Path Tracking Of Mobile Robot Research Articles

A robust interpolated model predictive control based on recurrent neural networks for a nonholonomic differential-drive mobile robot with quasi-LPV representation: computational complexity and conservatism

This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by a Linear Parameter Varying (LPV), and a Recurrent Neural Network (RNN) solves the constrained optimisation problem, providing optimal velocities. Moreover, an interpolation-based approach has been introduced to augment the region of attraction. The algorithm ensures stability in the presence of bounded disturbances through the inclusion of free control moves in the control law. The controller efficiency has been evaluated in two scenarios in a hospital setting. The simulation results illustrate that the proposed method performs better than nonlinear MPC and standard LPV-based MPC in terms of computational cost, disturbance rejection, and region of attraction.

MOBILE ROBOT TRACKING SYSTEM BASED ON MACHINE VISION AND LASER RADAR

The proposed solution addresses the issue of insufficient real-time performance and accuracy in mobile robot path tracking by introducing a system that combines machine vision and laser radar. In this study, the Broadcom BCM2711 microcontroller chip is connected to the RS232 communication interface for transmitting information to the ARM embedded processor. Users can access position distance, direction, and other robot-related data through the man-machine interface's LCD display in a Windows operating system environment. By initiating an adaptive position tracking algorithm program identified by the robot within the position tracking unit, mobile position tracking of the robot is achieved. Experimental results demonstrate significant improvements in both real-time performance and accuracy of this mobile robot tracking system.

Path planning and tracking of wheeled mobile robot: using firefly algorithm and kinematic controller combined with sliding mode control

Path planning and tracking of wheeled mobile robot: using firefly algorithm and kinematic controller combined with sliding mode control

Fast terminal sliding mode control of agricultural robots with permanent magnet synchronous motor servo systems based on an extended state observer for path tracking

In response to the challenges in mobile robot path tracking using model predictive control, where the predictive model weakens the controller’s ability to respond to sudden changes in the reference path curvature and heading, this paper proposes a composite control strategy suitable for agricultural robots. The strategy combines the maximum torque per ampere control and an Extended State Observer (ESO). The paper initially establishes a mathematical model for a Permanent Magnet Synchronous Motor (PMSM) considering aggregated disturbances. It designs a position tracking controller based on a non-singular terminal sliding mode and convergence law. This controller, employing a non-cascaded structure, replaces traditional position and velocity loop controllers and is proven to be stable with finite-time convergence through Lyapunov’s theorem. To enhance the system’s disturbance rejection capabilities further, the paper introduces an ESO to estimate system disturbances and applies it for feedforward compensation. The paper concludes by providing stability proof for the overall PMSM servo system in agricultural robots. Finally, the paper conducts simulations and experimental verifications based on the designed controller, demonstrating that the controller exhibits excellent path tracking performance, fast convergence, and robustness against external disturbances.

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning

Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning

Path Planning and Tracking of Wheeled Mobile Robot: Using Firefly Algorithm and Different Control Architectures

Path Planning and Tracking of Wheeled Mobile Robot: Using Firefly Algorithm and Different Control Architectures

Support Value-Based NFSMC for Wheeled Mobile Robot Path Tracking in Unknown Environments

Mobile robot navigation is an obscure condition that plays a vital role in different applications such as military purposes, navy, etc. now a day's lot of problems occur during navigation. To solve the navigation problem an RGB-Depth sensor along with support value-based neuro-fuzzy sliding mode controller techniques (SVB-NFSMC) are proposed. The RGB-Depth sensor is used to estimate the depth map using the linear coefficients along with the bilateral filter. Then the angular and linear velocity is calculated to define the angular position and the object movement. Then the calculation of support value is based on the above angular and linear estimation values. Here the depth map and support value as a training data set input are given to the controller. Subsequently, the sliding mode controller provides the guiding commands in terms of guidance law, control law to the robot system. The ANFIS and the GWO algorithm are used to optimize the parameters is given to the controller then the Lyapunov function is used to analyze the stability of the system. Finally, compare the proposed work with the existing AKH-NFIS and the other SMC methods provides better outcomes.

