# FRE 2026: Field Robot Event – Innovation & the Future (www.fieldrobotevent.eu) > The Field Robot Event is a global, student-driven competition dedicated to advancing autonomous agricultural robotics. Organized by an association of academic partners across Europe, the event invites university teams from around the world to design, build, and test field robots that tackle real-world farming challenges. All activities aim to foster innovation, sustainability, and the next generation of agri-tech leaders. FIELD ROBOT EVENT Welcome to the Field Robot Event Welcome to the Field Robot Event – the international arena where student teams push the boundaries of agricultural robotics. Here, innovation meets real-world application: autonomous robots designed and built by students compete on real or virtual fields, mastering tasks from row navigation to object detection and more. Join us on the journey toward smarter, more sustainable farming. INNOVATION Teams create cutting-edge robotic solutions for real agricultural tasks, transforming ideas into field-ready technology. COLLABORATION Students, supervisors, and industry experts share knowledge and work together, building a worldwide network of agri-robotics pioneers. SUSTAINABILITY Every challenge promotes environmentally conscious farming, encouraging robotics that reduce inputs and protect natural resources. CREATIVITY The freestyle challenge rewards bold thinking and imaginative approaches that push the limits of autonomous field work. EDUCATION Participants gain practical engineering and problem-solving skills, preparing them to shape the future of digital and autonomous agriculture. The Field Robot Event strives towards the following values: Supporters 2026 Want to learn more? Would you like to find out more about the Field Robot Event 2026 and how you can participate as a team or sponsor? Discover how innovation, collaboration, and cutting-edge field robotics are shaping the future of agriculture. Don’t hesitate to get in touch with us. Welcome to the Field Robot Event Welcome to the Field Robot Event – the international arena where student teams push the boundaries of agricultural robotics. Here, innovation meets real-world application: autonomous robots designed and built by students compete on real or virtual fields, mastering tasks from row navigation to object detection and more. Join us on the journey toward smarter, more sustainable farming. FRE 2026 – Field Robotics in Action The global field robotics community met from June 16–18, 2026 at the International DLG Crop Production Centre in Bernburg, Germany. Supporters 2026 Want to learn more? Would you like to find out more about the Field Robot Event 2026 and how you can participate as a team or sponsor? Discover how innovation, collaboration, and cutting-edge field robotics are shaping the future of agriculture. Don’t hesitate to get in touch with us. ## Sitemap: - [Support FRE: Help us advance our mission](https://www.fieldrobotevent.eu/support-us) Support us You would like to support us in our work? Only through support of companies and private individuals is it possible for us to keep the FRE alive. We want to give as many students and pupils as possible the opportunity to experience participating in an agricultural robotics competition. Therefore, the participation fees are very low and do not cover costs. The organization itself also depends on the voluntary commitment of its members and has no other sources of income apart from sponsors. We have two Categories of sponsors: FRE Event Sponsors: The FRE Event Sponsor support is not continuous support and is tailored to the specific event. What your support is mainly used for: • Cover part of the costs of accommodation for participants (overnight stays, meals, etc.). • Costs for setting up the venue (fields, tents, barriers, internet, etc.) • Event specific marketing materials What is your benefit: • Be visible with your logo on our website (Eventpage) • Your logo in the event flyer • Your logo on the event banner • If wanted you can be part of the Jury or Awarding Ceremony • You can be present with own promotion materials (one banner flag) at the event. The FRE organization supporters are our long-term supporters. They give the organization stability. What your support is mainly used for: • Provide organizational support for the event • Provide material to the event organizers that is not event specific • Maintaining the Website • Marketing and outreach to new teams What is your benefit: • Be visible with your logo on our website (Frontpage) • If wanted a LinkedIn post about your company and support • Your logo in each event flyer • Your logo in social media posts • Your logo on each event banner • If wanted you can be part of the Jury or Awarding Ceremony • You can be present with own promotion materials (up to 3 banner flags) at each event. • You are allowed to contact the teams with your HR-department FRE Organization Sponsor: Other kinds of support: If you are willing to give In-Kind donations like materials, robots, sensors, etc. needed for robotics, please contact us and we will bring you into contact with new teams that need your support. Become a financial team sponsor. If you want to support an individual team (for example to cover travel costs), we can bring you into contact with the team coordinators. If you would like to help us achieve greater reach, please feel free to contact us. Your assistance is greatly appreciated. Support us You would like to support us in our work? Only through support of companies and private individuals is it possible for us to keep the FRE alive. We want to give as many students and pupils as possible the opportunity to experience participating in an agricultural robotics competition. Therefore, the participation fees are very low and do not cover costs. The organization itself also depends on the voluntary commitment of its members and has no other sources of in... - [Contact us: Questions? Feel free to ask! ](https://www.fieldrobotevent.eu/contact) Contact us Want to learn more? Would you like to find out more about the Field Robot Event 2026 and how you can participate as a team or sponsor? Discover how innovation, collaboration, and cutting-edge field robotics are shaping the future of agriculture. Don’t hesitate to get in touch with us. Thank you for your message. We have successfully received your inquiry and appreciate your interest. Our team will review your message and get back to you as soon as possible with further information. In the meantime, feel free to explore our website or follow us on LinkedIn for the latest updates. Contact us Want to learn more? Would you like to find out more about the Field Robot Event 2026 and how you can participate as a team or sponsor? Discover how innovation, collaboration, and cutting-edge field robotics are shaping the future of agriculture. Don’t hesitate to get in touch with us. Thank you for your message. We have successfully received your inquiry and appreciate your interest. Our team will review your message and get back to you as soon as possible with further information. In the meantime, feel free to explore our website or follow us on LinkedIn for the latest updates. - [Imprint](https://www.fieldrobotevent.eu/imprint) Imprint - [Privacy Policy](https://www.fieldrobotevent.eu/privacy-policy) Privacy Policy - [About us: Field Robot Event - Agri-robotics since 2003](https://www.fieldrobotevent.eu/about) About us The Field Robot Event (FRE) was founded to motivate students to actively engage in the development of autonomous field robots and to experience agricultural robotics beyond laboratory environments. The idea was to create a practical and interdisciplinary platform where engineering, computer science, and agriculture come together. The first Field Robot Event took