Robotic technologies in horticulture: analysis and implementation prospects

• Autorzy: Hutsol Taras , Kutyrev Alexey , Kiktev Nikolay , Biliuk Mykola

Identyfikatory

DOI

10.2478/agriceng-2023-0009

Warianty tytułu

PL

Zautomatyzowane technologie w ogrodnictwie: Analiza i możliwości wdrożenia

• Języki publikacji: EN

Abstrakty

EN

The article contains an analytical review and perspectives of robotic technologies in horticulture. Trends in the growth of production, implementation, and sales of robots in various regions of the world are revealed. The analysis showed a lag in the introduction of agricultural robots compared to other sectors of the economy, as well as a significant gap between the countries of the Asian region and other continents. A review of technical means of three main components of ground agricultural robots is considered: navigation systems, sensors, and platform design. Examples of constructing a tree trajectory using the A* algorithm and using the Rviz visualization tools and the Github PathFindings graphical web service are given. As a result of the conducted research, the use of Lidar sensors is recommended, which will make it possible to design the route of robotic platforms, build maps by scanning a previously unknown surrounding space and updating the resulting map at each step of the algorithm in real time. The use of existing modern sensors with an optical rangefinder with a resolution of 4.5 million pixels, a frame rate of 25 frames per second and the ability to automatically adapt to the light level in combination with stereo cameras and GPS/GLONASS navigation will improve the positioning accuracy of robotic platforms and ensure autonomous operation. To perform basic technological operations for the care of plantings with row spacing of 2.5-4 m, a tree crown height up to 3-3.5 m with intensive technologies, the following design parameters of a robotic platform are required: agro-treatment of at least 1200 mm, adjustable track width of 1840-2080 mm, weight not more than 400 kg, load capacity not less than 1000 kg, the power of the power plant is not less than 5 kW.

PL

Niniejszy artykuł zawiera przegląd analityczny i perspektywę dla technologii robotycznych w ogrodnictwie. Przedstawiono kierunki rozwoju produkcji, wdrożenia oraz sprzedaży robotów w różnych regionach świata. Analiza pokazuje przepaść między wprowadzeniem robotów rolniczych w porównaniu do innych gałęzi gospodarki oraz dużą różnicę między krajami regionu Azji a innymi kontynentami. Wzięto pod uwagę przegląd trzech głównych części naziemnych robotów rolniczych: systemy nawigacji, czujniki oraz projekty platform. Przedstawiono przykłady konstrukcji trajektorii drzewa za pomocą algorytmu A* oraz narzędzi wizualizacyjnych Rviz oraz sieciową usługę graficzną Github PathFindings. W wyniki przeprowadzonego badania zarekomendowano stosowanie czujników Lidar, co pozwoli na zaprojektowanie trasy dla platform robotycznych, stworzenie mapy przez skanowanie znanej wcześniej otaczającej przestrzeni i aktualizację takiej mapy na każdym etapie algorytmu w czasie rzeczywistym. Zastosowanie istniejących nowoczesnych czujników z optycznym dalmierzem o rozdzielczości 4,5 miliony pikseli, częstotliwości wyświetlania klatek 25 klatek na sekundę i możliwości automatycznej adaptacji do poziomu światła w połączeniu z kamerami stereo, a nawigacja GPS/GLONASS ulepszy dokładność pozycjonowania automatycznych platform i zapewni działanie autonomiczne. Aby wykonać podstawowe operacje technologiczne na nasadzeniach o rozmieszczeniu 2,5-4 m, o wysokości korony drzewa do 3-3,5 m za pomocą intensywnych technologii, następujące parametry projektu platformy automatycznej są konieczne: agro-operacja co najmniej 1200 mm, szerokość jazdy 1840-2080 mm, waga nie przekraczająca 400 kg, obciążenie nie większe niż 1000 kg, moc silnika nie mniejsza niż 5 kW.

