There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured roa...There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.展开更多
Enhancing ride comfort has always constituted a crucial focus in the design and research of modern tracked vehicles,heavily reliant on the driving system's performance.While the road wheel is a key component of th...Enhancing ride comfort has always constituted a crucial focus in the design and research of modern tracked vehicles,heavily reliant on the driving system's performance.While the road wheel is a key component of the driving system,traditional road wheels predominantly adopt a solid structure,exhibiting subpar adhesion performance and damping effects,thereby falling short of meeting the demands for high-speed,stable,and long-distance driving in tracked vehicles.Addressing this issue,this paper proposes a novel type of flexible road wheel(FRW)characterized by a catenary construction.The study investigates the ride comfort of tracked vehicles equipped with flexible road wheels by integrating finite element and vehicle dynamic.First,three-dimensional(3D)finite element(FE)models of both flexible and rigid road wheels are established,considering material and contact nonlinearities.These models are validated through a wheel radial loading test.Based on the validated FE model,the paper uncovers the relationship between load and radial deformation of the road wheel,forming the basis for a nonlinear mathematical model.Subsequently,a half-car model of a tracked vehicle with seven degrees of freedom is established using Newton's second law.A random road model,considering the track effect and employing white noise,is constructed.The study concludes by examining the ride comfort of tracked vehicles equipped with flexible and rigid road wheels under various speeds and road grades.The results demonstrate that,in comparison to the rigid road wheel(RRW),the flexible road wheel enhances the ride comfort of tracked vehicles on randomly uneven roads.This research provides a theoretical foundation for the implementation of flexible road wheels in tracked vehicles.展开更多
A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dyn...A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.展开更多
Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do no...Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.展开更多
传统纯跟踪控制器在面向实际应用时往往难以较好地处理传感器信号延时、执行器响应滞后等因素带来的控制滞后问题以及欠缺内外部扰动干预下的自主抗干扰能力;同时在跟踪临近终点的目标点时,因前视距离作为控制律的分母且逐渐减小,导致...传统纯跟踪控制器在面向实际应用时往往难以较好地处理传感器信号延时、执行器响应滞后等因素带来的控制滞后问题以及欠缺内外部扰动干预下的自主抗干扰能力;同时在跟踪临近终点的目标点时,因前视距离作为控制律的分母且逐渐减小,导致输入转角值发生突变且伴随方向盘出现不稳定地晃动现象,影响行驶稳定性及人员乘坐体验感。针对此,提出了一种基于改进纯跟踪的路径跟踪控制器。首先,构建了纯跟踪控制器的基础模型并为改善纯跟踪控制的滞后影响,建立了一种结合规划路径和规划速度信息的道路预瞄模型。然后考虑系统因内部及外部环境影响产生的未知干扰并设计非线性扩张状态观测器(nonlinear expanded state observer,NESO)进行扰动估计及实时补偿,以提升纯跟踪控制器的抗干扰能力。并进一步通过转向稳定调节模型以改善纯跟踪控制器临近终点时的转角突变问题。最终,基于软件在环动力学仿真平台以不同初始航向及存在初始偏差工况下测试所提控制器的有效性,并进一步在实车平台进行闭环测试验证,实验结果表明改进的纯跟踪算法具有良好的跟踪精度和转向平顺性。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.62261160575,61991414,61973036)Technical Field Foundation of the National Defense Science and Technology 173 Program of China(Grant Nos.20220601053,20220601030)。
文摘There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.
基金Supported by National Natural Science Foundation of China (Grant No.11672127)Innovative Science and Technology Platform Project of Cooperation between Yangzhou City and Yangzhou University of China (Grant No.YZ2020266)+3 种基金Advance Research Special Technology Project of Army Equipment of China (Grant No.AGA19001)Innovation Fund Project of China Aerospace 1st Academy (Grant No.CHC20001)Fundamental Research Funds for the Central Universities of China (Grant No.NP2022408)Jiangsu Provincial Postgraduate Research&Practice Innovation Program of China (Grant No.SJCX23_1903)。
文摘Enhancing ride comfort has always constituted a crucial focus in the design and research of modern tracked vehicles,heavily reliant on the driving system's performance.While the road wheel is a key component of the driving system,traditional road wheels predominantly adopt a solid structure,exhibiting subpar adhesion performance and damping effects,thereby falling short of meeting the demands for high-speed,stable,and long-distance driving in tracked vehicles.Addressing this issue,this paper proposes a novel type of flexible road wheel(FRW)characterized by a catenary construction.The study investigates the ride comfort of tracked vehicles equipped with flexible road wheels by integrating finite element and vehicle dynamic.First,three-dimensional(3D)finite element(FE)models of both flexible and rigid road wheels are established,considering material and contact nonlinearities.These models are validated through a wheel radial loading test.Based on the validated FE model,the paper uncovers the relationship between load and radial deformation of the road wheel,forming the basis for a nonlinear mathematical model.Subsequently,a half-car model of a tracked vehicle with seven degrees of freedom is established using Newton's second law.A random road model,considering the track effect and employing white noise,is constructed.The study concludes by examining the ride comfort of tracked vehicles equipped with flexible and rigid road wheels under various speeds and road grades.The results demonstrate that,in comparison to the rigid road wheel(RRW),the flexible road wheel enhances the ride comfort of tracked vehicles on randomly uneven roads.This research provides a theoretical foundation for the implementation of flexible road wheels in tracked vehicles.
基金Foundation item： National Natural Science Foundation of China （60502019）
文摘A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.
文摘传统纯跟踪控制器在面向实际应用时往往难以较好地处理传感器信号延时、执行器响应滞后等因素带来的控制滞后问题以及欠缺内外部扰动干预下的自主抗干扰能力;同时在跟踪临近终点的目标点时,因前视距离作为控制律的分母且逐渐减小,导致输入转角值发生突变且伴随方向盘出现不稳定地晃动现象,影响行驶稳定性及人员乘坐体验感。针对此,提出了一种基于改进纯跟踪的路径跟踪控制器。首先,构建了纯跟踪控制器的基础模型并为改善纯跟踪控制的滞后影响,建立了一种结合规划路径和规划速度信息的道路预瞄模型。然后考虑系统因内部及外部环境影响产生的未知干扰并设计非线性扩张状态观测器(nonlinear expanded state observer,NESO)进行扰动估计及实时补偿,以提升纯跟踪控制器的抗干扰能力。并进一步通过转向稳定调节模型以改善纯跟踪控制器临近终点时的转角突变问题。最终,基于软件在环动力学仿真平台以不同初始航向及存在初始偏差工况下测试所提控制器的有效性,并进一步在实车平台进行闭环测试验证,实验结果表明改进的纯跟踪算法具有良好的跟踪精度和转向平顺性。
