Estimation of articulated angle in six-wheeled dump trucks using multiple GNSS receivers for autonomous driving (2312.02510v1)

Abstract: Due to the declining birthrate and aging population, the shortage of labor in the construction industry has become a serious problem, and increasing attention has been paid to automation of construction equipment. We focus on the automatic operation of articulated six-wheel dump trucks at construction sites. For the automatic operation of the dump trucks, it is important to estimate the position and the articulated angle of the dump trucks with high accuracy. In this study, we propose a method for estimating the state of a dump truck by using four global navigation satellite systems (GNSSs) installed on an articulated dump truck and a graph optimization method that utilizes the redundancy of multiple GNSSs. By adding real-time kinematic (RTK)-GNSS constraints and geometric constraints between the four antennas, the proposed method can robustly estimate the position and articulation angle even in environments where GNSS satellites are partially blocked. As a result of evaluating the accuracy of the proposed method through field tests, it was confirmed that the articulated angle could be estimated with an accuracy of 0.1$^\circ$ in an open-sky environment and 0.7$^\circ$ in a mountainous area simulating an elevation angle of 45$^\circ$ where GNSS satellites are blocked.

• The paper introduces a novel GNSS-based graph optimization approach to precisely estimate the articulated angle and vehicle position.
• It integrates RTK-GNSS data with geometric constraints from four antennas to ensure high accuracy despite varying satellite visibility.
• Field experiments validated the method's robustness, demonstrating reliable performance in both open-sky and challenging mountainous conditions.

Introduction to the Problem

The construction industry is undergoing a transformation with the integration of automation to counter labor shortages and improve efficiency. Specifically, autonomous operation of construction machinery such as six-wheeled articulated dump trucks is emerging as a solution. Among the challenges of automating such vehicles is the need for precise state estimation, notably the accurate determination of the vehicle's position and the measurement of its articulated angle—the pivotal joint angle which effectively steers the vehicle.

Approach for Estimation

A novel approach is introduced for estimating the position and the articulated angle of six-wheeled articulated dump trucks by implementing four GNSS (Global Navigation Satellite System) antennas mounted on the vehicle. The method involves graph optimization to process multiple constraints from GNSS data, allowing for accurate state estimation of the trucks. Explicitly, the method utilizes real-time kinematic (RTK) GNSS for precise point positioning, while geometric constraints are derived from the physical arrangement of the GNSS antennas on the truck.

Estimation Methodology

The core of the solution involves a factor graph structure where the objective is the minimization of errors derived from GNSS observations and geometric constraints. This is undertaken through leveraging RTK-GNSS data, alongside a factor for moving-base RTK-GNSS, which accounts for the relative movement between base and rover (or mobile) GNSS units on the vehicle. Further enhancing this are additional factors like GNSS Doppler velocity and baseline length between antennas, which are utilized for refining the state estimation.

Experiments and Results

Field experiments were conducted to evaluate the method's performance. The GNSS antennas fixed on a dump truck provided data which, post-processing, indicated highly accurate estimations of both position and articulated angles. Testing across environments with different GNSS signal blockage levels, typified by open skies versus mountainous terrain, showcased the robustness of the approach. While alternative methods struggled with decreasing GNSS satellite visibility, this new approach remained reliable, maintaining a high accuracy rate reminiscent of an open-sky environment.

Conclusion and Future Perspectives

This paper's findings significantly advance the capability for automated operation of construction machinery in diverse environments. The utilization of GNSS technology and graph optimization for state estimation proved effective in both open-sky conditions and simulated mountainous areas with limited GNSS visibility. Future works could involve integrating Inertial Navigation System (INS) data to further bolster state estimation under more challenging conditions, as well as real-time assessment of the articulated angle and positioning to fully support autonomous operations.