Path Tracking Research Articles

Artificial Flow Guidance Method for Combined Off-Tracking and Stability Improvement for Automated Articulated Vehicles

Abstract This paper considers an integrated control method combining autonomous driving and active steering. By using artificial flow guidance (AFG), the steering at all five axles on a tractor–semitrailer are synchronized for precise low-speed path tracking and improved lateral stability. AFG is a motion planning method which uses velocity vectors to guide the motion of the vehicle; here, two modification on AFG are proposed to form the integrated-AFG which improves the transient performance and generalizes the choice of the tracking points. Simulations rendered in TruckMaker show submillimeter level steady-state off-tracking for a broad speed range, with maximum deviation being 1 cm for a high-speed lane change; amplification of trailer lateral motion is also suppressed, with rearward amplification (RWA) < 1 maintained throughout. This control method is robust to changes in key parameters such as trailer mass and road friction coefficient. The methods presented provide a new foundation for implementing automated freight transportation on highways.

Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints.

The essence of biomimetics in human-computer interaction (HCI) is the inspiration derived from natural systems to drive innovations in modern-day technologies. With this in mind, this paper introduces a biomimetic adaptive pure pursuit (A-PP) algorithm tailored for the four-wheel differential drive robot (FWDDR). Drawing inspiration from the intricate natural motions subjected to constraints, the FWDDR's kinematic model mirrors non-holonomic constraints found in biological entities. Recognizing the limitations of traditional pure pursuit (PP) algorithms, which often mimic a static behavioral approach, our proposed A-PP algorithm infuses adaptive techniques observed in nature. Integrated with a quadratic polynomial, this algorithm introduces adaptability in both lateral and longitudinal dimensions. Experimental validations demonstrate that our biomimetically inspired A-PP approach achieves superior path-following accuracy, mirroring the efficiency and fluidity seen in natural organisms.

Path tracking of varying-velocity 4WS autonomous vehicles under tire force friction ellipse constraints

In this paper, a control scheme is presented to solve the problem of path tracking for varying-velocity autonomous vehicles with higher robust performance while friction force constraints of vehicle tires and ground are considered to avoid appearing the steering lost situation actively in the limit of the tire friction forces by using four wheel steering. To achieve these goals, a new expression of the tire forces and a new controlled system are proposed for the three degree of freedom vehicle model. A longitudinal input-saturation controller is designed to track the desired longitudinal velocity and attenuate the influence of lateral and yaw motions. The virtual control input method is used to design the input-saturation controller and get the desired steering angles to track the desired path for the lateral and yaw motions while a new control problem with desired forces tracking is considered to autonomously avoid the limit of lateral tire friction forces and the steering lost situation appearing. To test these results, lane changing, U-corner turning and straight-line driving for a varying-velocity vehicle are simulated with external disturbances and model uncertainties. Simulation results show that the higher robustness and precise path tracking performances are obtained.

Time-optimal control of ship manoeuvring under wave loads

Ship manoeuvrability of Maritime Autonomous Surface Ships (MASS) revolutionise the maritime industry. However, this paradigm shift necessitates the advancement of manoeuvring control models to meet the complex demands of autonomous navigation. This paper addresses the need for an improved manoeuvring control model for MASS, particularly concerning path planning and tracking in the presence of wave loads. The paper establishes a comprehensive mathematical model for ship manoeuvring, considering forces acting on the ship's hull, propellers, rudders, and wave loads. A time optimisation model using a spatial reformulation approach is introduced. A nonlinear Model Predictive Control (MPC) model is presented for path planning and tracking, with a case study investigating the influence of wave load and comparing two control strategies. 10%–20% of time consumption increases if the wave load exists. This research bridges the gap in existing literature by incorporating wave loads into MPC-based control models for MASS. The findings shed light on the significance of wave loads in ship manoeuvring and provide valuable insights into effective control strategies for autonomous vessels operating in real-world sea conditions.

