Pick the best strawberry

Pick the best strawberry

Problem being addressed

Labour shortage is a major challenge for many sectors including agriculture and bringing robotic arms to the field is a response to this global challenge of labour shortage. However​,​ precise​,​ reliable and fast motion planning is one of the key bottlenecks of a robotic fruit picker. A sophisticated robotic picking technology is only capable of successfully picking isolated strawberries whereas many of the strawberries are grown in clusters.

Solution

An interactive primitive-based planning strategy that features pushing actions in complex clusters. The proposed approach targets a specific application, the one of robotic harvesting, and tackles the problem of picking occluded fruits. Occlusion results normally from the variety of grown clusters. In order to generate different degrees of non-linearity in the system behavior, the planner learns two movement primitives from demonstrations, conditions the resulting primitive to pass through selective neighbors (movable obstacles), reasons then on the pushing direction of each of them and finally augments them with an updated pose.

Advantages of this solution

The suggested approach successfully performs the pushing movements and reaches the occluded ripe strawberry in different configurations.

Solution originally applied in these industries

agriculture

Agriculture Industry

Possible New Application of the Work

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

Electronics and Sensors Industry

A human may push/move objects to reach-and-pick a fruit or reach-and-grasp an object in a toolbox or fridge. Some previous studies researched some of such problems in robotic context. For instance, consider problems limited to 2-D problem of objects rearrangement on a flat surface, e.g. in a fridge or on a shelf, to reach and grasp the desired object in a cluttered scene. The suggested approach can be successfully used in robotics for space optimization, especially when objects are placed in clusters.

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