Accurate Path Tracking Research Articles

A Hierarchical Anti-Disturbance Path Tracking Control Scheme for Autonomous Vehicles Under Complex Driving Conditions

Path tracking is one of the most crucial issues of autonomous vehicles, but uncertain factors including road roughness and wind gusts could cause lateral deviation and even divergence of the control system. Therefore, this paper proposes a hierarchical anti-disturbance tracking architecture based on the steer-by-wire (SBW) system to improve tracking accuracy and dynamic stability for autonomous vehicles. The proposed architecture is robust strongly against different types of disturbances in the path tracking process by means of hierarchical decoupling. Firstly, in the upper-layer path tracking module, a robust model predictive control (MPC) method with a feedback correction term calculates the expected front-wheel steering angle. Secondly, in the lower-layer SBW module, a nonlinear high gain rack force observer is proposed to compensate for the generalized rack force disturbance. Then, an improved non-singular fast terminal sliding mode auxiliary control law based on the super-twisting algorithm is presented to attenuate the norm-bounded rack disturbance effectively. Simulation and experiment results show that the proposed control architecture can improve the path tracking accuracy and has strong interference resistance and robustness against both modeled and unmodeled disturbances.

A Model Predictive Control for Path Tracking of Electronic-Four-Wheel Drive Vehicles

This paper proposes a path tracking controller to assist the electronic-four-wheel drive (e-4WD) vehicle in following the desired path profile. It utilizes only readily available in-vehicle sensors and a standalone global positioning system (GPS). Noteworthy, both the lateral distance error and the heading angle error between the desired path profile and the vehicle are calculated using only a standalone GPS. Therefore, the proposed algorithm can be easily applied to mass-produced e-4WD vehicles. A model predictive control (MPC) is selected as the controller type, which derives optimal control input considering both state and input constraints of in-wheel motor (IWM) in the e-4WD vehicle. Due to the advantage of this MPC predicting the vehicle's future dynamic behavior in advance, it is possible to output a more preemptive and stable yaw moment for vehicle path tracking. Finally, a weighted least square (WLS) allocation that is suitable for redundant actuator configuration is utilized to distribute the optimal IWM torques to the front left and right wheels of the e-4WD vehicle. The major contributions of the proposed control algorithm are as follows. 1) It is the first time in this paper that the MPC is applied to path tracking of e-4WD vehicles. 2) The condensed constraint matrix of the proposed MPC has a structure that can be easily applied to the linear programming. Using the CarSim simulation and the real-car based experiment, the effectiveness of the proposed MPC algorithm is verified by a comparative analysis. Thus, the high path tracking accuracy of the proposed algorithm is confirmed.

Evaluation of the Path-Tracking Accuracy of a Three-Wheeled Omnidirectional Mobile Robot Designed as a Personal Assistant.

This paper presents the empirical evaluation of the path-tracking accuracy of a three-wheeled omnidirectional mobile robot that is able to move in any direction while simultaneously changing its orientation. The mobile robot assessed in this paper includes a precise onboard LIDAR for obstacle avoidance, self-location and map creation, path-planning and path-tracking. This mobile robot has been used to develop several assistive services, but the accuracy of its path-tracking system has not been specifically evaluated until now. To this end, this paper describes the kinematics and path-planning procedure implemented in the mobile robot and empirically evaluates the accuracy of its path-tracking system that corrects the trajectory. In this paper, the information gathered by the LIDAR is registered to obtain the ground truth trajectory of the mobile robot in order to estimate the path-tracking accuracy of each experiment conducted. Circular and eight-shaped trajectories were assessed with different translational velocities. In general, the accuracy obtained in circular trajectories is within a short range, but the accuracy obtained in eight-shaped trajectories worsens as the velocity increases. In the case of the mobile robot moving at its nominal translational velocity, 0.3 m/s, the root mean square (RMS) displacement error was 0.032 m for the circular trajectory and 0.039 m for the eight-shaped trajectory; the absolute maximum displacement errors were 0.077 m and 0.088 m, with RMS errors in the angular orientation of 6.27° and 7.76°, respectively. Moreover, the external visual perception generated by these error levels is that the trajectory of the mobile robot is smooth, with a constant velocity and without perceiving trajectory corrections.

