Path Tracking Algorithm Research Articles

Enhancing Passenger Comfort in Autonomous Vehicles Through Vehicle Handling Analysis and Optimization

Autonomous Vehicles (AVs) offer significant advantages in terms of traffic and fuel efficiency accident prevention, and reduced travel time. `Sense-plan-act' cycle of the AV has been designed and implemented to achieve a high level of active safety that considerably reduces the risk to passengers and pedestrians. However, within existing path planning and tracking algorithms, the subject of passenger comfort has received less attention compared to other AV topics. This paper is aimed at characterizing vehicle handling criteria and path profiles configuration in order to identify self-driving requirements for passenger comfort. The emphasis is given to the efficacy and usability of the classical handling analysis methods from a self-driving perspective. To these aims, we initially establish a framework which is defined by comfortable path profiles and a versatile handling model of an AV. Then, we introduce a directional path tracking unit which optimally implements AV's trajectories using a controller and a speed regulator. The path tracking unit includes a multi-objective optimizer enabling improvement the handling behavior of the AV. The integration of the proposed methodology into the state-of-the-art AVs' control system can lead to an enhanced comfort level.

Motion Control and Path Planning and Tracking Algorithm of Football Robot

Motion Control and Path Planning and Tracking Algorithm of Football Robot

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Applying computer vision to mobile robot navigation has been studied for over two decades. One of the most challenging problems for a vision-based mobile robot involves accurately and stably tracking a guide path in the robot limited field of view under high-speed manoeuvres. Pure pursuit controllers are a prevalent class of path tracking algorithms for mobile robots, while their performance is rather limited to relatively low speeds. In order to cope with the demands of high-speed manoeuvres, a multi-loop receding-horizon control framework, including path tracking, robot control, and drive control, is proposed in this paper. This is done within the vision guidance of differential-driving wheeled mobile robots (DWMRs). Lamé curves are used to synthesize a trajectory with G 2 -continuity in the field of view of the mobile robot for path tracking, from its current posture towards the guide path. The platform twist—point velocity and angular velocity—is calculated according to the curvature of the Lamé-curve trajectory, then transformed into actuated joint rates by means of the inverse-kinematics model; finally, the motor torques needed by the driving wheels are obtained based on the inverse-dynamics model. The whole multi-loop control process, initiated from Lamé-curve blending to computational torque control, is conducted iteratively by means of receding-horizon guidance to robustly drive the mobile robot manoeuvring close to the guide path. The results of numerical simulation show the effectiveness of our approach.

Path Tracking Control Based on Adaptive Control Period

Abstract In this paper, a path tracking algorithm of adaptive trajectory prediction is presented. To begin with, trajectory prediction equations were built from two degree of freedom vehicle dynamics model. Based on the assumption that the yaw rate of a vehicle does not change in a control period, the trajectory prediction equations are solved to obtain the vehicle’s target front wheel steering angle. By comparing the curves of trajectory, tracking error, and yaw angle of the vehicle, this paper analyzes the effect of control period on tracking accuracy and vehicle stability. Based on this, a path tracking algorithm of adaptive control period was designed using fuzzy control, adaptively adjusting control period according to lateral deviation and yaw rate. From the simulation analysis, it can be seen that optimized path tracking algorithm can satisfy the need for vehicle tracking under low speed, and the problem of vehicle runaway under high speed is overcome to a certain extent. The vehicle’s path tracking performance is significantly improved.

A Robust Numerical Path Tracking Algorithm for Polynomial Homotopy Continuation

We propose a new algorithm for numerical path tracking in polynomial homotopy continuation. The algorithm is “robust” in the sense that it is designed to prevent path jumping, and in many cases it can be used in (only) double precision arithmetic. It is based on an adaptive stepsize predictor that uses Padé techniques to detect local difficulties for function approximation and danger for path jumping. We show the potential of the new path tracking algorithm through several numerical examples and compare it with existing implementations.

