Advertisement

Unmanned Ground Vehicles in Precision Farming Services: An Integrated Emulation Modelling Approach

  • Dimitrios BechtsisEmail author
  • Vasileios Moisiadis
  • Naoum Tsolakis
  • Dimitrios Vlachos
  • Dionysis Bochtis
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 953)

Abstract

Autonomous systems are a promising alternative for safely executing precision farming activities in a 24/7 perspective. In this context Unmanned Ground Vehicles (UGVs) are used in custom agricultural fields, with sophisticated sensors and data fusion techniques for real-time mapping and navigation. The aim of this study is to present a simulation software tool for providing effective and efficient farming activities in orchard fields and demonstrating the applicability of simulation in routing algorithms, hence increasing productivity, while dynamically addressing operational and tactical level uncertainties. The three dimensional virtual world includes the field layout and the static objects (orchard trees, obstacles, physical boundaries) and is constructed in the open source Gazebo simulation software while the Robot Operating System (ROS) and the implemented algorithms are tested using a custom vehicle. As a result a routing algorithm is executed and enables the UGV to pass through all the orchard trees while dynamically avoiding static and dynamic obstacles. Unlike existing sophisticated tools, the developed mechanism could accommodate an extensive variety of agricultural activities and could be transparently transferred from the simulation environment to real world ROS compatible UGVs providing user-friendly and highly customizable navigation.

Keywords

Precision farming Robot Operating System UGV simulation Real-time navigation Orchard field 

Notes

Acknowledgements

The work was supported by the project “Research Synergy to address major challenges in the nexus: energy-environment-agricultural production (Food, Water, Materials)” - NEXUS, funded by the Greek Secretariat for Research and Technology (GSRT) – Pr. No. MIS 5002496.

