Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

Abstract

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency.

Stability Analysis of Planetary Rotor with Variable Speed Self Rotation and Uniform Eccentric Revolution in the Rubber Tapping Machinery

“…Meanwhile, the noise can be caused and may have harm on the operator. In recent years, many scholars have attempted to use intelligent rubber tapping machinery, including rubber tapping robots [8][9][10][11][12][13] and fixed rubber tapping machines [14][15][16][17] instead of manual rubber tapping. They want to realize the precise tapping depth control of intelligent rubber tapping machinery by introducing industrial ranging technology involving machine vision [18][19][20], laser ranging [21,22] and ultrasonic ranging [9].…”

A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

“…Nie et al [35] later took the optimized DBSCAN trunk recognition method as the front-end input of their SLAM system, which made it possible to synchronize the positioning and mapping between forests based on rubber trunk landmarks. Fang et al [36] used a distance-adaptive Euclidean clustering method combined with cylinder fitting to identify tree trunks. However, due to the complexity of the operating environment in the forest and the fragility of Euclidean cylindrical clustering dependent on geometry, it is difficult for the robot to separate the trunk point clouds from the background point clouds.…”