The first challenge that needs to be solved is robust and reliable mobility in the field, which is an unstructured environment. The consortium has already developed the Thorvald robot, which is capable of moving autonomously in both polytunnels and open fields.

The second large challenge is the identification and picking of the strawberry using a robotic arm. The berries need to be accurately localized in the field. We find the berries using a deep neural network and find the location in the three-dimensional space using 3D camera. The consortium has developed a novel and patent-pending gripper that will be used in the project. The gripper picks the berries by surrounding them entirely before picking. This makes us able to pick berries in the presence of uncertainties and moving berries.

The project will also develop methods for determining the ripeness prior to picking the berry. We will equip the gripper with internal sensors that can estimate the ripeness of the berries and evaluate the quality in a enclosed and controlled environment before harvesting. Advanced machine learning
will be used to give the robot the ability to reason and make decisions in the field, substantially improving the current state-of-the-art by inspecting each and every berry before picking. This will guarantee that diseased berries are not picked and ensure online quality control and grading of the berries.

Finally, the project will develop full on-robot and off-robot logistics of the harvested berries.