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Design, Development, and Field Testing of a Tomato Cluster Harvesting Robot
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With the aging population and labor shortage, the proportion of labor
costs in the production costs of tomato harvesting is increasing, making
the research and development of tomato harvesting robots urgent. This
paper develops a cherry tomato harvesting robot, consisting of a mobile
chassis, lifting platform, robotic arm, end effector, depth camera, and
control system, to achieve automated walking navigation, harvesting, and
basket loading. This paper outlines the system design and harvesting
scenario of the tomato harvesting robot, details the design and
implementation of the robot’s chassis and walking route planning, end
effector design, tomato recognition and positioning, robotic arm motion
planning, and control system. Furthermore, the architecture and control
flow of the software system are elaborated. Finally, field tests
validate the robot’s performance and harvesting success rate. Tests show
that the overall accuracy and recall rates of the visual recognition
model for tomato bunches are 85.04% and 88.71%, respectively, and for
the stalks are 82.72% and 81.51%, respectively, with a harvesting
success rate of 70.77%. These efforts provide beneficial exploration
for the future commercial application of cherry tomato harvesting
robots.
Title: Design, Development, and Field Testing of a Tomato Cluster Harvesting Robot
Description:
With the aging population and labor shortage, the proportion of labor
costs in the production costs of tomato harvesting is increasing, making
the research and development of tomato harvesting robots urgent.
This
paper develops a cherry tomato harvesting robot, consisting of a mobile
chassis, lifting platform, robotic arm, end effector, depth camera, and
control system, to achieve automated walking navigation, harvesting, and
basket loading.
This paper outlines the system design and harvesting
scenario of the tomato harvesting robot, details the design and
implementation of the robot’s chassis and walking route planning, end
effector design, tomato recognition and positioning, robotic arm motion
planning, and control system.
Furthermore, the architecture and control
flow of the software system are elaborated.
Finally, field tests
validate the robot’s performance and harvesting success rate.
Tests show
that the overall accuracy and recall rates of the visual recognition
model for tomato bunches are 85.
04% and 88.
71%, respectively, and for
the stalks are 82.
72% and 81.
51%, respectively, with a harvesting
success rate of 70.
77%.
These efforts provide beneficial exploration
for the future commercial application of cherry tomato harvesting
robots.
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