Słowa kluczowe

EN

robot; sensor; navigation; gardening; platform

PL

robot; czujnik; nawigacja; ogrodnictwo; platforma

• Wydawca: Polskie Towarzystwo Inżynierii Rolniczej
• Czasopismo: Agricultural Engineering
• Rocznik: 2023
• Tom: Vol. 27, No. 1
• Strony: 113--133
• Opis fizyczny: Bibliogr. 56 poz., rys., tab.

Twórcy

autor

Hutsol Taras

• University of Agriculture in Krakow
• Department of Mechanics and Agroecosystems Engineering, Polissia National University

• https://orcid.org/0000-0002-9086-3672

autor

Kutyrev Alexey

• Department of Technologies and Machines for Horticulture, Viticulture and Nursery, Federal Scientific Agroengineering Center VIM

• https://orcid.org/0000-0001-7643-775X

autor

Kiktev Nikolay

• Department of Automation and Robotic Systems, National University of Life and Environmental Science of Ukraine
• Department of Intelligent Technologies, Taras Shevchenko National University of Kyiv

• https://orcid.org/0000-0001-7682-280X

autor

Biliuk Mykola

• University of Agriculture in Krakow

• https://orcid.org/0000-0001-6842-7746

Bibliografia

• A Green Deal, (2022). Available online: https://ec.europa.eu/info/strategy/priorities/2019-2024/european-green-deal [Aaccessed on March 2022].
• Almasri, M., Elleithy, K., & Alajlan, A. (2015). Sensor fusion based model for collision free mobile robot navigation. Sensors, 16(1), 1-24. DOI: 10.3390/s16010024.
• Analytical review of the global robotics market 2019 [Analiticheskij obzor mirovogo rynka robototehniki 2019]. Online: https://www.sberbank.ru/common/img/uploaded/pdf/sberbank_robotics_review_2019_17.07.2019_m.pdf Accessed on 13.03.2023 [Accessed on March 2022] (In Russian).
• Andžāns, M., Bērziņš, J., Durst, J., Maskaliunaite, A., Nikitenko, A., Ķiploks, J., Rogers, J., Romanovs, U., Sliwa, Z., Väärsi, K., et al. Digital Infantry Battlefield Solution. Introduction to Ground Robotics, DIBS Project, Part I; Romanovs, U., Ed.; Milrem: Helsinki, Finland, 2016.
• Arnó, J., Martínez-Casasnovas, J., Ribes-Dasi, M. & Rosell, J. (2009). Review. PrecisionViticulture. Research topics, challenges and opportunities in site-specific vineyard management. Spanish Journal of Agricultural Research, 7(4), 779-790. DOI: 10.5424/sjar/2009074-1092.
• Astrand, B. & Baerveldt, A. J. (2005). A vision based row-following system for agricultural field machinery. Mechatronics, 15(2), 251-269. DOI: 10.1016/j.mechatronics.2004.05.005.
• Autonomous system for agricultural purposes such as spraying, tillage, fertilization, contour cut, harvest, and transportation (2019). Available from: http://www.raussendorf.de/en/fruit-robot.html [Accessed: March 2023.
• Autopilot (2019). Available from: http://www.trimble.com/Agriculture/autopilot.aspx [Accessed: March, 2022].
• Barnea, E., Mairon, R. & Ben-Shahar, O. (2016). Colour-agnostic shape-based 3D fruit detection for crop harvesting robots. Biosystems Engineering, 146, 57-70. DOI: 10.1016/j.biosystemseng.2016.01.013.
• Bechar, A. & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111. DOI: 10.1016/j.biosystemseng.2016.06.014.
• Bietresato, M., Carabin, G., Vidoni, R., Gasparetto, A. & Mazzetto, F. (2016). Evaluation of a LiDARbased 3D-stereoscopic vision system for crop-monitoring applications. Computers and Electronics in Agriculture, 124, 1-13. DOI: 10.1016/j.compag.2016.03.017.
• Bogue, R. (2016). Robots poised to revolutionise agriculture. Industrial Robot International Journal. 43(5), 450-456. DOI: 10.1108/IR-05-2016-0142.
