This study presents a comprehensive tomato harvesting robot system addressing three critical technical aspects: 1) optimal manipulator configuration design, 2) robust environmental recognition, and 3) efficient end effector control. For the manipulator configuration design, four different mounting configurations were systematically evaluated, with the vertical configuration featuring an offset end effector achieving the highest target reachability of 97.7%. For environmental recognition, a multi-sensor system that combines RGB and depth (RGBD) cameras and light detection and ranging (LiDAR) was implemented, utilizing depth filtering to suppress outliers. The end effector integrates suction and cutting mechanisms, employing a suction pad with conforming motion and Bowden cable-driven scissors. A bunch model was developed based on actual fruit bunches to create a testing environment with diversity and reproducibility. Field experiments conducted in a commercial greenhouse demonstrated continuous harvesting operations with a 68% suction success rate and a 45% overall harvesting success rate across 159 target fruits from 200 bunches. Additionally, the fruit position distribution in the field was measured, which can be utilized for layout optimization. This study contributes to advancing practical agricultural robotics by providing validated solutions for the three fundamental challenges in robotic crop manipulation.

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