Research on the Strawberry Picking Robot with Improved YOLO V11 and Adaptive Path Planning

DOI: https://doi.org/10.62517/jike.202504202

Author(s)

Dong Yu*

Affiliation(s)

College of Electronic Information, Southwest Minzu University, Chengdu, China *Corresponding Author.

Abstract

To address the challenges of target recognition difficulty, complex path planning, and low operational efficiency in strawberry picking, this paper designs and implements an intelligent strawberry picking robot that integrates YOLO V11 object detection and adaptive path planning technologies. The robot system consists mainly of a visual recognition module, motion control module, robotic arm picking module, and LiDAR module. The visual system is based on the YOLO V11 deep learning model, which incorporates the CA attention mechanism to achieve high-precision recognition and 3D localization of strawberry fruits. By integrating a depth camera with sensor fusion algorithms, the system effectively identifies strawberries under various stages of ripeness and occlusion. The navigation system adopts an adaptive path planning strategy based on ROS, combining global path planning with local obstacle avoidance algorithms to improve the robot's mobility efficiency and stability in the complex path conditions of greenhouse environments. The robotic arm and end-effector use coordinate transformation and posture planning to achieve flexible strawberry picking. This research provides a feasible solution for the automation and intelligence of strawberry picking operations.

Keywords

To address the challenges of target recognition difficulty, complex path planning, and low operational efficiency in strawberry picking, this paper designs and implements an intelligent strawberry picking robot that integrates YOLO V11 object detection an

References

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