• Bramley, R.G.V., Proffitt, A.P.B., Hinze, C.J., Pearse, B. & Hamilton, R.P. (2005). Generating benefits from Precision Viticulture through selective harvesting. Precision Agriculture, 5, 891-898.
• Bulanon, D. M., Burks, T. F. & Alchanatis, V. (2009). Image fusion of visible and thermal images for fruit detection. Biosystems Engineering, 103(1), 12-22. DOI: 10.1016/j.biosystemseng.2009.02.009.
• Freitas, G., Hamner, B., Bergerman, M. & Singh, S. (2012). A Practical Obstacle Detection System for Autonomous Orchard Vehicles. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Proceedings of a meeting held 7-12 October 2012 (pp. 3391-3398). Vilamoura-Algarve, Portugal. DOI: 10.1109/IROS.2012.6385638.
• Garcia, E. & Gonzalez-de-Santos, P. (2006). On the improvement of walking performance in natural environments by a compliant adaptive gait. IEEE Transactions on Robotics, 22(6), 1240-1253. DOI: 10.1109/TRO.2006.884343.
• Gongal, A., Amatya, S., Karkee, M., Zhang, Q. & Lewis, K. (2015). Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture, 116, 8-19. DOI: 10.1016/j.compag.2015.05.021.
• Gonzalez-de-Santos, P., Garcia, E. & Estremera, J. (2006). Quadrupedal Locomotion: An Introduction to the Control of Four-Legged Robots. London: SpringerVerlag; 2006. DOI|: 10.1007/1-84628-307-8.
• Hagras, H., Colley, M., Callaghan, V. & Carr-West, M. (2002). Online learning and adaptation of autonomous mobile robots for sustainable agriculture. Autonomous Robots, 13(1), 37-52. DOI: 10.1023/A:1015626121039.
• Hayashi, S., Shigematsu, K., Yamamoto, S., Kobayashi, K., Kohno, Y., Kamata, J. & Kurita, M. (2010). Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering, 105, 160-171. DOI: 10.1016/j.biosystemseng.2009.09.011.
• Hemming, J., Ruizendaal, J., Willem Hofstee, J. & van Henten, E. J. (2014). Fruit detectability analysis for different camera positions in sweet-pepper. Sensors, 14(4), 6032-6044. DOI: 10.3390/s140406032.
• Hiremath, Van der Heijden, G. W. A. M., Van Evert, F. K., Stein, A. & Ter Braak, C. J. F. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using aparticle filter. Computers and Electronics in Agriculture, 100,41-50. DOI: 10.1016/j.compag.2013.10.005.
• Khort, D., Kutyrev, A., Filippov, R. & Semichev, S. (2021). Development control system robotic platform for horticulture. In E3S Web of Conferences, 1st International Scientific and Practical Conference ITEEA 2021. 262, 01024. DOI: 10.1051/e3sconf/202126201024.
• Khort, D., Kutyrev, A., Kiktev, N., Hutsol, T., Glowacki, S., Kuboń, M., Nurek, T., Rud, A. & GródekSzostak, Z. (2022). Automated mobile hot mist generator: a quest for effectiveness in fruit horticulture. Sensors, 22, 8901. DOI: 10.3390/s22228901.
• Khort, D., Kutyrev, A., Smirnov, I. & Pupin, D. (2021). Development automated capture device for picking apples. E3S Web of Conferences, 285, 07025. DOI: 10.1051/e3sconf/202128507025.
• Khort, D., Kutyrev, A., Smirnov, I., Osypenko, V. & Kiktev, N. (2020). Computer vision system for recognizing the coordinates location and ripeness of strawberries. Communications in Computer and Information Science, 1158, 334-343. DOI: 10.1007/978-3-030-61656-4_22.
• Khort, D.O., Kutyrev, A.I. & Smirnov, I.G. (2022). Research into the Parameters of a Robotic Platform for Harvesting Apples. In: Hu, Z., Petoukhov, S., Yanovsky, F., He, M. (eds) Advances in Computer Science for Engineering and Manufacturing, ISEM 2021. Lecture Notes in Networks and Systems, vol 463. Springer, Cham. DOI: 10.1007/978-3-031-03877-8_13.