place in 2003 at Wageningen University. From the very beginning, the focus was on real fields, real crops, and real challenges. Students were encouraged to design, build, and test their robots under realistic outdoor conditions and to learn from both success and failure. Over the years, the Field Robot Event has grown into a well-established international competition. Long before agricultural robotics and digitalisation became widely discussed topics, the FRE provided an annual snapshot of the state of the art in field robotics and precision agriculture. Today, the Field Robot Event brings together student teams, research groups, and young engineers from many countries. Each year, they develop autonomous field robots that must solve practical agricultural tasks. The event supports education, innovation, and exchange and helps to train the next generation of experts in agricultural robotics. About us The Field Robot Event (FRE) was founded to motivate students to actively engage in the development of autonomous field robots and to experience agricultural robotics beyond laboratory environments. The idea was to create a practical and interdisciplinary platform where engineering, computer science, and agriculture come together. The first Field Robot Event took place in 2003 at Wageningen University. From the very beginning, the focus was on real fields, real crops, and real challenges. Students were encouraged to design, build, and test their robots under realistic outdoor conditions and to learn from both success and failure. Over the years, the Field Robot Event has grown into a well-established international competition. Long before agricultural robotics and digitalisation became widely discussed topics, the FRE provided an annual snapshot of the state of the art in field robotics and precision agriculture. Today, the Field Robot Event brings together student teams, research groups, and young engineers from many countries. Each year, they develop autonomous field robots that must solve practical agricultural tasks. The event supports education, innovation, and exchange and helps to train the next generation of experts in agricultural robotics. - [Field Robot Event: Organization](https://www.fieldrobotevent.eu/organization) Organization The FieldRobotEvent is organized through a collaborative and community-driven structure. There is no single permanent organizer. Instead, the event is carried by a loose association of long-standing participants and supporters who have accompanied the FRE for many years. Tasks such as rule development, task definition, technical coordination, jury organization, communication, and overall planning are distributed among this group according to experience and availability. This structure allows the event to remain flexible while benefiting from long-term knowledge and continuity. The current Chairs of the FieldRobotEvent are Prof. Dr. Stefan Stiene and Dr.-Ing. Jan Schattenberg. They coordinate the overall development of the event, support the local organizers, and ensure consistency across different editions. Each year, a university or research institution hosts the FieldRobotEvent and takes responsibility for local organization. This includes field preparation, infrastructure, logistics, safety, and on-site coordination. The host is supported by volunteers, task supervisors, and an international technical jury. Organization The FieldRobotEvent is organized through a collaborative and community-driven structure. There is no single permanent organizer. Instead, the event is carried by a loose association of long-standing participants and supporters who have accompanied the FRE for many years. Tasks such as rule development, task definition, technical coordination, jury organization, communication, and overall planning are distributed among this group according to experience and availability. This structure allows the event to remain flexible while benefiting from long-term knowledge and continuity. The current Chairs of the FieldRobotEvent are Prof. Dr. Stefan Stiene and Dr.-Ing. Jan Schattenberg. They coordinate the overall development of the event, support the local organizers, and ensure consistency across different editions. Each year, a university or research institution hosts the FieldRobotEvent and takes responsibility for local organization. This includes field preparation, infrastructure, logistics, safety, and on-site coordination. The host is supported by volunteers, task supervisors, and an international technical jury. - [Field Robot Event: All Teams](https://www.fieldrobotevent.eu/all-teams) Teams 2026 Coming soon: Get to know the teams of FRE 2026 here! FAQ Infrastructure Competition Accommodation / Food - [Field Robot Event: An agricultural robotics competition for student teams](https://www.fieldrobotevent.eu/the-event) The Event The Field Robot Event is a multi-day, hands-on competition held in a real agricultural environment. Teams work directly at the field, test their systems, improve their robots, and compete in clearly defined tasks. At the same time, the event offers many opportunities for exchange and discussion between teams, organizers, and experts. Each team brings its own autonomous field robot, developed in advance. The competition tasks focus on autonomous navigation, perception of plants and field structures, decision-making, and reliable operation under outdoor conditions. A typical Field Robot Event runs over four days: Day 1: Arrival, setup, field inspection, and test runs Day 2: Competition runs for Task 1 and Task 2 Day 3: Competition runs for Task 3 and Task 4 Day 4: Task 5 – the FreeStyle Task and closing activities The Event The Field Robot Event is a multi-day, hands-on competition held in a real agricultural environment. Teams work directly at the field, test their systems, improve their robots, and compete in clearly defined tasks. At the same time, the event offers many opportunities for exchange and discussion between teams, organizers, and experts. Each team brings its own autonomous field robot, developed in advance. The competition tasks focus on autonomous navigation, perception of plants and field structures, decision-making, and reliable operation under outdoor conditions. A typical Field Robot Event runs over four days: Day 1: Arrival, setup, field inspection, and test runs Day 2: Competition runs for Task 1 and Task 2 Day 3: Competition runs for Task 3 and Task 4 Day 4: Task 5 – the FreeStyle Task and closing activities FAQ Organization Infrastructure / Competition Accommodation / Food The FreeStyle Task is not part of the overall ranking. It gives teams the opportunity to present their own ideas related to agricultural technology. Throughout the event, teams have access to working areas close to the field. Accommodation is usually located nearby, which encourages informal exchange, joint problem solving, and networking. The Field Robot Event usually takes place in mid-June, aligning with the growing season and typical academic schedules. Event Locations 2003 Wageningen, Netherlands 2004 Wageningen, Netherlands Wageningen, Netherlands 2005 2006 Hohenheim, Germany 2007 Wageningen, Netherlands 2007 Wageningen, Netherlands 2008 Osnabrück, Germany 2008 Osnabrück, Germany 2009 Wageningen, Netherlands 2009 Wageningen, Netherlands 2010 Braunschweig, Germany 2011 Herning, Denmark 2012 Venlo, Netherlands 2013 Prague, Czech Republic 2014 Bernburg-Strenzfeld, Germany 2015 Hoče / Maribor, Slovenia 2016 Haßfurt, Germany 2017 Newport (Shropshire), United Kingdom 2018 Bernburg-Strenzfeld, Germany 2025 Milan, Italy 2024 Erwitte / Lippstadt, Germany 2023 Maribor, Slovenia 2022 Mannheim, Germany 2021 Virtual 2020 Event cancelled 2019 Heilbronn, Germany 2026 Bernburg, Germany The FreeStyle ... - [Team Son of The Sun](https://www.fieldrobotevent.eu/team-son-of-the-sun) Team