Vision-Based Control of an Industrial Vehicle in Unstructured Environments

Most of the previously proposed vehicle controllers are developed with the no-skid assumption, also presume that full states of the vehicle are available, which are usually violated for industrial tractors. This article dedicates to building a practical vision-based antiskid tracking control framework for industrial tractors. For that, we first present a novel nonlinear visual-inertial estimator (VIE) to estimate the real-time position and velocity of the tractor in unstructured environments (GPS-denied, no prior feature maps, etc.). Subsequently, VIE-based kinematic and dynamic controllers are designed for trajectory tracking. The proposed kinematic controller is easy to implement. It works well even if the vehicle is towing payloads since the sideslip angle can be reactively estimated and compensated. Besides, by fully considering vehicle dynamics, the VIE-based dynamic controller can be compatible with occasions that require higher control performance. It is proved by Lyapunov theory that the asymptotic stability of the closed-loop estimation-control system is guaranteed. Extensive full-scale experiments on an autonomous tractor are implemented to show the validity of the VIE-based control system in unstructured environments.

Automated summary

This article introduces a novel VIE estimator and VIE control framework for industrial tractors, addressing the challenges of incomplete vehicle state availability and unstructured environments in industrial settings. The system is proven to be effective through extensive real-world experiments.