An Improved Fuzzy Logic Control Method for Path Tracking of an Autonomous Vehicle

Highlights This study focused on the path tracking accuracy of a two-wheel steering, two-wheel drive autonomous vehicle. An improved fuzzy logic control method (IFM) was designed to control the steering angle of the front wheels. The IFM was better than traditional pure pursuit and fuzzy logic control methods for automatic path tracking. Abstract. This article presents a path-tracking algorithm based on an improved fuzzy logic control method (IFM) that generates optimal steering angles for an autonomous wheeled vehicle to track a sequence of waypoints. The path-tracking algorithm was designed to focus on the vehicle performance, including tracking accuracy, stability, and convergence speed. A prototype vehicle with two-wheel steering and two-wheel drive (2WS-2WD) was developed as a test platform and equipped with a series of sensors for evaluating the IFM. In this study, we considered two variables, i.e., lateral deviation (d) and heading deviation (θ), as inputs for the control system. The output (δ) was the steering angle of the front wheels as controlled by an electric linear actuator. A maximum steering angle strategy was adopted for rapid convergence when the vehicle was far from the desired path or when the heading deviation was large. An update-line distance algorithm was used for finding the best turning time as the vehicle moved closer to the next waypoint. To validate the proposed algorithm, testing was performed to compare traditional pure pursuit (TPP), traditional fuzzy logic control method (TFM), and the IFM in tracking two types of paths (straight and rectangular). Traveling at 0.5 m s-1 and with a signal sampling period of 0.5 s, the prototype converged to the desired straight path from different initial states. When the vehicle reached a stable state, the steady-state error was between 5.0 and 6.5 cm with a mean of ≤6.0 cm. The settling distance in the straight path test was between 125.0 and 422.3 cm, and the convergence time was in the range of 11.5 to 16.0 s; the greater the initial deviation of the vehicle was, the greater the settling distance and settling time were. In the 15 m x 14 m rectangular path test, the overall mean error of the IFM was 14.4 cm when the initial lateral and heading deviations were 0, and the maximum deviation was 67.4 cm, which occurred at the corners. The test results show that the proposed IFM had better accuracy, stability, and convergence speed for path tracking when compared to the TPP and TFM algorithms.

An improved hybrid error control path tracking intelligent algorithm for omnidirectional AGV on ROS

In order to improve the accuracy and stability of intelligent omnidirectional AGV path tracking based on Mecanum wheels, an improved intelligent hybrid error control path tracking method is proposed. The method combines the angular velocity of the intelligent AGV vehicle with the error correction of longitudinal velocity as the coupling estimation error. The coupling estimation error and the improved pure tracking algorithm are combined as the lateral control of the intelligent AGV car, while the PID control is used as the vertical control to further reduce the error interference. The ROS simulation results showed that compared with the tracking effect of the traditional pure tracking algorithm, the tracking path of the improved intelligent hybrid error control path tracking algorithm is closer to the real path, which greatly improved the trajectory deviation phenomenon, and the path tracking accuracy and stability are significantly improved.

Path Tracking Control Based on Model Predictive Control With Adaptive Preview Characteristics and Speed-Assisted Constraint

As one of the research focuses in the field of intelligent driving, improving the performance of path tracking has become a goal for many scholars. Among many path tracking control algorithms, model predictive control (MPC) controllers are widely used due to their excellent control performance. However, the traditional MPC control has shortcomings because it does not consider the particularity of the driving car with preview driving characteristics, i.e., it is only directly controlling from the vehicle state. To fully retain the advantages of the MPC controller and simultaneously exert the preview characteristic of the intelligent vehicle to improve the path tracking performance, this work proposes a model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The algorithm mainly consists of two parts: The MPC controller with adaptive preview characteristic is proposed based on the lateral error and target curvature; The longitudinal vehicle speed-assisted constraint algorithm based on the largest ideal lateral acceleration becomes a supplementary part of the algorithm as a supplementary constraint of the MPC controller. Series of simulations based on the Simulink/CARSIM software individually verified the model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The proposed adaptive preview strategy is suitable for the path tracking algorithm controlled by the MPC controller and improves the path tracking performance.

