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

Jiahao Fang, Yongliang Shi*, Jianhua Cao, Yao Sun, Weimin Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Article number3717
JournalRemote Sensing
Volume15
Issue number15
DOIs
Publication statusPublished - Aug 2023

Keywords

  • active navigation
  • factor graph
  • hybrid map
  • pose tracking
  • trunk detection

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Fang, J., Shi, Y., Cao, J., Sun, Y., & Zhang, W. (2023). Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection. Remote Sensing, 15(15), Article 3717. https://doi.org/10.3390/rs15153717