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

Author:

Fang Jiahao12ORCID,Shi Yongliang3ORCID,Cao Jianhua4ORCID,Sun Yao2,Zhang Weimin1

Affiliation:

1. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

2. Automotive Walking Technology (Beijing) Co., Ltd., Beijing 100071, China

3. Institute for AI Industry Research, Tsinghua University, Beijing 100084, China

4. Rubber Research Institute of Chinese Academy of Tropical Agricultural Sciences, Danzhou 571101, China

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.

Funder

Automotive Walking Technology (Beijing) Co., Ltd.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference54 articles.

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