Two-wheeled Mobile Robot Research Articles

A new hybrid learning control system for robots based on spiking neural networks

This paper presents a new hybrid learning and control method that can tune their parameters based on reinforcement learning. In the new proposed method, nonlinear controllers are considered multi-input multi-output functions and then the functions are replaced with SNNs with reinforcement learning algorithms. Dopamine-modulated spike-timing-dependent plasticity (STDP) is used for reinforcement learning and manipulating the synaptic weights between the input and output of neuronal groups (for parameter adjustment). Details of the method are presented and some case studies are done on nonlinear controllers such as Fractional Order PID (FOPID) and Feedback Linearization. The structure and the dynamic equations for learning are presented, and the proposed algorithm is tested on robots and results are compared with other works. Moreover, to demonstrate the effectiveness of SNNFOPID, we conducted rigorous testing on a variety of systems including a two-wheel mobile robot, a double inverted pendulum, and a four-link manipulator robot. The results revealed impressively low errors of 0.01 m, 0.03 rad, and 0.03 rad for each system, respectively. The method is tested on another controller named Feedback Linearization, which provides acceptable results. Results show that the new method has better performance in terms of Integral Absolute Error (IAE) and is highly useful in hardware implementation due to its low energy consumption, high speed, and accuracy. The duration necessary for achieving full and stable proficiency in the control of various robotic systems using SNNFOPD, and SNNFL on an Asus Core i5 system within Simulink’s Simscape environment is as follows:– Two-link robot manipulator with SNNFOPID: 19.85656 hours– Two-link robot manipulator with SNNFL: 0.45828 hours– Double inverted pendulum with SNNFOPID: 3.455 hours– Mobile robot with SNNFOPID: 3.71948 hours– Four-link robot manipulator with SNNFOPID: 16.6789 hours.This method can be generalized to other controllers and systems like robots.

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

Trajectory Optimization for Adaptive Deformed Wheels to Overcome Steps Using an Improved Hybrid Genetic Algorithm and an Adaptive Particle Swarm Optimization

Two-wheeled mobile robots with deformed wheels face low stability when climbing steps, and their success rate in overcoming steps is affected by the trajectory. To address these challenges, we propose an improved hybrid genetic and adaptive particle swarm optimization (HGAPSO) algorithm to optimize the deformed wheels’ trajectory for overcoming steps. HGAPSO optimizes the maximum and minimum values of the inertial weight and learning factors of the adaptive particle swarm algorithm utilizing the region-wide search capabilities of the genetic algorithm, which substantially improves the convergence speed and adaptability. Furthermore, the analysis of the motion of the deformed wheel overcoming the steps and the examination of the potential interference during the operation are used to construct a wheel’s center-of-mass route based on fifth-order Bézier curves. Comparative simulation experiments of the trajectories optimized using different optimization algorithms under the same working conditions are designed to demonstrate the efficacy of the proposed HGAPSO algorithm in optimizing the trajectory of the deformed wheel overcoming the step. Simulation experiments were conducted using the HGAPSO algorithm to optimize the trajectories of deformation wheels for overcoming steps of various sizes. These optimized trajectories were then compared to unoptimized ones. The results showed that the HGAPSO-optimized trajectories significantly improved the success rate and stability of the mobile robot in overcoming steps.

Iterative-learning-based tracking control of a two-wheeled mobile robot with model uncertainties and unknown periodic disturbances

In this paper, we develop an adaptive iterative learning approach to investigate the trajectory tracking control issue for a class of two-wheeled mobile robots subject to model uncertainties and unknown disturbances. First, we derive the nonlinear velocity error dynamics. Then a parameterization-based adaptive iterative learning control scheme is adopted to achieve precise tracking, along with logic-based update law for the estimated period and bound of the disturbance. Moreover, the boundness of all the closed-loop signals is rigorously analyzed based on the Lyapunov stability theory to provide the theoretical foundation for the proposed method. The experimental results show the efficacy and viability of our results.

Control of mobil robots in fuzzy environment

In this work, the application of fuzzy hardware control to the dynamic movement of an autonomous two-wheeled mobile robot under uncertainty is considered the most relevant topic of the day. There are also adverse reactions. These led to an imprecise mathematical model for the two-wheeled robotic test rig. Therefore, the goal of developing a control system for a two-wheeled robot with a fuzzy logic control algorithm was set. The development of fuzzy logic control systems according to modern applications is very important to create intelligent robots that can be used in wider applications. In this work, a fuzzy control model for a non-linear two-wheeled robot system is designed, followed by a real-time implementation of the developed control in a robot device. Finally, the experimental results of the system were analyzed and discussed, and the closed-loop response of the fuzzy logic two-wheeled robot system with PID control was tested. The fuzzy control has a better response than the PID controller. A similar effect was observed for the normalized speed of the motors, as less energy was required to balance and stabilize the two-wheeled robot with fuzzy control. One of the main advantages of the method proposed in the article is to enable the autonomous two-wheeled mobile robot to operate without collision with obstacles and with high dynamic accuracy in a low-obstacle environment. As a result of the simulation, the effectiveness and reliability of the change avoidance behavior in uncertain environments and the speed control of the two-wheeled mobile robot platform of the proposed fuzzy control strategy are realized.

