第三介质作用下坡道对轮轨黏着特性影响

利用JD-1轮轨模拟试验机,通过设计坡道试验夹具实现了不同坡道接触条件的轮轨模拟试验,研究了第三介质条件下坡度对轮轨界面黏着特性的影响规律.结果表明:在干态、水介质和防冻液介质条件下,上下坡道工况的轮轨黏着系数均低于平直轨道工况;随坡道坡度的增加,黏着系数呈下降趋势且下坡道的黏着系数下降更为明显;与水介质相比较,轮轨界面存在防冻液时更容易引起低黏着现象;上坡道工况下,随速度和轴重的增加轮轨界面黏着系数呈略微下降的趋势.     

2D FE–DEM analysis of tractive performance of an elastic wheel for planetary rovers

We have been developing a simulation program for use with soil–wheel interaction problems by coupling Finite Element Method (FEM) and Discrete Element Method (DEM) for which a wheel is modeled by FEM and soil is expressed by DEM. Previous two-dimensional FE–DEM was updated to analyze the tractive performance of a flexible elastic wheel by introducing a new algorithm learned from the PID-controller model. In an elastic wheel model, four structural parts were defined using FEM: the wheel rim, intermediate part, surface layer, and wheel lugs. The wheel rigidity was controlled by varying the Young’s Modulus of the intermediate part. The tractive performance of two elastic wheels with lugs for planetary rovers of the European Space Agency was analyzed. Numerical results were compared with experimentally obtained results collected at DLR Bremen, Germany. The FE–DEM result was confirmed to depict similar behaviors of tractive performance such as gross tractive effort, net traction, running resistance, and wheel sinkage, as in the results of experiments. Moreover, the tractive performance of elastic wheels on Mars was predicted using FE–DEM. Results clarified that no significant difference of net traction exists between the two wheels.     

Characterisation of martian soil simulants for the ExoMars rover testbed

The European Space Agency (ESA) ExoMars mission involves landing a rover on the surface of Mars on an exobiology mission to extend the search for life. The locomotion capabilities of the ExoMars rover will enable it to use its scientific instruments in a wide variety of locations. Before it is sent to Mars, this locomotion system must be tested and its performance limitations understood. To test the locomotion performance of the ExoMars rover, three martian regolith simulants were selected: a fine dust analogue, a fine Aeolian sand analogue, and a coarse sand analogue. To predict the performance of the ExoMars rover locomotion system in these three regolith simulants, it is necessary to measure some fundamental macroscopic properties of the materials: cohesion, friction angle, and various bearing capacity constants. This paper presents the tests conducted to determine these properties. During these tests, emphasis was placed on preparing the regolith simulants at different levels of density in order to evaluate its impact on the value of the parameters in particular. It was shown that compaction can influence the Bekker coefficients of pressure-sinkage. The shear properties are consistent with the critical state model at normal stresses similar to those of the ExoMars rover in all but one of the simulants, which showed behaviour more consistent with transitional soil behaviour. It is necessary to give due consideration to these variations to ensure a robust test regime is developed when testing the tractive ability of the ExoMars mobility system.     

A systematic approach to reliably characterize soils based on Bevameter testing

Although a lot of information about soil parameter identification exists in literature, there is currently no algorithm who makes use both of state of the art identification methodologies and incorporating statistical analysis. In this paper a state of the art soil parameter identification method is presented including the calculation of its standard deviations and a proper weighting of the objective function. With this algorithm and a Bevameter with advanced sensor and actuator technology a test campaign is started to find a reliable soil preparation, which is applicable to a large planetary rover performance testbed. Furthermore, the preparation method has to be valid and stable for various types of dry, granular and frictional soils, typically used for planetary rover testing in space robotics, since the result of pre-tests show that the soil parameters are highly depending on the preparation. Besides preparation, the soil parameters are also influenced by different Bevameter test setup variables. Thus, the effect of the penetration velocity as well as the penetration tool geometry for pressure–sinkage tests on soil parameters is investigated. For shear tests the influence of the dimension of the shear ring is analysed as well as the variation of the grouser height, the number of the grousers and the increase of the rotational shear velocity. The results of the extensive test campaign are evaluated by the proposed identification algorithms.     