Son of The Sun Team members Berk Özyurt, Ece Atmaca, Berk Ermiş, Mehmet Efnan Koyunlu, Emirhan Minaz, Neval Demiroğlu, Sevgi Pinar, Deniz Othan, Bilge Öztürk Team captain’s name Berk Özyurt Instructor(s) Prof. Dr. İlker Hüseyin Çelen, University of Tekirdağ Namık Kemal, Fakulty of Agriculture, Tekirdağ/Türkiye Description of the team and robot Since 2012, our team has represented our country and university on national and international platforms through innovative projects in agricultural robotics. AgroSense is designed as an autonomous agricultural robot capable of precise field navigation, environmental perception, and targeted intervention. Equipped with a PWM-controlled nozzle system for spot spraying, the robot is developed to detect target treatment areas and apply pesticides accurately, improving application efficiency while reducing chemical usage and environmental impact Robot specifications Participating in the FRE for the 4th time The robot uses ROS 2 for autonomous navigation, sensor data processing and machine control. LiDAR and odometry data are used for localization and mapping, while camera-based perception is planned for detecting target areas. Python and C++ are used for software development. Controller system software description: Jetson Orin NX 16 GB is used as the main onboard computer. Slamtec Aurora is used for LiDAR-based perception and odometry. Motor drivers control the four-wheel drive system, and additional microcontrollers are used for actuator and spraying control. Controller system hardware description: The robot uses ROS 2 Nav2 for autonomous navigation. Slamtec Aurora provides LiDAR and odometry data for localization, mapping, and path planning. The robot autonomously follows the planned route, detects target treatment areas using onboard perception systems, and performs precise spot spraying through PWM-controlled nozzles. The integrated control architecture enables real-time decision-making and accurate operation under field conditions. Short strategy description for navigation and applications: - LabTedarik - NetVay - ESKO - Ercan Tarım Makinaları These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 58 x 63 x 40 Weight (kg): 36 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4/4 Drivetrain concept/ max. speed (m/s): Four-wheel drive and swerve drive system Max. Speed=2 m/s Turning radius (cm): 0 (with all wheels rotation, the robot rotates around itself) Battery type / capacity (Ah): Li-Po battery, 22.2 V / 10 Ah Total motor power (W): 60 No. of sensors internal/ external: Sensor type: Internal: 1 / External: 3 Slamtec Aurora LiDAR, camera module, IMU, Battery monitoring sensor (v... - [Team FloriBot](https://www.fieldrobotevent.eu/team-floribot) Team FloriBot Team members Norika Schneider, Timo Zimmerman, Aaron Stark, Marvin Hack, Fabio Schaaf, David Santiago Alzate Leon Team captain’s name Benedict Bauer Instructor(s) Prof. Dr.-Ing. Torsten Heverhagen, Benedict Bauer, Heilbronn University of Applied Sciences, Faculty of Mechanics and Electronics, Heilbronn, Germany Description of the team and robot We are a team of electrical engineering and mechatronics students with strong expertise in electronics, software development, and mechanical engineering. With our robot Floribot 4.0, featuring all-wheel drive and a passive articulated joint, we improve hardware proven in previous events to develop robust and adaptable solutions for autonomous field robotics. Robot specifications Participating in the FRE since 2012 ROS and PLC software Controller system software description: SEW DHE41B and SEW CMP ELVCD, Nvidia Jetson AGX Xavier, Raspberry Pi 4B Controller system hardware description: Navigation within the crop rows is based on a simple algorithm that uses the robot’s position relative to the row center to determine its speed and steering angle. The overall project is developed by orchestrating state-of-the-art large language models for the software design. The resulting components are then containerized using Docker to improve overall system handling. Short strategy description for navigation and applications: SEW EURODRIVE, Ingenieurbüro Stöger, Agria-Werke GmbH These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 43 x 132 x 45 Weight (kg): Approx. 60 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4/4 Drivetrain concept/ max. speed (m/s): All-wheel drive articulated steering / 2 m/s Turning radius (cm): 37,5 Battery type / capacity (Ah): Stiga SBT 5048 AE 2 x 5 Ah Total motor power (W): 4 x 300 No. of sensors internal/ external: Sensor type: Encoder (IFM RM9003) for articulated joint, 2 x Lidar (TIM551), 2 x Depth camera (Intel Realsense D435i) and some more - [Team AIRLab POLIMI](https://www.fieldrobotevent.eu/team-airlab-polimi) Team AIRLab POLIMI Team members Mirko Usuelli, Leonardo Gargani, Mattia Caon, Ettore Castelli, Simone Edmondo Bedini, Riccardo Serraino, Giuliano Livi, Giulio Cestele Team captain’s name Mirko Usuelli Instructor(s) Mirko Usuelli, Leonardo Gargani – Politecnico di Milano, Italy Academic supervisors: Prof. Matteo Matteucci, Dr. Simone Mentasti Description of the team and robot The AIRLab Team is part of one of the oldest research laboratories in AI and robotics in Italy at Politecnico di Milano. Through the Robotics course, the lab offers students the opportunity to apply their knowledge to real-world agricultural challenges by participating in the Field Robot Event. The team, composed of MSc Computer and Automation Engineering students, develops autonomous robots using AI, computer vision, and perception techniques. Founded in 2022, it has quickly grown, earning several medals in recent years while helping train the next generation of robotics engineers. Robot specifications Participating in the FRE since 2022 We rely on a modular, dockerized architecture composed of interconnected submodules running ROS 2 Humble on Ubuntu 22.04. Our software stack integrates both C++ and Python: C++ is primarily used for core robotics functionalities such as navigation and low-level perception, while Python is adopted for the visual perception framework. The overall system is coordinated through finite state machines and behavior trees, selected according to the specific task requirements. Controller system software description: The robot is equipped with four independent wheels, enabling dynamic adaptation of its kinematics at runtime. Depending on the task, the system can switch between skid-steering, Ackermann, and omnidirectional configurations. The platform includes two computing units: a primary Intel Core i7-based system responsible for robot control, and a secondary NVIDIA Jetson unit dedicated to vision processing. Controller system hardware description: Our navigation strategy is primarily based on LiDAR (laser-scan) data, using a custom approach that clusters crop vegetation along the sides of the robot. This enables robust row-following through goal-point tracking combined with geometry-based regularization, particularly effective in sparse scenarios. For turning maneuvers, the system leverages the geometric structure at the end of each row to estimate the robot’s orientation and executes a sequence of closed-loop motions. This ensures smooth curvature alignment with the crop layout before entering the next row. Short strategy description for navigation and applications: NovaLabs , Oversonic Robotics, Agricola Moderna These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm)... - [Team FarmBeast](https://www.fieldrobotevent.eu/team-farmbeast) Team FarmBeast Team members Benjamin Založnik, Emil Maltar, Jaša Jernej Rakun Kokalj, Luka Kramolc, Matija Slapšak, Matic Presečnik, Niko Korošec, Oscar Fernando Hernandez Lopez, Rok Friš, Žiga Breznikar Team captain’s name Jaša Jernej Rakun Kokalj Instructor(s) Prof. Miran Lakota, Chair of