Path Tracking Approach Research Articles

Improved Model Predictive Control Path Tracking Approach Based on Online Updated Algorithm with Fuzzy Control and Variable Prediction Time Domain for Autonomous Vehicles

The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load.

Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.

Vision-based autonomous navigation stack for tractors operating in peach orchards

Recently, camera-based approaches have been proposed to replace expensive LiDAR or real-time kinematic (RTK) based solutions for the unmanned tractors operating in an orchard-like environment. However, constructing a completely autonomous navigation stack with a low-costly camera and onboard computer presents extra difficulties, such as a computationally efficient perception model, rapid path planning without costly SLAM, and the capacity to avoid obstacles. In this paper, a novel vision-based autonomous navigation stack is devised to address challenges of the wholly autonomous tractor using stereo camera and inertial measurement unit (IMU) that are inexpensive. The computational pipeline consists primarily of three modules: (1) the multi-task perception network, (2) the frame transformation algorithm, and (3) the motion planning module. The multi-task perception network detects tree trunks, obstacles, and traversable areas simultaneously from the RGB image with high efficiency (69 FPS) and accuracy (mAP@.5 of 96.7% and mIoU of 98.1%). For global path planning and trajectory prediction, the frame transformation algorithm integrates the downsized the navigational features and transforms them to the tractor frame from the image frame. The motion planning module then uses the fused data to plan the appropriate path (i.e., the center of the tree row, U-turn path, or J-turn path) and search for the optimal trajectory utilizing the optimized dynamic window approach (DWA) for path tracking. In the peach orchard, the proposed stack is implemented on our autonomous retrofitted tractor and its efficacy is demonstrated compared with the human-driving tractor.

End-effector path tracking of a 14 DOF Rover Manipulator System in CG-Space framework

AbstractIn order to resolve redundancy and path planning of a high DOF mobile manipulator using conventional approaches like Jacobian and a pseudoinverse method, researchers face the limitation of computational load and delay in response. If such kind of mobile manipulator is traversing the rough terrain, then conventional methods become too costly to implement due to the handling of redundant joints, obstacles, and wheel-terrain interaction. A few optimization-based redundancy resolution approaches try incorporating wheel-terrain interaction but fail in real-time response. This paper describes a 14 DOF Rover Manipulator System’s end-effector path-tracking approach using CG-Space framework to incorporate wheel-terrain interaction. CG-Space means the center of gravity (CG) locus of the Rover. The Rover’s CG is calculated while traversing over 3D terrain using a multivariable optimization method and a 3D point cloud image of the actual terrain and stored as CG-Space over the given terrain. First of all, we decide which part of the system moves to track the path, that is, arm or Rover, depending upon the manipulator’s work volume and manipulability measure restrictions. The next task is to obtain the Rover pose according to the end-effector path using a simple arm’s inverse kinematic solution between the CG-Space and end-effector task space without resolving redundancy. Meanwhile, obstacles and non-traversable regions are avoided in CG-Space. On diverse 3D terrains, simulation and experimental results support the suggested CG-Space framework for end-effector tracking.

Improved Linear Quadratic Regulator Lateral Path Tracking Approach Based on a Real-Time Updated Algorithm with Fuzzy Control and Cosine Similarity for Autonomous Vehicles

Path tracking plays a crucial role in autonomous driving. In order to ensure the real-time performance of the controller and at the same time improve the stability and adaptability of the path tracking controller, a lateral path control strategy based on the improved LQR algorithm is proposed in this paper. To begin with, a discrete LQR controller with feedforward and feedback components is designed based on the error model of vehicle lateral dynamics constructed by the natural coordinate system. Then, a fuzzy control method is applied to adjust the weight coefficients of the LQR in real time according to the state of the vehicle. Furthermore, an update mechanism based on cosine similarity is designed to reduce the computational effort of the controller. The improved LQR controller is tested on a joint Simulink–Carsim simulation platform for a two-lane shift maneuver. The results show that the control algorithm improves tracking accuracy, steering stability and computational efficiency.

Waypoint-Based Path Tracking Approach For Self-Organized Swarm Robots

In this paper, a waypoint-based path tracking approach is suggested for the swarm robots to follow the desired path in an organized way. In the study, the applicability of the waypoint-based path tracking on the swarm robots that show flexible and scalable behavior has been demonstrated. To evaluate the proposed path planing approach with regard to scalability and flexibility, simulations have been applied in with/without obstacle arenas with different numbers of robots and according to different lookahead distances. With the proposed approach, each swarm robots exhibit swarm behavior in an organized manner depending on the distance of the lookahead to the path to track in the with / without obstacle arenas.

