Contact Residue for Simultaneous Removal of Vehicle’s Frequency and Surface Roughness in Scanning Bridge Frequencies Using Two Connected Vehicles

Two factors are critical to the effectiveness of the vehicle scanning method for bridge frequencies. One is the frequency of the test vehicle itself. This can be eliminated by using the vehicle–bridge contact point response calculated from the vehicle response. The other is the surface roughness of the bridge, which can be removed by using the residual response of two connected vehicles. In this paper, it is demonstrated for the first time that both vehicle’s frequency and surface roughness can be simultaneously eliminated using the contact residue of two connected vehicles. Theoretically, a formulation is presented for both the contact response and residues. In the numerical study, the contact response is demonstrated to outperform the vehicle response as more bridge frequencies can be identified, while the contact residue is verified to work well for various surface roughnesses, vehicle spacings, and bridge damping ratios. For damped bridges with rough surfaces, the contact residue enables us to extract the first three bridge frequencies.

Numerical Parametric Study on the Effectiveness of the Contact-Point Response of a Stationary Vehicle for Bridge Health Monitoring

Indirect bridge health monitoring is conducted by running an instrumented vehicle over a bridge, where the vehicle serves as a source of excitation and as a signal receiver; however, it is also important to investigate the response of the instrumented vehicle while it is in a stationary position while the bridge is excited by other source of excitation. In this paper, a numerical model of a stationary vehicle parked on a bridge excited by another moving vehicle is developed. Both stationary and moving vehicles are modeled as spring–mass single-degree-of-freedom systems. The bridges are simply supported and are modeled as 1D beam elements. It is known that the stationary vehicle response is different from the true bridge response at the same location. This paper investigates the effectiveness of contact-point response in reflecting the true response of the bridge. The stationary vehicle response is obtained from the numerical model, and its contact-point response is calculated by MATLAB. The contact-point response of the stationary vehicle is investigated under various conditions. These conditions include different vehicle frequencies, damped and undamped conditions, different locations of the stationary vehicle, road roughness effects, different moving vehicle speeds and masses, and a longer span for the bridge. In the time domain, the discrepancy of the stationary vehicle response with the true bridge response is clear, while the contact-point response agrees well with the true bridge response. The contact-point response could detect the first, second, and third modes of frequency clearly, unlike the stationary vehicle response spectra.

Snow-covered obstacles’ effect on vehicle mobility

The Mobility in Complex Environments project used unmanned aerial systems (UAS) to identify obstacles and to provide path planning in forward operational locations. The UAS were equipped with remote-sensing devices, such as photogrammetry and lidar, to identify obstacles. The path-planning algorithms incorporated the detected obstacles to then identify the fastest and safest vehicle routes. Future algorithms should incorporate vehicle characteristics as each type of vehicle will perform differently over a given obstacle, resulting in distinctive optimal paths. This study explored the effect of snow-covered obstacles on dynamic vehicle response. Vehicle tests used an instrumented HMMWV (high mobility multipurpose wheeled vehicle) driven over obstacles with and without snow cover. Tests showed a 45% reduction in normal force variation and a 43% reduction in body acceleration associated with a 14.5 cm snow cover. To predict vehicle body acceleration and normal force response, we developed two quarter-car models: rigid terrain and deformable snow terrain quarter-car models. The simple quarter models provided reasonable agreement with the vehicle test data. We also used the models to analyze the effects of vehicle parameters, such as ground pressure, to understand the effect of snow cover on vehicle response.

Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains

In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.

Vehicle model response in frequency domain

The offered article deals with one of the possibilities of numerical analysis of the vehicle response in frequency domain. It works with quarter model of vehicle. For the selected computational model of vehicle it quantifies the Frequency Response Functions (FRF) of both force and kinematic quantities. It considers the stochastic road profile. The Power Spectral Density (PSD) of the road profile is used as input value for the calculation of Power Spectral Density of the response. Al calculations are carried out numerically in the environment of program system MATLAB. When we know the modules of FRF or the Power Response Factors (PRF) of vehicle model the calculation of vehicle response in frequency domain is fast and efficient.