• Khort; D., Kutyrev, A., Filippov, R., Kiktev, N. & Komarchuk, D. (2019). Robotized Platform for Picking of Strawberry Berries. In 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), 8-11 Oct. 2019, Kyiv, Ukraine. DOI: 10.1109/PICST47496.2019.9061448.
• Kiktev, N., Didyk, A. & Antonevych, M. (2020). Simulation of Multi-Agent Architectures for Fruit and Berry Picking Robot in Active-HDL. In 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, 2020, 635-640. DOI: 10.1109/PICST51311.2020.9467936.
• Kormen, T., Leyzerson, Ch., Rivest, R. & Stein, K. (2011). Algorithms: construction and analysis [Algoritmy: postroenie i analiz]. Moscow, Williams Publishing House, 1296.
• Kutyrev, A., Kiktev, N., Jewiarz, M., Khort, D., Smirnov, I., Zubina, V., Hutsol, T., Tomasik & M.; Biliuk, M. Robotic Platform for Horticulture: Assessment Methodology and Increasing the Level of Autonomy (2022). Sensors, 22, 8901. DOI: 10.3390/s2222890.
• Kutyrev, A., Kiktev, N., Kalivoshko, O. & Rakhmedov, R. (2023). Recognition and Classification Apple Fruits Based on a Convolutional Neural Network Model. In Selected Papers of the IX International Scientific Conference "Information Technology and Implementation" (IT&I-2022). Conference Proceedings. Kyiv, Ukraine, November 30-December 02, 2022. CEUR Workshop Proceedings, 3347, 90-101. https://ceur-ws.org/Vol-3347/Paper_8.pdf.
• Lakkad, S. (2004). Modeling and simulation of steering systems for autonomous vehicles. Master thesis. The Florida State University, US. https://www.academia.edu/37918621/Modeling_and_Simulation_of_Steering_Systems_for_Autonomous_Vehicles.
• Lee, E.A. & Seshia, S.A. (2017). Introduction to Embedded Systems - A Cyber-Physical Systems Approach. 2nd ed. Cambridge, Massachusetts: MIT Press. https://ptolemy.berkeley.edu/books/leeseshia/
• Linz, A., Ruckelshausen, A., Wunder, E. & Hertzberg, J. (2014). Autonomous service robots for orchards and vineyards: 3d simulation environment of multi sensorbased and applications. In 12th International Conference on Precision Agriculture, At: Sacramento, CA, USA https://www.hs-osnabrueck.de/fileadmin/HSOS/Homepages/COALA/Veroeffentlichungen/2014-ICPA_2014_Autonomous_Service_Robots_for_Orchards_and_Vineyards_3D_Simulation_Environment_of_Multi_Sensor_Based_Navigation_and_Applications.pdf.
• Lipiński, A. J., Markowski, P., Lipiński, S., & Pyra, P. (2016). Precision of tractor operations with soil cultivation implements using manual and automatic steering modes. Biosystems Engineering, 145:22-28. DOI: 10.1016/j.biosystemseng.2016.02.008.
• Luan, P.G. & Thinh, N. T. (2020). Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots. Applied Sciences, 10, 3355.
• Lysenko, V.P., Bolbot, I.M., Lendiel, T.I., Amirgaliyev, Y., Nurseitova, K. et al. (2021). Mobile robot with optical sensors for remote assessment of plant conditions and atmospheric parameters in an industrial greenhouse. Proceedings of SPIE - The International Society for Optical Engineering, 2021. doi: 10.1117/12.2613975.
• New Automated Agricultural Platform-Kongskilde Vibro Crop Robotti. Available from: http://conpleks.com/robotech/new-automated [Accessed: March 2023].
• Nof, S. Y. (2009). Springer handbook of automation (pp. 1379-1396). S. Y. Nof (Ed.). Berlin, Heidelberg: Springer Berlin Heidelberg.
