Research Progress of Autonomous Robot Navigation Technology for Indoor Farm
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摘要:
介绍了视觉导航、激光导航、卫星导航和路标导航等机器人导航技术,分析了不同类型的机器人导航技术在特定应用场景下的导航精度、实时性、适应性。根据这些导航技术的特点,结合在梅州市金绿现代农业发展有限公司室内养殖场开展的导航技术试验结果,指出了在室内养殖场内应用单一导航技术存在的问题,并展望多种导航方法信息融合的导航技术在室内养殖场的应用前景,期望为突破机器人饲喂、运输、采摘关键技术,解决制约农业智能化、无人化发展难题提供思路。
Abstract:Robot navigation technologies such as vision navigation, laser navigation, satellite navigation and road sign navigation were introduced, and navigation accuracy, real-time and adaptability of different types of robot navigation technologies in specific application scenarios were analyzed.According to characteristics of these navigation technologies, combined with results of navigation technology test conducted in the indoor farm of Meizhou Golden Green Modern Agriculture Development Co., Ltd., problems existing in application of single navigation technology in indoor breeding farms were pointed out, and application prospect of navigation technology based on information fusion of multiple navigation methods in indoor breeding farms were looked forward, hoping to provide ideas for breaking through key technologies of robot feeding, transportation and picking, and solving problems restricting development of intelligent and unmanned agriculture.
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Keywords:
- indoor farm /
- robot /
- autonomous navigation technology /
- visual navigation technology /
- sensor
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表 1 基于传统图像处理的导航路径提取结果对比
Table 1. Comparison of navigation path extraction results based on traditional image processing
作业
对象图像预处理 图像分割 特征提取 导航线
拟合图像
分辨率/
像素处理
时间/ms准确率/
平均距
离误差玉米行 像素归一化 过滤算法、最大类
间方差法基于在静态感兴趣区域内利用特征点聚类算法拟合玉米行识别线更新和优化动态感兴趣区域 最小二乘法 1 920 × 1080 97.56 96% 玉米根茎 2G-R-B灰度图像 最大类间方差法 高斯滤波器平滑累加曲线、极值法等剔除伪特征点 最小二乘法 1280 × 720 236.00 92% 水稻 畸变矫正、高斯
滤波、灰度化与
二值化基于2G-R-B超红特征
模型的综合阈值法垂直投影值动态设定感兴趣区域,水平扫描拟合关键点 三次B样条曲线拟合法 1292 × 964 38.00 4.23 cm 表 2 神经网络处理的导航路径提取方法
Table 2. Navigation path extraction method based on neural network processing
网络 导航线拟合 数据集/幅 图像分辨率 处理时间/识别率 平均距离误差/cm U-Net 扫描边缘信息提取法三次B样条曲线拟合 1300 1280 ×720 154.0 ms 4.4 YOLOV3 最小二乘法 800 1024 ×768 95.37% 3.3 Faster R-CNN 三次样条插值法,最小二乘法 1200 3024 × 4032 90.00% 5.2 -
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