Optimization of a bionic end-effector for automated cherry tomato harvesting using Response Surface Methodology and...

Area of Science:

• Agricultural Engineering
• Robotics
• Biomimetics

Background:

• Cherry tomato harvesting is labor-intensive, necessitating automated solutions.
• End-effectors are critical components for robotic harvesting systems.
• Bionic designs offer potential for enhanced end-effector functionality.

Purpose of the Study:

• To design and optimize a novel bionic end-effector for cherry tomato harvesting.
• To improve the efficiency and success rate of automated cherry tomato picking robots.
• To provide a theoretical foundation for future end-effector development.

Main Methods:

• Developed a three-gripper end-effector mimicking vulture leg phalangeal structure.
• Employed Response Surface Methodology (RSM) with Box Behnken Design (BBD) for optimization.
• Varied parameters: Width (WDMP), Thickness (TDMP), Angle (AEDP), and Distance (DPAS); optimized for fruit retention (DCDP) and damage risk (DCMP).

Main Results:

• Identified optimal parameters: WDMP 16 mm, TDMP 5 mm, AEDP 90°, DPAS 7.5 mm.
• Experimental validation showed a harvesting success rate of at least 88%.
• The bionic end-effector demonstrated effective cherry tomato retention and safe handling.

Conclusions:

• The bionic end-effector design significantly enhances cherry tomato harvesting efficiency.
• The optimization strategy successfully identified parameters for high harvesting success.
• This research offers a valuable theoretical basis for robotic end-effector design in agriculture.