Simulation Study to Develop Implement Control and Headland Turning Algorithms for Autonomous Tillage Operations

This article reports a simulation study conducted using a three-dimensional tractor-driving simulator to develop an implement control algorithm and evaluate the tillage coverage and field efficiencies of a virtual autonomous tractor following paths generated based on different headland turning methods. To minimize the no-tilled or unnecessarily tilled areas that occurred in our previous study, a tillage implement control algorithm was designed by enabling the raising or lowering of a three-point hitch with appropriate delay times. The effects of headland turning methods, i.e., X-shaped, R-shaped, and C-shaped turns, on path tracking and full-path simulation of autonomous tillage operations were studied. The results of the simulation studies were evaluated in terms of tracking error, skipped area, and field efficiency. The developed implement control algorithm effectively reduced both no-tilled and unnecessarily tilled areas in comparison with those obtained without the implement control algorithm. The magnitudes of changes in no-tilled and unnecessarily tilled areas were from 4.5 to 0.4 m2 and from 4.6 to 0.3 m2, respectively. In a study in which an autonomous tractor followed a desired path in a virtual field of 100 m × 40 m at a constant traveling speed of 4 km h−1 designed to investigate the influence of the three different headland turning methods, the simulator allowed a quantifiable comparison of tillage operation performance for the various headland turns by showing lateral deviations < 9 cm, heading angle errors < 16°, ratios of skipped area < 2%, and field efficiencies ranging from 81.3 to 86.6%. Full-path simulation tests of the autonomous tillage operation conducted in virtual rectangular fields with a length of 100 m and various widths showed that field efficiency was inversely proportional to the field width. The use of the 3D tractor-driving simulator was effective for designing tillage implement control algorithms and studying the effects of headland turning methods on path tracking. These results are applicable to the development of path generation and tracking algorithms suitable for autonomous tillage operations.

Research on control method and evaluation system of unmanned ground vehicle group change

In this paper, a novel formation control system, composed of path planning and tracking, was designed. A master-slave control model, based on the main vehicle, was proposed to solve the potential accumulation, transmission and amplification errors. To be specific, the referenced trajectory was initially generated by integrating a dynamic window and potential field in the process of transforming. Then a path tracking algorithm based on Hermite curve was introduced, which can make the group change process more stable and accurate. The performance of the formation control system was evaluated, in terms of the formation response and capacity of the formation stability in the simulated scenario. As a result, the vehicle control is simulated, the vehicle can quickly and precisely track the variation of the expected position, the maximum error is 0.1 m, and the speed error of the vehicle following can be controlled within 0.2 km / h, which can keep the stability of the vehicle formation better.

Sequential Time-Optimal Path-Tracking Algorithm for Robots

This paper focuses on minimum-time path tracking, a subproblem in motion planning of robotic systems. We generate a time-optimal velocity profile for robotic manipulators taking into account kinematic and dynamic constraints. Based on the special structure of the constraints (called peaked constraints), profile generation is formulated as a linear programming (LP) problem. The LP-based control problem is solved by a sequential optimization method. The presented algorithm has reduced computational time compared to a general LP solver.

Double-DQN based path smoothing and tracking control method for robotic vehicle navigation

Automatically tracking paths with large curvatures smoothly and tightly is an acknowledged challenge in robotic vehicle navigation. This is particularly true for some navigation applications in which pre-defined paths such as crop rows in agricultural applications must be followed accurately. This article presents a novel path smoothing and tracking control method based on Double Deep Q-Network (Double DQN) for an automated robotic vehicle. This Double-DQN-based controller utilized a deep reinforcement learning method that scored all potential actions at the current state according to their performance index and selected the best performer as the output of the network. In this study, a path-tracking algorithm was self-developed with a deep Q-network trained by driving a rover in a simulated virtual environment. The algorithm was tested in both simulation and on a grass field to follow paths with multiple sharp turns. The performance was compared with that of the Pure-Pursuit Control (PPC) algorithm. The results showed that the Double DQN-based control dramatically reduced the settling time and the overshoot at the corner at higher forward speeds at a minor expense of slightly increased rise time and steady state error.

