Journal of Terramechanics最新文献

Traversing soft terrain poses a major challenge for planetary wheeled rovers, and various studies have demonstrated ways to enhance rover mobility by transforming the wheel structure or adjusting the wheel’s stiffness, which results in a change in wheel contact area on the terrain. This paper presents a novel idea using the jamming mechanism for modulating the wheel’s stiffness. The developed wheel consists of the core body, wheel outer rim, inner flexure, and cable tension mechanism. The jamming mechanism is realized by adjusting the cable tension inserted between the outer rim of the wheel. The wheel stiffness measuement test confirms that the wheel with low stiffness can reduce its stiffness for 75% of the high stiffness configuration. The wheel’s traversability on soft terrain are also evaluated based on slip ratio and current consumption. The results demonstrate that the lower-stiffness configuration outperforms the higher-stiffness wheel under various conditions. These findings, being consistent with previous works on flexible wheels, highlight the potential benefits of the jamming-based stiffness-adjustable wheel for rough terrain traverse with various payload conditions.

Estimating the mechanical properties of snow from imagery is an essential part of over-snow vehicle autonomy. However, snow surfaces that differ widely in strength, traction, and motion resistance tend to appear a uniform bright white in visible or broadband infrared imagery, and it is difficult to determine where an oversnow vehicle should operate from imagery alone. In this work we determine the optimal fusion of filtered broadband shortwave infrared (SWIR) imagery to separate snow types with different mechanical properties by appearance. We demonstrate vastly increased discrimination skill in distinguishing snow types using a small number of SWIR cameras in both field and laboratory settings, and also identify sources of environmental context that can improve lookahead sensing for oversnow vehicles. Overall, we show that a small set of inexpensive SWIR filters is a powerful tool for over-snow autonomy and motion planning.

Wheeled mobile robots, rovers, are highly effective in lunar exploration. However, the lunar regolith can cause wheel slippage, resulting in an inability to travel for the rover. A single-wheel testbed is usually used to analyze a rover wheel’s driving performance. Our experiment can control the rotation and translation of the wheels separately, realizing experiments in any slippage condition. Moreover, this testbed can conduct experiments using regolith simulant with a cohesive property, in addition to Toyoura sand, which is non-cohesive sand collected from the earth.

This paper presents the results of a driving test on two types of loose soil: Toyoura sand and regolith simulant (FJS-1). The wheel used in the experiment is the preliminary version of the actual flight model of a 10 kg class lunar exploration microrover. The results reveal that the traction performance on both sands improves as the slip ratio increases. The performance did not depend on velocity but on vertical load. It should be noted that the cohesive simulant shows a higher difference in traction performance than Toyoura sand. These findings, measured in detail from the low-slip to the high-slip range, contribute to the actual driving operation of the rover missions.

Traction and ground pressure are key aspects of modern off-highway machinery. On the one hand, the machinery must be able to move successfully on rough terrain, on the other hand, the soil cannot be excessively ruined, particularly in agriculture fields that must be as productive as possible. In this regard, when the soil is very sensitive to ground pressure and slip efficiency, tracks are often mounted on agricultural tractors rather than wheels. Regrettably, it significantly diminishes the multi-purpose functionality of modern agricultural tractors, which is an essential feature. To offer higher pulling efficiency, reduced ground pressure, and greater multi-purpose functionality, an agricultural tractor fitted with a rear bogie axle is hereby presented. A market analysis is carried out to demonstrate the potential of such a vehicle. Subsequently, an ideal agricultural tractor is proposed for benchmarking purposes and as the baseline for designing the bogie axle application. Their pulling performance is evaluated by using a custom-made spreadsheet, while a novel coefficient named Pull on Pressure is introduced to assess off-road mobility. Ultimately, the two variations of the agricultural tractors undergo testing on vehicle dynamics simulation software to conduct an initial comparative analysis.

This paper presents a novel parameter identification method for DEM-FEM coupling model to investigate the trafficability of off-road tires on granular soils. Initially, an experimental device is developed to measure the bulk responses of granular materials i.e., angles of repose and shear. A series of numerical tests, including the Plackett-Burman tests, steepest-climbing tests and three-factor orthogonal tests, are then performed to formulate the mathematical regression and constraint equations. These equations establish the correlation between the three key model input parameters (namely, coefficients of static friction of acrylic wall-particle and particles, and coefficient of restitution of acrylic wall-particle) and the aforementioned bulk responses. After that, the non-dominated sorting genetic algorithm II (NSGA-II) is implemented to iteratively calculate the equations based on the multi-objective optimization method to obtain the optimal solution set. Finally, the effectiveness and feasibility of the parameter identification method are confirmed by comparing the results of indoor soil-bin tests and the corresponding numerical simulations in terms of tire sinkage, ruts and soil deformation and flow.

