MSc Project

Investigation into Harvesting Soft Fruit Clusters

We propose an investigation into harvesting soft fruit clusters with robotics. Separating the problem into two distinct problems: motion planning to grasp the soft fruit (task 1); motion planning to release the soft fruit from the cluster (task 2). We will apply probabilistic movement primitives updated with model predictive control to task 1 and a probabilistic movement primitive architecture to task 2. We intend to test task 1 on strawberry clusters and task 2 on mushroom clusters.

PhD Project

Data-driven methods for detecting fruit and planning harvesting actions in dense cluster (DPFH)

Selective harvesting of soft fruit is a very challenging problem involving computer vision, motion planning, motion control, scheduling, and optimisation. State-of-the-art (SOTA) robotic system for selective harvesting of soft fruits are still far away from a human performance level, partially because the conventional planning cannot provide a feasible solution for the robot to reach-and-pick the soft fruit.   

DPFH aims to develop SOTA method for interactive motion planning and control for picking soft-fruits in a cluster where the robot needs to push the occluding pieces away to (i) better detect, segment and localise a ripe fruit and (ii) reach-and-pick the ripe fruit. The effectiveness of the developed methods in this project will be demonstrated in simulation environments, in the lab with toy strawberries and in the strawberry field.