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

Abstract

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.