With the advent of artificial intelligence, the analysis of systems related to complex processes has become possible or easier. The interaction of the traction factor of off-road vehicles with soil or other uncommon surfaces is one of the complex mechanical problems, which has been very difficult to model and analyze in conventional and previous methods due to numerous and variable parameters. This review article delves into the imperative and progression of integrating AI algorithms within the realm of modeling and predicting target parameters in Terramechanics engineering. Such endeavors are especially pertinent to predicting various soil properties, including soil compaction, traction, energy consumption, deformation, and associated factors. The application of AI encompasses various facets, including modeling and predicting traction, soil sinkage, rut depth, contact area, soil stress, density, and energy wasted on the traction device’s movement on the soil. The present study evaluates the solutions and benefits offered by AI-based methodologies in addressing soil-machine interaction challenges. Furthermore, the study investigates the constraints inherent in utilizing these methodologies.

On soft terrain, the rover wheels are easy to slip, sink, or even fail to move. This paper designs a soil-plowing wheel which is two-sided closed and without tread. The discrete element simulation shows that the wheel could grasp soil through both sides and plowing soil and that the ability to gain drawbar pull is not significantly reduced. The wheel is fabricated and tested to measure its sinkage, slip rate and drawbar pull. The wheel has high sinking, high slip and high drawbar pull. And the wheel is tested to verify the passability on five terrains of flat ground, climbing, out of sinkage, obstacle crossing and hard ground. The wheel exhibits good passability in all terrains. The soil-plowing wheel is tested verify the passability on three terrains of obstacle crossing, out of sinkage and climbing and using a three-rockers six-wheels rover. The wheel can pass through all terrain. More importantly, the wheel has an excellent ability to get out of sinkage. And it takes only 25.43 s for all six wheels to get out of sinkage. It is believed that the structure and test results of this wheel are valuable for the subsequent development of unmanned rover wheel.

Bulldozers are one of the major off-road machine systems for cutting and transporting granular materials during earthmoving operations. With the growing demand for energy-efficient and accelerated optimization design cycles and automated earthmoving processes, researchers and engineers are exploring methods to model the soil-to-bulldozer interaction. This study proposes a similitude scaling law for the soil and scaled blade systems, providing an alternative approach to costly and time-consuming field-based design verification and validation for product engineering of Ground-Engaging Tools (GETs). In this soil bin study, we examined the interaction between scaled bulldozer blades and cohesive-frictional artificial soil, aiming to establish scaling laws of geometrically scaled blade ratio to two blade performance responses, soil reaction forces and soil mass. A randomized complete block design with five replications was conducted in a soil bin using five 3D printed geometric scales of the blade, λ = 1/8, λ = 1/9, λ = 1/11, λ = 1/13, and λ = 1/15, with λ = 1 representing the full-scale geometry of a Caterpillar D3K2 XL bulldozer blade. Blade soil cutting forces were measured using a load cell instrumented blade dynamometer carriage on a cohesive-frictional artificial soil in the bin. Each scaled blade traveled at a constant speed of 213 mm/s and the tool depth was set to 30 % of the blade height. After reaching full load, the cut soil mass and pile dimensions (height, width, and rupture length) above the undisturbed soil were also measured. A scaling law model was established between soil horizontal reaction forces and the five geometric blade scale ratios with a high coefficient of determination, R², of 0.9898. Similarly, the scaling law (R² = 0.9951) was established between the five geometric scales and soil mass. The findings demonstrate that a scaling law model can be used for predicting the soil horizontal reaction force and soil load. The scaling law can be utilized for optimizing energy and productivity, enhancing GET product design optimization, and developing algorithms for energy-efficient automation of earthmoving processes.

Difference thresholds of whole-body vibration is important to determine perceptibility of changes in vehicle vibration and can be used to guide ride comfort improvements. It is postulated that estimated difference thresholds in a laboratory setting should be applicable to real-world driving conditions given that the stimuli are similar. This study considers the aspect of vehicle vibration associated with the stimulus. A validated non-linear SUV vehicle model is simulated on a 4-poster test rig and driven in a straight-line over a rough road. This allows for the vehicle vibration to be compared between vertical excitation only (4-poster) and complete excitation (straight-line driving) by the road profile at each of the four wheels. Results show that differences in the seat vibration exists between the 4-poster test rig and straight-line driving simulations. These differences are larger than difference thresholds implying that they would most probably be perceivable. Further investigations are needed to determine the influence of differences in vibration stimuli on difference thresholds.