Design and Reconfiguration Analysis of the Trunk Mechanism for a Reconfigurable Wheeled Mobile Platform

Abstract This paper proposes a reconfigurable wheeled mobile platform (RWMP) consisting of two two-wheeled mobile robots and a reconfigurable trunk. The reconfigurable trunk is a 6R multi-mode single-loop mechanism (SLM) that is obtained by inserting two revolute (R) joints with intersected axes into a planar rhombus 4R mechanism. The 6R mechanism has reconfigurable characteristics owing to changes in the wrench system. All six motion modes and their constraint equations of the 6R mechanism are obtained by solving the closed-loop equation based on the D-H transformation matrix. The analysis shows that the mechanism has six single-DOF motion modes, including a planar rhombus 4R mode, two overconstrained spatial 6R modes, and three coaxial 1R modes. The motion characteristics of the six motion modes are identified using screw theory. The six transition configurations among different modes are identified by combining the constraint equations of each mode. The locomotion modes of the RWMP are designed based on the reconfiguration analysis of the trunk mechanism. The locomotion effect of the RWMP under two confined spaces is verified by simulation analysis and prototype experiment.

A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots.

There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students' learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students' learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students' creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students' learning outcomes and stimulate their active learning and creativity.

Load Cell-Based Two-Wheeled Mobile Robot Control with Proportion al–Integral–Derivative and Fuzzy Proportional–Integral–Derivative Methods

Load Cell-Based Two-Wheeled Mobile Robot Control with Proportion al–Integral–Derivative and Fuzzy Proportional–Integral–Derivative Methods

Experimental Research on Avoidance Obstacle Control for Mobile Robots Using Q-Learning (QL) and Deep Q-Learning (DQL) Algorithms in Dynamic Environments

This study provides simulation and experimental results on techniques for avoiding static and dynamic obstacles using a deep Q-learning (DQL) reinforcement learning algorithm for a two-wheel mobile robot with independent control. This method integrates the Q-learning (QL) algorithm with a neural network, where the neural networks in the DQL algorithm act as approximators for the Q matrix table for each pair (state–action). The effectiveness of the proposed solution was confirmed through simulations, programming, and practical experimentation. A comparison was drawn between the DQL algorithm and the QL algorithm. Initially, the mobile robot was connected to the control script using the Robot Operating System (ROS). The mobile robot was programmed in Python within the ROS operating system, and the DQL controller was programmed in Gazebo software. The mobile robot underwent testing in a workshop with various experimental scenarios considered. The DQL controller displayed improvements in computation time, convergence time, trajectory planning accuracy, and obstacle avoidance. As a result, the DQL controller surpassed the QL algorithm in terms of performance.

Trajectory-tracking Control of Two-wheeled Mobile Robot with a Cascaded Time-scale Transformation

Trajectory-tracking Control of Two-wheeled Mobile Robot with a Cascaded Time-scale Transformation

Safe trajectory tracking control by input-to-state constrained safety control barrier function (ISCSf-CBF)

Safe trajectory tracking, which involves following the target trajectory while avoiding obstacles, is crucial for mobile robots. In this paper, we propose a robust safety trajectory tracking controller based on the input-to-state stable tracking control Lyapunov function (ISS-TCLF) and the input-to-state constrained safety control barrier function (ISCSf-CBF). This controller makes it possible to realize trajectory tracking and obstacle avoidance while satisfying state constraints in the presence of disturbances. The proposed controller is applied to a two-wheeled mobile robot and its effectiveness is confirmed by computer simulations.

