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Issue 5 ， 2023
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Inversion method of soil Mohr-Coulomb parameters based on dynamic cavity expansion model
Abstract：To achieve accurate and rapid interpretation of in-situ soil mechanical parameters and quantitatively evaluate military equipment mobility， an inversion method for soil Mohr-Coulomb parameters is established based on the dynamic cavity expansion model using the resistance-velocity relationship， acceleration and velocity curves， and the relationship between depth and initial velocity measured during the impact penetration process. It indicates that accurate fitting of the resistance coefficients is critical for interpreting Mohr-Coulomb parameters. Precise results could be derived using the measured velocity curve. Based on the experimental results reported by Forrestal et al. using the proposed method， the relative errors of soil cohesion and internal friction angle interpretation are 2.14% and 9.77%， respectively. Furthermore， the solution domain of the Mohr-Coulomb parameters under the dynamic cavity expansion model is revealed， and parameter sensitivity is investigated. The proposed inversion method resolves the problems of unclear physical images and strong parameter dependency in traditional semi-empirical interpretation methods. It could provide a new approach for rapidly determining soil Mohr-Coulomb parameters and assessing soil bearing capacity in complex geological environments.Keywords：dynamic cavity expansion model;soil strength;Mohr-Coulomb criterion;parametric inversion- 206
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Published：2023-03-22 -
Test and verification of rut recognition on complex ground
Abstract：The geomorphological characterization of Mars is complex. Therefore， to ensure the safe driving of rover， it is essential to understand the surface state around the rover through images captured by on-board digital cameras. The images are first preprocessed using stereo vision to create an aerial view， which is then divided into equal-sized blocks. Next， calibration and prediction datasets are created， containing 315 and 135 datapoints， respectively. Based on these datasets， a neural network model is developed. Finally， the image is classified using the resulting classification model to identify the region of interest. The results of the classification show that its accuracy on the calibration and prediction datasets using ResNet50 is 75.56% and 81.48%， respectively. This method can help researchers characterize the surface types around UGVs and identify the regions of interest that may provide more valuable information from the images. It can also be used for traversability prediction， risk assessment， and automatic path planning.Keywords：Mars rover;trafficability;terramechanics;computer vision;slip ratio- 126
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Published：2023-03-22 -
Research progress of soil
in -situ penetration technology and penetration mechanismAbstract：Soil in-situ penetration technology is an important method for obtaining its physical and mechanical parameters with the advantages of accuracy， rapidity， and continuity. It has a wide range of applications in marine geotechnical and planetary surface geological investigation， and off-road mobility assessment of military vehicles. In this study， the development， instrumentation， and basic principles of soil in-situ penetration technology have been systematically summarized， and engineering applications in the civil and defense fields are introduced. Based on the typical in-situ penetration techniques of cone penetration test and free fall penetrometer， three aspects of the progress of the penetration mechanism in theoretical analysis， numerical simulation， and experimental methods were concluded. Finally， the soil in-situ penetration technology was prospected， providing a theoretical basis and an engineering reference for future development.Keywords：soil in-situ penetration technology;physical and mechanical parameters of soil;cone penetration test;free fall penetrometer;penetration mechanism- 131
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Published：2023-03-22
Terramechanics
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A visual SLAM algorithm based on adaptive inertial navigation assistant feature matching