Biosystems Engineering, Faculty of Agriculture and Life Sciences, University of Maribor, Hoče, Slovenia. Dr. Jurij Rakun, Chair of Biosystems Engineering, Faculty of Agriculture and Life Sciences, University of Maribor, Hoče, Slovenia. Dr. Mitja Truntič, Laboratory for Power Electronics, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia. Description of the team and robot FarmBeast is an interdisciplinary educational and research initiative at the University of Maribor focused on autonomous robotic systems for precision agriculture. The project connects students and researchers from agriculture, computer science, electrical engineering and mechanical engineering, enabling hands-on development of AI-based sensing, navigation and robotic manipulation technologies. FarmBeast supports practical learning, innovation and applied research in smart farming and agricultural robotics. In recent years, the project has been led by the Faculty of Agriculture and Life Sciences, University of Maribor in close cooperation with the Faculty of Mechanical Engineering, University of Maribor and the Faculty of Electrical Engineering and Computer Science, University of Maribor. Robot specifications Participating in the FRE since 2008, except 2017 Linux Ubuntu, Robot Operating System Controller system software description: Raspberry Pi 4 Model B (low level computer) Intel NUC 7i7BNH (high level computer) Controller system hardware description: Custom infield navigation algorithm based on Velodyne / Intel Realsense RGB(D) and IMU readings. Short strategy description for navigation and applications: SMTd.o.o, CLAAS, EMSISO d.o.o, Tuli d.o.o, IHS d.o.o, AzureFIlm d.o.o, ODrive Robotics, Inc., Rehar d.o.o. These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 99 x 50 x 51 Weight (kg): 80 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4 Drivetrain concept/ max. speed (m/s): 3 Turning radius (cm): 0 Battery type / capacity (Ah): 12 Total motor power (W): 800 No. of sensors internal/ external: Sensor type: Velodyne VLP-16 multichannel LIDAR ||SICK TIM310 LIDAR sensor,2 x RGBD camera, IMU (Xsens) - [Team RoboTO](https://www.fieldrobotevent.eu/team-roboto) Team RoboTO Team members Letizia D’Angelo, Matteo Vecchia, Francesca Itta, Niccolò Cossu, Niccolò Malgeri, Dario Lupo, Vitor Viana de Paula, Marco Ambrosio, Alessandro Navone, Umberto Albertin Team captain’s name Letizia D’Angelo Instructor(s) Marco Ambrosio, Alessandro Navone, Umberto Albertin, Politecnico di Torino, Interdepartmental Centre for Service Robotics (PIC4SeR), Turin, Italy Description of the team and robot Team Roboto from Politecnico di Torino is a coalition of engineering students from various backgrounds. Split into 5 divisions, we are united by a single goal: engineering the future, one robot at a time. We build the perfect environment to develop robots with special technical features every year. We take part in international competitions and are proud to be the first European team to compete in the RoboMaster University League. Today we continue to grow, aiming at new challenges and global events like FRE 2026. Robot specifications Participating in the FRE for the second time - 3D LIDAR - Livox MID-360 - Point cloud fi ltering and segmentation - 3x Depth camera - Realsense D455 - Detection of object through DNNs image processing - IMU - Dead reckoning and state estimation Controller system software description: - Compute Unit- Nvidia Jetson Orin NX 64GB - Traction Motor Controllers - 4x DJI RoboMaster C620 - Speed control via CAN-bus for the traction motors (M3508). - Steering Motor Controllers - 4x Integrated MIT mode Drivers (CubeMars AK70-10) - Position control via CAN-bus for independent wheel orientation - Low-Level Microcontroller - DJI RoboMaster Development Board Type C with STM32 main controller- Real-time calculation of electronic Ackermann/swerve steering kinematics and centralized CAN-bus communication with all motor drivers. Controller system hardware description: - Task 1, 2, 3 - Maize fi eld navigation: this task is performed by analyzing the point cloud provided by the 3D LIDAR. It allows our robot to infer where the maize plants are located and to fi nd the best path in between the rows. When the end of the row is detected an heuristic algorithm is used to infer where the other rows are and to enter in the next correct row. - Task 4 - Open fi eld navigation: to fulfi l this task our team exploits state-of-the-art SLAM algorithms and a smart heuristic to cover all the working fi eld and to approach the point of interest with a convenient attitude. Short strategy description for navigation and applications: - Politecnico di Torino - PIC4SeR These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 45 x 60 x 35 Weight (kg): 15 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4/4 Drivetrain concept/ max.... - [Team Carbonite](https://www.fieldrobotevent.eu/team-carbonite) Team Carbonite Team members Maximilian Duchet, Fabian Hölscher, Noah Schittenhelm, Maximilian Scheible Team captain’s name Fabian Hölscher Instructor(s) Niels Fenkl, Lukas Locher; both: Schülerfoschungszentrum Südwürttemberg, Germany. Description of the team and robot We are a student team (ages 15 to 18) from the SFZ Südwürttemberg, Standort Überlingen. For most members this will be the second time participating in the FRE. The FRE will be a great opportunity to test how the field robot we are currently building performs under real field conditions. Robot specifications Participating in the FRE since 2010 Self-developed ros2 code Controller system software description: From STM32 stepper driver (self-developed board) with serial to USB converter to ros nodes on main computer Controller system hardware description: Self-developed algorithms that use LiDAR data for end of row detection and anotomous steering in the corn rows Short strategy description for navigation and applications: Sick, Wilhelm Stemmer-Stiftung, Baden-Württemberg-Stiftung (MikroMakroMint) These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): n. a. Weight (kg): n. a. Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 3/2 Drivetrain concept/ max. speed (m/s): differential with two geared stepper motors Turning radius (cm): 0 Battery type / capacity (Ah): LiPo (16 000 mAh) Total motor power (W): 2 x 200 No. of sensors internal/ external: Sensor type: 1x gyroskop, 1x camera, 1x LiDAR Scanner - [Team FREDT](https://www.fieldrobotevent.eu/team-fredt) Team FREDT Team members Enrico Schleef, Anna Ricken, Tobias Lamping Team captain’s name Enrico Schleef Instructor(s) Dr.-Ing. Jan Schattenberg, Technische Universität Braunschweig, IMN - Institut für mobile Maschinen und Nutzfahrzeuge, 38106 Braunschweig Description of the team and robot Helios Evo is the current robot generation of FREDT. It is based on our proven chassis with four wheel drive and all wheel ackermann steering. The main component of Helios Evo is the multifunctional body in which the central electrical distribution and battery-management-unit as well as the agricultural rear lift system with 20 kg load capacity are integrated. The rear lift system is equipped with integrated electrical- and fl uid-lines. The vehicle body also includes the main computer and the cooling and light system. A dual LIDAR Sensor System is used for improved driving stability and navigation. Robot specifications Participating in the FRE since 2006 Two LIDAR at different heights are used to approximate the distances between the rows of maize plants. In addition, it is easier to recognise obstacles such as leaves from different angles. By processing, it is possible to determine the centre of the robot to the plant rows to predict how the robot can drive through the rows as fast as possible. Controller system software description: The navigation runs on a NUC 11 i7-1165G7, NVDIA