Journal of Terramechanics最新文献

Accurately estimating surficial soil moisture and strength is integral to determining vehicle mobility and is especially important over large spatial extents at fine resolutions. While methods exist to estimate soil strength across landscapes, they are empirical and rely on class average soil behavior. The Strength of Surficial Soils (STRESS) model was developed to estimate moisture-variable soil strength with a physics-based approach. However, there is a lack of field data from a diverse landscape to evaluate the STRESS model. To test the STRESS model, a field study was conducted in northern Colorado. Soil moisture and strength were measured on 10 dates. Data from the surficial layer (0–5 cm) were used to test the STRESS model and determine if soil strength trends could be estimated from topographical and soil textural differences. High variability was observed in soil strength measurements stemming from fine-scale terrain features and user variability. Observations show lower soil strengths for greater soil moistures, steeper slopes, higher vegetation, and lower soil fines content. STRESS estimated rating cone index values with a mean relative error of 37.5 %, while a pre-existing model had a mean relative error of 47.4 %. The STRESS model outperforms the current RCI prediction method, but uncertainty remains large.

Based on decoupled technique and superposition principle, an applied mathematical modelling method was developed to analyze soil-roadway-vehicle interaction and roadway sinkage for a rapidly deployable foldable roadway. A tensionless soil-structure model was applied to model the interaction between the soil and the roadway. The roadway is flexible longitudinally and rigid transversely. The three-dimensional (3D) plate-like problem was decoupled by two two-dimensional (2D) structural models, a longitudinal membrane-like structural model and a transverse elastic beam model. The total sinkage of the roadway is the superposition of the calculations of these two structural models. The mathematical modelling is consistent with the experimental result and its rationality has been verified.

In the realm of numerical simulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to align with the mechanical responses of actual soil. In pursuit of a rapid and precise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models. This method calibrates the corresponding discrete element microparameters based on the macroscopic Mohr–Coulomb parameters derived from actual soil direct shear tests. The distinct contribution lies in the creation of a dataset that bridges the soil microparameters and macroparameters through simulated direct shear tests, which serves as training data for machine learning algorithms. Additionally, an adaptive particle swarm optimization neural network algorithm is proposed to represent the nonlinear relationships among parameters within the dataset, thus achieving intelligent calibration. To verify the reliability of the proposed soil calibration model in the context of vehicle mobility simulations, a co-simulation is conducted using a vehicle multibody dynamics simulation model and the calibrated soil model, with validation conducted across multiple criteria.

This study explores the potential of Precision Agriculture (PA) and Smart Farming (SF) using cutting-edge technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Unmanned Aerial Vehicles (UAVs) to address global challenges such as food shortages and population growth. The research focuses on recent developments in SF, including data collection, analysis, visualization and viable solutions, highlighting the role of IoT and 5G networks. The paper also discusses the application of robots and UAVs in agricultural tasks, showcasing their integration with IoT, AI, Deep Learning (DL), Machine Learning (ML), and wireless communications. Moreover, Smart Decision Support Systems (SDSS) are explored for real-time soil analysis and decision-making. The study underscores the significance of these technologies in PA, propelling traditional farming practices into an era of intelligent and sustainable farming solutions. This Overview is grounded in a thorough analysis of 80 recent research articles, covering the period from 2019 to 2023, within the domain of SF. This study highlights notable trends and advancements in this ever-evolving sector. Furthermore, this paper delves into the nuances of addressing particular challenges prevalent in developing nations, strategies aimed at surmounting constraints related to infrastructure and resource availability, and the pivotal role of governmental and private sector support in fostering the growth of Smart Agriculture (SA).

Tracked vehicles are widely used in transportation, excavation, and site investigation due to their advantages of maneuverability and off-road ability. Static stability of tracked vehicles has been characterized with the static stability factor (SSF) and the static roll threshold (SRT) by assuming the ground as perfectly rigid. Considering the interaction between the vehicle and the ground, the traditional studies assess the static stability of vehicles under uni-directional loads by ignoring coupling effect between the load components. This paper enhances the traditional framework by evaluating the static stability of tracked vehicles under multi-directional loads. The two tracks of a tracked vehicle are considered as two parallel shallow foundations. The failure envelope method is adopted to capture the maximum allowable loads through swipe test and fixed ratio test. A new coefficient, factor of stability (FOS), is introduced to quantify the static stability of the tracked vehicles. Various track configurations and load combinations have been considered. The judgment process of tracked vehicle’s stability and the calculation method of FOS are detailed. The results show that the FOS obtained from the enhanced framework is more conservative than that from the traditional one.

