Vehicle Guidance Research Articles

Immersive virtual simulation system design for the guidance, navigation and control of unmanned surface vehicles

The simulation of Guidance, Navigation and Control (GNC) is a key issue to provide an economical way to test and improve the various algorithms in the development and research of Unmanned Surface Vehicle (USV). However, most of the existing simulation systems are offline with limited visualization scene, and unable to interact with real sensors mounted on the USV, thus failing to display the immersive and real-time motion of USV for researchers. In this paper, a virtual simulation system is developed, which provides an open and online platform for the immersive and visualized GNC simulation of USVs. Two modes are designed with one to provide a virtual simulation with simulated sensors and USVs, while another to provide a visualization of real GNC tasks driven by the in-situ data. To accomplish these two modes, the lower computer works to interact with simulated or real sensors, and calculate the mathematical model of USVs, while the host computer works to obtain the data from lower computer, and implement the GNC of USVs, which is finally and immersively displayed in the virtual scene. Moreover, an open interface is designed in this system that allows the researchers to test their own algorithms in the GNC of USVs. The sliding mode control (SMC) controller, hybrid navigation algorithm as well as LOS-based formation navigation algorithm are proposed to serve as two case studies in the single-USV and multi-USV scenarios. The results verify the effectiveness of the proposed virtual simulation system in the immersive and visualized simulation of GNC for USVs.

Predefined-time fractional-order time-varying sliding mode control and its application in hypersonic vehicle guidance law

Predefined-time fractional-order time-varying sliding mode control and its application in hypersonic vehicle guidance law

Towards Oblivious Guidance Systems for Autonomous Vehicles

We consider encrypted guidance systems for straight-line path following. With cloud-computing technology, we can outsource computations in guidance systems to third-party providers, increasing scalability and enabling guidance-as-a-service. However, by remotely hosting a conventional guidance system on third-party infrastructure, we leak information such as the path of the vehicle. Potential customers may consider these leaks a serious breach of confidentiality, which limits the practical use of such systems. Therefore, to make cloud-based guidance systems viable, we would like to design guidance systems that we can host remotely without revealing confidential information to the host. To this end, we show that we can construct guidance systems that operate on encrypted position measurements, encrypted waypoints, and the bearing between each waypoint by using homomorphic encryption, effectively preventing the cloud host from identifying the vehicle's position. We show that the proposed guidance laws are locally exponentially stable and that the induced computational latency is appropriate for the real-time guidance of autonomous vehicles. Through field experiments, we demonstrate that an encrypted guidance system is practical and allows an unmanned surface vehicle to follow an encrypted path. The originality of this work lies in conceptualizing, designing, and experimentally validating an encrypted guidance system, unlike other studies that considered encrypted control systems.

A Dual-Sided LCLC Topology for AGV Wireless Charging System With Low Leakage EMF

The leakage electromagnetic field (EMF) generated during the wireless charging for automatic guidance vehicle will affect the regular operation of communication equipment and human safety. To reduce the leakage EMF, a dual-sided LCLC topology is introduced and optimized. The core idea is to construct the currents opposite to that flowing through the power transmission coils on primary and secondary sides, respectively. On this basis, connecting the shielding coils in the reverse current branches can generate the canceling magnetic field opposite to the leakage EMF. Thus, a low leakage magnetic coupler is established via the combination of power transmission coils and shielding coils. To minimize the leakage EMF, the specific parameters of magnetic coupler and dual-sided LCLC topology are optimized, and an experimental prototype is built to verify the shielding effect. The results show that when the output power is 1 kW, the shielding effect reaches 84.70%, and the power transfer efficiency is improved by 0.907%. Moreover, in the case of coil misalignment, there is still over 53.15% shielding effect by using the proposed method.

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

AbstractCooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy.