RTX2060, 16GB RAM, 1 TB SSD. It contains steering the motor for driving and steering servos for turning as well as data analysis by several sensors, which are localised in front of the robot. In addition, there are several micro-controller based systems used for battery management and all other functions concerning task-implements (e.g. servos, rear power lift, sprayer, ...). All systems are connected by CAN-Bus. Controller system hardware description: Because of the ackermann steering our robot is very efficient at driving with relatively high speed in straight or curved lines Given the higher speed we designed and tuned our software and hardware systems for fast reaction times and minimal delay (keep it simple and fast) Short strategy description for navigation and applications: Technische Universität Braunschweig , Institut für mobile Maschinen und Nutzfahrzeuge, Technische Universität Braunschweig, Carolo-Wilhelmina Stiftung These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 35 x 69 x 40 Weight (kg): 30 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4 Drivetrain concept/ max. speed (m/s): Ackermann Steering, 3.5 m/s Turning radius (cm): 75 Battery type / capacity (Ah): 2x Li-Ion 155Wh Total motor power (W): 250 No. of sensors internal/ external: Sensor ty... - [Team HSM-TERRA](https://www.fieldrobotevent.eu/team-grasshopper) Team HSM-TERRA Team members Arshpreet Singh, Anish Paul, Charles Sunil Swaris, Mukul Bimbra, Sharad Vettankarakav Sanjeev, Tony Mathew, Priyam Bhatnagar Team captain’s name Priyam Bhatnagar Instructor(s) Prof. Dr.-Ing. Frank Schrödel, Hochschule Schmalkalden, Automatisierungstechnik/Antriebstechnik/Robotertechnik Description of the team and robot Team HSM-TERRA from Hochschule Schmalkalden develops an autonomous agricultural field robot for the Field Robot Event 2026. The robot uses LiDAR, Intel RealSense vision, IMU sensing, an Intel NUC onboard computer, and VESC motor control for autonomous navigation and precision agriculture applications such as row navigation, obstacle avoidance, and crop monitoring. Robot specifications Participating in the FRE since 2023 ROS-based controller software integrating LiDAR, RGB-D camera, and IMU data for SLAM, perception, navigation, and real-time motor control via VESC. Controller system software description: Intel NUC embedded computer for processing and control, VESC motor controller for motor actuation and speed control, integrated with LiDAR, Intel RealSense camera, and IMU sensors for robotic navigation and operation. Controller system hardware description: The robot performs autonomous navigation in maize field rows using LiDAR, Intel RealSense vision, and IMU-based localization. Sensor fusion and real-time control enable stable row following, obstacle avoidance, and turning at row ends. The system is designed for precision agriculture applications including crop-row navigation, plant health monitoring, biodiversity detection, and soil spot treatment. Short strategy description for navigation and applications: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 40.2 x 81.71 x 53.5 Weight (kg): 64 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4/2 Drivetrain concept/ max. speed (m/s): Motor/ Gearbox, 5kmph Turning radius (cm): 35 Battery type / capacity (Ah): LiFePO4 battery, 20 Total motor power (W): 250 No. of sensors internal/ external: Sensor type: 3, LiDAR sensor, Depth camera (Intel RealSense), Inertial Measurement Unit (IMU) - [Team Robatic Bullseye](https://www.fieldrobotevent.eu/team-robatic) Team Robatic Bullseye Team members Willemijn Deen, Isa Gooijer, Jelle van Iersel, Niels Olijerhoek, Lot Sturkenboom, Nynke Schippers, Jorn van Soesbergen, Arjan Huisman Team captain’s name Jorn van Soesbergen Instructor(s) Sam Blauw, Thomas Versmissen, Wageningen University & Research, Agricultural Biosystems Engineering, The Netherlands Description of the team and robot Every year, a student team from Wageningen University & Research participates in the Field Robot Design course. The bullseye robot has been upgraded annually with improved hardware and adaptations for new tasks. The software is also continuously developed and refined each year by a new group of students building on previous work. Robot specifications Participating in the FRE since Team since 2003 Bullseye since 2012 ROS2 Jazzy, Python, C++ Controller system software description: Intel VPro i7 8th generation and NVIDIA Jetson Xavier AGX, Maxon motor controllers Controller system hardware description: For task 1, 2 and 3 we make the robot stay between the rows with the LIDAR and switch rows when it no longer detects plants next to it. The stereocameras are used for the detection tasks. For task 4 and 5 we are planning to use SLAM in combination with nav2. Short strategy description for navigation and applications: LEMKEN, Aurea Imaging, Kverneland Group, Maxon, Wageningen University These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 40 x 130 x 75 Weight (kg): 65 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4 Drivetrain concept/ max. speed (m/s): 4-wheel drive max 6 m/s Turning radius (cm): 40 Battery type / capacity (Ah): 2 x 7500 mAh 6 cell LiPo (2 x 166.6 Wh) Total motor power (W): 4 x 25 W (steering) and 4 x 150 W (drive motors) No. of sensors internal/ external: Sensor type: 2 x LIDARs, IMU, 2 x Zed 2i stereo camera - [Team TH[e] OWL](https://www.fieldrobotevent.eu/team-the-owl) Team TH[e] OWL Team members Prof. Dr. Burkard Wrenger, Matthias Valentin Meer, Lukas Helmke, Jannick Klante, Till Kunisch, Timon Hilber, Simon Brinkmann, Carl-Linus Meulenbergh, Niklas Meißner, Thies Müller, Bjarne Nielsen, Kim Alina Heymann Team captain’s name Jannick Klante Instructor(s) Prof. Dr. Burkhard Wrenger, Technische Hochschule Ostwestfalen-Lippe, University of Applied Sciences and Arts, Höxter, Germany Description of the team and robot We are a mixed team from Precision Farming at TH OWL in Höxter. Our members cover Organization (S. Brinkmann, K. Heymann), Hardware (L. Helmke, T. Müller, J. Klante, T. Hilber, B. Nielsen, N. Meißner), and Software (T. Kunisch, L. Helmke). The robot uses an AgileX Scout Mini 4WD chassis as the base, with a LattePanda Sigma. It is equipped with a 3D-printed frame that supports a 3D LiDAR, one RealSense camera, two RGB cameras, and an IMU. The software enables autonomous navigation, row-following, and semantic perception for agricultural tasks. Robot specifications Participating in the FRE since 2022 A reactive row-following concept based on sensor fusion. It calculates a dynamic centerline between crop rows in real-time, allowing the robot to navigate autonomously without GPS or pre-existing maps. Controller system software description: The robot utilizes a 3D LiDAR for long-range structural row detection and obstacle sensing, an RGB-D camera for color-based vegetation identification (Excess Green Index), and an IMU/Odometry for motion estimation and heading stability. Controller system hardware description: Rows are detected by segmenting 3D point clouds based on height (Z-filtering) and color information. The filtered data is projected into a 2D grid where a Hough Transform algorithm identifies the parallel lines of the crop rows to determine the navigation path. Short strategy description for navigation and applications: To be determined These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 61,2 x 58 x 24,5 Weight (kg): 35 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4/4 Drivetrain concept/ max. speed (m/s): Skid steer / 3m/s Turning radius (cm): 0 Battery type / capacity (Ah): Lithium / 24V 15Ah Total motor power (W): 4 x 250 No. of sensors internal/ external: Sensor type: To be determined - [Team Acorn](https://www.fieldrobotevent.eu/team-acorn) Team Acorn Team members Simon Balzer, Justus Braun, Fabian Buitkamp, Jan