An Optimized Path Tracking Approach Considering Obstacle Avoidance and Comfort

An Optimized Path Tracking Approach Considering Obstacle Avoidance and Comfort

Curriculum-based reinforcement learning for path tracking in an underactuated nonholonomic system

Underactuated mechanical systems with nonholonomic constraints find applications in bioinspired robotics, such as snake-like robots and more recently in fish-like aquatic robots. Animal locomotion suggests that in such bioinspired robots, gaits or cyclic changes in kinematics or shape variables lead to efficient and agile motion. Path tracking in such nonholonomic systems that are not purely kinematic can be a challenging problem. In this paper we consider the problem of path tracking by a modified Chaplygin sleigh with a ‘tail’ which is a four degree of freedom nonholonomic system, possessing a single internal reaction wheel as an actuator. We develop a curriculum based deep Reinforcement Learning (RL) optimal control approach for simultaneous velocity and path tracking for this system. The curriculum based learning approach first leads to a policy for optimal tracking of limit cycles in a reduced velocity space and then in a next step to track a path. This curriculum approach allows an RL agent to learn the ’mechanics on invariant manifolds’ of the system and can be a useful approach in the motion control of high degree of freedom robots with increasing model complexity.

A Voltage Vector Residual Estimation Method Based on Current Path Tracking for T-Type Inverter Open-Circuit Fault Diagnosis

This article proposes a current path tracking approach that uses a current path state to correlate device status and operating mode of an inverter under normal and abnormal conditions. Based on graph theory, a node-path modeling method is proposed and applied in the T-type inverter topologies. The model reflects system state transitions, given healthy or faulted transistors. Effective conduction paths and node potentials can be derived with the model to indicate circuit faults. In addition, a hierarchical diagnosis method is proposed for single-transistor open-circuit (OC) faults based on estimated output voltage vectors. The diagnosis involves two steps. First, an OC fault can be detected by following a residual between an actual voltage vector and its reference voltage vector calculated from duty ratios of intended current paths. Next, faulty transistors sharing similar features can be distinguished by adopting a current path state reconfiguration approach. Diagnosis variables can be obtained from the controller, and their errors, because of sampling, signal delay, and dead time, are taken into account to avoid misdiagnosis. Experimental results verify the robustness and effectiveness of the proposed method.

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.

An integrative approach for path planning and tracking of a shape-aware mobile robot in structured environment using vision sensor

An integrative approach for path planning and tracking of a shape-aware mobile robot in structured environment using vision sensor

A Reinforcement Learning-Based Adaptive Path Tracking Approach for Autonomous Driving

The path tracking system is a crucial component in fully autonomous vehicles. While many methods provide state-of-the-art tracking performance, they tend to emphasize tracking accuracy for safety, at the price of ride quality. Although methods based on a dynamic model and optimization theory guarantee improved tracking performance, a high-fidelity model is unavailable in most autonomous systems, and the complex optimization process may increase the computational burden. Therefore, for practical, real-life systems, it is urgent to develop a tracking method with a simple, effective control framework for real-time implementation. Moreover, the tracking approach should adaptively balance between tracking accuracy and the passenger experience. With that in mind, a simple tracking scheme with adaptive lateral and longitudinal control approaches is proposed. For lateral control, a pure pursuit (PP) geometric controller is used as a basic tracking framework to design the steering method. A detailed analysis shows that PP inefficiently reduces the lateral error. A PID controller is integrated with PP by a customized reinforcement learning model to better deal with tracking error by trading off between PP and PID. Moreover, a rough-to-fine velocity adaptation method is proposed to adjust the speed according to the geometry of the predefined path and the real-time tracking feedback. The proposed controlling approach is tested in different path tracking scenarios. The results show that the approach can adaptively change the weights of PP and PID to maintain a balance between tracking error (within $\pm \text{0.5}\;\text{m}$ ) and jerk (within $\pm 0.5\;\text{m}{\tt /} \text{s}^3$ ), and can adapt to high-speed scenarios (up to $\text{80}\;\text{km/h}$ ). Finally, a field test is carried out to validate the practicability.

An integrative approach for path planning and tracking of shape aware mobile robot in structured environment using vision sensor

A shape-aware path planning algorithm is necessary for real-time execution of a task by a mobile robot whereas path planning algorithms available in literature consider mobile robot as a point object. Current work proposes a shape-aware A* path planning approach with a heuristic function to accommodate the shape of mobile robot. In this paper, the detection, tracking and control of the robot has been carried out for the mobile robot while performing a task in a structured environment. For the implementation and validation, an overhead camera is used to capture the images of obstacles in the task space and published in ROS platform. The captured images are also processed using OpenCV software for detection and tracking using Kanade-Lucas-Tomasi (KLT) and Kalman filter algorithms for different test scenarios. The proposed approach accurately detects and tracks the shape aware mobile robot with percentage error ranging from 6%-10% in different cases.