EASS: An automatic steering system for agricultural wheeled vehicles using fuzzy control

Automatic steering is a crucial technology for achieving automatic navigation and unmanned driving of agricultural machinery. In this research, agricultural wheeled vehicles with the HPAS (Hydraulic Power Assistant Steering) system were selected as the research object and the EASS (Electric Automatic Steering System) was proposed by using fuzzy control to realize effective steering instead of human drivers. The PID (Proportional Integral Differential) algorithm was used to process the difference between the desired and actual steering angles and to realize automatic control of rotation of directional wheels. Fuzzy control was adopted to achieve self-adjusting of PID parameters and to optimize working state of the steering motor in real-time for different agricultural wheeled vehicles under various operating conditions. Fuzzy control rules were explored and formulated by analyzing variation of the steering angle under different resistive torque values. For evaluation of its feasibility, the newly developed EASS was applied in automatic navigation of a rice transplanter, a tractor, and a high-clearance sprayer. Field testing results showed that the maximum path tracking lateral error and heading error were 4.39 cm and 2.13°, respectively. And the maximum RMS (Root Mean Square) errors were 3.75 cm and 1.02°, respectively. Therefore, the fuzzy control-based the EASS in this research was applicable to various agricultural wheeled vehicles.

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs.

Cooperative Control of Path Tracking and Driving Stability for Intelligent Vehicles on Potholed Road

When a vehicle hits a pothole, the reaction force of the pothole on the tire causes an imbalance in the driving forces on both sides of the vehicle and may cause the wheels to jump, potentially resulting in path deviation and roll instability. To address these issues, a cooperative control system for path tracking and driving stability is proposed. Roll stability is achieved by the roll moment generated by reasonably distributing the driving torque among the four IWMs. The necessary roll moment is calculated by the designed robust sliding mode control (RSMC), which enhances the transient performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_∞</i> by 48.9%. The path tracking is ensured by active front steering (AFS) based on model predictive control (MPC), and the front wheel angle is used as the disturbance of the stability control. Simulation and bench test results demonstrate that the developed controller can maintain vehicle stability even when the greatest path deviation is 0.03 m, whereas conventional direct yaw-moment control (DYC) and AFS controllers fail to achieve the same level of control. These findings provide a theoretical foundation and serve as a reference for cooperative control on potholed roads.

Integrated Vehicle Dynamics Modeling, Path Tracking, and Simulation: A MATLAB Implementation Approach

Integrated Vehicle Dynamics Modeling, Path Tracking, and Simulation: A MATLAB Implementation Approach

Fixed-time Sliding Mode-based Adaptive Path Tracking Control of Maize Plant Protection Robot via Extreme Learning Machine

During the maize middle and late periods, the soil between rows is soft and also involved with weeds and straw. When the plant protection robot (PPR) moves on the soil, there exists uncertain shear perturbation because of the shear action and pressure subsidence, leading to the difficulty of the controller design. In this work, we propose an adaptive path tracking control (PTC) considering disturbances for the PPR. The disturbance of PRR in contact with soil is first revealed according to Bekker pressure subsidence and Janosi shear models, through which the plant model of PPR system is established. Then, we propose an adaptive fixed-time sliding mode (AFTSM)-based PTC to achieve excellent path tracking performance, where an extreme learning machine (ELM) estimator is developed, releasing the requirement for bound derivations in the control design. Using the fixed-time control and the ELM techniques in the proposed control, a remarkable control performance is well ensured, i.e., high-accuracy tracking, fast convergence, and excellent robustness. Experimental studies on a PPR are executed for demonstrating the validity and good performance of the designed controller.