Under Actuated Unmanned Submarine Path Tracking Method in Complex Environment

Cui, Y.-N.; Dong, C., and Ren, J., 2019. Under actuated unmanned submarine path tracking method in complex environment. In: Gong, D.; Zhu, H., and Liu, R.(eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 149–152. Coconut Creek (Florida), ISSN 0749-0208.With the vigorous development of China's shipbuilding industry in recent years, the research of underactuated unmanned submarines has gradually come into people's vision. Underactuated Unmanned Submarine is driven by propeller thrust and yaw moment. The input freedom of the control system of under-actuated unmanned submarine is very small. Because of its low altitude, which control is more difficult than general underactuated mechanical systems because it belongs to a special nonholonomic system. At the same time, the working conditions of submarines are very complicated. It will be disturbed by the marine environment such as living things and water flow. These velocities increase the difficulty of motion control and tracking of underactuated unmanned submarines. With the development of society, people's demand for underactuated unmanned submarines is increasing, which is mainly used in military and marine development. So, the study of underactuated unmanned submarines has important practical significance. Firstly, this paper designs the path tracking and control algorithm based on Lyapunov theory by establishing the model. Then, the correctness of the test results is verified by simulation. The final results show that the model and algorithm can effectively realize the tracking control of submarine and improve the tracking accuracy.

Research on Path Tracking Algorithm of Autopilot Vehicle Based on Image Processing

Smart cars are the result of the combination of the latest technological achievements in the fields of artificial intelligence, sensors, control science, computer, and network technology with the modern automobile industry. Intelligent cars usually have functions, such as automatic shifting, automatic driving, and automatic road condition recognition. The research of intelligent car technology involves many disciplines. This thesis focuses on the field of smart car visual navigation, focusing on image denoising, image information recognition, extraction, and pattern recognition control algorithms. The traditional trajectory tracking algorithm is mainly used in industrial computer or high-performance computer. The computational complexity leads to poor real-time control, and it is easily interfered by external complex terrain environment and internal disordered electromagnetic environment during vehicle driving. In general, on a regular basis, by the image analysis of the driver or the driver information, the image information is proposed using way trace processing technology, vehicle tracking control method and automatic driving rules. The simulation and experimental results show that the proposed control methods and rules used to carry out automatic driving vehicle are feasible. The algorithm reduces the complexity of the algorithm, improves the real-time and stability of the control and finally achieves a good trajectory tracking effect of the car on high-speed automatic driving.

Energy-efficient Real-time Deployment of Mobile Sensors in Disaster-prone Location

Recent research works on real-time robot-assisted mobile sensor deployment have become rapid, as they tend to accomplish problem-solving tasks at both safe location and unsafe location. This paper proposes a technique to achieve a real-time energy-efficient deployment of mobile sensors in a disaster-prone location using a path-tracking algorithm. An enhanced path-tracking algorithm was introduced to be able to deploy mobile sensors in both presence and absence of obstacles. The simulation result investigates the real-time performances of the mobile sensor nodes deployment with respect to factors such as the minimal energy consumption of the nodes with neighbourhood sharing scheme, the end-to-end system threshold and the time variance for the deployed sensor nodes to reach the target location and back to the base station.

Energy-efficient real-time deployment of mobile sensors in disaster-prone location

Recent research works on real-time robot-assisted mobile sensor deployment have become rapid, as they tend to accomplish problem-solving tasks at both safe location and unsafe location. This paper proposes a technique to achieve a real-time energy-efficient deployment of mobile sensors in a disaster-prone location using a path-tracking algorithm. An enhanced path-tracking algorithm was introduced to be able to deploy mobile sensors in both presence and absence of obstacles. The simulation result investigates the real-time performances of the mobile sensor nodes deployment with respect to factors such as the minimal energy consumption of the nodes with neighbourhood sharing scheme, the end-to-end system threshold and the time variance for the deployed sensor nodes to reach the target location and back to the base station.