An unscented Kalman filter based velocity estimation method for articulated steering vehicle using a novel dynamic model

This paper presents a novel velocity estimation method for an articulated steering vehicle to improve the automation and safety of articulated steering vehicle. The estimation method process is done in three steps: First, the articulated steering vehicle's tire forces are calculated separately against articulated steering vehicle's asymmetric tire load transfers. Then the tire forces are used to build a concise dynamic model, which only needs to consider the single centroid's dynamics in a fixed direction of the vehicle body. Finally, an unscented Kalman filter estimator is developed further based on the articulated steering vehicle dynamics model for velocity estimation. The main contribution of this paper to the related literature lies in two aspects. On the one hand, by converting the velocity vectors of each vehicle body to a fixed coordinate system, a concise dynamic model is built to avoid complex calculations and high sensor costs. On the other hand, an additional moment equilibrium equation is introduced to assist the simplified estimator in calculating the asymmetric tire dynamics of articulated steering vehicle, which ensures the accuracy and universality of the method. The simulation results from ADAMS/MATLAB indicate that the proposed model can effectively reflect the dynamics of articulated steering vehicle even under obvious load transfer. In addition, the developed unscented Kalman filter estimator can obtain accurate and robust estimation results in several typical vehicle manoeuvres.

Dynamic Model Based Research on Intelligent Vehicle Steering and Obstacle Avoidance

In order to improve road safety, promote the development of autonomous driving, and realize the stability of unmanned vehicles during steering and obstacle avoidance, this paper establishes a lateral dynamic model and a cyclotron curve model based on the 3-degree-freedom vehicle dynamics model. Taking no collision between cars and timely obstacle avoidance as the optimization goal, this paper discretizes the model, a prediction model is established, a soft constraint solver is designed, and a relaxation factor is added to the performance index function. Based on the model prediction and control principle, the obstacle avoidance trajectory planning and tracking of unmanned vehicles is integrated and controlled, and the solution is converted into a quadratic programming problem according to the constraints and rolling optimization characteristics. Finally Matlab/Prescan joint simulation is used to verify the steering and obstacle avoidance quality of the model. The results show that the model has excellent safety, stability and comfort in non-highspeed situations.

Part B: Steering and control of an autonomous vehicle in emergency lane change maneuvers based on skilled driver’s performance

This paper considers the vehicle control process in emergency lane change (ELC). The control method is divided into two parts: before crossing the obstacle and after crossing it. The first part is done by a feed-forward controller inspired by the driver’s behavior pattern in the ELC maneuver. This controller is compensated by a neural network system and two proportional-integral (PI) and proportional-derivative (PD) controllers, which correct the output of the feed-forward controller to follow the path more accurately. The second part consists of two PD controllers responsible for controlling and steering the vehicle in the main path after crossing the obstacle. The main novelty of the proposed method is that the computational burden is low because of using a feed-forward controller, which is the main source of the calculation’s burden. So as a result, the controller system can cover the ELC maneuver in under 2 seconds in various lateral displacements to bypass the obstacle and prevent collisions. The co-simulations are performed in CarSim software and MATLAB/Simulink, which shows that the control system can appropriately steer the vehicle and follow the desired at different speeds, in the presence of road friction and parameter uncertainty.

Hierarchical coordinated control of multi-axle steering for heavy-duty vehicle based on tire lateral and longitudinal forces optimization

Traditional multi-axle steering vehicle often adopts linear state feedback control, which is difficult to ensure high-precision trajectory tracking under all working conditions. Moreover, due to the failure to consider the optimal distribution of tire lateral and longitudinal forces, tires are prone to problems such as uneven load rates and wear. In this paper, considering the over-redundant and nonlinear characteristics of three-axle vehicle, a hierarchical coordinated control strategy is proposed. In the upper layer, the trajectory tracking controller is designed based on the robust nonlinear sliding mode control theory. In the middle layer, based on the optimization conditions of tire load rate and dissipative energy, the lateral and longitudinal forces optimal distribution controller is constructed under the constraint of friction circle. In the lower layer, the lateral and longitudinal forces are finally converted into tire angles and torques with the tire inverse model. The results show that the hierarchical coordinated control strategy can ensure that the multi-axle vehicle can achieve high-precision trajectory tracking under all-terrain load conditions, and the load rate and wear of each tire are relatively uniform. The coordinated control strategy proposed in this paper considers the influence of nonlinear characteristic of vehicle and tire lateral and longitudinal forces distribution on steering coordination, which can provide an important theoretical basis for the further improvement of steering coordination of multi-axle vehicle.