• Ochoa, S.F., Fortino, G. & Di Fatta, G. (2017). Cyber-physical systems, internet of things and big data. Future Generation Computer Systems, 75, 82-84. DOI: 10.1016/j.future.2017.05.040.
• Okamoto, H. & Lee, W.S. (2009). Green citrus detection using hyperspectral imaging. Comput. Electron. Agric., 66(2), 201-208. DOI: 10.1016/j.compag.2009.02.004.
• Pasichnyk, N., Komarchuk, D., Lysenko, V., Opryshko, O., Miroshnyk, V., Shvorov, S., ... & Lendiel, T. (2020, October). Substantiation of the Choice of the Optimal UAV Flight Altitude for Monitoring Technological Stresses for Crops of Winter Rape. In 2020 IEEE 6th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC) (pp. 141-145). IEEE.
• PROJECT ACTIVATE, (2022) Online: https://ammoniaengine.org/ [Accessed on 15 March 2022].
• Sgorbissa, A. & Zaccaria, R. (2012). Planning and obstacle avoidance in mobile robotics. Robotics and Autonomous Systems, 60, 628-638. DOI: 10.1016/j.robot.2011.12.009.
• Sharma, K. R., Honc, D., & Dušek, F. (2014, September). Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors an approach based on Kalman filter. In 2014 International Conference on Applied Electronics (pp. 263-266). IEEE.
• Silwal, A. Davidson, J., Karkee, M., Mo, C., Zhang, Q. & Lewis, K. (2016). Effort towards robotic apple harvesting in Washington State. In Proceedings of the 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, Orlando, FL, USA, 17-20 July 2016. DOI: 10.13031/aim.20162460869.
• Skvortsov, E.A. Improving the Efficiency of Robotization of Agriculture [Povyshenie Jeffektivnosti Robotizacii Sel’skogo Hozjajstva]. Ph.D. Thesis, Federal State Budgetary Educational Institution of Higher Education “Ural State Agrarian University”, Yekaterinburg, Russia, 2017; p. 182. (In Russian).
• Smirnov, I., Kutyrev, A. & Kiktev, N. (2021). Neural network for identifying apple fruits on the crown of a tree. In E3S Web of Conferences. International scientific forum on computer and energy Sciences, WFCES 2021, 01021. DOI: 10.1051/e3sconf/202127001021.
• Stentz, A., Dima, C., Wellington, C., Herman, H. & Stager, D. (2002). A system for semi-autonomous tractor operations. Autonomous Robots, 13(1), 87-104. DOI: 10.1023/A:1015634322857.
• Vaeljaots, E., Lehiste, H., Kiik, M. & Leemet, T. (2018). Soil sampling automation case-study using unmanned ground vehicle. Eng. Rural Dev., 17, 982-987. DOI: 10.22616/ERDev2018.17.N503.
• Van Henten, E. J., Van Tuijl, B.A.J, Hoogakker, G.J., Van Der Weerd, M. J., Hemming, J., Kornet, J. G. & Bontsema, J. (2006). An autonomous robot for de-leafing cucumber plants grown in a highwire cultivation system. Biosyst. Eng., 94 (3), 317-323. DOI: 10.1016/j.biosystemseng. 2006.03.005.
• We put machines to work (2019). Available from: http://www.precisionmakers.com/greenbot/ [Accessed: March 2023].
• Weltzien, C., Harms, H.-H. & Diekhans, N. (2006). Automotive Radar Sensor for Object. Agricultural Engineering, 61(5), 250-251. DOI: 10.15150/lt.2006.1114.
• Westling, F., Underwood, J. & Örn, S. (2018). Light interception modelling using unstructured LiDAR data in avocado orchards. Computers and Electronics in Agriculture, 153, 177-187. DOI: 10.1016/j.compag.2018.08.020.
• Zong, C. G., Ji, Z. J., Yu, Y. & Shi, H. (2020). Research on obstacle avoidance method for mobile robot based on multisensor information fusion. Sensors and Materials, 32(4), 1159-1170. DOI: 10.18494/SAM.2020.2540.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).