Optimal path tracking control for intelligent four-wheel steering vehicles based on MPC and state estimation

Four-wheel steering (4WS) vehicles have better stability control and path following performance than front-wheel steering (FWS) vehicles. Aiming at this characteristic, a new four-wheel active steering control strategy is proposed. For intelligent vehicle path tracking and nonlinear vehicle system state estimation, a path tracking control algorithm based on the MPC algorithm is designed to analyze the stability of the vehicle and set up constraints to achieve accurate tracking of the reference path. Automotive dynamic control systems require information on system variables. For example, the sideslip angle of electric vehicles cannot be measured directly. Based on UKF theory and the information input of low-cost sensors on the vehicle, an estimator is designed to estimate vehicle sideslip angle and yaw rate. According to the difference between the estimated value and the ideal value of the vehicle state, the LQR optimal controller is designed to realize the optimal control of the front and rear steering. And compared with the dynamic simulation results of front-wheel steering, proportional control four-wheel steering and yaw rate feedback four-wheel steering. The experimental results of the simulation platform show that the path tracking 4WS state feedback optimal control method has good lateral control stability and path tracking accuracy.

Path tracking control of an autonomous vehicle with model-free adaptive dynamic programming and RBF neural network disturbance compensation

The performance of the model-based controller is always affected by the uncertainty and nonlinearity of the model parameters in the vehicle path tracking process. To address this issue, a novel path tracking controller based on model-free adaptive dynamic programming (ADP) is proposed for autonomous vehicles in this paper. To be specific, the proposed controller obtains information from the online state and front-wheel angle input data which are repeatedly used to calculate the controller gain iteratively. So, this controller features not requiring accurate knowledge of vehicle model parameters for controller development. Meanwhile, the path tracking performance of the autonomous vehicle will be inevitably disturbed by unknown nonlinear external disturbance. To approximate this disturbance, the learning characteristics of Radial Basis Function Neural Network (RBFNN) are applied to generate compensation for the front-wheel angle. Afterward, the weight updating law of RBFNN is derived by Lyapunov function to ensure the stability and convergence of the whole system. Finally, Hardware in the loop (HIL) test results demonstrate that the proposed ADP-RBF controller can improve the comprehensive performance of the vehicle path tracking control system and achieve the balance between path tracking accuracy and minimum sideslip angle.

Compliant Human–Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator

In this article, we aim to achieve manual guidance of a robot manipulator to perform tasks that require strict path following and would benefit from collaboration with a human to guide the motion. The robot can be used as a tool to increase the accuracy of a human operator while remaining compliant with the human instructions. We propose a dual-loop control structure where the outer admittance control loop allows the robot to be compliant along a path considering the projection of the external force to the tangential-normal-binormal (TNB) frame associated with the path. The inner motion control loop is designed based on a modified sliding mode control (SMC) law. We evaluate the system behavior to forces applied from different directions to the end-effector of a 6-DOF industrial robot in a linear motion test. Next, a second test using a 3D path as a tracking task is conducted, where we specify three interaction types: free motion (FM), force-applied motion (FAM), and combined motion with virtual forces (CVF). Results show that the difference of root mean square error (RMSE) among the cases is less than 0.1 mm, which proves the feasibility of applying this method for various path-tracking applications in compliant human–robot collaboration.

Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory

In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

A Vehicle Handling Inverse Dynamics Method for Emergency Avoidance Path Tracking Based on Adaptive Inverse Control

In this paper, a new adaptive inverse control (AIC) method is presented to solve vehicle handling inverse dynamics problem and achieve path tracking control for emergency avoidance. The designed AIC system includes two crucial modules: the model identifier and the inverse model controller which are both the nonlinear adaptive filters constituted by neural networks. Firstly, they are both trained offline and connected with the vehicle to be the initial states of AIC system. Then, the back-propagation (BP) algorithm is used to adjust the weight parameters of the model identifier to identify the nonlinear characteristics of vehicle in real time. Simultaneously, the inverse model controller is used to be the controller of AIC system and the controller's weight parameters are tuned by the back-propagation through model (BPTM) algorithm online. A novel adaptive learning rate is introduced into the BP algorithm and the BPTM algorithm to guarantee the stability of neural networks. Finally, the inverse model controller can be regarded as the inverse of vehicle and used to solve required steering wheel angle according to the desired path to realize path tracking. The double lane change (DLC) road is used to be the desired path for emergency avoidance. The simulation results illustrate that the AIC system could realize accurate path tracking although there exist external disturbances and the AIC is an effective method to deal with the handling inverse dynamics problem for emergence avoidance.