Analyzation of the Application Scenarios of Ackerman Geometry based on Vehicle Steering Model

A complete set of models about vehicle steering are established to testify the feasibility of Ackerman geometry applied in various scenarios. The relationship between different parameters from cars and how they affect the steering ability is studied by Python. Linear model and nonlinear model are built to adapt to small and big slip angle. The mechanism section is tested with data from two types of cars: BMW M3 and Chevrolet Cavalier. We find that M3 has a better steering capability since it can generate more lateral force with less sideslip. The relationship between slip angle and lateral force illuminates our research about Ackerman geometry. The result indicates that regular Ackerman principle is beneficial in the big wheel steering angle with low-speed conditions, because it can reduce the slip angle and tire-road interaction, making cornering smoother and reducing tire wear. However, in small wheel steering angle with high-speed conditions, the Ackerman principle has some limitations and should be amended, which adds to the slip angle and thus increase the lateral force to get better steering. That is basically the reason why Reverse Ackerman is widely applied in Formula 1.

Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles

Articulated steering vehicles (ASVs), with brilliant maneuverability and efficiency, are being widely applied in mining, construction, agriculture, and forestry. However, their special structures result in them having complex dynamic characteristics, but there are no reliable models for further research. This study established a simulation platform with the dynamic model of ASVs, where the subsystems of the power train, steering systems, tires, and frames were also included. The dynamic model was validated with field test data of typical working cycles, in which the focus was on longitudinal and lateral motions and the characteristics of steering and power train systems. Then, the distribution of hydraulic and drive power was revealed using the simulation platform and test data. For a load–haul–dump (LHD) vehicle with a 6 m3 capacity, the maximum power of the system was about 289 kW; the power of the motor accounted for the majority of the power at the beginning stage of loading, being about 74%, and then the hydraulic power dominated in the later stage of loading. During the transport stage, the power of the motor accounted for about 79% of the total power. Finally, the influence of the dynamic parameters on lateral and longitudinal motions was analyzed based on the validated platform.

Learning-based interfered fluid avoidance guidance for hypersonic reentry vehicles with multiple constraints

To address the problem of no-fly zone avoidance for hypersonic reentry vehicles in the multiple constraints gliding phase, a learning-based avoidance guidance framework is proposed. First, the reference heading angle determination problem is solved efficiently and skillfully by introducing a nature-inspired methodology based on the concept of the interfered fluid dynamic system (IFDS), in which the distance and relative position relationships of all no-fly zones can be comprehensively considered, and additional rules are no longer needed. Then, by incorporating the predictor–corrector method, the heading angle corridor, and bank angle reversal logic, a fundamental interfered fluid avoidance guidance algorithm is proposed to steer the vehicle toward the target zone while avoiding no-fly zones. In addition, a learning-based online optimization mechanism is used to optimize the IFDS parameters in real time to improve the avoidance guidance performance of the proposed algorithm in the entire gliding phase. Finally, the adaptability and robustness of the proposed guidance algorithm are verified via comparative and Monte Carlo simulations.