Carstens, Philipp Gehricke, Eduard Gode, Frederik Hartmann, Isaak Ihorst, Jannik Jose, Andreas Klaas, Justus Klingner, Malte Klöpping, Julian Kronenberg, Luca Otto, Natalie Puls, Philipp Schein, Philipp Schlinge, Lena Schötker, Arthur Schreiber, Christopher Sieh, Marco Tassemeier, Nico Thiessen Team captain’s name Justus Braun, Fabian Buitkamp Instructor(s) Christopher Sieh , Osnabrück University, Institute of Computer Science, Computer Engineering Group, Osnabrück, Germany and Osnabotics e. V., Osnabrück, Germany Eduard Gode, Osnabrück University of Applied Sciences, Autonome, kollaborative Agrar- und Sensorsysteme, Osnabrück, Germany and Osnabotics e. V., Osnabrück, Germany Description of the team and robot Acorn is the name of our primary robot for the FRE and the team behind it. Started in 2004 at the University of Applied Sciences Osnabrück, the team expanded in 2019 when the University of Osnabrück joined. Last year, we founded the non-profit Osnabotics e. V. to advance robotics in Osnabrück and the surrounding region through knowledge sharing and supporting student teams. We are excited for our first FRE as Osnabotics e. V. here at the DLG Feldtage, where our robots will tackle new tasks for the agricultural industry. Robot specifications Participating in the FRE since Since 2004 yearly with varying team names The compute unit is running ROS2 and gathers information via Ethernet or USB sensors processed in nodes. The nodes provide ROS actions, which are triggered and managed by State Machines and Behavior Trees. This abstraction enables us to quickly adapt to problems coming up during the event. Apart from ROS, Acorn uses CAN, CUDA, TensorRT, PointCloudLibrary, OpenCV, ONNX, YOLO for communication, model inference and processing. Controller system software description: The main compute unit is a NVIDIA Jetson Orin AGX connected to 2 Arduino Nanos für fast pattern controls or visual status and detection signals. Controller system hardware description: Acorn detects rows and plants using a 3D laser scanner and two 3D depth cameras. The field is evaluated from point cloud data to generate a drive path, which is then followed by a customized navigation controller. The robot follows this path, while the 3D cameras are used for detection tasks in the rows. Short strategy description for navigation and applications: AMAZONEN-Werke H. Dreyer SE & Co. KG, Allied Vision Technologies GmbH, ioTech GmbH and additional donators featured on our website. These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 45 x 90 x 64 Weight (kg): 22 Commercial or prototype: Prototype Total no. of wheels / no.... - [Team SemTechno by TribeLab](https://www.fieldrobotevent.eu/team-semtechno-by-tribelab) Team SemTechno by TribeLab Team members Pedro Rodrigues, Francisco Oliveira Team captain’s name Pedro Rodrigues Instructor(s) Dr. José Sarmento, INESC TEC, TribeLab Description of the team and robot We are the team SemTechno by TribeLab, and we are participating in the Field Robot Event for the first time. We are composed by 9 elements and 1 instructor, containing BSc, MSc and PhD students, and we are all members of the Tribe Laboratory at INESC TEC. In the FRE 2026, the team will be represented by two of its members and the instructor. Our robot consists of a modified Traxxas E-Maxx RTR wheelbase, adapted for autonomous field navigation. It integrates a custom sensor module mounted on its chassis, which includes two 2D LiDAR sensors and two cameras for environmental perception. Additionally, the wheelbase has been mechanically modified to support a double Ackermann steering configuration, improving maneuverability and precision in structured agricultural environments. Robot specifications Participating in the FRE since 2026 The robot is controlled via ROS2, with each sensor having its respective node which reads the input data and publishes the values as a ROS2 topic. That way it is possible for the different modules to communicate between them if required during the tasks. There’s also a main node (supervisor), which is responsible to aggregate the different data from the remaining nodes and make the controller decisions for the navigation of the robot. Controller system software description: - Motor (steering): Servo motors - Motor (wheel drive): DC motors - Computer: Raspberry Pi Compute Module 5 - Microcontroller: RP2040 based board - Camera: Raspberry Pi AI Cameras - LiDAR: Hokuyo UST-10LX and UST-20LX - Signals: Colored LEDs Controller system hardware description: Navigation: The navigation will be based in the two LiDARs, one on the front and another on the back of the robot, to detect the lines. We will have a node that will understand the semantic of the field to decide if the robot is inside one line, outside the line and the current line number. The robot will have a localisation node that will be able to localise the current position of the robot in relation to the field. The robot has a custom controller for the double ackermann setup also allowing us to control the curvature radius and the linear velocity. Perception: We trained two YOLO11n for the task 2 and 3. One of the models is responsible to detect the structures representing the plants with diseases (Task 2) and the other model detects the 3 classes of insects (Task 3). On both tasks, the perception is conducted on both sides at the same time, since we have one camera on each side covering the plant rows. When a detection is achieved on Task 2, an additional line is added to a CSV file stating the class detected, the row number of the corn field and the distance from the start. Also, there are 6 LEDs on the top of the robot, a set of red, green an... - [NMBU Robotics](https://www.fieldrobotevent.eu/nmbu-robotics) Team NMBU Robotics Team members William Fredrik Bakke Dahl, Nikolai Klemp Petersen, Nicolai Lütken Terland, Marius Kirkeby Huseby, Emilie Hope Petersen, Mikael Hulleberg, Joep Westplate Team captain’s name William Fredrik Bakke Dahl Instructor(s) Weria Khaksar, NMBU Robotics, Norway. Description of the team and robot We are all students at NMBU studying applied robotics. Our robot, Peik, is a compact modular robot designed for easy maintenance and quick prototying. This year, we aim to solve the tasks with cheaper depth cameras, as apposed to 3D LiDAR. Robot specifications Participating in the FRE since 2022 We are using our depth cameras for 3D Visual SLAM, colapsed into a 2D map passed to Nav2. The rows are extracted using clustering. These clusters are used to calculate the apropriate waypoits that are passed to Nav2. Controller system software description: Peik is controlled with a Jetson Orin AGX connected to four Odrive S1 motor controllers. Peik is made inhouse from a mix of custum and commercially availeble parts. Controller system hardware description: The rows are extracted using clustering. These clusters are used to calculate the apropriate waypoits that are passed to Nav2. Short strategy description for navigation and applications: NU NMBU – The Career fund These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 37 x 44 x 40 Weight (kg): 20.45 Commercial or prototype: Prototype Total no. of wheels / no. driven wheels: 4 Drivetrain concept/ max. speed (m/s): 4 Wheel Skid Steer Turning radius (cm): 0 Battery type / capacity (Ah): Dewalt 18v Tool battery Total motor power (W): 360 No. of sensors internal/ external: Sensor type: 2x Realsense D435if 9 axis IMU - [CAU Robot Team](https://www.fieldrobotevent.eu/team-cau) CAU Robot Team Team members Xia Hong, Yu Nuo, Zheng Lei, Zhang Qi, Chu Yuxing, Yang Wenchao, Xia Junlin, Xu Guangkuo, Liu Yihao Team captain’s name Xia Hong Instructor(s) Associate Professor Xin Wang, College of Engineering, China Agricultural University, Beijing, China Associate Professor Tian Wenbin, College of Engineering, China Agricultural