内蒙古农业大学学报（自然科学版）2026，Vol.47Issue(1)：66-76,11.DOI:10.16853/j.cnki.1009-3575.2026.01.009
基于RT-DETR视觉定位和机械臂路径规划的马铃薯分拣系统
Potato Sorting System Based on RT-DETR Visual Positioning and Manipulator Path Planning
摘要
Abstract
With the advancement of the potato staple food strategy in China,the cultivation area of potatoes has been expanding.However,existing segmented potato harvesters still face challenges in sorting and cleaning efficiency,with a high reliance on manual labor.Particularly,during the sorting process,it is difficult to effectively distinguish potatoes from soil clods and stones of similar shapes and sizes based on physical characteristics,leading to continued dependence on manual sorting at conveyor belts.To address this issue,this study designed a potato sorting system based on the deep learning target detection and robotic arm path planning,aim-ing to reduce the labor intensity and improve the sorting efficiency through automation technology.This would promote the wide-spread application of segmented potato harvesters and support the implementation of the potato staple food strategy.The potato sort-ing system consisted of two main components,i.e.,visual positioning and robotic arm path planning.The visual positioning module employed the RT-DETR(Real-Time DEtection TRansformer)algorithm to identify potatoes,soil clods,and stones,and to locate the targets.The camera calibration and hand-eye calibration were utilized to achieve the transformation between the world coordinate system,camera coordinate system,image coordinate system,and pixel coordinate system,while the spatial coordinates of the tar-gets were obtained based on the conveyor belt height.The robotic arm path planning module established the robotic arm link coordi-nate system using Denavit-Hartenberg(D-H)parameters,realized forward kinematic modeling through homogeneous transformation matrices,and simplified inverse kinematic solutions using a geometric approach to derive analytical expressions for joint angles.Path planning was performed using cubic polynomial interpolation,genarating smooth trajectories by setting initial and final angles and velocity boundary conditions,ensuring smooth acceleration changes in each joint for precise robotic arm movement.Experimen-tal results demonstrated that the RT-DETR lightweight detection model deployed on the PyTorch framework achieved an accuracy of 93.3%and a recall rate of 93%on the dataset.Under conditions of stacking,soil covering,and strong light,the system achieved recognition accuracies of 97.5%,90.3%,and 95%,respectively.By integrating the OpenCV and the Socket communication proto-col,the system's positioning error was controlled to≤1.342 mm.Combined with a progressive sorting strategy and cubic polynomial trajectory planning,the system achieved a sorting success rate of 92.9%and a false detection rate of 1.3%.The experiments validat-ed the system's robustness under various environmental conditions,effectively addressing challenges such as target overlap,morpho-logical variations,and environmental interference.Additionally,the system's average sorting cycle was 8.7 seconds per item,dem-onstrating its practical application value.The successful application of this system provided a feasible solution for the automated sort-ing of segmented potato harvesters,demonstrating significant practical value.Future work may focus on optimizing algorithms and hardware configurations to enhance the system's real-time performance and stability for more complex field operations.关键词
马铃薯除杂/深度学习/目标检测/路径规划/机械臂分拣Key words
Potato impurity removal/Deep learning/Target detection/Path planning/Mechanical arm sorting分类
农业科技引用本文复制引用
孔祥,吴英思,刘飞,玄德正,张安斌..基于RT-DETR视觉定位和机械臂路径规划的马铃薯分拣系统[J].内蒙古农业大学学报（自然科学版）,2026,47(1):66-76,11.基金项目
内蒙古自治区自然科学基金项目(2021MS05067) （2021MS05067）