Coordination control of active front steering and direct yaw moment control based on stability judgment for AVs stability enhancement

A coordination control strategy based on stability judgment is presented for autonomous vehicles (AVs) aiming to enhance the handling and stability performance. Firstly, the stability judgment scheme is used to evaluate the real-time stability level of vehicles based on the Self-Organizing Feature Map (SOFM) neural network and K-Means algorithm. Secondly, a coordination controller of active front steering (AFS) and direct yaw moment control (DYC) is designed to track the desired vehicle motion. To enhance the handling and stability of AVs, the weights of AFS and DYC controllers are adaptively adjusted according to the vehicle stability level. Finally, the effectiveness of the proposed method is verified in co-simulation environment of CarSim and Simulink, and a rapid control prototyping test is implemented to evaluate the feasibility and robustness. The results indicate that the stability judgment scheme and coordination control strategy for AVs can not only satisfy the requirements of path tracking accuracy but also enhance the handling and stability performance.

Feedback control of a pneumatically driven soft finger using a photoelastic polyurethane bending sensor

ABSTRACT In recent years, pneumatically driven soft-fingered hands constructed from polymer materials have been developed to grasp variously shaped objects. In general, the motion-control ability of these hands is limited to grasping objects. A dexterous manipulation requires pinching and in-hand manipulation. However, this is difficult because soft-fingered hands face two major problems of low positioning accuracy and vibration during high-speed motion. These problems can be solved with feedback control by using a soft sensor that does not impair finger softness. This study proposes a pneumatically driven soft-finger system with a soft-position sensor formed from photoelastic polyurethane that measures the soft-finger curvature through changes in the light intensity. A sensor feedback control for the soft finger is also established in this paper. Consequently, in a series of basic experiments, the sensor feedback control achieved precise positioning and continuous accurate path tracking. Finally, pinching motions and object orientation control were achieved by accurate continuous-path control of two soft fingers.

Path tracking based on model predictive control with variable predictive horizon

Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.

Parallel collaborative planning for the coupled system of underground heavy-load robot

At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.

A Novel Algorithm for Path Planning of the Mobile Robot in Obstacle Environment

—In this paper, a design method of smoothing the path generated by a novel algorithm is proposed, which makes the mobile robot can more rapidly and smoothly follow the path and reach the target point. No matter the attitude vector angle is an acute angle or obtuse angle, there is no doubt that we can find the right curve, including polar polynomial curves and piecewise polynomial functions, which makes the path length and the circular arc tend to be similar and guarantees the shorter path length. In the condition of meeting the dynamic characteristics of the mobile robot, the tracking speed and quality are improved. Therefore, the symmetric polynomial curve and the piecewise polynomial function curve are used to generate a smooth path. This novel algorithm improves the path tracking accuracy and the flexibility of the mobile robot. At the same time, it expands the application range of mobile robot in structured environment.

Fuzzy-Torque Approximation-Enhanced Sliding Mode Control for Lateral Stability of Mobile Robot

Accurate path tracking and stability are the main challenges of lateral motion control in mobile robots, especially under the situation with complex road conditions. The interaction force between robots and the external environment may cause interference, which should be considered to guarantee its path tracking performance in dynamic and uncertain environments. In this article, a flexible lateral control scheme is considered for the developed wheel-legged robot, which consists of a cubature Kalman algorithm to evaluate the centroid slip angle and the yaw rate. Furthermore, a fuzzy compensation and preview angle-enhanced sliding model controller to improve the tracking accuracy and robustness. Finally, some simulations and experimental demonstrations using the four-wheel-legged robot (BIT-NAZA) are carried out to illustrate the effectiveness and robustness, and the proposed method has achieved satisfactory results in high-precision trajectory tracking and stability control of the mobile robot.

