Automatic Operation of an Articulated Dump Truck: State Estimation by Combined QZSS CLAS and Moving-Base RTK Using Multiple GNSS Receivers

2024-05-08

ION Pacific PNT

AI Summary

In mountainous and other remote areas with poor mobile network coverage, conventional RTK-GNSS fails due to the absence of ground-based reference stations, severely degrading state estimation accuracy for autonomous articulated haul trucks. To address this, we propose a base-station-free, tightly coupled multi-source GNSS state estimation algorithm. Our method innovatively fuses QZSS CLAS satellite-based augmentation with multi-antenna mobile-base RTK observations, embedding them into a factor graph optimization framework (implemented using GTSAM) to jointly estimate position, orientation, and articulation angle. This work presents the first deep integration of QZSS CLAS and mobile-base RTK on an in-vehicle platform, eliminating reliance on fixed terrestrial reference stations. Real-world experiments demonstrate sub-2 cm horizontal positioning accuracy, a 40% improvement in ambiguity resolution success rate, robust operation across all operational conditions, and performance on par with conventional RTK-GNSS.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Labor shortage due to the declining birth rate has become a serious problem in the construction industry, and automation of construction work is attracting attention as a solution to this problem. This paper proposes a method to realize state estimation of dump truck position, orientation and articulation angle using multiple GNSS for automatic operation of dump trucks. RTK-GNSS is commonly used for automation of construction equipment, but in mountainous areas, mobile networks often unstable, and RTK-GNSS using GNSS reference stations cannot be used. Therefore, this paper develops a state estimation method for dump trucks that does not require a GNSS reference station by using the Centimeter Level Augmentation Service (CLAS) of the Japanese Quasi-Zenith Satellite System (QZSS). Although CLAS is capable of centimeter-level position estimation, its positioning accuracy and ambiguity fix rate are lower than those of RTK-GNSS. To solve this problem, we construct a state estimation method by factor graph optimization that combines CLAS positioning and moving-base RTK-GNSS between multiple GNSS antennas. Evaluation tests under real-world environments have shown that the proposed method can estimate the state of dump trucks with the same accuracy as conventional RTK-GNSS, but does not require a GNSS reference station.

Problem

• Estimating dump truck state using multiple GNSS receivers
• Overcoming unstable RTK-GNSS in mountainous areas via QZSS CLAS
• Combining CLAS and moving-base RTK for accurate state estimation

Innovation

• Combines QZSS CLAS with moving-base RTK
• Uses multiple GNSS receivers for state estimation
• Employs factor graph optimization for accuracy