Digital Twin-based Collision Avoidance System for Autonomous Excavator with Automatic 3D LiDAR Sensor Calibration

Abstract

This paper proposes a real-time collision avoidance system with automatic three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor calibration as a means of meeting the increasing demand for safety in construction automation. Although a typical system requires object detection to prevent collisions with obstacles in the workspace, practical safety performance relies heavily on detection accuracy and processing time delays. To achieve both robustness and operational efficiency while increasing safety, we propose a system that determines the possibility of a collision from the observed point cloud and the posture of an excavator without detecting objects. This is achieved by introducing an excavator model synchronized with a real one as a digital twin and evaluating the overlap between the volume occupied by the model and the point cloud observed by the 3D LiDAR sensor. Moreover, the algorithm to estimate the position and orientation of the 3D LiDAR was developed utilizing a digital twin and the probabilistic sequential estimation technique. The proposed system was successfully demonstrated through experiments using a real excavator, making us confident that deploying the system, from the installation of LiDAR to normal operation, could be fully automated.