Kinematic Model Of Vehicle Research Articles

Motion Control of a Low-Cost Underwater Vehicle with Three-Position Cross Rudder

Underwater vehicles are widely employed as platforms for marine monitoring and operations, with rudders playing a crucial role in controlling their movements. Traditional underwater vehicles utilize servos to steer rudders, which results in high production and maintenance costs. To reduce the manufacturing costs of small underwater vehicles, this paper proposes a design featuring a three-position cross rudder driven by a binary-controlled electromagnet. Additionally, a novel virtual rudder angle controller is developed, which utilizes PWM frequency conversion to perform rudder control, ensuring motion performance comparable to that of conventional underwater vehicles. By establishing kinematic and dynamic models of a typical underwater vehicle (REMUS100), a corresponding virtual rudder angle control strategy is introduced. Based on the binary-controlled three-position cross rudder and virtual rudder angle controller, the effectiveness of the motion control method is verified by numerical simulation. The results confirm that the proposed virtual rudder angle controller successfully executes turning and spatial path-tracking tasks. The binary-controlled three-position cross rudder, together with the virtual rudder angle controller, offers a cost-effective solution for the practical application of small unmanned underwater vehicles, particularly for disposable underwater vehicles that do not require recovery.

PID control for the water-air hybrid aquatic-aerial vehicle’s exit based on simulated annealing genetic algorithm optimization

Abstract A hybrid aquatic-aerial vehicle is a special equipment with the ability to perform amphibious operations in water and air, and its control accuracy and stability during the water outflow process are crucial for ensuring the safety and operational efficiency of the vehicle. This article first constructs the kinematic and dynamic models of the hybrid aquatic-aerial vehicle and designs the basic control strategy based on a PID controller. Given the limitations of a single PID control strategy in specific contexts, this study proposes a PID controller based on genetic algorithm optimization and introduces a simulated annealing algorithm in the optimization process of the genetic algorithm to make the adjustment of controller parameters more precise and efficient. Through simulation verification, it was found that the optimized PID controller improved the performance of the aircraft during the outflow stage, indicating that this optimization control strategy has a positive impact on improving the operational performance of the aircraft.

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Abstract This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems.

Model predictive control for autonomous vehicle path tracking through optimized kinematics

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

Localization robustness improvement for an autonomous race car using multiple extended Kalman filters

In this paper, we introduce a vehicle localization method designed for the SZEnergy race car, which competes in the Shell Eco-marathon. The proposed method comprises four different extended Kalman filter-based localization algorithms and a selection algorithm that determines the most suitable one based on vehicle speed, GNSS availability, and signal quality. The low-speed Kalman filters are based on a kinematic vehicle model while the high-speed variants are based on a dynamic vehicle model. Several measurements were performed during test maneuvers to evaluate the performance of the filters. The proposed method succesfully handles sensor miscalibration and GNSS outages.

Multi-mode vehicle pose estimation under different GNSS conditions

The integrated navigation system combining the global navigation satellite system (GNSS) and inertial navigation system (INS) is a crucial method for pose estimation in the field of autonomous driving technologies. Nevertheless, the accuracy of pose estimation is severely compromised when GNSS signals are obstructed or disrupted. To address this issue, this study introduces a multi-mode pose estimation framework designed to ensure accurate pose estimation even under unstable GNSS conditions. By integrating vehicle kinematics model that considers steering characteristics (VKMSC) and the convolutional neural network-long short-term memory (CNN-LSTM) neural network (NN) model into various estimation modes, the framework enhances the robustness of the integrated navigation system against signal interference. The system dynamically selects the optimal estimation strategy based on the degree of GNSS signal disruption. The proposed method has been validated through real-vehicle experiments, which demonstrate its efficacy in providing precise pose estimation across a spectrum of interference scenarios. Under the multipath and non-line-of-sight (MP/NLOS) mode, compared to the integrated navigation system and the fusion of traditional vehicle kinematic models, the proposed method improved positional estimation accuracy by 61.8 % and 19.7 %, respectively. In GNSS outage mode, the proposed method increased the estimation accuracy by 36.5 % and 12.0 %, respectively, compared to the INS navigation system assisted by the VKMSC and CNN-LSTM network model. The proposed method effectively reduces pose estimation errors in the integrated navigation system during interference and suppresses data fluctuations, thereby enhancing the system's precision and robustness.

Path Planning Algorithm of Orchard Fertilization Robot Based on Multi-Constrained Bessel Curve

Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot’s driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots.

Adaptive path tracking control for autonomous vehicles with sideslip effects and unknown input delays

This paper presents an adaptive path-tracking control of autonomous ground vehicles with sideslip effects and time-varying input delays. To handle the problem of unknown time-varying input delays, the vehicle kinematic model is transformed into a novel chain model, and a delay-based auxiliary integral term is introduced for the estimation of the unknown terms caused by time-varying delays. To avoid modeling errors caused by existing linear approximation methods, a new adaptive path-tracking control strategy is proposed, such that the optimal tracking performance is achieved by appropriately adjusting certain design parameters. In addition, the inherent complexity explosion issue is avoided with the aid of a boundary estimation strategy. All signals of the closed-loop systems are guaranteed to be bounded. Simulation results illustrate the effectiveness of the method proposed.

Design and Analysis of a Lunar Crewed Vehicle With a Novel Versatile Compliant Suspension Mechanism

Abstract A lunar crewed vehicle (LCV) with improved maneuverability, mobility, and ride comfort is required for astronauts to conduct long-range scientific investigations and resource utilization on the Moon’s surface. This paper concentrates on designing a novel multi-functional compliant suspension for LCV to improve the above-mentioned performance. First, based on the requirement of high-speed traversing on the rough Lunar terrain, the required type of suspension motion is identified and the demanded suspension mechanism is obtained through structural evolution. Then, the kinematic analysis of the proposed suspension mechanism is conducted, and the steering kinematic model of the whole vehicle is established. A compliance analysis is completed, taking into account the actual design characteristics of the suspension mechanism. A multi-degrees-of-freedom dynamics model of the vehicle is developed, considering both wheel–ground separation and the deformation of wheels and soil. Simulations are conducted to verify full vehicle performance with the proposed suspension, and the results reveal that the design features better mobility and comfort in rough terrain with minimum turning radius, peak longitudinal acceleration, and root mean square reduced by 9.5%, 45.1%, and 21.4%, respectively.

Traversable map construction and robust localization for unstructured road environments1

Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation.

Path tracking control for autonomous vehicles using hybrid fault-tolerant approach

Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach.

Developing inverse motion planning technique for autonomous vehicles using integral nonlinear constraints

The study considers issues of elaborating and validating a technique of autonomous vehicle motion planning based on sequential trajectory and speed optimization. This method includes components such as representing sought-for functions by finite elements (FE), vehicle kinematic model, sequential quadratic programming for nonlinear constrained optimization, and Gaussian N-point quadrature integration. The primary novelty consists of using the inverse approach for obtaining vehicle trajectory and speed. The curvature and speed are represented by integrated polynomials to reduce the number of unknowns. For this, piecewise functions with two and three degrees of freedom (DOF) are implemented through FE nodal parameters. The technique ensures higher differentiability compared to the needed in the geometric and kinematic equations. Thus, the generated reference curves are characterized by simple and unambiguous forms. The latter fits best the control accuracy and efficiency during the motion tracking phase. Another advantage is replacing the nodal linear equality constraints with integral nonlinear ones. This ensures the non-violation of boundary limits within each FE and not only in nodes. The optimization technique implies that the spatial and time variables must be found separately and staged. The trajectory search is accomplished in the restricted allowable zone composed by superposing an area inside the external and internal boundaries, based on keeping safe distances, excluding areas for moving obstacles. Thus, this study compares two models that use two and three nodal DOF on optimization quality, stability, and rapidity in real-time applications. The simulation example shows numerous graph results of geometric and kinematic parameters with smoothed curves up to the highest derivatives. Finally, the conclusions are made on the efficiency and quality of prognosis, outlining the similarities and differences between the two applied models.

Trajectory Tracking Control Algorithm of Two-wheel Rutting Model

In the development of unmanned two-wheeled self-balancing vehicle, it is very important to consider the trajectory tracking of unmanned two-wheeled self-balancing vehicle. In order to study the track tracking problem of unmanned two-wheeled self-balancing vehicle, the kinematic model of unmanned two-wheeled vehicle and the relation between its roll Angle and the kinematic model were established. The simulation platform is built to realize the unmanned two-wheeled vehicle and realize the circular track tracking at 5m/s, and the tracking performance of the track tracking controller under the circular track is verified.

Trajectory Planning Framework Combining Replanning and Hierarchical Planning

To enable autonomous vehicles to respond quickly to changes in the surrounding environment and to generate safe and stable trajectory, we propose a safety-based hierarchical trajectory planning method that divides the planning module into two parts: the planner and the replanning monitor. In the planner, we first propose a fast linear trajectory planning method that uses the mass point model instead of the vehicle model and linearizes the collision avoidance constraint using the Big-M method for linear programming (Big-M) to obtain a linear programming model. The complete vehicle kinematic model is then built in the nonlinear programming stage, the collision constraints and cost functions are refined, and the rough solution of the linear programming is brought into it to obtain the exact solution. The replanning monitor is divided into safety and comfort monitors. The safety monitor will always pay attention to the changes in the surrounding environment and calculate whether the vehicle is in danger of collision in the future period, while the comfort monitor is more concerned with the comfort of driving the vehicle; when the monitor requirements are not met, replanning will be carried out. By simulating the driving environment, the proposed algorithm can form a safe and comfortable trajectory, which verifies the rationality of the proposed method.

An improved model predictive control method for path tracking of autonomous vehicle considering longitudinal velocity

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature.

A Model Decomposition Kalman Filter for Enhanced Localization of Land Vehicles

This paper investigates a GPS-dead reckoning fusion localization problem for land-vehicles. We first give a nonlinear vehicle kinematics model, which is used as the transition model for the fusion filter. Then a decomposition rule is designed to resolve the transition model into a set of model families. Based on decomposed model series expansion, a fusion filter is derived to estimate the vehicle position based on Gaussian approximation. The fusion filter can guarantee higher real-time localization accuracy than other linearization-based approaches, since it is derived on the basis of the decomposed process model and the series expansion scheme involves a loss function to ensure minimum truncated orders. Numerical simulations and real-world experiments results demonstrate that our method is more accurate and reliable than the state-of-the-art methods for vehicle localization under various driving conditions.

Time-optimal trajectory planning for autonomous parking of tractor-semitrailer vehicle based on piecewise Gauss pseudospectral method

For the autonomous parking problem of the tractor-semitrailer vehicle, a piecewise Gauss pseudospectral method is proposed as a trajectory planning scheme in this paper. Firstly, the trajectory planning problem is transformed into an optimal control problem by establishing a vehicle kinematics model for the low-velocity scene, the dynamic constraints including the physical constraints, the collision avoidance constraints, and the endpoint constraints. The optimal control problem are discretized into a nonlinear programming problem by piecewise Gauss pseudospectral method according to the movement area during parking process. The shortest time to complete parking is taken as the objective function. The Gauss pseudospectral method is further adopted to solve the nonlinear programming problem in the two parking stages. Three parallel parking conditions with different lengths of parking spaces and three vertical parking conditions with different widths of parking spaces are set for simulation verification based on Matlab/Simulink. The simulation results show that the proposed method can uniformly and effectively solve the trajectory planning problem of the tractor-semitrailer vehicle. Compared with the traditional pseudospectral method, a shorter parking time and a significantly higher convergence rate can be got with the method, which further indicates that the proposed method has good effectiveness and robustness.

Tridimensional vector path abstracting and trajectory tracking control on ramps of full self-driving vehicle

Under the constraints of time and the corresponding vehicle location, trajectory tracking control of full self-driving vehicles on ramps requires higher speed to overcome the increase in the driving distance caused by the road slope. Two-dimensional trajectory tracking methods are not applicable to roads with various gradients in reality. Here we discuss a series of studies on tridimensional vector path abstracting and trajectory tracking, collectively designing a network physical model with meaningful nodes for describing real roads and a trajectory tracking controller suitable for ramps. Almost all kinds of paths can be characterized by several simple polynomials, which are then used as the path demand in trajectory tracking control. A vehicle kinematics model considering its vertical location and slope angle attitude is formulated, based on which, model predictive control algorithm is used for the trajectory tracking controller design by regulating the vehicle speed and front wheel steering angle. The developed approach is downloaded into an intelligent control unit and tested in the real world by using a self-driving test vehicle to fully realize the practical application.

An adaptive cascade predictive control strategy for connected and automated vehicles

SummaryConnectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

Coupled lateral and longitudinal trajectory tracking control with a modified vehicle kinematics model for autonomous vehicles

Abstract The paper proposes a coupled lateral and longitudinal trajectory tracking control algorithm based on model predictive control (MPC) to enhance the precision and stability of trajectory tracking of autonomous vehicles. The coupled MPC controller is developed based on a modified vehicle kinematics model, which simultaneously takes the steering angle and the longitudinal speed as control variables. The modified vehicle model and the coupled MPC controller are verified by CarSim and MATLAB/Simulink co-simulation experiments. Simulation results demonstrate that, at the cost of 12 percent increase in average computational time, the proposed coupled MPC controller leads to an approximate 48 percent reduction in tracking error compared to the benchmarking coupled lateral and longitudinal MPC controller based on the classic kinematics model.