Accurate Path Tracking by Adjusting Look-Ahead Point in Pure Pursuit Method

Path tracking is an essential aspect of the navigational process of self-driving cars. Especially, accurate path tracking is important for not only normal urban roads but also narrow and complex roads such as parking lot and alleyway. The pure pursuit method is one of the geometric path-tracking methods. Using this method, the look-ahead point can be selected far away and the control input is computed in real-time, which is advantageous when the given path is not smooth or when the path is specified using waypoints. Moreover, this method is more robust to localization errors than the model-based path-tracking method. However, the original pure pursuit method and its variants have limited tracking performance. Therefore, this paper proposes a new method that heuristically selects a look-ahead point by considering the relationship between a vehicle and a path. Using this new look-ahead point, the vehicle can stably converge to the desired path and track the path without encountering the cutting-corner problem. The proposed method was tested using simulation and our self-driving car platform. Our results show that the vehicle tracks the desired path more accurately using our proposed algorithm than using the previous pure pursuit methods.

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

A study pointed out that the delay time of the driver's nervous system has a significant effect on the roll stability of the vehicle. However, the existing researches on vehicle rollover prevention control rarely consider the influence of driver factors on vehicle roll stability. Aiming at this problem, a vehicle roll stability and path tracking control strategy considering driver in the loop is proposed to assist different types of drivers. It includes the supervisory decision layer and execution layer. The supervisory decision layer selects the corresponding control mode according to the driver's steering wheel angle change rate, path tracking deviation and vehicle roll stability information. The execution layer includes three modes: human-machine shared steering, active braking and integrated chassis control. The human-machine shared steering and active braking modes assist the driver to improve the roll stability and path tracking accuracy. The integrated chassis control mode is used for the automatic driving of vehicles under emergency conditions. Simulation results show that the proposed control strategy can effectively improve the vehicle roll stability and path tracking accuracy, and reduce the driver's operating burden.

Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants’ movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible.

Research on Path Tracking and Anti-roll Control of Commercial Vehicle Based on Takagi-Sugeno Fuzzy Model

Abstract With the increasing demand for intelligent commercial vehicles, the path tracking technology of commercial vehicle autonomous driving systems has become a research hotspot. However, commercial vehicles are prone to roll over when they are making an emergency obstacle avoidance or making a sharp turn on the road surface with high adhesion coefficient, resulting in serious traffic accidents. In this paper, a steering control method based on the T-S fuzzy model for automatic driving of commercial vehicles is proposed, which takes path tracking and anti-roll as control objectives. Firstly, the three-degree-of-freedom model of commercial vehicle and the vehicle-road dynamic model was established to obtain the vehicle-road general model. Then, considering the influence of the nonlinear change of vehicle speed on the system performance, the speed is taken as the variable parameter of the system. Taking the speed as the prerequisite variable, the fuzzy rule is designed, and the vehicle-road model is transformed into a T-S fuzzy model. Then, the fuzzy controller is designed according to the Lyapunov method and the parallel distributed compensation (PDC) algorithm. Finally, sufficient conditions for the system to satisfy the H∞ performance index under the action of the fuzzy controller are given in the form of linear matrix inequality (LMI). LMI toolbox is used to solve the problem, and the robustness of the system is guaranteed. Simulation results show that the system can ensure the accuracy of path tracking and greatly reduce the risk of vehicle rollover.

Autonomous Tracking Control for Four-Wheel Independent Steering Robot Based on Improved Pure Pursuit

Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation, especially for future intelligent robots. In this paper, an improved pure pursuit control method is proposed for the path tracking control problem of a four-wheel independent steering robot. Based on the analysis of the four-wheel independent steering model, the kinematic model and the steering geometry model of the robot are established. Then the path tracking control is realized by considering the correlation between the look-ahead distance and the velocity, as well as the lateral error between the robot and the reference path. The experimental results demonstrate that the improved pure pursuit control method has the advantages of small steady-state error, fast response and strong robustness, which can effectively improve the accuracy of path tracking.