Predictive Scheduling of Collaborative Mobile Robots for Improved Crop-transport Logistics of Manually Harvested Crops

TL;DR

This paper introduces a predictive stochastic scheduling system for collaborative robots that transport trays during manual strawberry harvesting, significantly reducing non-productive walking time and increasing harvest efficiency.

Contribution

It develops and evaluates a novel co-robotic system with a scheduling algorithm that optimizes harvest logistics, demonstrating substantial efficiency improvements in real-world trials.

Findings

Harvest efficiency increased by around 10%.

Non-productive time reduced by 60%.

System applicable to other manually harvested crops.

Abstract

Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This work presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this work can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.