A model-based method of tire-road friction estimation for articulated steering vehicles

Articulated steering vehicles (ASV) are widely used in many industries for their high efficiency and excellent maneuverability. The autonomous driving and intelligent control of ASV are extremely critical owing to the operation characteristics. As a very important parameter, the tire-road friction coefficient (TRFC) determines the extreme tire force directly in the process of intelligent control. However, it cannot be obtained with the existing methods for the harsh environment and special structure of ASV. This paper proposed a two-layer model-based method of tire-road friction coefficient estimation for ASV. The dynamic models of ASV in the XY plane, including the longitudinal and lateral models of frames, tire forces, and steering system models, are established first. The dynamic models are embedded into the upper layer with a Kalman filter (KF) to estimate the tire forces in longitudinal and lateral directions. During the process, some self-contained sensors, including the state sensors of frames and steering system, are used to provide the observation data. In the lower layer, a recursive least square (RLS) method with a forgetting factor is used to obtain the TRFC and tire stiffness parameters with the aid of the tire model. The simulation and field test are carried out to validate the method under comprehensive conditions, in which different steering commands, velocities, and roads are included. The simulation and field test results show that the forgetting factor has a significant influence on the convergence and robustness of the proposed method. The forgetting factor τ = 0.95 is used in the field test, the estimation result of dry concrete road friction coefficient is around 0.83. The results indicated that the proposed method can obtain the TRFC and tire parameters dynamically for ASVs.

다자유도 구형 트랙션 전동기의 틸팅 안정성 향상을 위한 구조에 관한 연구

The technology related to the multi-axis spherical motor is a technology that implements a three-dimensional motion, and various countries and research institutes are currently conducting prior research. A motor using a spherical type rotor can dramatically increase the degree of freedom of vehicle steering and robot motion according to the expansion of the tilt angle that determines the movable range of the rotation axis. when a multi-axis control system is used by adding mechanical axes, the size of the motor increases due to additional devices such as complex power conversion devices such as multiple gears and links. To solve this size problem, a spherical motor that does not use a mechanical axis can significantly affect the size. However, if a mechanical axis is not used, a problem in stability, such as shaking, occurs during driving. In this paper, we proposed an auxiliary structure to improve the stability of an inductive spherical by using a permanent magnet instead of a mechanical shaft.

Automation of haulers for debris removal in tunnel construction

AbstractTunnel construction is one of the areas of civil engineering where automation systems can increase both safety and productivity. That is why, to promote a continuous improvement and optimization of construction processes, the implementation of intelligent transport systems and, specifically, autonomous vehicles has been proposed as a catalyst for public works construction activity, including tunnels, reducing personnel, and operation costs, as well as improving safety. Under this scope, this paper presents the development of the automation system for a 25‐ton hauler truck, focusing on the task of removing debris from public works, including tunnels. This automation system has been designed, developed, and tested under real conditions in the facilities of two companies dedicated to civil engineering works, including vehicle guidance with and without gobal positioning system, obstacle detection, and tele‐operation for load/unload tasks. The main novelty of the system is the capacity of easy integration into the production system of the works, with a capacity to switch between manual, tele‐operation, or autonomous driving modes, and with specific sensors and control systems for combining indoor and outdoor navigation. The presented work includes the description of the automation of the vehicle, the general architecture, the environment perception, and the tele‐operation capacity, including indoor navigation and obstacle detection algorithms. The proposed system has been implemented and tested in real conditions, demonstrating the performance of the system and the benefit of the automation for this application.

Causes of Understeer and New Solutions

This study will discuss the problem of vehicle understeer, in order to explore the situation that the vehicle steering action can not be completed smoothly. With the development of the automobile industry, the vehicles produced by many automobile manufacturers still have the problem of insufficient steering. So we take BMW M3 as the research objects. So far, the known causes of vehicle understeer are camber, automobile suspension, tire wear and so on. The above three problems have been explained in other papers. On their basis, we propose that when turning, the size of the steering angle is related to whether it can turn, and at the same time, the steering angle is related to the turning time. So we make a conjecture whether changing the steering time can improve the problem of understeer. In order to verify this conjecture, we model the vehicle, analyzed various data and charts, and finally came to the conclusion that changing the steering time can improve the problem of understeer. I hope our article can provide a new idea for the future research on understeer.

