Rut tracking and steering control for autonomous rut following

Abstract

Ruts formed as a result of vehicle traversal on soft ground are used by expert off road drivers because they can improve vehicle safety on turns and slopes thanks to the extra lateral force they provide to the vehicle. In this paper we propose a rut detection and tracking algorithm for Autonomous Ground Vehicles (AGVs) equipped with a laser range finder. The proposed algorithm utilizes an Extended Kalman Filter (EKF) to recursively estimate the parameters of the rut and the relative position and orientation of the vehicle with respect to the ruts. Simulation results show that the approach is promising for future implementation.

Laser‐based rut detection and following system for autonomous ground vehicles

“…Then a set of average templates {𝕋 i : $i = 1, 2, 3, 4\}$ was constructed for the quadrants as shown in Figure 9. However, it is expected that a larger vehicle may encounter a wider variety of rut shapes and therefore may require a larger number of templates, which can be generated by using a template scaling procedure such as the one used in Ordonez et al (2009b). …”

“…However, the approach of Ordonez et al (2009a) did not make use of the spatiotemporal coherence that exists between the detections from consecutive laser scans while the vehicle is in motion. The research of Ordonez, Chuy, Collins, and Liu (2009b) incorporates the spatiotemporal coherence between measurements by using a rut detection and tracking module based on an extended Kalman filter (EKF) that recursively estimates the parameters of the ruts (tracking) and uses these estimates to improve the detection of the ruts for subsequent laser scans. In addition, the EKF generates smooth state estimates of the relative position and orientation (i.e., the ego state) of the vehicle with respect to the ruts, which are the inputs to the steering control system used to follow the ruts.…”

“…In addition, the EKF generates smooth state estimates of the relative position and orientation (i.e., the ego state) of the vehicle with respect to the ruts, which are the inputs to the steering control system used to follow the ruts. However, the work presented in Ordonez et al (2009b) included only simulation results.…”

“…The main contributions of this paper are the inclusion of a new rut detection method based on a path planner, which is used to detect a set of ruts (from multiple candidates) that point in the general direction of the goal and to provide the initial state estimates required by the rut tracking and the steering control system. In addition, the paper presents an experimental evaluation of the rut tracking module and steering controller originally proposed and simulated in Ordonez et al (2009b).…”

Rut Detection and Following for Autonomous Ground Vehicles

“…However, the approach of [20] did not make use of the spatio-temporal coherence that exists between the detections from consecutive laser scans while the vehicle is in motion. The research of [21] incorporates the spatio-temporal coherence between measurements by using a rut detection and tracking module based on an extended Kalman filter (EKF) that recursively estimates the parameters of the ruts (tracking) and uses these estimates to improve the detection of the ruts for subsequent laser scans. In addition, the Kalman filter also generates smooth state estimates of the relative position and orientation (i.e., the ego-state) of the vehicle with respect to the ruts, which are the inputs to the steering control system used to follow the ruts.…”