Dynamic simulations of various types of off-road vehicles, from planetary rovers to agricultural equipment, have long relied on well-established semi-empirical terramechanics models. While these models do have drawbacks and reliability issues that have been addressed by numerous works in the decades since the models were first introduced, semi-empirical approaches remain one of the few ways to simulate realistic wheel-soil interaction in real-time. One of their drawbacks is their assumption that the terrain is a flat plane. The models work by integrating normal and shear stresses along the wheel-terrain contact patch. The normal stress at each point along the contact patch is determined using an equation that computes soil pressure based on semi-empirical parameters, the dimensions of the wheel and the sinkage, which is determined based on the distance between the point and the plane that defines the terrain. Other works simplify the rough terrain contact problem by defining an equivalent contact plane at each time step in order to continue to be able to use semi-empirical models - modified to work with slanted planes - to compute the interaction forces. In this work, we propose a new, modified version of the semi-empirical model in which interaction forces for a wheel travelling on rough terrain can be computed without the need to use an equivalent contact plane. To highlight the advantages of our proposed approach, we compare our simulation results to the results of simulations using an existing approach for modelling a wheel travelling over rough terrain using traditional semi-empirical models.

Reducing the position and attitude deviation of the planetary rover while driving is an important issue that needs to be considered in the design and controller development of the new types of planetary rovers at this stage. It is also the basis for whether the rovers can carry out exploration missions with high precision requirements on the complex terrain of planetary surfaces. A systematic study of the deviation problems generated by planetary rovers under the most basic open-loop path control is of great significance to improve the effectiveness of planetary detection. In this study, based on simulated Martian terrain and soil, planetary rover driving experiments under various scenes were conducted to test the resulting position and attitude deviation and evaluation indexes under different path types, terrain distributions, driving speeds and steering radius. By combining the experimental phenomena, the action characteristics of single wheel with ground and its influence on the state of the whole vehicle during the deviation generation process are analyzed. And finally, the discussion and conclusion are directed to how to optimize the planetary rover path control. These systematic experiments and analyses can provide valuable references for researchers engaged in the development of mobile controllers for planetary rovers.

Precision tillage (PT) is an innovative method that aims to take mechanical actions in the soil only where it is needed to curb the impact of heavy machinery usage on the soil. This research explores the use of remote sensing to measure relevant soil parameters to implement a PT strategy. This was achieved by combining traditional soil properties measurements and a non-contact approach based on taking hyperspectral camera (HSC) data in the field. Six methods were generated and divided into two sets to determine soil properties to make PT decisions. The first set includes mathematical functions that were generated from the ground true data (GTD). The second set includes functions that were generated from the remotely sensed HSC data and have a relationship with the methods in the first set. It was possible to tune the functions’ parameters to increase the accuracy. In addition, prediction error categories set at 5 % intervals were used to select the best method. The results show that a tuned method based on the GTD has an overall error below 5 %, and a tuned method based on HSC data has an overall error below 10 %.

The discrete element method (DEM) has been widely used to simulate varied sand particles interacting with earthmoving machines. However, past research using DEM barely addressed accurate verification and validation for different sand moisture content. Therefore, the main purpose of this paper is to reveal the range of key parameters of an adhesive force model used in the DEM simulation corresponding to the specific sand moisture content. We considered the bulldozing phenomenon to be typical earthmoving work and performed the bulldozing experiments under different sand moisture levels to investigate the bulldozing force variations. Subsequently, the DEM simulation with an adhesive force model calculated the bulldozing force corresponding to the experimental results. The values for two adhesive parameters, i.e., a scaling magnitude and the maximum adhesive distance between particles, were adjusted such that the maximum bulldozing force calculated in the DEM coincides with that of the experiments under different moisture contents. The experimental verification of the DEM revealed the relationship curves between these two key parameters corresponding to the different moisture content. The relationship obtained in this paper implies that the DEM simulation carefully adjusting the adhesive force parameters can reproduce machine interaction on moist sand environments accurately.

This paper presents a discrete element/finite element (DE/FE) coupling method to investigate the trafficability of off-road tires on wet granular terrains. Firstly, a DE model of the wet terrain is established, and a linear contact model is adopted to describe the interaction between particles, while the adhesion effect between wet particles is simulated by a liquid bridge force model. An FE model of an off-road tire is then developed, and the Yeoh hyperelastic material model is used to describe the large deformations and nonlinear mechanical behaviors of the off-road tire. Furthermore, numerical simulations of the angle of repose of wet particles are compared with experimental studies to verify the effectiveness of the DE/FE coupling method. Finally, the tire traction behavior under different complex working conditions is predicted by the presented DE/FE coupling approach. The simulation results show that the absolute value of tire sinkage increases almost linearly (the sinkage is 97.1 mm at 25% moisture content) with the rise of moisture content among particles. The rate of change of sinkage is greater for small friction coefficients ($<$ 0.3) than that for large friction coefficients ($⩾$0.3). The drawbar pull experiences a rapid increase for the slide friction coefficient with a range 0.3 and 0.7, after which the rate of change slows down ($⩾$0.7). However, the drawbar pull exhibits an opposite trend as the tire pressure and height of the tread pattern increase. Numerical results also indicate that the smaller the slide friction coefficient, the larger the soil deformation, flow, and failure area in wet granular terrains.