Design of Fruit Harvesting Robot System Based on a Reachability and Inverse Reachability Map

This paper proposes a fruit-harvesting robot system that improves harvesting efficiency by utilizing a Reachability Map (RM) and an Inverse Reachability Map (IRM). The proposed system accurately detects fruit locations using You Only Look Once version 5 (YOLOv5)–based object detection and camera calibration. Through coordinate transformation and hand–eye calibration, the manipulator is precisely guided to the fruit’s 3D position. During the construction of the reachability map, the reachability index, manipulability isotropy, and harvesting index are jointly considered to quantitatively evaluate manipulator performance. Fruits accessible by the manipulator are prioritized for harvesting. For fruits that cannot be directly reached, the system computes the optimal base pose using the inverse reachability map, enabling the mobile manipulator to reposition itself for harvesting. To further enhance efficiency, multiple fruits are grouped to minimize unnecessary movements. The integrated system is implemented on the Robot Operating System 2 (ROS 2), where fruit detection, autonomous navigation, and harvesting are executed as independent nodes to support scalable and modular operation. Finally, the proposed system is validated in a simulated orchard environment, confirming its effectiveness in improving autonomous fruit-harvesting performance.