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

Abstract

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.

Rubber Tree Tapping Position Detection on Trunk-Covered RGB-D Images for Automation Platform

“…Especially the work of Zhang et. al [2], which showed an autonomous rubber tree navigation vehicle with 2D LiDAR and gyroscope sensors. The vehicle travels along the tree row and aligns itself to each tree with a robot arm at a tap-able distance.…”

“…Many developments of robot vision show the use of cameras in ways that the acquired images can be simply processed by the algorithm to detect the objects with a small amount of time on mobile platforms where computation powers are considerably limited. We present a camera with a light positioning technique for capturing rubber tree images which is similar to Zhang's autonomous vehicle navigation pattern [2] and the automatic rubber latex farming robot patents such as [3], [4]. We chose the Intel RealSense camera D400 series [5] (D415 model) because it meets the requirements for Zhang's autonomous vehicle system.…”

Research on Vision System Calibration Method of Forestry Mobile Robots

“…In developing the forestry vision system, researchers initially used LiDAR or a camera. LiDAR can be used to detect the diameter and position of trees in forest environments [5]- [8], and a camera can capture the color and texture features of the forest environment for detection and classification [9]- [12]. A three-dimensional (3D) LiDAR can obtain more complete shape and position information, but it is highly expensive.…”

Improvement of Productivity of the Moderate Metabolism Clone GT 1 of Hevea Brasiliensis Muell. Arg. by Early Upward Tapping in Côte d'Ivoire

“…In the natural rubber growing region of Africa, the management of rubber plantation is carried out according to the tapping panel management standard scheme [1]. It consists of latex harvesting on the downward panels (BO) and upward panels (HO).…”

“…This revealed that most productive systems, especially the earliest applied upward tapping, further weaken the rubber trees [5,6] and exacerbate their sensitivity to TPD. The feasibility of upward tapping from the fourth year of semi-spiral downward tapping (early upward tapping relative to the current tapping panel management standard; [1]), is investigated by reducing the bark panel length to quarter of a spiral to avoid overexploitation of trees [7]. To achieve this upward tapping after increasing periods of downward tapping was conducted at two sites in South-East and Central-West of Côte d'Ivoire, for eight years.…”