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

Nonlinear Model Predictive Control for Mobile Medical Robot Using Neural Optimization

Mobile medical robots have been widely used in various structured scenarios, such as hospital drug delivery, public area disinfection, and medical examinations. Considering the challenge of environment modeling and controller design, how to achieve the information from the human demonstration in a structured environment directly arouse our interests. Learning skills is a powerful way that can reduce the complexity of algorithm in searching space. This is especially true when naturally acquiring new skills, as mobile medical robot must learn from the interaction with a human being or the environment with limited programming effort. In this article, a learning scheme with nonlinear model predictive control (NMPC) is proposed for mobile robot path tracking. The learning-by-imitation system consists of two levels of hierarchy: in the first level, a multivirtual spring-dampers system is presented for imitation of the mobile robot's trajectories; and in the second level, the NMPC method is used in the motion control system. The NMPC strategy utilizes a varying-parameter one-layer projection neural network to solve an online quadratic programming optimization via iteration over a limited receding horizon. The proposed algorithm is evaluated on a mobile medical robot with an emulated trajectory in simulation and three scenarios used in the experiment.

Optimization of Type-2 Fuzzy Logic Controller Design Using the GSO and FA Algorithms

This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm optimization (GSO) method, where the performance of both methods is observed and tested in the optimization of a fuzzy controller for path tracking of an autonomous mobile robot. The main contribution of this work is finding the best method that generates an optimal vector of values for the membership function optimization of the fuzzy controller. This with the goal of improving the performance of the controller and thus the trajectory generated by the autonomous robot is closer to the desired trajectory. It should be noted that the fuzzy controller that is optimized is an interval type-2 fuzzy controller, which has a greater capability for managing uncertainty than a type-1 fuzzy controller. In this case, the limiting membership functions in the interval type-2 fuzzy sets are themselves type-1 fuzzy sets that define the footprint of uncertainty. Type-2 fuzzy controllers have been shown in previous works to handle better the control of robotic systems under noisy and dynamic conditions and this is why their optimal design is very important. Simulation results show that GSO outperforms FA in the optimal design of interval type-2 fuzzy controllers.

Iterative learning control for path tracking of service robot in perspective dynamic system with uncertainties

A novel iterative learning control (ILC) for perspective dynamic system (PDS) is designed and illustrated in detail in this article to overcome the uncertainties in path tracking of mobile service robots. PDS, which transmits the motion information of mobile service robots to image planes (such as a camera), provides a good control theoretical framework to estimate the robot motion problem. The proposed ILC algorithm is applied in accordance with the observed motion information to increase the robustness of the system in path tracking. The convergence of the presented learning algorithm is derived as the number of iterations tends to infinity under a specified condition. Simulation results show that the designed framework performs efficiently and satisfies the requirements of trajectory precision for path tracking of mobile service robots.

An RFID-Based Mobile Robot Localization Method Combining Phase Difference and Readability

A novel radio frequency identification (RFID)-based mobile robot global localization method combining two kinds of RFID signal information, i.e., phase difference and readability, is proposed. Specifically, a phase difference model and a classification logic strategy based on readability are built and integrated into a particle filter localization algorithm. Compared with existing RFID localization methods, the proposed localization method can achieve competitive localization performance in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. A series of real experimental tests were performed, and the results show that the proposed method can localize a mobile robot with centimeter-level position accuracy and satisfactory attitude angle accuracy when the distance between adjacent reference tags is approximately 60 cm, even if all RFID devices are commercial off-the-shelf (COTS). The proposed method provides a promising option for mobile robot localization applications, such as path tracking of mobile robots. Note to Practitioners —Mobile robot localization is a key technology for its location-based services. Considering that radio frequency identification (RFID) is entirely unaffected by light interference and has a globally unique ID, RFID has been regarded as a localization sensor with broad application prospects. This article proposes an RFID-based mobile robot global localization method combining phase difference and readability, by which the mobile robot can be accurately localized in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. The experimental results indicate that the proposed method can localize the mobile robot with good performance, including centimeter-level position accuracy and satisfactory attitude angle accuracy. The proposed method can effectively contribute to many practical applications, such as the path tracking of a mobile robot.