Intelligent Enhanced Mobile Robotics Navigation: Integrating Neural Networks with Type-2 Fuzzy Logic for Dynamic Environments

Intelligent mobile robots move on uncertain grounds, thus requiring good navigation strategies for things like path tracking and obstacle avoidance. This research uses an Omni-drive mobile robot to autonomously approach given objectives in different situations encountered in static and dynamic environments. The paper compares two distinct controllers – fuzzy logic controller and neural network controller- that lead the mobile robot towards its destination without hitting obstacles. These are responsible for adjusting the linear and angular velocities of a mobile robot which makes adaptive navigation possible during real-time. The experimental results have depicted the adaptability of each controller as well as its efficiency especially when dealing with uncertainties involved with the mobile robot navigation system. By systematically evaluating and contrasting them, this study brings out the best performance between Fuzzy Logic and Neural Network Controllers regarding enhancing the autonomy and robustness of Mobile Robots. This research helps to advance knowledge in autonomous systems for practical applications, which will give rise to more efficient navigational techniques for mobile robots; thus, efficient systems that are autonomous become more reliable today. The results show that these controllers are effective in safely steering the robot from its starting point to a specified destination without hitting obstacles.

Path tracking and stability control of 4WID electric vehicles based on variable prediction horizon MPC

Path tracking and stability control of 4WID electric vehicles based on variable prediction horizon MPC

Path tracking and stability control of 4WID electric vehicles based on variable prediction horizon MPC

Path tracking and stability control of 4WID electric vehicles based on variable prediction horizon MPC

Path tracking and Handling Stability Coordinated Control of 4WS and DYC for Distributed in-Wheel Motor Drive Electric Vehicle Under Extreme Conditions

Path tracking and Handling Stability Coordinated Control of 4WS and DYC for Distributed in-Wheel Motor Drive Electric Vehicle Under Extreme Conditions

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

A Heuristic Motion Planning Algorithm for a Mobile Robot With Nonholonomic Constraints

This paper proposes a heuristic motion planning algorithm to enhance optimal path determination and tracking for a differential drive mobile robot within a global environment. The proposed method uses the A‐star search algorithm to discover the optimal path between the start and goal vertices. This algorithm employs precise distance values and heuristic distance values to compute and expand the vertex for identifying a suitable path. When the path is generated, the movement of the robot is regulated by an attractive potential field (APF) and a repulsive potential field (RPF). The APF assists the robot in following its designed path, while the RPF is employed to avoid obstacles. Subsequently, the APF plays a role in helping the robot return to its trajectory. In case of obstacles obstruct the path, the A‐star algorithm identifies a new alternative route starting from the current position to the goal, and the robot navigates along this path. During a series of scenario evaluation, the algorithm demonstrates the ability to present optimal solutions in complex environments. To validate the effectiveness of the system, the simulation is executed with the Webots R2021a software. Then, experimental verification for the designed robot demonstrates the system’s high accuracy in tracking and obstacle avoidance on a specific map.

Efficient Path Planning and Tracking Control of Autonomous Vehicles in Park Scenarios Based on Sparrow Search and Stepwise Prediction

Efficient Path Planning and Tracking Control of Autonomous Vehicles in Park Scenarios Based on Sparrow Search and Stepwise Prediction

Distributed Nonlinear Predictive Control for Unmanned Air-Ground Vehicles

This paper addresses the formation path tracking for a heterogeneous system made up of a steering car and a quadcopter. System modelling is based on Nonlinear Model Predictive Control (NMPC), which is a proper solution when considering a heterogeneous feet as not only it deals with non-linearities of the dynamic model but it is also a way to generate trajectories that can be communicated to other agents. Moreover, an Extended Kalman Filter (EKF) is employed to estimate the non-measurable ground robot state variables. Two separate simulations in Simulink for each agent have been carried out leading to promising results in terms of tracking errors. Indeed, tests show that the proposed solution allows the two vehicles to follow the reference aligned with a deviation of a few centimeters.

Vehicle Path Tracking Control Using Pure Pursuit With MPC-Based Look-Ahead Distance Optimization

Vehicle Path Tracking Control Using Pure Pursuit With MPC-Based Look-Ahead Distance Optimization

Task Accuracy Enhancement for a Surgical Macro-Micro Manipulator With Probabilistic Neural Networks and Uncertainty Minimization

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

Finite-Time Autonomous Vehicle Path Tracking Control Based on State Constraint

Finite-Time Autonomous Vehicle Path Tracking Control Based on State Constraint