University, Beijing, China Description of the team and robot We developed a compact, high-precision, fully autonomous field robot designed to complete official FRE competition tasks in complex, unstructured outdoor agricultural environments. The robot integrates mechatronic control, multi-sensor fusion, and artificial intelligence algorithms, with all design, assembly, and debugging independently completed by our student team. Robot specifications Participating in the FRE since First time Perception & Data Analysis: For environmental sensing, the system relies exclusively on a forward-facing RGB camera. The perception pipeline extracts the navigation path through a real-time adaptive image binarization algorithm (e.g., ExG and Otsu’s thresholding) to segment crop rows from the soil background. Shape and centroid analysis are applied to the binarized image to dynamically calculate the robot's lateral offset and yaw error relative to the target pathway centre. Controller system software description: The computed trajectory errors are processed via an optimized PID controller to determine velocity commands (Twist), directly commanding the AgileX SCOUT mini base over an industrial CAN bus at 50Hz. The controller incorporates velocity smoothing to mitigate wheel slip on loose field soil and stabilize the camera's field of view. For fault-tolerant safety, the software continuously telemeters the SCOUT mini's built-in motor current and temperature sensors, triggering an immediate safety stop sequence if a vision blackout or motor overload (e.g., weed entanglement) is detected. Controller system hardware description: This robot features a highly robust, purely vision-based farmland navigation system built upon ROS 2 and the Scout Mini skid-steer chassis.For Task 1 (Straight Crop Rows), the system utilizes Inverse Perspective Mapping (IPM) perspective stretching to compensate for missing-crop gaps. This is combined with HSV segmentation and quadratic polynomial fitting to extract the baseline, achieving centered cruising via a PD controller.For Task 2 (Complex Curved Rows), a cubic polynomial is employed to reconstruct the path curvature, incorporating a tangent pose feedforward control mechanism.At the low-level control layer, the system integrates a depth-vision-based near-field Artificial Potential Field (APF) with an Instantaneous Centers of Rotation (ICRs) model, enabling collision avoidance deceleration and high-precision slip compensation to ensure safe and smooth operations across all working conditions. Short strategy description for navigation and applications: CLAAS These are the commercial... - [Team LCASTOR](https://www.fieldrobotevent.eu/team-lcastor) Team LCASTOR Team members Rajitha de Silva, Jonathan Cox, Elliot Smith, Emmanuel Soumo, Yasmin Alderson, Amy-Rose Masic Team captain’s name Rajitha de Silva Instructor(s) Dr. Riccardo Polvara, University of Lincoln, UK Description of the team and robot LCASTOR is the University of Lincoln’s robotics competition team, uniting postdocs, PhD, master’s and undergraduate students from the Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute for Agricultural Technology (LIAT). For FRE 2026, we will represent the UK with an improved holonomic AgileX Ranger Mini field robot, strengthened by technologies emerging from our students’ research outputs, to tackle crop-row navigation, weed and pest detection, and freestyle agri-tech demonstrations. Robot specifications Participating in the FRE since 2025 The platform supports command control via CAN and secondary development using AgileX software/SDK resources. For FRE 2026, the LCASTOR system can combine sensor data analysis, autonomous crop-row navigation, perception for weed/pest detection, and machine-control logic developed from student research outputs. Controller system software description: AgileX Ranger Mini 2.0 chassis with integrated drive and steering motor control, CAN communication interface, 48 V battery system, external power interface, and a team-added NVIDIA Jetson AGX Orin / Orin Nano onboard computer for perception, navigation, sensor-data processing, and high-level robot control, together with the required sensing payload. Controller system hardware description: The robot will use row following and row switching as separate navigation behaviours, with the overall mission orchestrated through Nav2 behaviour trees. An independent vision system will run in parallel for object detection, enabling vision-based actions such as weed, pest, crop or obstacle identification to be triggered during autonomous operation. Short strategy description for navigation and applications: AgriForwards CDT at University of Lincoln , Douglas Bomford Trust These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 50.0 × 73.8 × 33.8 Weight (kg): 75 Commercial or prototype: Commercial Total no. of wheels / no. driven wheels: 4/4 Drivetrain concept/ max. speed (m/s): 4WD omnidirectional/holonomic platform with four-wheel independent steering and in-wheel drive motors; max. speed approx. 1.5–1.67 m/s Turning radius (cm): 0 cm in spin mode; approx. 81 cm in Ackermann mode Battery type / capacity (Ah): 48 V lithium iron phosphate battery / 24 Ah Total motor power (W): 1,800 W total installed motor power: 4 × 350 W drive motors + 4 × 100 W steering motors No. of sensors internal/ external: Sensor type: Base platform:... - [Team LEO-Poland](https://www.fieldrobotevent.eu/team-leo-poland) Team LEO-Poland Team members Robert Florek, Błażej Rosół, Paweł Szerszeń Team captain’s name Błażej Rosół Instructor(s) mgr inz. Robert Florek, Complex of Agricultural Schools in Żarnowiec Poland Description of the team and robot Our team consists of students of the Technical School of Mechanization of Agriculture and Agrotronics. They are interested in the application of new technologies and robots in agricultural production. They also run farms where they want to use new solutions for the production of healthy food in the future. Our robot is controlled by a Raspberry Pi computer, with the Rasberian + ROS system. For autonomous driving, we used a camera and software written in Python that detects rows of plants (corn). The rover has 4 wheels, which are driven by a separate engine. We used MATLAB software to distinguish objects. Robot specifications Participating in the FRE since - The ride is carried out using image analysis from a camera installed on the robot. Controller system software description: - Raspberry Pi 5 - A single-board computer developed by Raspberry Pi Ltd. - Camera Arducam 12.3MP 477M HQ Camera Module for Raspberry Pi with 158°(D) M12 Wide Angle Lens. - Bühler Motors 1.61.077.414 - x4 - Equipped with an STM32F401 microcontroller Controller system hardware description: - cfg/ColorMask.cfg – A ROS1-specific file defining dynamic parameters. - CMakeLists.txt – A file with package building instructions (standard ROS configuration). - config/corn.yaml – A configuration file with color mask values. The parameters were experimentally selected in a corn field to maximize crop detection while filtering out noise (weeds, grass, and other unwanted vegetation). - config/field_rider.yaml – The main configuration of the driving program. - launch/color_mask.launch – A launch file for the color mask calibration tool. - launch/field_rider.launch – Launch file for the row-riding algorithm. o Arguments - scripts/color_mask.py – Source code for the program used to find and calibrate the color mask value. - scripts/field_rider.py – Source code for the control logic responsible for autonomous row-riding. Short strategy description for navigation and applications: - These are the commercial team sponsors & partners: Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch W x L x H (cm): 44.5x42.4x30.0 Weight (kg): 7 Commercial or prototype: - Total no. of wheels / no. driven wheels: 4 Drivetrain concept/ max. speed (m/s): 0.4 m/s linea Turning radius (cm): - Battery type / capacity (Ah): 12.6V 2A Li-ion charger Total motor power (W): 12 W x 4 motor No. of sensors internal/ external: Sensor type: Camera, lidar - [Teams 2026](https://www.fieldrobotevent.eu/teams-2026) Teams 2026 LCASTOR FREDT HSM-Terra Robatic Bullseye LEO-Poland NMBU Robotics RoboTO SemTechno by TribeLab SON OF THE SUN TH[e] OWL CAU Robot Team Carbonite AIRLab Polimi Acorn Farmbeast FloriBot Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch - [Supporters 2026](https://www.fieldrobotevent.eu/supporters-2026) Supporters FRE 2026 DLG (German Agricultural Society) is a leading international, non-profit organisation with over 30,000 members, dedicated to advancing agriculture and the food industry through innovation, knowledge exchange and practical solutions. Founded in 1885, DLG connects science, industry and practice, promoting sustainable, efficient and future-oriented farming worldwide. A key platform for this exchange is the DLG Feldtage, where pioneering technologies in crop production are brought to life. As part of this, the FarmRobotix initiative showcases cutting-edge developments in robotics, automation and AI – and provides the stage for the Field Robot Event, where international teams demonstrate the future of autonomous field operations in real conditions. By linking research, industry and young talent in formats like the Field Robot Event, DLG actively drives the development and real-world application of next-generation agricultural robotics. AgDoIT thrives for knowledge-based agriculture and makes field research as smart and safe as never before. Through our IsoFarmResearch system, we help farmers, consultants, the scientific community, start-ups and the agricultural industry worldwide to gain evidence and deeper insights from real-world field trials and to accelerate innovation, productivity and sustainability. We support the Field Robot Event in order to support education, innovation, and exchange for the next generation practical agriculture. The HARTING Technology Group is a leading global provider of industrial connectivity. Around 6,500 employees are active across the globe in 42 sales companies, 14 production facilities and seven R&D sites. HARTING connectivity solutions are used to transmit data, signals and power in numerous industrial sectors. Among others, in transportation, e-mobility, automation and mechanical engineering as well as in generation, storage and distribution of renewable energy. In the 2024/25 business year the family owned and managed company generated sales of 1.1 billion euros. NEXAT is a pioneer in agricultural technology, redefining the future of farming. As a family-owned company, we have developed a globally unique modular crop production system that integrates all stages of plant production into a single autonomous-ready carrier vehicle. We are not a traditional manufacturer, but an innovation-driven company. The world of NEXAT moves faster: from idea today to field application tomorrow. Zauberzeug develops intelligent robotic systems for a sustainable future. Based in Germany, our interdisciplinary team unites mechanical engineering, electronics, software, AI, design and computer vision. With products like Field Friend, Learning Loop and Robot Brain, we turn research into practical solutions for autonomous mobile machines — advancing agricultural robotics to reduce manual labor, support sustainable farming and bring autonomy to real-world applications. GRIMME Landmaschinenfabrik was founded in 1936... - [Field Robot Event 2026: Tasks & Contest](https://www.fieldrobotevent.eu/tasks2026) Dates and Facts The 23th Field Robot Event took place as part of the DLG-Feldtage, 16 to 18 June 2026 Venue: International DLG Crop Production Centre Bernburg, Germany. The Contest at a Glance The competition is structured over multiple tasks that test distinct robot capabilities. Teams begin in basic navigation and progressively face more advanced challenges—such as precise sensing, classification, mapping, and manipulation. The final “freestyle” challenge allows each team to demonstrate an original concept or application beyond the standard tasks. Judges evaluate performance not only in terms of accuracy or speed but also in safety, reliability, robustness in outdoor conditions, and creativity. Past tasks have included autonomous row navigation, gap traversal, object/weed detection, mapping, and classification. The contest is typically held outdoors; in some editions, online or hybrid formats or virtual fields may also be involved. Field Robot Event 2026 – Tasks Overview The Field Robot Event 2026 challenged autonomous robots to perform realistic agricultural tasks, including navigation, plant health detection, biodiversity monitoring, and targeted soil treatment. A creative freestyle task highlighted innovative and sustainable solutions. Click the button to download the full task description with rules, scoring, and illustrations. Dates and Facts The 23th Field Robot Event took place as part of the DLG-Feldtage, 16 to 18 June 2026 Venue: International DLG Crop Production Centre Bernburg, Germany. The Contest at a Glance The competition is structured over multiple tasks that test distinct robot capabilities. Teams begin in basic navigation and progressively face more advanced challenges—such as precise sensing, classification, mapping, and manipulation. The final “freestyle” challenge allows each team to demonstrate an original concept or application beyond the standard tasks. Judges evaluate performance not only in terms of accuracy or speed but also in safety, reliability, robustness in outdoor conditions, and creativity. Past tasks have included autonomous row navigation, gap traversal, object/weed detection, mapping, and classification. The contest is typically held outdoors; in some editions, online or hybrid formats or virtual fields may also be involved. Field Robot Event 2026 – Tasks Overview The Field Robot Event 2026 challenged autonomous robots to perform realistic agricultural tasks, including navigation, plant health detection, biodiversity monitoring, and targeted soil treatment. A creative freestyle task highlighted innovative and sustainable solutions. Click the button to download the full task description with rules, scoring, and illustrations. - [Organisation Team 2026](https://www.fieldrobotevent.eu/organisation-team-2026) Organisation Team 2026 Vincent Omondi Ochieng’ University of Applied Sciences Anhalt Local contact person John Schmidt University of Applied Sciences Anhalt Local contact person Charity Wanjiku Wangui University of Applied Sciences Anhalt Local contact person Prof. Dr. Stefan Stiene University of Applied Sciences Osnabrück Local event chair Dr.-Ing. Jan Schattenberg Technische Universität Braunschweig Local event chair Robert Everwand Agrotech Valley Forum e. V. Osnabrück Administrative Office Vivien Vetter Agrotech Valley Forum e. V. Osnabrück Administrative Office Phillip Hildner Agrotech Valley Forum e. V. Osnabrück Administrative Office Dr. Michaela van Eickelen Agrotech Valley Forum e. V. Osnabrück Administrative Office - [Photos FRE 2026](https://www.fieldrobotevent.eu/photos-fre-2026) FRE 2026 - [FRE Archive: Tasks and Documents 2004–2025](https://www.fieldrobotevent.eu/archive) Archive 2023 - Maribor, Slovenia 2026 - Bernburg, Germany 2025 - Milan, Italy 2024 - Erwitte, Germany 2022 - Mannheim, Germany 2021 - Virtual Task 2004 - 2018 Proceedings 2004 - 2018 Other Information Vielen Dank, dass Sie uns kontaktiert haben „In der aktuellen Version haben wir die Unterstützung für Lottie-Dateien hinzugefügt. Um sie zu nutzen, besuchen Sie lottiefiles.com, wählen Ihre Favoriten aus und laden sie in ein „Bild“-Element in Ihrem Projekt hoch