271–272.
  15. [15] X. Gao, J. Li, L. Fan, Q. Zhou, K. Yin, J.Wang, C. Song, L. Huang, and Z. Wang, (2018) “Review of wheeled mobile robots’ navigation problems and application prospects in agriculture" IEEE Access 6: 49248–49268. DOI: 10.1109/ACCESS.2018.2868848.
  16. [16] K. Zhu and T. Zhang, (2021) “Deep reinforcement learning based mobile robot navigation: A review" Tsinghua Science and Technology 26(5): 674–691. DOI: 10.26599/TST.2021.9010012.
  17. [17] “Bosch’s Giant Robot Can Punch Weeds to Death - IEEE Spectrum. https://spectrum.ieee.org/bosch-deepfield-robotics-weed-control. accessed: Jan. 26, 2022.
  18. [18] A. Roshanianfard, N. Noguchi, H. Okamoto, and K. Ishii, (2020) “A review of autonomous agricultural vehicles (The experience of Hokkaido University)" Journal of Terramechanics 91: 155–183. DOI: 10.1016/j.jterra.2020.06.006.
  19. [19] W. Zhao, X. Wang, B. Qi, and T. Runge, (2020) “Ground-level Mapping and Navigating for Agriculture based on IoT and Computer Vision" IEEE Access: DOI: 10.1109/ACCESS.2020.3043662.
  20. [20] W. Yuan, Z. Li, and C.-Y. Su, (2021) “Multisensor-Based Navigation and Control of a Mobile Service Robot" IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(4): 2624–2634. DOI: 10.1109/TSMC.2019.2916932.
  21. [21] F. Rovira-Mas, V. Saiz-Rubio, and A. Cuenca-Cuenca, (2021) “Augmented Perception for Agricultural Robots Navigation" IEEE Sensors Journal 21(10): 11712–11727. DOI: 10.1109/JSEN.2020.3016081.
  22. [22] N. Gupta, M. Khosravy, S. Gupta, N. Dey, and R. G. Crespo, (2022) “Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm" International Journal of Parallel Programming 50(1): DOI: 10.1007/s10766-020-00671-1.
  23. [23] E. Horvath, C. Hajdu, and P. Koros. “Novel Pure- Pursuit Trajectory Following Approaches and their Practical Applications”. In: Cited by: 4. 2019, 597–602. DOI: 10.1109/CogInfoCom47531.2019.9089927.
  24. [24] X. Wang, G. Tan, Y. Dai, F. Lu, and J. Zhao, (2020) “An Optimal Guidance Strategy for Moving-Target Interception by a Multirotor Unmanned Aerial Vehicle Swarm" IEEE Access 8: 121650–121664. DOI: 10.1109/ACCESS.2020.3006479.
  25. [25] J. Lopez, P. Sanchez-Vilarino, R. Sanz, and E. Paz, (2021) “Efficient Local Navigation Approach for Autonomous Driving Vehicles" IEEE Access 9: 79776–79792. DOI: 10.1109/ACCESS.2021.3084807.
  26. [26] T. Bouwmans, (2014) “Traditional and recent approaches in background modeling for foreground detection: An overview" Computer Science Review 11-12: 31–66. DOI: 10.1016/j.cosrev.2014.04.001.
  27. [27] J. S. Kulchandani and K. J. Dangarwala. “Moving object detection: Review of recent research trends”. In: Cited by: 87. 2015. DOI: 10.1109/PERVASIVE.2015.7087138.
  28. [28] G. Qi, H.Wang, M. Haner, C.Weng, S. Chen, and Z. Zhu, (2019) “Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation" CAAI Transactions on Intelligence Technology 4(2): 80–91. DOI: 10.1049/trit.2018.1045.
  29. [29] J. Borenstein and Y. Koren, (1989) “Real-Time Obstacle Avoidance for Fast Mobile Robots" IEEE Transactions on Systems, Man and Cybernetics 19(5): 1179–1187. DOI: 10.1109/21.44033.
  30. [30] J. Borenstein, Y. Koren, et al., (1991) “The vector field histogram-fast obstacle avoidance for mobile robots" IEEE transactions on robotics and automation 7(3): 278–288.
  31. [31] W. Zhang, H. Cheng, L. Hao, X. Li, M. Liu, and X. Gao, (2021) “An obstacle avoidance algorithm for robot manipulators based on decision-making force" Robotics and Computer-Integrated Manufacturing 71: DOI: 10.1016/j.rcim.2020.102114.
  32. [32] Z. Chong, B. Qin, T. Bandyopadhyay, M. Ang, E. Frazzoli, and D. Rus. “Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment”. In: Cited by: 90. 2013, 1554–1559. DOI: 10.1109/ICRA.2013.6630777.
  33. [33] Y. Li and J. Ibanez-Guzman, (2020) “Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems" IEEE Signal Processing Magazine 37(4): 50–61. DOI: 10.1109/MSP.2020.2973615.
  34. [34] U. Weiss and P. Biber, (2011) “Plant detection and mapping for agricultural robots using a 3D LIDAR sensor" Robotics and Autonomous Systems 59(5): 265–273. DOI: 10.1016/j.robot.2011.02.011.
  35. [35] O. C. Barawid Jr., A. Mizushima, K. Ishii, and N.Noguchi, (2007) “Development of an Autonomous Navigation System using a Two-dimensional Laser Scanner in an Orchard Application" Biosystems Engineering 96(2): 139–149. DOI: 10.1016/j.biosystemseng.2006.10.012.
  36. [36] V. Subramanian, T. F. Burks, and A. Arroyo, (2006) “Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation" Computers and Electronics in Agriculture 53(2): 130-143. DOI: 10.1016/j.compag.2006.06.001.
  37. [37] Y. Ji, S. Li, C. Peng, H. Xu, R. Cao, and M. Zhang, (2021) “Obstacle detection and recognition in farmland based on fusion point cloud data" Computers and Electronics in Agriculture 189: DOI: 10.1016/j.compag.2021.106409.
  38. [38] N. Li, L. Guan, Y. Gao, S. Du, M. Wu, X. Guang, and X. Cong, (2020) “Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system" Remote Sensing 12(19): 1–21. DOI: 10.3390/rs12193271.
  39. [39] M. Samuel, M. Hussein, and M. B. Mohamad, (2016) “A review of some pure-pursuit based path tracking techniques for control of autonomous vehicle" International Journal of Computer Applications 135(1): 35–38.
  40. [40] J. Lowenberg-DeBoer, K. Behrendt, M.-H. Ehlers, C. Dillon, A. Gabriel, I. Y. Huang, I. Kumwenda, T. Mark, A. Meyer-Aurich, G. Milics, K. O. Olagunju, S. M. Pedersen, J. Shockley, and D. Rose, (2022) “Lessons to be learned in adoption of autonomous equipment for field crops" Applied Economic Perspectives and Policy 44(2): 848–864. DOI: 10.1002/aepp.13177.
  41. [41] O. Bawden, D. Ball, J. Kulk, T. Perez, and R. Russell. “A lightweight, modular robotic vehicle for the sustainable intensification of agriculture”. In: 02-04-December-2014. Cited by: 24. 2014.

