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

文献类型： 外文期刊

作者： Fang, Jiahao 1 ; Shi, Yongliang 3 ; Cao, Jianhua 4 ; Sun, Yao 2 ; Zhang, Weimin 1 ;

作者机构： 1.Beijing Inst Technol, Sch Mechatron Engn, Beijing 100081, Peoples R China

2.Automot Walking Technol Beijing Co Ltd, Beijing 100071, Peoples R China

3.Tsinghua Univ, Inst AI Ind Res, Beijing 100084, Peoples R China

4.Chinese Acad Trop Agr Sci, Rubber Res Inst, Danzhou 571101, Peoples R China

关键词： active navigation; pose tracking; factor graph; trunk detection; hybrid map

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 15 期

页码：

收录情况： SCI

摘要： 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.

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