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

Abstract

An important off-road driving rule is to keep the vehicle wheels in existing ruts when possible. Rut following has the following benefits: (1) it prevents the ruts from serving as obstacles that can lead to undesirable vehicle vibrations and even vehicle instability at high speeds; (2) it improves vehicle safety on turns by utilizing the extra lateral force provided by the ruts to reduce lateral slippage and guide the vehicle through its path; (3) it improves the vehicle energy efficiency by reducing the energy wasted on compacting the ground; and (4) it increases vehicle traction when traversing soft terrains such as mud, sand, and snow. This paper first presents a set of field experiments that illustrate the improved energy efficiency and traction obtained by rut following. Then, a laser-based rut detection and following system is proposed so that autonomous ground vehicles can benefit from the application of this off-road driving rule. The proposed system utilizes a path planning algorithm to aid in the rut detection process and an extended Kalman filter to recursively estimate the parameters of a local model of the rut being followed. Experimental evaluation on a Pioneer 3-AT robot shows that the proposed system is capable of detecting and following ruts in a variety of scenarios. C

Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications?

“…Terrestrial laser scanning has also been used successfully in many other applications ranging from sediment deposition and erosion (Stenberg et al 2016) to mapping landslides (Jaboyedoff et al 2012) and estimating forest stand characteristics (Liang et al 2016). Mounting a LiDAR sensor to a moving vehicle has been reported by Ordonez et al (2011), Laurent et al (2012, Hyyti and Visala (2013) and Kage and Matsushima (2015). Until recently this has been considered too costly for rut depth extraction in timber harvesting.…”

Track-Index-Guided Sustainable Off-Road Operations Using Visual Analytics, Image Intelligence and Optimal Delineation of Track Features

“…This meets the requirements of both manual and autonomous navigation. Thus, the earlier works on the rut or track following vehicles as presented by [3,4,23] can be improved even further. The proposed methodology can support making intelligent on-board decisions for delineating track zones.…”

“…Investigators conducted a study of the rut following robotic movement on unpaved terrain [3]. Monocular-camera-based off-road track detection for the path following robot movement is proposed [4].…”

Rut Detection and Following for Autonomous Ground Vehicles

“…However, the work presented in [21] included only simulation results. A deliberative system used to select the best rut to follow among several candidates was then proposed in [22]. In addition, [22] provided experimental results showing that the proposed rut detection and following approach was able to handle scenarios with S-shaped ruts, broken ruts, shallow ruts, ruts that are not directly in front of the vehicle, and scenarios with multiple ruts.…”