3,625 research outputs found
In Situ Soil Property Estimation for Autonomous Earthmoving Using Physics-Infused Neural Networks
A novel, learning-based method for in situ estimation of soil properties
using a physics-infused neural network (PINN) is presented. The network is
trained to produce estimates of soil cohesion, angle of internal friction,
soil-tool friction, soil failure angle, and residual depth of cut which are
then passed through an earthmoving model based on the fundamental equation of
earthmoving (FEE) to produce an estimated force. The network ingests a short
history of kinematic observations along with past control commands and predicts
interaction forces accurately with average error of less than 2kN, 13% of the
measured force. To validate the approach, an earthmoving simulation of a bladed
vehicle is developed using Vortex Studio, enabling comparison of the estimated
parameters to pseudo-ground-truth values which is challenging in real-world
experiments. The proposed approach is shown to enable accurate estimation of
interaction forces and produces meaningful parameter estimates even when the
model and the environmental physics deviate substantially.Comment: 10 pages, 6 figures, to be published in proceedings of 16th
European-African Regional Conference of the International Society for
Terrain-Vehicle Systems (ISTVS
A Robust Localization Solution for an Uncrewed Ground Vehicle in Unstructured Outdoor GNSS-Denied Environments
This work addresses the challenge of developing a localization system for an
uncrewed ground vehicle (UGV) operating autonomously in unstructured outdoor
Global Navigation Satellite System (GNSS)-denied environments. The goal is to
enable accurate mapping and long-range navigation with practical applications
in domains such as autonomous construction, military engineering missions, and
exploration of non-Earth planets. The proposed system - Terrain-Referenced
Assured Engineer Localization System (TRAELS) - integrates pose estimates
produced by two complementary terrain referenced navigation (TRN) methods with
wheel odometry and inertial measurement unit (IMU) measurements using an
Extended Kalman Filter (EKF). Unlike simultaneous localization and mapping
(SLAM) systems that require loop closures, the described approach maintains
accuracy over long distances and one-way missions without the need to revisit
previous positions. Evaluation of TRAELS is performed across a range of
environments. In regions where a combination of distinctive geometric and
ground surface features are present, the developed TRN methods are leveraged by
TRAELS to consistently achieve an absolute trajectory error of less than 3.0 m.
The approach is also shown to be capable of recovering from large accumulated
drift when traversing feature-sparse areas, which is essential in ensuring
robust performance of the system across a wide variety of challenging
GNSS-denied environments. Overall, the effectiveness of the system in providing
precise localization and mapping capabilities in challenging GNSS-denied
environments is demonstrated and an analysis is performed leading to insights
for improving TRN approaches for UGVs.Comment: 15 pages, 9 figures, 2 tables, to be published in The Proceedings of
the Institute of Navigation GNSS+ 2023 conference (ION GNSS+ 23
Correction to: Accuracy of surface strain measurements from transmission electron microscopy images of nanoparticles
Abstract Unfortunately, after publication of this article [1], it was noticed that the name of the fifth author was incorrectly displayed as Jakob Schiøz. The correct name is Jakob Schiøtz and can be seen in the corrected author list above. The original article has also been updated to correct this error
Accuracy of surface strain measurements from transmission electron microscopy images of nanoparticles
Additional file 1. Section S1. Measuring the center of mass. Figure S1. Definition of integration regions for center of mass calculations. Figure S2. Comparison of center of mass positions with peak positions. Figure S3. Magnitudes of thermal vibrations. Figure S4. Comparison of our method with GPA. Figure S5. Negative defocus measurements. Figure S6. Planar strain errors for increasing tilt. Figure S7. Surface strain errors for increasing tilt
Correction to: Accuracy of surface strain measurements from transmission electron microscopy images of nanoparticles
Unfortunately, after publication of this article [1], it was noticed that the name of the fifth author was incorrectly displayed as Jakob Schiøz. The correct name is Jakob Schiøtz and can be seen in the corrected author list above. The original article has also been updated to correct this error
Gas-Kinetic-Based Traffic Model Explaining Observed Hysteretic Phase Transition
Recently, hysteretic transitions to `synchronized traffic' with high values
of both density and traffic flow were observed on German freeways [B. S. Kerner
and H. Rehborn, Phys. Rev. Lett. 79, 4030 (1997)]. We propose a macroscopic
traffic model based on a gas-kinetic approach that can explain this phase
transition. The results suggest a general mechanism for the formation of
probably the most common form of congested traffic.Comment: With corrected formula (3). For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.htm
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