用ADINA确定热电厂磨煤机打击轮磨损极限

利用ＡＤＩＮＡ程序计算磨损后打击轮的应力分布状态;根据强度理论,得到磨损极限,为打击轮维修提供了依据．实践证明,不超过磨损极限的打击轮修复以后可以满足使用精度要求,其使用期限与新的打击轮相同．磨损极限的确定为国家创造了经济效益     

不同介质对轮轨增黏与磨损特性影响

利用MMS-2A摩擦磨损试验机研究了水介质下砂、氧化铝、研磨子对轮轨增黏与磨损特性影响.结果表明:干态和水介质下随蠕滑率增大黏着系数呈先增加后小幅降低并趋于稳定.水介质下氧化铝介质的增黏效果最好,研磨子最差;砂介质使轮轨磨损严重且塑性流变层最厚,轮轨试样表面剥落严重;氧化铝介质下轮轨磨损量较砂介质小,塑性流变层轻微;研磨子介质下的轮轨试样磨损量最小,磨损表面较为光滑;落叶显著降低黏着系数,且易形成落叶浆,使接触表面产生划痕;落叶工况下撒砂能增加黏着系数且能去除落叶浆,但易造成轮轨试样的剥落损伤.     

单点修整工具磨损对砂轮磨削表面的影响

在磨削加工过程中,砂轮表面的切削刃分布情况与磨削工件表面特征直接相关,而砂轮表面特征又与修整过程相联系,为研究单点金刚石笔的磨损和修整参数对磨削工件表面的影响,利用数值模拟和试验测量的方法揭示了砂轮修整及磨削过程,在修整重叠率为3时,讨论了修整参数与工件表面形貌特征的关系,并获得金刚石笔的磨损对磨削表面的影响.结果表明:金刚石笔初期修整阶段,磨削工件表面的峰谷分布明显具有规律性,其0.12 mm的变化周期完全为修整导程的复映;金刚石笔磨损后,工件截面曲线特征的规律性减弱,测量的表面粗糙度由0.52μm减小到0.38μm,最后导致工件表面烧伤;若调整修整导程保持原重叠率,则表面粗糙度增大为0.81μm.     

特性拦阻材料的台架实验装置研制

本文建立了单轮负载、平台拖动式的台架实验装置,并开展了验证实验。该装置主要包括固定框架、吊篮、活动平台、机轮夹持组件和传感器。将拦阻材料安装在活动平台上,通过牵引活动平台实现机轮对拦阻材料的碾压。该装置可以更换不同机轮、安装不同性能的拦阻材料;实验条件包括机轮胎压、机轮承重等参数;传感器可以测量实验过程中机轮受到的拦阻力、机轮压入深度等参数。该装置可以为研究机轮规格、胎压、承重、拦阻材料性能等对拦阻力的影响提供实验手段。验证实验结果表明,所建立的装置可以满足机轮与特性拦阻材料力学模型研究的需要。     

A dynamic terramechanic model for small lightweight vehicles with rigid wheels and grousers operating in sandy soil

This paper presents a validated dynamic terramechanic model for rigid wheels with grousers that may be used for planetary and terrestrial mobile robots operating in loose sandy soil. The proposed model is based on established analytical terramechanic theories and incorporates two new dimensionless empirical coefficients. The additional terms in the model are based on existing soil mechanic theories that vary as a function of soil properties, slip conditions, and vehicle loading. The proposed model was able to capture and predict the dynamic oscillations observed in experimental data from a single-wheel testbed for the sinkage, drawbar pull and normal load. For the operating conditions tested in this research the simulation results using the proposed model show an improvement over traditional terramechanic models for capturing the dynamic effects of grousers.     

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.