Application of a 3D tractor-driving simulator for slip estimation-based path-tracking control of auto-guided tillage operation

Successful adoption of auto-steering agricultural vehicles in Korean paddy fields depends on the ability to accurately follow a full path, including straight and curved paths for agricultural tasks and headland turning in each row, in the presence of sliding caused by wet ground conditions in relatively small fields. To improve the performance of an autonomous tillage tractor originally developed in our previous study, this article describes a simulation study, conducted using a 3D computer simulator, that accounts for the slippery motion of a virtual tractor on ground with varying adherence properties, In addition, this study performed field evaluation of an auto-guided tillage tractor equipped with a slip estimation-based path-tracking algorithm that is robust to slippage in paddy fields. The test platform was built with a 60-kW tractor equipped with an RTK-GPS (real-time kinematic-global positioning system) and IMU (inertial measurement unit) system, a navigation controller that could estimate the sideslips of the tractor in real-time, and a three-point hitch dynamometer that could measure tillage drafts. The results of the computer simulation confirmed that the slip estimation-based path-tracking algorithm was superior in guiding the tractor along curved paths on the ground with relatively low coefficients of cornering stiffness by reducing the lateral deviations as compared with those obtained with the conventional look-ahead distance method. In field tests run in an arable field, the autonomous tillage tractor equipped with the slip observer algorithm demonstrated improved performance as compared to the previously developed system by reducing the RMSE (root mean square error) for lateral deviation on curved paths from 29 cm to 15 cm.

Performance analysis of vertically offset overlapped propulsion system based quadrotor in an aerial mapping mission

In this paper, the authors present the performance analysis of a Vertically Offset Overlapped Propulsion System (VOOPS)-based quadrotor in an aerial mapping mission. The dynamic model of the VOOPS quadrotor with the effect of overlapping propellers and the profile drag has been derived and simulated. A path-tracking mission is taken as an example for aerial survey. The controller used for this task is presented, followed by the response study of the attitude and the position controller with standard test inputs. A graphical interface has been built to select the area to be mapped by defining a polygon around it, and waypoints for lawn-mower type survey grid were generated based on the direction of wind. The path-tracking algorithm is presented along with course correction and simulations were performed with both conventional and VOOPS quadrotor. An experimental vehicle based on the proposed VOOPS concept has been built, tested on the same path, and the results are discussed. The results show that the VOOPS quadrotor is capable of performing the aerial mapping mission with quick response and good accuracy.

Tracking Error Learning Control for Precise Mobile Robot Path Tracking in Outdoor Environment

This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than $10$ cm error in off-road terrain.

Automatic extraction of coronary centerline based on model-mapped and inertia-guided minimum path from CTA images

It has been a challenging but significant research topic to extract the centerlines of the coronary arteries of the cardiac computed tomography angiography volume in clinical applications. A new method is proposed to full-automatically extract and recognize the centerlines of the major branches instead of manual interaction in this paper, which employs a new path tracking algorithm that combines direction features from atlases and inertia features from previous extracted centerline points, referred to as model-mapped and inertia-guided minimum path. This method first registers a pre-constructed coronary model that contains the right coronary artery, the left anterior descending artery, the left circumflex artery coronary artery to the target cardiac computed tomography, to provide initial reference positions and direction information of the coronary artery. After getting the reference regions based on the registration, the two ostia positions are detected automatically by the learning-based method, which is based on the probability boosting tree and 3D Haar features, in the region of interest of the cardiac computed tomography volume. The starting points are then as the ostia in the evolution of minimum path. Meanwhile, to boost the robustness of the evolution, the tracked path of the last step is used to generate the inertia-driven force. Finally, based on a new automatic endpoint detection algorithm, the longest centerline of a particular coronary branch can be extracted with the proposed method. We tested the robustness of our method in the Rotterdam Coronary Artery Algorithm Evaluation framework. The proposed method is fully automatic and obtains the optimal effect among the fully automatic methods in Rotterdam framework.