Estimation of log-gripping position using instance segmentation for autonomous log loading

ABSTRACT

Autonomous forestry machinery is necessary both to ensure safety and improve productivity. Previous research related to automation technology for forestry machinery has mainly focused on autonomous driving; research on log loading/unloading is still in progress. To automate the loading and unloading of logs, it is necessary to evaluate the errors of several processes quantitatively: detecting logs in the environment, estimating the gripping position, and controlling the machine. This paper focuses on the development of an autonomous log loading operation. This study aims to propose an estimation method for log gripping position based on log detection using instance segmentation. Evaluation of the proposed system shows that the root mean square errors in the radial, axial, and vertical directions are 0.162, 1.526, and 0.140 m for sparse logs, 0.384, 0.271, and 0.119 m for dense logs, and 0.764, 1.022, and 0.194 m for unorganized logs, respectively. Our results demonstrate that the proposed method is sufficiently accurate to achieve gripping of a single log; however, the accuracy is insufficient for gripping one in a dense group of logs accurately.

KEYWORDS:

• Automation
• autonomous forestry machinery
• forestry robot
• log detection
• stereo camera

Acknowledgements

Authors would like to thank the staff of Forestry Agency Forest Mechanization Center for technical assistance with the experiments and providing us with the field.

Authors acknowledge the use of ChatGPT for grammar check in the preparation of this research paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the research program on the development of innovative technology grants [JPJ007097] from the Project of the Bio-oriented Technology Research Advancement Institution (BRAIN) and JSPS KAKENHI Grant Number [JP20K15560].