Critical Saturation Function Based Sliding Mode Control for Path Tracking of Mobile Robot

This paper proposes a new sliding mode controller, as to enhance the performance of path tracking of three-wheeled mobile robot system. To begin with, backstepping design is used to construct a new sliding mode control law for better dynamic response and robustness. Next, in order to reduce chattering, the symbolic function is replaced with the critical saturation function in the conventional reaching law. At last, the simulation results show that backstepping design combined with saturation function can not only improve the response rate and robustness of the system, but also effectively reduce the chattering.

Anti‐sideslip path tracking of wheeled mobile robots based on fuzzy model predictive control

When a wheeled mobile robot is travelling at high longitudinal velocities and turning, it may sideslip. Except for a few researchers, this issue has not received much attention. However, there is a high possibility that sideslip may cause danger, so the authors think this problem needs to be solved urgently. The mechanism of preventing sideslip is simple, which is to reduce the longitudinal velocity of the robot when turning. But, how to slow down is a question worth studying. To this end, they have proposed two schemes, but these two schemes have poor real-time performance and poor robustness to positioning errors, respectively. In order to solve the above problems, considering that the real-time and robustness of fuzzy control are very good, they propose a controller by combining robust control and model predictive control in this Letter. The fuzzy control is used to adjust the longitudinal velocity according to the state of the robot, and the model predictive control is used to achieve path tracking. According to the simulation results, the proposed fuzzy model predictive controller does have small errors, small sideslip, good real-time performance, and good robustness to positioning errors.

Sliding Mode Control Based on Inversion Design and Bounded Input Constraint for Path Tracking of Mobile Robot

In order to solve the problem of path tracking of mobile robot, a sliding mode control strategy is proposed. Firstly, a sliding mode controller based on the inversion algorithm is designed, and the low-pass filter is used to reduce the interference in the control process. On this basis, for solving the problem that the dynamic performance of the previous controller is not ideal, the bounded input constraint is used to improve the control law. The simulation results show that the improved controller not only has good tracking effect and fast dynamic response, but also ensures the global asymptotically stability.

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.

Wheeled Mobile Robot Path Planning and Path Tracking Controller Algorithms: A Review

Wheeled Mobile Robot Path Planning and Path Tracking Controller Algorithms: A Review

Path Tracking of Wheeled Mobile Robots Based on Dynamic Prediction Model

In the field of path tracking control for wheeled mobile robots, researchers generally believe that the motion of robots meets non-holonomic constraints. However, the robot may sideslip by centrifugal force when it is steering. This kind of slip is usually uncontrollable and dangerous. In order to prevent sideslip and improve the performance of path tracking control, we propose a controller based on tire mechanics. Moreover, the new controller is based on the model predictive control, and this control method has proven to be suitable for path tracking of wheeled mobile robots. The proposed controller was verified by simulation and compared with a model predictive controller based on kinematics (non-holonomic constraints). As for the simulation results, the maximum values of displacement error, heading error, lateral velocity, and slip angle of the dynamic-based model predictive controller are 0.2086 m, 0.1609 rad, 0.1546 m/s, and 0.0576 rad, respectively. Compared with the kinematics-based model predictive controller, the maximum values of the above-mentioned parameters of the dynamic-based controller reduce by 86.55%, 72.25%, 96.30%, and 90.02%, respectively. The above-mentioned results show that the proposed controller can effectively improve the accuracy of path tracking control and avoid sideslip.