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Forward Navigation for Autonomous Unmanned Vehicle in Inter-Row Planted Agriculture Field

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Control Engineering in Robotics and Industrial Automation

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 371))

Abstract

In a well-structured plantation field throughout the world, the crops are usually planted in such long rows, which indirectly has created small pathways in between two consecutive rows. This pathway has been traveled by the farmers to move around in the field either by foot or machine and so that they can monitor, harvest as well as provide a maximum plant scouting through the field. This activity is tiring and stressful as the size of the field is getting bigger. To facilitate this issue, normally tractor and machine have been used. Unfortunately, this machine still requires a worker to operate it, and therefore the workforce problem is still unsolved as more workers and working shifts are needed for this process. Furthermore, the use of tractor is still limited in the plantation field that has pathways that are small in size, which is less than the size of the tractor itself. Therefore, in this work, a small-scaled unmanned vehicle is developed by structuring its forward navigation operation in a small-sized pathway which is approximately 1 m. Since the size of the unmanned vehicle is very small, it hinders the usage of bulky equipment such as laser scanner and laptop to be carried away during the operation. To overcome this issue, with the usage of small discrete sensor such as the infrared sensor, an embedded and automated navigation of the unmanned vehicle is developed to manipulate the minimal space in between the rows in order to navigate in the entire farm. A Bezier curve is applied to draft out the desired path and series of navigation schemes are deployed in order to maneuver the unmanned vehicle on the planned path as well as maintain its position to be in the middle of the path to avoid collision with the available trees or landmarks. Test results indicate that this work has demonstrated a simple and robust algorithm as well as low-level requirement for a small-scaled unmanned vehicle to travel between the crops in a forward navigation.

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References

  1. Xue Jinlin, X.L.: Autonomous agricultural robot and its row guidance. In: International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2010. IEEE (2010)

    Google Scholar 

  2. Vijay Subramaniam, T.F.B., Arroyyo, A.A.: Development of machine vision and laser radar based autonomous vehicle guidance system for citrus grove navigation. J. Comput. Electron. Agricul. 53(2006/9), 130–143 (2006)

    Google Scholar 

  3. Deyle, T.: SICK LMS 291 Laser RangeFinder (LIDAR) (2009). Available from http://www.hizook.com/projects/sick-lms-291-laser-rangefinder-lidar

  4. Fengyu Zhou, B.S., Guohui, T.: Bezier curve based smooth path planning for mobile robot. J. Inf. Comput. Sci. 8(12), 2441–2450 (2011)

    Google Scholar 

  5. Jaakko Jutila, K.K., Visala, A.: Tree measurement in forest by 2D laser scanning. In: Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation. IEEE, Jacksonville, FL, USA (2007)

    Google Scholar 

  6. Benson, E.R., Zhang, J.F.R.Q.: Machine vision-based guidance system for an agricultural small-grain harvester. Am. Soc. Agricul. Eng. 46(4), 1255–1264 (2003)

    Google Scholar 

  7. Sebastian Thrun, J.J.L.: Simultaneous Localization and Mapping. Springer Handbook of Robotics, pp. 871–889 (2008)

    Google Scholar 

  8. Guo-Quan Jiang, C.-J.Z., Yong-Shieng, S.: A Machine based crop rows detection for agricultural robots. In: Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition. Qingdao, China (2010)

    Google Scholar 

  9. Stefan Ericson, B.Å.: Row-detection on an agricultural field using omnidirectional camera. In: The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, Taiwan (2010)

    Google Scholar 

  10. Xue Jinlin, J.W.: Vision-based guidance line detection in row crop fields. In: 2010 International Conference on Intelligent Computation Technology and Automation (2010)

    Google Scholar 

  11. SØren Hansen, E.B., Andersen, J.C., Ravn, O., Andersen, N., Poulsen, N.: Orchard navigation using derivative free Kalman filtering. In: 2011 American Control Conference. San Francisco, California, USA (2011)

    Google Scholar 

  12. Kim Son Dang, J.K., Eaton, R., Kwok, N.M.: Modelling and control of the GreenWeeder for crop row tracking. In: International Conference on Advance Robotics, 2009, ICAR 2009. Sydney, Australia (2009)

    Google Scholar 

  13. Chen, C. et al.: Trajectory planning for omni-directional mobile robot based on bezier curve, trigonometric function and polynomial. In: 5th International Conference on Intelligent Robotics and Application, ICIRA ‘12. Springer, Montreal, Canada (2012)

    Google Scholar 

  14. Tijmen Bakker, H.W., van Asselt, K., Bontsema, J., Tang, L., Muller, J., van Straten, G.: A vision based row detection system for sugar beet. Comput. Electron. Agricul. 60, 87–95 (2008)

    Google Scholar 

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Thamrin, N.M., Arshad, N.H.M., Adnan, R., Sam, R. (2022). Forward Navigation for Autonomous Unmanned Vehicle in Inter-Row Planted Agriculture Field. In: Mariappan, M., Arshad, M.R., Akmeliawati, R., Chong, C.S. (eds) Control Engineering in Robotics and Industrial Automation. Studies in Systems, Decision and Control, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-74540-0_7

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