自然环境下柑橘采摘机器人识别定位系统研究

Research and Experiment on Recognition and Location System for Citrus Picking Robot in Natural Environment

为了准确理解柑橘采摘机器人在自然环境下的作业场景,获取采摘目标及周围障碍物的位置信息,构建了基于卷积神经网络和Kinect V2相机的识别定位系统。首先,对采摘场景中的果树提出5类目标物分类准则,包含1类可采摘果实和4类障碍物目标;然后,在YOLO V3(You only look once)卷积层模块中添加3层最大池化层,对预测候选框进行K-means聚类分析,增强模型对枝叶类物体特征的提取能力,实现采摘场景的准确理解;最后,采用Kinect V2相机的深度图映射得到采摘目标和障碍物的三维信息,并在自然环境下进行了避障采摘作业。实验结果表明,构建的识别定位系统对障碍物和可采摘果实的识别综合评价指数分别为83.6%和91.9%,定位误差为5.9 mm,单帧图像的处理时间为0.4 s,采摘成功率和避障成功率分别达到80.51%和75.79%。

For citrus picking robot in natural environment,the accurate recognition and location vision system is one of the key factors ensuring the efficiency and safety of picking operations.In order to make the robot not only acquire the location information of the picking target accurately but also the surrounding obstacles,a novel obstacle recognition and location system based on Kinect V2 and improved you only look once(YOLO V3)algorithm was proposed.Firstly,five classification principles of citrus tree in natural orchard were defined,including one class that the fruit can be picked directly and four obstacle classes.Secondly,three maximum pooling layers were added to the convolution module of the YOLO V3 structure and K-means clustering analysis was conducted on anchor box to enhance the feature extraction performance of branches and leaves of the convolution neural network.Finally,three-dimensional coordinates of the classification targets were obtained by using the Kinect V2 depth mapping to guide obstacle-avoiding picking operation.The experimental results showed that the F 1 scores of obstacles and normal fruits were 83.6%and 91.9%,respectively,the positioning error was 5.9 mm and the processing time of each frame was 0.4 s,the success picking rate was 80.51%and success rate of obstacle avoidance was 75.79%.The research results provided a basis and guide for the picking path planning and obstacle avoidance of robotic harvesting task in natural scene.