Autonomous Fruit Picking


Inspire robotics gripper testing


Vision model testing


xArm5 testing in simulation


xArm5 testing in real


A Master Thesis project on the investigation and usage of robot arm for fruit picking.

Robotics in the agricultural sector has a great potential to simplify tasks, reduce fatigue for laborers, and perform repetitive tasks persistently. Tasks like fruit detection, grasping, packaging, etc. are employed for robotic fruit harvesting. The project aims to add value to the fruit harvesting task with robots by simplifying and testing novel and smart vision- based solutions. The deployment of robots for fruit picking has been a challenging task due to uncertainties involved in the environment, etc. The scope of the work is narrowed down to fruit localization, grasping, and placing tasks. The project targets simulation and experimentation validation for the fruit grasping approach, however, the scope of the work is limited to robotic manipulation with vision.

The state-of-the-art methodologies in fruit harvesting with robots focus on a two-stage process, in which first the fruit location is identified, and then further processing is implemented with sphere fitting in fruit point cloud points, centroid estimation axis estimations, and so on. The existing challenges are occlusion, fruit shape estimation, and interference from leaves and branches, slippage, etc. The project is in collaboration with the Institute of Mechanism Theory, Machine Dynamics and Robotics (IGMR), RWTH Aachen as part of Thesis. The next step of the project is combining the autonomous manipulation with the autonomous navigation to perform autonomous fruit harvesting. Subsequent sections will outline the objectives and their descriptions.