Our research focusses on learning behaviour of robot systems and how to implement this in our future farming and food production systems.
The size of a skilled workforce that accepts repetitive tasks under harsh climatic conditions in arable farming is decreasing rapidly. There’s a growing need to automate labour-intensive and repetitive tasks such as monitoring and spraying operations in fields.
Will you join us?
Mobile robotics help growers to perform operations in their fields without spending tedious hours in their tractor cabins. Furthermore, farmers are not exposed to crop protection chemicals anymore when the robots take over their jobs in the field. However, taking away people from the farm operations is not the only task for robotics. We want robots to perform multiple tasks throughout the year. Not only harvesting apples, but also weeding in spring and sorting fruit or vegetables from the storage in winter time. This requires robots to learn their tasks in an easy way. Robots should not be task specific anymore, they should learn new operations easily from demonstration or from humans directly. This requires new, innovative and flexible robot systems. Deep learning and artificial intelligence are required to teach robot systems their goals of harvesting apples and weeding fields. Our research focusses on learning behaviour of robot systems and how to implement this in our future farming and food production systems.