Journal of Terramechanics最新文献

The deep sea polymetallic nodule mining vehicle maneuverability depends on the vehicle track parameters and track configuration. The traction force offered by the deep sea soil is very limited for the mining vehicle during dynamic operating conditions on the seabed and it is very critical to maneuver against the external resistances. The present study strives to arrive at optimum track parameters for enhancing the traction of the vehicle for the pre-determined seabed conditions. The efficacy of the four tracks in Inline and Offset track configurations on the soft soil has been compared. To improve the traction force estimation, the existing mathematical model was modified with the inclusion of dynamic variation of shear stress-shear displacement characteristics and variation in shear residual displacement concerning the track parameters. The modified mathematical model was solved in a well-established mathematical tool and found that there are 30 percent improvements in the traction force generation for the offset configuration over inline track configuration. The optimum track length to width ratio ($L/b$) was also estimated for the given contact area to configure the vehicle track for improvement of the traction. Further, a Multi-Body Dynamic (MBD) analysis has been carried out in commercially available soil-machine interaction tool for the inline and offset track configurations with actual measured seabed soil parameters. The MBD analysis proved that the sinkage and vehicle gradient is significantly increased in the inline track configuration due to disturbance created by the front tracks. The simulation results confirm that the offset track configuration is suitable for the deep sea soil conditions for handling the higher payload of a deep sea mining vehicle.

The soil in southwest China is a cohesive soil in which discrete cohesive particles and aggregates coexist. In view of the problem that there are many studies on discrete cohesive particles and a lack of research on aggregates, discrete element software DEM is used to conduct a study on cohesive particles and agglomerates parameter calibration. The angle of repose is selected as the target value to calibrate the simulation parameters of sticky particles. Then, the simulation parameters of the viscous particles are used as the basis for the calibration of the contact parameters of the agglomerates, and shear experiments are conducted on the agglomerates, with the ultimate shear depth and ultimate shear force as target values. The results show that the parameters of the agglomerate are: Normal Stiffness per unit area is 5.627 × 10⁸ N/m³, Shear Stiffness per unit area is 4.359 × 10⁸ N/m³, Critical Normal Stress is 3.5 × 10⁶ Pa, Critical Shear Stress is 4.5 × 10⁶ Pa and Bonded Disk Radius is 5.43 mm. Through the particle sliding friction angle test and the agglomerate compression test, it was verified that the errors of sticky particles were 0.30 % and 0.37 % respectively, and the error of agglomerates was 1.69 %.

Vegetation override is an important aspect of off-road ground vehicle mobility. For autonomous ground vehicles (AGV), path-planning in off-road environments may be informed by the predicted resistance of vegetation in the navigation environment. However, there are no prior measurements on the override resistance of small stems ($<$2.5 cm) and groups of small stems on medium-sized ($\approx$1000 kg) vehicles. In this work, a series of override measurements for clumps of small vegetation that are relevant for off-road navigation by intermediate-sized AGV is presented. The development and calibration of a custom-made pushbar system with integrated load cells for directly measuring override forces is also presented, and a comparison of the results of the experiments to models developed for override of larger single stems is made. It is found that for clumps of small vegetation, the total override force is best predicted by the diameter of the largest stem in the clump. Additionally, it is found that the equations developed for larger stems under-predict the override forces exerted by smaller stems by about a factor of two.

The interaction between deformable terrain and wheels significantly affects wheel mobility. To accurately predict vehicle mobility or optimize wheel design, an analysis of this interaction is essential. This study develops a hybrid terramechanics model (HTM) that integrates the semi-empirical model (SEM) and the discrete element method (DEM) using artificial neural networks (ANNs). The model overcomes the limitations inherent in SEM and DEM approaches. We used DEM simulations to analyze the impact of wheel design parameters and slip ratio on terrain behavior. ANNs were subsequently developed to predict dynamic sinkage in real time based on these results. A new concept, termed bulldozing angle, was introduced to define additional terrain–wheel contact caused by dynamic sinkage. Based on this concept, we predicted the bulldozing resistance exerted on the wheel. By combining SEM, ANNs, and DEM, we developed an HTM capable of terrain behavior analysis. Lastly, we conducted a comparative analysis between the SEM, HTM, and actual test data. The results confirmed that the predictive accuracy of the HTM surpassed that of the SEM across all slip ratios.

Modeling the wheel-soil interaction of small-wheeled robots in granular media is important for robot design and control. A wide range of applications, from earthmoving for construction and farming vehicles to navigating rough terrain for Mars rovers, motivate the need for a model that can predict the force response of a wheel and the terrain shape afterward. More importantly, the speed, accuracy, and generality of the model should be considered for real-world applications. In this paper, we offer a straightforward sand deformation simulator to simulate the soil surface and integrate it with 3D-RFT in order to capture the soil motion caused by the wheel. The proposed method is able to: (1) estimate three-dimensional interaction forces of arbitrarily shaped wheels traveling in granular media; (2) simulate the soil displacement from the trajectory; and (3) perform the force calculation in real-time at 60 Hz.

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.