Abstract：This paper proposes a feature-matching algorithm based on an adaptive search radius to improve the accuracy of SLAM localization and mapping. This method can overcome the problem in which the search radius of feature matching is fixed in the traditional algorithm， leading to a high mismatching rate of the visual odometer in high dynamic motion. The algorithm first extracts and matches the features of the left and right images of the binocular camera and obtains the three-dimensional coordinates of the map points. Second， the camera pose is predicted by the measured values of the pre-integral inertial measurement unit. Then， the covariance of the predicted pose is calculated according to the error propagation law. Finally， the predicted pose is used to project the map points to the image to get the corresponding pixel coordinates. According to the error in pixel coordinates， the most likely radius of the map point is determined. Experimental results show that this method can effectively reduce the search radius of feature matching and significantly improve the accuracy of image feature matching. The position and pose accuracy of the tracking thread in the ORB-SLAM3 system is improved by approximately 38.09%， and the system's whole position and pose accuracy is improved by approximately 16.38%. This method can provide an adaptive region constraint for each feature point， improve the accuracy of feature point matching， improve the precision of position and pose estimation of the whole SLAM system， and build a more accurate dense map.Keywords：computer vision;vision odometry;feature matching;adaptive threshold;inertial navigation- 93
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Published：2023-03-22 -
Research on background photon characteristics and filtering methods for ICESat-2 data
Abstract：ICESat-2 is the world's first laser altimetry satellite to use photon counting technology. It provides high-precision， three-dimensional， large-scale ground data that can be obtained rapidly. Altimetry data obtained using photon detection systems generally contain ground signals and background signals， such as atmospheric scattering， which must be filtered to obtain ground information. To analyze the distribution characteristics of ICESat-2 background and signal photons and the performance of the point cloud filtering algorithm， we selected data from six types of land cover （city， sea ice， desert， vegetation， ocean and ice sheet （or glacier）） in different observation conditions. A statistical analysis of the background rate was then performed. The results show that the average background rate of daytime observation data is 106 points per second， which is much higher than that of nighttime observation data （104 points per second）. The background rate of the weak beam is equivalent to that of the strong beam. Among the six types of land cover， the ice sheet （or glacier） has the highest background rate. Based on the statistical results， 21 datasets and seven representative point cloud filters were selected for denoising experiments. After analyzing the accuracy， we can conclude that the improved local density method with recall， accuracy， and F-measure values greater than 0.90 has the best denoising performance and is relatively stable. Finally， the performance， characteristics， and applicability of the seven filtering algorithms were summarized and analyzed. This can serve as a reference for the subsequent data use and selection of filtering algorithms.Keywords：ICESat-2;laser altimetry;photon counting LiDAR;photon denoising;background rate- 84
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Published：2023-03-22 -
Semantic segmentation of multi-source remote sensing data self-adaptive fusion with independent branch network
Abstract：Existing deep learning-based terrain classification methods are mainly for remote sensing imagery； however， the spatial information of point clouds is underutilized. Specifically， the fusion of heterologous features is insufficient for point clouds and imagery. To utilize multi-source features fully， we propose a self-adaptive fusion classification method of multi-source remote sensing data based on independent branch network in this study. First， three-dimensional （3D） and two-dimensional （2D） networks are used to extract the semantic features of registered LiDAR point clouds and remote sensing imagery. From the 3D space， the features of imagery are then sampled and aligned with those of point clouds. Finally， a nonlinear self-adaptive feature fusion module is proposed to realize the fusion of multi-source semantic features. The experimental results indicate that the proposed method achieves an average classification accuracy of 85.87% on the vegetation， building， and ground of the ISPRS multi-source remote sensing dataset. Through network training， multi-source remote sensing data can be more data feature-adaptive fused and classified； further， the accuracy is significantly improved by 10.12% compared with the 3D classification result. The proposed independent branch fusion network can realize interactive learning and deep fusion of 2D and 3D data， and it provide a new idea for terrain classification based on remote sensing multimodal data fusion.Keywords：LiDAR point cloud;remote sensing imagery;multi-source data;independent branch network;self-adaptive feature fusion- 68
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Published：2023-03-22 -
Rotation-invariant 2D views-3D point clouds auto-encoder