References

  1. 1.
    Bechtsis, D., Tsolakis, N., Vlachos, D., Iakovou, E.: Sustainable supply chain management in the digitalisation era: the impact of automated guided vehicles. J. Cleaner Prod. 142(4), 3970–3984 (2017)CrossRefGoogle Scholar
  2. 2.
    Srai, J.S., Gregory, M.J.: A supply network configuration perspective on international supply chain development. Int. J. Oper. Prod. Manage. 28(5), 386–411 (2008)CrossRefGoogle Scholar
  3. 3.
    Bechar, A., Vigneault, C.: Agricultural robots for field operations: concepts and components. Biosyst. Eng. 149, 94–111 (2016)CrossRefGoogle Scholar
  4. 4.
    Tsolakis, N., Bechtsis, D., Srai, J.S.: Intelligent autonomous vehicles in digital supply chains: from conceptualisation, to simulation modelling, to real-world operations. Bus. Process Manage. J. (2018, In Press)Google Scholar
  5. 5.
    Walker, G.H., Stanton, N.A., Young, M.S.: Feedback and driver situation awareness (SA): a comparison of SA measures and contexts. Transp. Res. Part F: Traffic Psychol. Behav. 11, 282–299 (2008)CrossRefGoogle Scholar
  6. 6.
    Finomore, V., Matthews, G., Shaw, T., Warm, J.: Predicting vigilance: a fresh look at an old problem. Ergonomics 52, 791–808 (2009)CrossRefGoogle Scholar
  7. 7.
    Kaber, D.B., Endsley, M.R.: The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theor. Issues Ergon. Sci. 5, 113–153 (2004)CrossRefGoogle Scholar
  8. 8.
    Billings, C.E.: Aviation Automation: The Search for a Human-Centered Approach. Lawrence Erlbaum Associates, Mahwah (1996)Google Scholar
  9. 9.
    Ho, Y.-C., Liu, H.-C., Yih, Y.: A multiple-attribute method for concurrently solving the pickup-dispatching problem and the load-selection problem of multiple-load AGVs. J. Manufact. Syst. 31(3), 288–300 (2012)CrossRefGoogle Scholar
  10. 10.
    Zheng, H., Negenborn, R.R., Lodewijks, G.: Closed-loop scheduling and control of waterborne AGVs for energy-efficient inter terminal transport. Transp. Res. Part E: Logistics Transp. Rev. 105, 261–278 (2017)CrossRefGoogle Scholar
  11. 11.
    Tremblay, N., Fallon, E., Ziadi, N.: Sensing of crop nitrogen status: opportunities, tools, limitations, and supporting information requirements. Horttechnology 21(3), 274–281 (2011)CrossRefGoogle Scholar
  12. 12.
    Bochtis, D.D., Sørensen, C.G.: The vehicle routing problem in field logistics. Biosyst. Eng. 104(4), 447–457 (2009)CrossRefGoogle Scholar
  13. 13.
    Bochtis, D.D., Sørensen, C.G., Busato, P.: Advances in agricultural machinery management: a review. Biosyst. Eng. 126, 69–81 (2014)CrossRefGoogle Scholar
  14. 14.
    Wulfsohn, D., Aravena Zamora, F., Potin Téllez, C., Zamora Lagos, I., García-Fiñana, M.: Multilevel systematic sampling to estimate total fruit number for yield forecasts. Precis. Agric. 13(2), 256–275 (2012)CrossRefGoogle Scholar
  15. 15.
    Prieto-Araujo, E., Olivella-Rosell, P., Cheah-Mañe, M., Villafafila-Robles, R., Gomis-Bellmunt, O.: Renewable energy emulation concepts for microgrids. Renew. Sustain. Energy Rev. 50, 325–345 (2015)CrossRefGoogle Scholar
  16. 16.
    Auat Cheein, F.A., Carelli, R.: Agricultural robotics: unmanned robotic service units in agricultural tasks. IEEE Ind. Electron. Mag. 7(3), 48–58 (2013)CrossRefGoogle Scholar
  17. 17.
    Cariou, C., Lenain, R., Thuilot, B., Berducat, M.: Automatic guidance of a four-wheel-steering mobile robot for accurate field operations. J. Field Robot. 26(6–7), 504–518 (2009)CrossRefGoogle Scholar
  18. 18.
    Christiansen, P., Nielsen, L.N., Steen, K.A., Jørgensen, R.N., Karstoft, H.: DeepAnomaly: combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16(11), 1904 (2016)CrossRefGoogle Scholar
  19. 19.
    Eaton, R., Katupitiya, J., Siew, K.W., Howarth, B.: Autonomous farming: modeling and control of agricultural machinery in a unified framework. In: Proceedings of 15th International Conference on Mechatronics and Machine Vision in Practice, pp. 499–504 (2008)Google Scholar
  20. 20.
    García-Pérez, L., García-Alegre, M.C., Ribeiro, A., Guinea, D.: An agent of behaviour architecture for unmanned control of a farming vehicle. Comput. Electron. Agric. 60(1), 39–48 (2008)CrossRefGoogle Scholar
  21. 21.
    Bengochea-Guevara, J.M., Conesa-Muñoz, J., Andújar, D., Ribeiro, A.: Merge fuzzy visual servoing and GPS-based planning to obtain a proper navigation behavior for a small crop-inspection robot. Sensors 16(3), 276 (2016)CrossRefGoogle Scholar
  22. 22.
    Duggal, V., Sukhwani, M., Bipin, K., Reddy, G.S., Krishna, K.M.: Plantation monitoring and yield estimation using autonomous quadcopter for precision agriculture. In: 2016 IEEE International Conference on Robotics and Automation (ICRA2016), pp. 5121–5127 (2016)Google Scholar
  23. 23.
    Bechtsis, D., Tsolakis, N., Vlachos, D., Srai, J.S.: Intelligent autonomous vehicles in digital supply chains: a framework for integrating innovations towards sustainable value networks. J. Cleaner Prod. 181, 60–71 (2018)CrossRefGoogle Scholar
  24. 24.
    Farinelli, A., Boscolo, N., Zanotto, E., Pagello, E.: Advanced approaches for multi-robot coordination in logistic scenarios. Robot. Auton. Syst. 90, 34–44 (2017)CrossRefGoogle Scholar
  25. 25.
    Clearpath Robotics. https://www.clearpathrobotics.com. Accessed 29 Mar 2018
  26. 26.
    SAGA Robotics. https://sagarobotics.com/. Accessed 29 Mar 2018
  27. 27.
    Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2149–2154 (2004)Google Scholar
  28. 28.
    Bochtis, D.D., Sørensen, C.G., Green, O.: A DSS for planning of soil-sensitive field operations. Decis. Support Syst. 53(1), 66–75 (2012)CrossRefGoogle Scholar
  29. 29.
    Moisiadis, V., Bechtsis, D., Menexes, G., Vlachos, D., Iakovou, E., Bochtis, D.: Intelligent autonomous vehicles in industrial environments. In: 6th ICMEN International Conferences, Thessaloniki, Greece, pp. 207–2012 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Automation EngineeringAlexander Technological Educational Institute of ThessalonikiThessalonikiGreece
  2. 2.Institute for Bio-Economy and Agri-Technology (IBO)Centre for Research and Technology Hellas (CERTH)ThessalonikiGreece
  3. 3.Centre for International Manufacturing, Institute for Manufacturing, Department of Engineering, School of TechnologyUniversity of CambridgeCambridgeUK
  4. 4.Department of Mechanical EngineeringAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations