Path Tracking Research Articles

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.

Research on layered control of path tracking for unmanned industrial vehicles based on fully hydraulic steering leakage compensation

Industrial vehicles work in complex terrain, the variable centre of mass position, leakage nonlinearity of full hydraulic steering and other issues lead to poor path tracking stability and accuracy. In this paper, an intelligent hierarchical controller for industrial vehicles’ path tracking is designed with full hydraulic steering leakage compensation, including an upper decision layer and a lower execution layer. The upper decision layer observes the pavement-tyre adhesion coefficients through an extended Kalman filter algorithm, and uses the real-time observations of the adhesion coefficients to establish a variable constraint control for the linear time-varying MPC, and thus performs the path tracking control. The lower actuator layer receives the upper layer’s target steering wheel angle output and performs steering operations. Based on the establishment of a mathematical model of full hydraulic steering considering the leakage characteristics, it analyses the leakage disturbance factors and constructs a fuzzy feed-forward steering leakage compensation controller to compensate for the leakage disturbances in real time for path tracking steering. Simulation and experimental results show that the lateral acceleration, sideslip angle, tyre side slip angle and tracking error of intelligent industrial vehicles under different loads and working conditions are improved by more than 32.4%, 35.8%, 40.2% and 45.8% respectively, and the designed hierarchical controller for the path tracking of intelligent industrial vehicles, which considers the compensation of all-hydraulic steering leakage, can effectively improve the path tracking stability and tracking accuracy of intelligent industrial vehicles under different loads and working conditions.

Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

Searching for a Cheap Robust Steering Controller

The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a cohesive way of evaluating all developed controllers under a wide variety of environmental conditions. This work serves as a step in this direction. Four controllers are tested on five maneuvers representing highways and collision avoidance trajectories. Each controller and maneuver combination is repeated on five sets of environmental conditions or Operational Design Domains (ODDs). The design of these ODDs ensures the translation of these experimental results to real-world applications. The commercial software, CarSim 2020, is extended with Simulink models of the environment, sensor dynamics, and state estimation performances to perform highly repeatable and realistic evaluations of each controller. The results of this work demonstrate that most of the combinations of maneuvers and ODDs have existing cheap controllers that achieve satisfactorily safe performance. Therefore, this field’s research efforts should be directed toward finding new control problems in lateral path tracking rather than proposing new controllers for ODDs that are already solved.

Path tracking with personalized handling stability constraints matched to driving styles

In the path-tracking control process, drivers have different expectations of the lateral response of vehicles. To meet drivers’ expectations of handling stability, this paper proposes a model predictive control (MPC)-based path-tracking controller with personalized handling stability constraints matched to driving style. Firstly, the categories of driving styles are obtained by applying the K-Means algorithm to the collected driving data. Then the sideslip angle-longitudinal velocity map is constructed based on the nonlinear dynamics model of the vehicle to generate the front-wheel steering angle constraints that are matched with the maximum sideslip of the vehicle expected by drivers of different styles. After that, an MPC-based path-tracking controller has been built, and the front-wheel steering angle is incorporated into the MPC controller. Furthermore, compared to the strategy of directly using sideslip angle constraints for handling stability control, this approach avoids introducing additional constraint variables, leading to improved real-time performance. Simulation experiments are conducted to compare the dynamic vehicle responses between the handling stability control that takes into account the driving style and the control system that does not consider the driving style. In addition, the subjective evaluation is conducted to verify the proposed path-tracking control method that considers the stability constraints of driving style. The evaluation results show that the proposed method can obtain better subjective evaluation results than a path-tracking controller that does not consider the driving style. Finally, through the hardware-in-the-loop experiments, the real-time computational capability of the proposed controller is verified.

Intricate multiple scattering features of artificial facilities in X-Band SAR images

Abstract. Due to the intricate distortion and reflection geometry of the SAR signal, it is typically difficult to determine the multiple scattering of large artificial objects in SAR images. This work presents a scattering point path tracking model that utilizes the real three-dimensional dimensions of targets, based on the geometric optics method. Three different artificial structures, including light poles, cable stayed bridges, and power transmission lines, are carefully analysed in time-series SAR images with their simulated multiple scattering results. The results demonstrate that the routes determined by the model are consistent with the multiple scattering features on SAR images. Moreover, the time-series data demonstrate that ripples in the water's surface have a significant impact on the multi-scattering features of power lines and bridges. The double scattering features of the light pole provides a novel approach to the process of permanent scatterers (PS) in urban areas. The instances presented in this study demonstrate the effectiveness of the scattering point path tracking model in identifying the various artificial facility targets on different reflective surfaces. It will be a useful tool for deciphering the multiple scattering of large artificial structures when their 3D model is known.

A tube-based model predictive control method for intelligent vehicles path tracking

A tube-based model predictive control method for intelligent vehicles path tracking

Types of lichen at Tenggulun Restoration Station, Leuser Ecosystem Area (LEA), Aceh Tamiang, Aceh Province, Indonesia

Lichen is an organism formed by two organisms between algae and fungi (mutualistic symbiosis). The main part of lichen is the thallus. This study aims to identify types of lichen in the Tenggulun Restoration Station in Leuser Ecosystem Area (LEA) Aceh Tamiang, Aceh Province, Indonesia using the explore method. The research data is species names, characters, and ecological conditions which include altitude, humidity, temperature, pH, and light intensity. Sampling was carried out on all types of substrates along the tracking path. The results of a study found 25 species of lichen from 21 genus and 13 families with 3 different types of thallus. The most common types of thallus lichen found were grouped into 20 types of crustose, 4 types of foliose, and 1 type of filamentous. Ecological conditions at study sites are varied.

Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation.

This article presents a neuroadaptive fault-tolerant control method for path tracking of multiinput multioutput (MIMO) systems in the presence of modeling uncertainties and external disturbances. In dealing with modeling uncertainties, neural networks (NNs) with diversified activation/basis functions are considered, with which we establish a set of control algorithms that are robust against uncertainties, adaptive to unknown parameters, and tolerant to actuation faults. This is the first work that explicitly takes into account the neural weights uncertainties and activating function uncertainties in multiple layered neural networks in control design. In addition, we apply the developed control algorithms to unmanned ground vehicles (UGVs) with actuator failures. With the aid of Lyapunov stability theory, it is shown that the proposed control is able to drive the vehicle along the desired path with high precision and all the internal signals are uniformly ultimately bounded (UUB) and continuous. Both theoretical analysis and numerical simulation confirm the effectiveness of the designed strategy.

Voronoi-based 3D phase field model and numerical simulations of granite crack propagation

The distribution of mineral grains within underground rocks and materials is crucial for understanding geological processes and the stability of engineering structures. However, numerical simulation methods that consider 3D mineral particles are still immature. This study presents a 3D phase-field fracture model of mineral grains based on Voronoi polygons, making some beneficial attempts in this regard. The phase-field method does not rely on mesh reconstruction or crack path tracking and can simulate complex crack propagation behavior. Meanwhile, the Voronoi polygon can better describe the microscopic structure of rock with grain boundaries. Rooted in Voronoi polygons, this model efficiently captures the complex geometric shapes of mineral grains and accurately characterizes their positions, shapes, and sizes. Therefore, this study combines Voronoi polygons to establish a 3D phase-field model. Through two numerical simulation cases, the model demonstrates its effectiveness in simulating the impact of three-dimensional mineral grains on crack propagation behavior, showing that cracks tend to propagate at the boundaries or inside softer mineral grains. The modeling approach proposed in this paper has good reference value for numerical simulation studies considering the microscopic structure of rocks.

A Tube-Based Model Predictive Control for Path Tracking of Autonomous Articulated Vehicle

This paper presents tube-based Model Predictive Control (MPC) for the path and velocity tracking of an autonomous articulated vehicle. The target platform of this study is an autonomous articulated vehicle with a non-steerable axle. Consequently, the articulation angle and wheel torque input are determined by the tube-based MPC. The proposed MPC aims to achieve two objectives: minimizing path tracking error and enhancing robustness to disturbances. Furthermore, the lateral stability of the autonomous articulated vehicle is considered to reflect its dynamic characteristics. The vehicle model for the MPC is formulated using local linearization to minimize modeling errors. The reference state is determined using a virtual controller based on the linear quadratic regulator to provide the optimal reference for the MPC solver. The proposed algorithm was evaluated through a simulation study with base algorithms under noise injection into the sensor signal. Simulation results demonstrate that the proposed algorithm achieved the smallest path tracking error, compared to the base algorithms. Additionally, the proposed algorithm demonstrated robustness to external noise for multiple signals.

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

Trajectory Tracking Control of Variable Sweep Aircraft Based on Reinforcement Learning.

An incremental deep deterministic policy gradient (IDDPG) algorithm is devised for the trajectory tracking control of a four-wing variable sweep (FWVS) aircraft with uncertainty. The IDDPG algorithm employs the line-of-sight (LOS) method for path tracking, formulates a reward function based on position and attitude errors, and integrates long short-term memory (LSTM) units into IDDPG algorithm to enhance its adaptability to environmental changes during flight. Finally, environmental disturbance factors are introduced in simulation to validate the designed controller's ability to track climbing trajectories of morphing aircraft in the presence of uncertainty.

Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose

Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose

Bi-level path tracking control of tractor semi-trailers by coordinated active front steering and differential braking

In recent years, autonomous tractor semi-trailers have been considered a promising solution for transportation on highways, ports, and large logistics centers to improve safety and efficiency. However, due to the complicated dynamics introduced by articulation structure, there are still challenges in achieving high-precision path tracking and stability control for tractor semi-trailers, especially in critical scenarios. In this work, a bi-level path tracking control scheme for the autonomous tractor semi-trailer is proposed. At the higher level, a robust model predictive controller coordinating active front steering and differential braking is devised for addressing path tracking, stability control and robustness for an autonomous tractor semi-trailer. At the lower level, incorporating a logic switching mechanism, the designed optimal braking torque distribution module and the PID-based longitudinal control module prioritize the implementation of differential braking and target speed tracking. Simulation results demonstrate that the proposed control strategy offers remarkable path tracking performance in three designed testing scenarios.

Learning model predictive controller for wheeled mobile robot with less time delay

The weak spots have been examined, a solution has been suggested, the solution has been applied, and a comparison between the simulation and experimental test results has been given in this work. In order to do this, a nonlinear model predictive controller and a linear quadratic regulator controller have been utilized to control a four-wheel mobile robot during modeling and implementation. But the combination of these classic and modern controllers with machine learning can greatly help to make these controllers work more accurately; As a result, in order to increase the accuracy of the performance of these controllers, by training neural networks of multilayer perceptrons, the controllers have been made intelligent. Controllers with cost function have coefficients as weighting to the matrix of system state variables and control input, which are greatly affected by changing these two weighting matrices of problem solving and optimization. For this reason, it is necessary to extract these two matrices for each separate path in order to improve the performance of the controller by trial and error. But by applying the proposed network which is trained with a new algorithm, not only the performance accuracy has increased, but the network extracts these two matrices without the need to spend human energy. Additionally, by training additional neural networks to optimally extract the benefit of the forecast horizon, fewer calculations and faster solution times have been achieved, helping to reduce the current time delays, particularly in the implementation of the nonlinear controller on the robot in the experimental mode. In the hardware section, the solution time has been shortened by looking at and utilizing operators like the U2D2 interface and the pixy camera, which are quicker than the standard procedure. The information indicates that the proposed intelligence technology reacts 40%–50% quicker than the NMPC standard procedure. The suggested algorithm has also minimized the path tracking error since it extracts the optimal gain at each stage. Moreover, a hardware mode solving speed comparison between the recommended and conventional methods is given. In this domain, a 33% increase in solving speed has been noted.

Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving

Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model.

Path Tracking and Anti-Roll Control of Unmanned Mining Trucks on Mine Site Roads

Path Tracking and Anti-Roll Control of Unmanned Mining Trucks on Mine Site Roads

A three level NPC inverter fault detection and identification using voltage vector residual evaluation based on current path tracking method

This research describes open circuit detection and fault location for active 3-level NPC (neutral point clamped) inverter switches during normal and connected to the neutral point. This work introduces the current path tracking method to correlate the operating mode of NPC inverter and switch status in abnormal and normal conditions. Furthermore, a hierarchal diagnosis approach is introduced for open circuit faults using output Voltage Vector Residual Evaluation (VVRE). The node potential and conduction path can be derived from the model to indicate the NPC inverter circuit faults. Two steps are involved in the diagnosis. First, OC fault can be determined by analyzing the difference between the reference voltage and actual voltage vector resulting from the specified current path utilization ratio. Therefore, using a current path state reconstruction method, we can distinguish faulty transistors with similar properties. The simulated results demonstrate the robustness and efficiency of the proposed approach.