The programmable steering machine for the electric lightweight vehicle

In this paper a programmable steering machine (PSM) and the lightweight electric powered vehicle, designed and made at the Kazimierz Pulaski University of Technology and Humanities in Radom (UTH Radom) have been presented. Both these technical objects are the result of the project carried out by the Student Research Group ?Turbodoładowani?. The steering machine has been developed with the programmable algorithms allowing to execute a controllable movement of the vehicle steering wheel. After execution, the system does not need an interaction with the driver. For this reason, a higher repetition of the vehicle traction measurements can be achieved. Such confirmation obtained in tests within which the time waveforms of rotation angle of the steering wheel by a set value of 45, 90, 180 and 360 degrees was recorded. In particular, the accuracy index for mentioned test conditions was calculated. Obtained results, expressed by the average value of the sensitivity index were lower than 2% within the tests carried out for ?45 degree maneuvers. In case of other tests i.e., for ?90, ?180 and ?360 degree maneuvers the accuracy index value was lower than 0.3%. In this way, it was confirmed that the tested PSM reached the appropriate operating parameters necessary for vehicle traction tests.

Demonstration And Analysis of The Guiding Control System for Aerospace Crafts

The development of space vehicle guidance and control systems gradually becomes a focus in recent research as a key to space vehicles’ information processing and control in the space environment. In general, among the popular methods in G&C systems, artificial intelligence-based algorithms contain a wide prospect. This study offers descriptions of the current methods in AI-based algorithms, proposed challenges involved in this field, and described the recent achievements in solving these issues. To be specific, the basic descriptions of the controlling systems as well as the various state-of-art machine learning approaches that are suitable for the G&C system are demonstrated and analyzed including ANN, DNN, RL, SVM, DRL, etc. According to the analysis, the challenges as well as the drawbacks of the advanced scenarios are estimated as well as propose the future prospects to improve the performance of landing. Overall, these results shed light on guiding further exploration of aerospace vehicles.

Control Analysis of Vehicle Steering System based on Closed-Loop Control Algorithm

When a four-wheel vehicle turns and the steering centre of the front and rear wheels is one point, it is regarded as the theoretical optimal steering of the vehicle and the goal of optimal control. Aiming at the commonly used multi-link steering system, the system control was analysed, according to the structural characteristics and angle relationship. Based on the closed-loop control algorithm, the optimal control of the steering system was designed and analysed, according to the steering control quantity obtained by the target path and parameter feedback. At the same time, the state variables and prediction parameters of the system were analysed. Based on the actual vehicle and steering optimal control system, the changes of various parameters of vehicle operation under no-load condition and turning at 30 km/h were analysed and compared with the theoretical optimal value to verify the effectiveness of the control system. The results show that: the state variables of the control system are predicted by discrete analysis and half step integration, and the accuracy is high. In the steady state turning condition, with the lifting of the vehicle, the radius of the steering track gradually increases, and the vehicle shows obvious understeer characteristics. Under the two working conditions, the characteristic curves obtained by the steering control system are highly consistent with the theoretical values, and the peak error of the two is controlled within 5%, indicating the reliability of the optimal control algorithm, which provides a reference for such design optimization.

Robust two-degrees of freedom linear quadratic gaussian position control for the front axle actuator of a steer-by-wire system

The Steer-by-Wire (SbW) system is a key technology for highly automated driving. For automated lateral vehicle guidance, the precise position control of the SbW Front Axle Actuator is an essential prerequisite. This contribution presents the modeling, control design, nominal performance, and stability analysis as well as the robustness analysis of the position control for the SbW Front Axle Actuator. Based on a nonlinear model of the plant a simplified linear system model is derived. This model yields the basis for the design of a Two-Degrees of Freedom Linear Quadratic Gaussian Control (2DOF LQG control), which allows an independent design of the command and the disturbance response. Besides an evaluation of the nominal control behavior, μ-analysis is applied to assess the robustness of performance and stability. Finally, real vehicle tests for different driving maneuvers are presented to verify simulation results.