Abstract：The unsupervised representation learning of point clouds is crucial for understanding and analyzing point clouds， and a 3D reconstruction-based autoencoder is an important architecture in unsupervised learning. To address the rotation interference and insufficient feature learning capability of existing autoencoders， this study proposes a rotation-invariant 2D views-3D point clouds autoencoder. First， a local fusion global rotation-invariant feature conversion strategy is designed. For the local representation， the input point clouds are transformed into handcrafted rotation-invariant features； for the global representation， an alignment module based on PCA is proposed to align the rotating point clouds under the same pose to exclude the rotation interference while complementing the global information. Then， for the encoder， the local and non-local module are designed to fully extract the local spatial features and non-local contextual correlations of the point cloud and model the semantic consistency between different levels of features. Finally， a PCA alignment-based decoding method for 2D-3D reconstruction is proposed for reconstructing the aligned 3D point clouds and 2D views such that the point-cloud representation output from the encoder integrates rich learning signals from the 3D point clouds and 2D views. Experiments demonstrate that the recognition accuracies of this algorithm are 90.84% and 89.02% on the randomly rotated synthetic dataset ModelNet40 and real dataset ScanObjectNN， respectively. Moreover， the learned point-cloud representations achieve good discriminability without label supervision and have a good rotational robustness.Keywords：three-dimensional point cloud;auto-encoder;representation learning;rotational invariance- 71
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Published：2023-03-22 -
Key techniques for three-dimensional completion： a review
Abstract：The inference of complete three-dimensional （3D） shape and semantic scene information from partial observations is crucial for various applications， such as autonomous driving， robotic vision， and metaverse ecosystem construction. Research on 3D completion has primarily focused on 3D-shape， 3D-scene， and 3D-semantic scene completion. In this paper， we systematically summarize and analyze recent relevant studies concerning these 3D completion tasks. First， for 3D-shape completion， the research progress is reviewed from two aspects： traditional shape completion and deep learning-based shape completion. Second， for 3D-scene completion， the research progress is reviewed from two aspects： the scene completion method based on model fitting and the scene completion method based on a generative approach. For 3D-semantic scene completion， the coupling characteristics between the two tasks of scene completion and semantic segmentation are analyzed， and the research progress is reviewed from three aspects： the depth map-based semantic scene completion method， the depth map-based semantic scene completion method with color images， and the point cloud-based semantic scene completion method， according to the different forms of input data. Finally， we analyze the current problems and future development trends of 3D completion tasks， aiming to provide a reference for related studies in this emerging field in 3D vision.Keywords：shape completion;scene completion;semantic scene completion;3D vision- 89
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Published：2023-03-22
Three-dimensional topographic mapping
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Design and analysis of the manned lunar vehicle for emergency with deployable structure
Abstract：The development of an emergency return vehicle for lunar exploration is crucial for future manned Chinese lunar exploration missions. To meet the needs of emergency life insurance and short distance movement on the moon surface， this study designs a cubic emergency lunar vehicle of China （CELV） from the perspective of safety， comfort， operation reliability， and working space. Several modules， such as body configuration， folding mode， driving mode， chassis structure， suspension steering， and wheel， are designed and optimized. The results show that the vehicle can achieve a high folding ratio of more than 17， with a simplified steering structure， improved transmission efficiency， as well as greater adaptability， stability， and comfort during travel.Keywords：manned lunar vehicle;deployable structures;structural design- 92
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Published：2023-03-22 -
Research on soft ground vehicle intrusion law based on CDEM-MPM coupling model
Abstract：Vehicles with varying loads deform soft-soil grounds， and the depth of their wheel intrusion is a crucial parameter for evaluating vehicle trafficability on soft soil. This study introduces a novel method that combines the Continuum-Discontinuum Element Method （CDEM） and Material Point Method （MPM） to create a coupling model of wheel and soft-soil interaction and simulate their interaction process. The model analyzes the relationship between different variables， including the wheel load； elastic modulus， strength parameters （cohesion and friction angle）， and surface stress of the soft soil； and intrusion depth， to quantify the law of wheel intrusion depth on soft soil. The results indicate that the intrusion depth positively correlates with the wheel load， with a relative variation of approximately 179%， and negatively correlates with the elastic modulus and strength parameters in a non-linear manner， with relative variations of approximately 23% and 164%， respectively. When the wheel load is constant， the intrusion depth is more sensitive to the strength parameters of the soft soil. The three-dimensional relationship established between the strength parameters and intrusion depth provides a theoretical foundation for key parameter measurements in evaluating vehicle trafficability on soft soil.Keywords：CDEM-MPM;soft-soil deformation;intrusion depth;soft-soil strength;trafficability- 84
