3D-Simulation Data-Making Trial to present and analyze Small-sized Farmlands Fields with Car-shaped Robot, ROS2, SLAM and Foxy for Real agricultural workers

3D-Simulation Data-Making Trial to present and analyze Small-sized Farmlands Fields with Car-shaped Robot, ROS2, SLAM and Foxy for Real agricultural workers



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In this study, we create various application systems focusing on agricultural (agri-) field data digitalization issues that will benefit traditional agri-researchers, workers, and their respective managers. We obtain three-dimensional (3D) information on agri-environments (e.g., rice fields, farmlands) via roaming robots with sensors. Robot-controlled middleware, such as robot operating systems (ROS), are often used for such robots. Thus, we selected car-shaped robot (NANO-RT1), ROS2, and the SLAM-based system. The car-shaped robot-based system operates sensor units uniformly. With this technology, we can recognize our location at an unknown place, and the robot can run. There are challenges in accurately presenting quantitative accuracy data for this type of study. We address this by providing average and standard deviation (SD) data for certain situations using five algorithms: (1) Hector-SLAM, (2) G-mapping, (3) Karto-SLAM, (4) Core-SLAM, and (5) Lago-SLAM. We believe the proposed holistic system has the potential to improve not only agri-businesses, but also agri-skills and overall security levels.

Keywords: 3D-Simulation data-making, car-shaped robot, NANO-RT1 Robot, ROS2, SLAM, Foxy, agricultural site
  1. S. Zhao, Z. Zhang, D. Xiao, and K. Xiao. A Turning Model of Agricultural Robot Based on Acceleration Sensor. IFAC-Papers OnLine, vol. 49, no. 16, pp. 445–450, Jan. 2016.  |   Google Scholar
  2. Y. Funami, S. Kawakura, and K. Tadano. Development of a Robotic Arm for Automated Harvesting of Asparagus. European Journal of Agriculture and Food Sciences, vol. 2, no. 1, pp. 1–10, Jan. 2020.  |   Google Scholar
  3. S. Rivera. Securing Robots: An Integrated Approach for Security Challenges and Monitoring for the Robotic Operating System. Doctoral dissertation at University of Luxembourg, Luxembourg, May 2021.  |   Google Scholar
  4. M. Cebe, E. Erdin, K. Akkaya, H. Aksu, and S. Uluagac. Block4forensic: An integrated lightweight blockchain framework for forensics applications of connected vehicles. IEEE Communications Magazine, vol. 56, no. 10, pp. 50–57, Oct. 2018.  |   Google Scholar
  5. G. Marques, and R. Pitarma. Agricultural environment monitoring system using wireless sensor networks and IoT. Proceedings of IEEE 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6, June 2018.  |   Google Scholar
  6. M. Cebe, E. Erdin, K. Akkaya, H. Aksu, and S. Uluagac. Block4forensic: An integrated lightweight blockchain framework for forensics applications of connected vehicles. IEEE Communications Magazine, vol. 56, no. 10, pp. 50–57, Oct. 2018.  |   Google Scholar
  7. A. Zujevs, V. Osadcuks, and P. Ahrendt. Trends in robotic sensor technologies for fruit harvesting: 2010-2015. Procedia Computer Science, vol. 77, pp. 227–233, Jan. 2015.  |   Google Scholar
  8. R. Vidoni, R. Gallo, G. Ristorto, G. Carabin, F. Mazzetto, L. Scalera, and A. Gasparetto. An Agricultural Mobile Robot Prototype for Proximal Sensing and Precision Farming. Proceedings of ASME (American Society of Mechanical Engineers) 2017 International Mechanical Engineering Congress and Exhibition, no. V04AT05A057, pp. 1–7, Jan. 2017.  |   Google Scholar
  9. V. F. Tejada, M. F. Stoelen, K. Kusnierek, N. Heiberg, and A. Korsaeth. Proof-of-concept robot platform for exploring automated harvesting of sugar snap peas. Precision Agriculture, vol. 18, no. 6, pp. 952–972, April 2017.  |   Google Scholar
  10. L. N. Smith, W. Zhang, M. F.Hansen, I. J. Hales, and M. L. Smith. Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field. Computers in Industry, vol. 97, pp. 122–131, 2018.  |   Google Scholar
  11. L. Siefke, V. Sommer, B. Wudka, and C. Thomas. Robotic systems of systems based on a decentralized service-oriented architecture. Robotics, vol. 9, no. 78, Sep. 2020.  |   Google Scholar
  12. S. Dragule. Programming Robots for Activities of Everyday Life. Doctoral dissertation at Department of Computer Science and Engineering, Chalmers University of Technology, 2021.  |   Google Scholar
  13. V. Mayoral, A. Hernández, R. Kojcev, I. Muguruza, I. Zamalloa, A. Bilbao, and L. Usategi. The shift in the robotics paradigm-The Hardware Robot Operating System (H-ROS); an infrastructure to create interoperable robot components. Proceedings of IEEE of NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 229–236, July 2017.  |   Google Scholar
  14. E. A. Esquivel-Barboza, L. F. Llamas, P. Vázquez, F. Bellas, and E. Arias-Méndez. Adapting Computer Vision Algorithms to Smartphone-based Robot for Education. Proceedings of Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA) 2020, pp. 51–56, Oct. 2020.  |   Google Scholar
  15. V. Strobel, E. Castelló Ferrer, and M. Dorigo. Managing byzantine robots via blockchain technology in a swarm robotics collective decision-making scenario. Proceedings of the 17th International Conference on Autonomous Agents and Multi Agent Systems, pp. 541–549, July 2018.  |   Google Scholar
  16. R. Shamshiri, I. Hameed, et. al. Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison. Int. J. Agric. & Biol. Eng., vol. 11, no.4, pp. 15–31, April 2018.  |   Google Scholar
  17. G. Lumer-Klabbers, J. O. Hausted, J. L. Kvistgaard, H. D. Macedo, M. Frasheri, and P. G. Larsen. Towards a Digital Twin Framework for Autonomous Robots. Proceedings of 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1254–1259, July 2021.  |   Google Scholar
  18. M. Pantano, T. Kamps, S. Pizzocaro, G. Pantano, M. Corno, and S. Savaresi. Methodology for Plant Specific Cultivation through a Plant Identification pipeline. Proceedings of 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 298–302, Nov.2020.  |   Google Scholar
  19. R. Cale. Modeling and Control of a Four-wheel Autonomous Agricultural Robot. Master's thesis at NTNU, 2021.  |   Google Scholar
  20. G. Deng, Y. Zhou, Y. Xu, T. Zhang, and Y. Liu. An Investigation of Byzantine Threats in Multi-Robot Systems. Proceedings of 24th International Symposium on Research in Attacks, Intrusions and Defenses, pp. 17–32, Oct. 2021.  |   Google Scholar
  21. S. Macenski and I. Jambrecic. SLAM Toolbox: SLAM for the dynamic world. Journal of Open Source Software, vol. 6, no. 61, pp. 1-7, May 2021.  |   Google Scholar

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[1]
Kawakura, S. and Shibasaki, R. 2021. 3D-Simulation Data-Making Trial to present and analyze Small-sized Farmlands Fields with Car-shaped Robot, ROS2, SLAM and Foxy for Real agricultural workers. European Journal of Electrical Engineering and Computer Science. 5, 6 (Nov. 2021), 17–21. DOI:https://doi.org/10.24018/ejece.2021.5.6.373.

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 Shinji Kawakura
 Google Scholar |   EJECE Journal
 Ryosuke Shibasaki
 Google Scholar |   EJECE Journal