Time-optimal path tracking for robots a numerical integration-like approach combined with an iterative learning algorithm

PurposeThe purpose of this paper is to propose a precise time-optimal path tracking approach for robots under kinematic and dynamic constraints to improve the work efficiency of robots and guarantee tracking accuracy.Design/methodology/approachIn the proposed approach, the robot path is expressed by a scalar path coordinate and discretized into N points. The motion between two neighbouring points is assumed to be uniformly accelerated motion, so the time-optimal trajectory that satisfies constraints is obtained by using equations of uniformly accelerated motion instead of numerical integration. To improve dynamic model accuracy, the Coulomb and viscous friction are taken into account (while most publications neglect these effects). Furthermore, an iterative learning algorithm is designed to correct model-plant mismatch by adding an iterative compensation item into the dynamic model at each discrete point before trajectory planning.FindingsAn experiment shows that compared with the sequential convex log barrier method, the proposed numerical integration-like (NI-like) approach has less computation time and a smoother planning trajectory. Compared with the experimental results before iteration, the torque deviation, tracking error and trajectory execution time are reduced after 10 iterations.Originality/valueAs the proposed approach not only yields a time-optimal solution but also improves tracking performance, this approach can be used for any repetitive robot tasks that require more rapidity and less tracking error, such as assembly.

Uncertainty-based Human Motion Tracking with Stable Gaussian Process State Space Models

Abstract Data-driven approaches are well suited to represent human motion because arbitrary complex trajectories can be captured. Gaussian process state space models allow to encode human motion while quantifying uncertainty due to missing data. Such human motion models are relevant for many application domains such as learning by demonstration and motion prediction in human-robot collaboration. For goal-directed tasks it is essential to impose stability constraints on the model representing the human motion. Motivated by learning by demonstration applications, this paper proposes an uncertainty-based control Lyapunov function approach for goal-directed path tracking. We exploit the model fidelity which is related to the location of the training and test data: Our approach actively strives into regions with more demonstration data and thus higher model certainty. This achieves accurate reproduction of the human motion independent of the initial condition and we show that generated trajectories are uniformly globally asymptotically stable. The approach is validated in a nonlinear learning by demonstration task where human-demonstrated motions are reproduced by the learned dynamical system, and higher precision than competitive state of the art methods is achieved.

On-Line Obstacle Detection, Avoidance, and Mapping of an Outdoor Quadrotor Using EKF-Based Fuzzy Tracking Incremental Control

The unknown two-dimensional obstacles, including different shapes, numbers, locations, and in the vicinity of the planning path for an outdoor quadrotor, are on-line detected by a 360-degree Li-Dar Sweep Scanse. Based on the comparison among detected triangles containing extreme points of each obstacle, the central orientation of the triangle with the maximum detected distance and the angle larger than the specific threshold is assigned to fly a specific distance for obstacle avoidance. Nevertheless, the quadrotor probably encounters a dead end and is then navigated to a specific altitude for the continuous procedure of obstacle detection. By on-line detection at different waypoints for the same obstacle, an obstacle mapping is also established by all the obstacle detections. Since the sensing signals of an outdoor quadrotor are easily affected by stochastic noise and the quadrotor is operated in “x” configuration, an extended Kalman filter based fuzzy tracking incremental control (EKF-FTIC) is employed to simultaneously execute the path tracking, obstacle avoidance, and target approach. Finally, the experiments with 5 unknown obstacles in the neighborhood of a planning path connecting start point with end point validate the effectiveness and feasibility of the proposed method.

A novel control approach for path tracking of a force-controlled two-wheel-steer four-wheel-drive vehicle

This paper presents a novel approach for a force-controlled over-actuated two-wheel-steer four-wheel-drive vehicle to achieve accurate path tracking through control allocation. The control algorithm is developed by combining embedded integrator model predictive control and sequential quadratic programming–based control allocation. Based on the offset model presented in this paper, the model predictive control can generate the virtual accelerations. Using these virtual accelerations in control allocation, the actual control inputs are obtained. The proposed method is then used in a simulation to demonstrate its successful implementation in path tracking.

A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles

In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics. Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T–S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework. By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed $\mathcal {H}_{\infty }$ level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T–S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

Drone Package Delivery: A Heuristic approach for UAVs path planning and tracking

In this paper we propose a new approach based on a heuristic search for UAVs path planning with terrestrial wireless network tracking. In a previous work we proposed and exact solution based on an integer linear formulation of the problem. Unfortunately, the exact resolution is limited by the computation complexity. In this case, we propose in this paper a new approach based on a heuristic search. More precisely, a heuristic adaptive scheme based on Dijkstra algorithm is proposed to yield a simple but effective and fast solution. In addition, the proposed solution can cover a large area and generate a set of optimum and near optimum pa ths according to the drone battery capacities. Finally, the simulation results show that the drone tracking is sustainable even in noisy wireless network environment.

Methods of Path Identification and Processing in Freescale Intelligence Car

It is based on the Eighth National College "Freescale" intelligent vehicle competition, this contest requirements to make a model car which can identify the road on the model vehicle platforms independently. If the model car completes contest in accordance with the line for the shortest time that it is winning. Therefore, researching various types of track and the path tracking approach of each group model car is particularly important. First it describes the various types of track, and then describes track recognition principle of each group by using corresponding sensors in detail, finally it analysis how to use sensor to deal with all kinds of circuit method, so that the model car can be quickly and securely through a variety of track, and in the shortest time to reach the end.