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Published：2023-03-22 -
Study on super-wetting clay soil trafficability of track equipment
Abstract：To solve the trafficability problem of track equipment on super-wetting clay soil， this study investigates the evaluation indexes of track equipment trafficability on super-wetting clay soil， and suggests revised trafficability rules of the track equipment on super-wetting clay-soil ground using the equipment driving speed， pretension， ground grade， and soil cohesion modulus. First， soil samples of clay soil are collected and the mechanical properties are tested. The parameters of mechanical properties and ground elevation information are thus obtained， and the power spectral density function of ground roughness is developed. Combined with specific structural parameters of the track equipment， this study also constructed a ground trafficability simulation model of track equipment on super-wetting clay soil， and it considered the load wheels’ sinkages as evaluation targets. Based on the results obtained， the maximum settlement of the road wheel is 315.01 mm， which is less than the ground clearance of track equipment. In addition， it analyzed the influencing factors of track-equipment's trafficability and obtained the changing laws of the crawler equipment trafficability on super-wet clay soil using the equipment speed， crawler pretension， ground grade， and soil cohesion modulus. The research results of this paper have good potential for engineering application， and can provide theoretical guidance and technical support for track equipment design and optimization.Keywords：clay soil;track;trafficability;evaluation index- 80
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Published：2023-03-22 -
Trafficability analysis for Mars rover： present and development
Abstract：The surface of Mars is a complex terrain covered with soft soil， which poses significant risks of subsidence， high slip， or even collapse for rovers exploring the planet. A thorough analysis of rover trafficability and effective path planning are crucial in mitigating these risks. This paper discusses a comprehensive review of the strategies and methods employed by the Sojourner， Spirit， Opportunity， Curiosity， Perseverance， and Zhurong rovers and the latest advances in trafficability assessment based on multi-information fusion， deep learning， and data-driven techniques. Finally， this paper outlines the prospects for advancing the field of rover trafficability assessment.Keywords：Mars rover;trafficability;terramechanics;slip ratio;planetary exploration- 87
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Published：2023-03-22
Trafficability evaluation
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CDEM-DEM analysis of the coupled wheel-ground dynamics behavior
Abstract：The dynamic mechanical wheel-ground interaction is extremely complex when off-road vehicles are driven on soft soil pavements， and the extent of wheel sagging and traversability are of significant interest in the field of vehicle ground mechanics. This study proposes a calculation approach that uses the continuum-discontinuum element method （CDEM） and discrete element method （DEM） in combination to analyze the dynamic performance of wheels on soft-soil pavements. In the proposed method， the wheel is modeled using CDEM elements， the soft-soil pavement is modeled using DEM particles， and a penalty spring is used to connect both models. The application of dynamic torque to the wheel allows for the accurate simulation of the friction， rolling， and forward motion of the wheel on the soft-soil pavement. By combining CDEM and DEM， the study explores the effect of wheel patterns and roadblocks on the dynamic behavior of the vehicle during driving. The results show that both patterned and glossy wheels leave visible ruts on the soft-soil road surface， with patterned tires having better traversability than glossy ones. The rotation speed of patterned wheels is slower， but their translation speed is much faster than that of glossy wheels， with a translation-to-linear speed ratio of 12.78% for patterned wheels and 2.80% for glossy wheels. During driving on soft-soil roads， glossy wheels sink deeper into the ground compared to patterned wheels with the same weight. Furthermore， when driving over roadblocks， the degree of ground undulation has a direct impact on vehicle energy consumption， with greater undulation requiring more energy.Keywords：wheel sagging;soft soil pavement;CDEM;DEM;numerical simulation- 118
