This paper addresses the labor shortages and high costs in traditional agriculture by designing an intelligent orchard picking robot. The robot leverages the ROS operating system, NVIDIA Jetson Nano, and a tracked chassis to navigate complex terrains. Key technologies include SLAM for real-time mapping, A* for path planning, and YOLOv5 for fruit detection. Experiments conducted in a simulated orchard environment demonstrate the robot's effectiveness in autonomous navigation and accurate fruit recognition, achieving 95% recognition accuracy in clear conditions and 75% under occlusion. The results showcase the robot's potential to significantly enhance productivity and reduce labor demands in orchard harvesting.


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    Title :

    Design and Implementation of Intelligent Orchard Robot


    Contributors:
    Wu, Jiaming (author) / Ge, Yuxuan (author) / Wu, Jianxiong (author) / Cao, Jin (author) / Feng, Junhao (author)


    Publication date :

    2024-12-13


    Size :

    3579075 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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