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A Rubber-Tapping Robot Forest Navigation and Information Collection System Based on 2D LiDAR and a Gyroscope

机译：基于二维LiDAR和陀螺的橡胶攻牙机器人森林导航与信息采集系统

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Natural rubber is widely used in human life because of its excellent quality. At present, manual tapping is still the main way to obtain natural rubber. There is a sore need for intelligent tapping devices in the tapping industry, and the autonomous navigation technique is of great importance to make rubber-tapping devices intelligent. To realize the autonomous navigation of the intelligent rubber-tapping platform and to collect information on a rubber forest, the sparse point cloud data of tree trunks are extracted by the low-cost LiDAR and a gyroscope through the clustering method. The point cloud is fitted into circles by the Gauss–Newton method to obtain the center point of each tree. Then, these center points are threaded through the Least Squares method to obtain the straight line, which is regarded as the navigation path of the robot in this forest. Moreover, the Extended Kalman Filter (EKF) algorithm is adopted to obtain the robot’s position. In a forest with different row spacings and plant spacings, the heading error and lateral error of this robot are analyzed and a Fuzzy Controller is applied for the following activities: walking along one row with a fixed lateral distance, stopping at fixed points, turning from one row into another, and collecting information on plant spacing, row spacing, and trees’ diameters. Then, according to the collected information, each tree’s position is calculated, and the geometric feature map is constructed. In a forest with different row spacings and plant spacings, three repeated tests have been carried out at an initial speed of 0.3 m/s. The results show that the Root Mean Square (RMS) lateral errors are less than 10.32 cm, which shows that the proposed navigation method provides great path tracking. The fixed-point stopping range of the robot can meet the requirements for automatic rubber tapping of the mechanical arm, and the average stopping error is 12.08 cm. In the geometric feature map constructed by collecting information, the RMS radius errors are less than 0.66 cm, and the RMS plant spacing errors are less than 11.31 cm. These results show that the method for collecting information and constructing a map recursively in the process of navigation proposed in the paper provides a solution for forest information collection. The method provides a low-cost, real-time, and stable solution for forest navigation of automatic rubber tapping equipment, and the collected information not only assists the automatic tapping equipment to plan the tapping path, but also provides a basis for the informationization and precise management of a rubber plantation.
机译：天然橡胶因其卓越的品质而广泛用于人类生活。目前，手动攻丝仍是获得天然橡胶的主要方法。在攻丝行业中对智能攻丝设备的需求非常迫切，而自主导航技术对于使橡胶攻丝设备变得智能化至关重要。为了实现智能挖胶平台的自主导航，并在橡胶林上进行信息采集，通过低成本的LiDAR和陀螺仪通过聚类的方法提取树干的稀疏点云数据。通过高斯-牛顿法将点云拟合成圆形，以获得每棵树的中心点。然后，通过最小二乘法将这些中心点穿线以获得直线，该直线被视为该森林中机器人的导航路径。此外，采用扩展卡尔曼滤波器（EKF）算法来获取机器人的位置。在行距和行距不同的森林中，分析该机器人的航向误差和侧向误差，并针对以下活动应用模糊控制器：沿着具有固定侧向距离的一行行走，在固定点处停止，从从一排到另一排，并收集有关植物间距，行间距和树木直径的信息。然后，根据收集到的信息，计算每棵树的位置，并构建几何特征图。在行距和植物间距不同的森林中，以0.3 m / s的初始速度进行了三个重复的测试。结果表明，均方根（RMS）侧向误差小于10.32 cm，这表明所提出的导航方法提供了很好的路径跟踪。机器人的定点停止范围可以满足自动敲击机械臂的要求，平均停止误差为12.08 cm。在通过收集信息构建的几何特征图中，RMS半径误差小于0.66 cm，RMS植物间距误差小于11.31 cm。这些结果表明，本文提出的导航过程中的信息收集和递归构造方法为森林信息的收集提供了解决方案。该方法为自动橡胶攻丝设备的森林导航提供了一种低成本，实时，稳定的解决方案，所收集的信息不仅有助于自动橡胶攻丝设备规划攻丝路径，而且为信息化和实用化提供了依据。橡胶园的精确管理。

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