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Published：2023-03-22 -
Location and path planning in underground parking lot for intelligent vehicles
Abstract：To solve the location and path planning challenges for intelligent vehicles in GPS-restricted underground parking lots， we employ a vision-based approach that combines binocular offline mapping and monocular online localization to trace the intelligent vehicles within the scene. The improved path planning algorithm is used to plan the global route. First， the system uses a binocular camera to capture scenes at multiple nodes in the underground parking lot， which are then stored in different layers of a hierarchical map. The monocular camera is subsequently utilized to perform coarse-to-fine map matching and calculate the pose of the vehicle， ensuring high-speed and accurate positioning. Finally， the improved path planning algorithm is achieved by detecting intersections and generating a roadmap. The experimental results demonstrate that the average monocular positioning error and average error rate are 1.3 m and 7.4%， respectively， the planning time is reduced from approximately 5 s to less than 0.2 s， and the path length is shortened by approximately 9.56%. This positioning and navigation system is practical for real-time positioning and path planning of intelligent vehicles in parking lot environments.Keywords：intelligent vehicle;indoor location;parking lot navigation;path planning;intersection roadmap（IRM）- 76
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Published：2023-03-22 -
Research on off-road path optimization algorithm based on Bekker theory improved genetic algorithm
Abstract：With the development of equipment intelligence， vehicle path planning in complex off-road environments has become a key technology， which is integral to the development of military forces and the intelligence of military equipment. Several factors affect vehicle performance in off-road environments， such as obstacles， road potholes， and mud. Most path optimization algorithms for traditional urban roads are designed for existing roads and do not meet the requirements of path optimization in complex off-road environments with many unknown risks. The path optimization algorithm is less， which considers the complicated soil geological conditions of the off-road environment. Thus， based on the Bekker ground mechanics theory and improved genetic algorithm， this study proposes an improved genetic algorithm， which considers the influences of soil on the vehicle. The shortest path travel time was taken as the optimization goal， and a path optimization algorithm suitable for off-road environments was implemented. In this study， the modeling and path optimization of a field environment with obstacles and various soils were conducted. The results demonstrated that the optimization algorithm established the coupling effect between the mechanical characteristics of ground and vehicle. The obstacles， soil characteristics， and vehicle characteristics in the field environment were evaluated comprehensively， and a safe， efficient， and smooth field path for vehicles was obtained in the complex off-road environment. This algorithm provides a reference for establishing the connection between topographic mechanics and the path optimization algorithm.Keywords：off-road environment;path optimization;Bekker theory;genetic algorithm- 72
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Published：2023-03-22 -
Research progress of off-road path optimization algorithm for equipment
Abstract：With the continuous development of military intelligence， equipment path optimization in off-road environments has become one of the most important research fields in recent years and plays an important role in promoting the development of military forces. It also promotes military intelligence and improves agricultural production efficiency. However， due to the complexity and uncertainty of an off-road environment， an optimization algorithm based on the existing road cannot be directly applied to path optimization. Therefore， based on the research status of equipment path optimization technology in off-road environments at home and abroad， this study summarizes the two research aspects of environment modeling and path optimization methods. First， the off-road path optimization algorithm was divided into single-equipment path optimization and multi-equipment path optimization. Then， the applicable scope and the advantages and disadvantages of each method were expounded. Aiming at the dynamics problem in the field environment， the equipment path optimization algorithm under the dynamics constraint was emphasized. Finally， this study outlines the future development direction of the off-road path optimization algorithm.Keywords：off-road environment;Intelligent;environment modeling;path optimization- 77
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Published：2023-03-22
Path planning
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