Latest Articles

Dynamic Analysis of the UHPFRC High-rise Building under High Explosion using Coupled Eulerian-Lagrangian Approach
Dynamic Analysis of the UHPFRC High-rise Building under High Explosion using Coupled Eulerian-Lagrangian ApproachVolume 28, Issue 2 (In Press)

Read more: ...

NSGA-II Algorithm-Based Wide Area Measurement (WAMS) Allocation with Considering Reliability
NSGA-II Algorithm-Based Wide Area Measurement (WAMS) Allocation with Considering ReliabilityVolume 28, Issue 2 (In Press)

Read more: ...

Predicting Demand for Emergency Ambulance Services: A Comparative Approach
Predicting Demand for Emergency Ambulance Services: A Comparative ApproachVolume 27, Issue 10

Read more: ...

Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education Management
Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education ManagementVolume 28, Issue 2 (In Press)

Read more: ...

River bank erosion prediction using multivariable linear regression
River bank erosion prediction using multivariable linear regressionVolume 27, Issue 12 (In Press)

Read more: ...

Optimization of Ultrasound-Assisted Pectin Extraction from Durian Rind
Optimization of Ultrasound-Assisted Pectin Extraction from Durian RindVolume 28, Issue 2 (In Press)

Read more: ...

Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTM
Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTMVolume 28, Issue 2 (In Press)

Read more: ...

Response Prediction Analysis of RC Frame Structures Using Support Vector Machine Algorithm
Response Prediction Analysis of RC Frame Structures Using Support Vector Machine AlgorithmVolume 28, Issue 2 (In Press)

Read more: ...

Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb core
Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb coreVolume 28, Issue 2 (In Press)

Read more: ...