Precision Harvesting in Cluttered Environments: Integrating End Effector Design with Dual Camera Perception

TL;DR

This paper presents a novel robotic harvesting system for cluttered high tunnel environments, integrating dual camera perception and end effector design to improve precision and efficiency in fruit localization and picking.

Contribution

The study introduces a codesigned framework combining global and eye-in-hand cameras with an end effector, enabling precise localization and reliable harvesting in cluttered environments.

Findings

Achieved 85.0% fruit detection success rate in field tests.

Average harvesting time per fruit was 10.98 seconds.

Demonstrated effective operation in high tunnel, cluttered environments.

Abstract

Due to labor shortages in specialty crop industries, a need for robotic automation to increase agricultural efficiency and productivity has arisen. Previous manipulation systems perform well in harvesting in uncluttered and structured environments. High tunnel environments are more compact and cluttered in nature, requiring a rethinking of the large form factor systems and grippers. We propose a novel codesigned framework incorporating a global detection camera and a local eye-in-hand camera that demonstrates precise localization of small fruits via closed-loop visual feedback and reliable error handling. Field experiments in high tunnels show our system can reach an average of 85.0\% of cherry tomato fruit in 10.98s on average.