Kinematic Modeling and Trajectory Tracking of Two-Wheeled Mobile Robot Manipulators at the Acceleration Level

Kinematic Modeling and Trajectory Tracking of Two-Wheeled Mobile Robot Manipulators at the Acceleration Level

Path Planning and Trajectory Tracking Control for Two-Wheel Mobile Robot

The mobile robot is a system that can work in various environments. This means that the robot must be able to navigate without delay and avoid any obstacles placed within the boundaries of its movement. Designing mobile robots that can be intelligently managed and operate autonomously when traveling from one place to another requires at least two steps. To start with, path planning is required to prevent motion collisions. Tracking the robot's trajectory is a crucial second task. The primary goal of this study is to find the quickest and safest path between the two positions. In this work, we investigated the path planning of a mobile robot with dynamic, and dynamic obstacles with moving goal environments using RRT, BiRRT, and HA* algorithms. These algorithms are easy, computationally inexpensive, and simple to use. They have been chosen for numerous real-time path-planning applications. The DDMR's kinematic model has been utilized in this paper to control path tracking, and a PID controller has been proposed to reduce tracking deviations between the robot's actual route and the reference trajectory. This work introduced the PSO, FPA, CSA, SSA, BWOA, and proposed HBPO optimization techniques for obtaining PID parameters (k_p,k_i,k_d) for improved mobile robot trajectory tracking. The simulation results have been examined using three trajectory shapes: step, circular, and infinite. The simulation findings reveal that HA* outperforms the other algorithms by generating collision-free pathways that are smoother and shorter than their RRT and BiRRT equivalents. On the other hand, the proposed HBPO outperforms the other methods. The HBPO method converges quicker than the other proposed algorithms.

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan.

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera's field of view. A single monocular sensor's sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system's dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera's field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera's field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.

Real time adaptive PID controller based on quantum neural network for nonlinear systems

In this paper, an adaptive proportional-integral-derivative (PID) controller based on a quantum neural network (APIDC-QNN) is introduced. In the proposed neural network structure, three neural layers are used namely: input layer, hidden layer, and output layer. The input and hidden layers use three and six quantum-processing neurons respectively. In contrast, in the output layer, the multiplication processing is used as an activation function free of the output weights which aims to reduce the number of tunable parameters. The output layer with three neurons represents the adaptive PID controller parameters. In this work, the tunable parameters are updated using the Lyapunov stability criterion to guarantee optimization and learning stability. In addition, the plant identification based on the Wiener-type model and quantum neural network is introduced to estimate the sensitivity of the plant output to the control signal. The proposed neural network structure yields only 30 tunable parameters for the proposed controller and 13 tunable parameters for the proposed identifier while the conventional neural network structure in other works requires 90 tunable parameters just for the controller. To evaluate the performance of the proposed APIDC-QNN, it is practically operated on a non-holonomic two-wheel mobile robot (NHWMR). Moreover, to investigate the robustness, superiority, and convergence speed of the proposed APIDC-QNN, some experimental tasks are discussed. The simulation and practical results show the powerful processing and the superiority of the proposed APIDC-QNN over that designed based on conventional neural networks by recording the minimum values of the performance indices.

Fish Swarmed Kalman Filter for State Observer Feedback of Two-Wheeled Mobile Robot Stabilization

Over the past few decades, there have been significant technological advancements in the field of robots, particularly in the area of mobile robots. The performance standards of speed, accuracy, and stability have become key indicators of progress in robotic technology. Self-balancing robots are designed to maintain an upright position without toppling over. By continuously adjusting their center of mass, they can maintain stability even when disturbed by external forces. This research aims to achieving and maintaining balance is a complex task. Self-balancing robots must accurately sense their orientation, calculate corrective actions, and execute precise movements to stay upright. Eliminating disturbances and measurement noise in self-balancing robot can enhance the accuracy of their output. One common technique for achieving this is by using Kalman filters, which are effective in addressing non-stationary linear plants with unknown input signal strengths that can be optimized through filter poles and process covariances. Additionally, advanced Kalman filter methods have been developed to account for white measurement noise. In this research, state estimation was conducted using the Fish Swarm Optimization Algorithm (FSOA) to provide feedback to the controller to overcome the effects of disturbances and noise in the measurements through the designed filter. FSOA mimics the social interactions and coordinated movements observed in fish groups to solve optimization problems. FSOA is primarily used for optimization tasks where finding the global optimal solution is desired. The results show that the use of an optimized Kalman filter with FSOA on a two-wheeled mobile robot to handle system stability reduces noise values by 38.37%, and the system reaches a steady state value of 3.8 s with a steady error of 0.2%. In addition, by using the proposed method, filtering disturbances and measurement noise in self-balancing robot can help improve the accuracy of the self balancing robot’s output. System response becomes faster towards stability compared to other methods which are also applied to two-wheeled mobile robots.

Robust Fault Estimation and Fault-Tolerant Control for Discrete-Time Systems Subject to Periodic Disturbances

To enhance the reliability of digital automation systems in the Industry 4.0 era, this paper investigates a robust fault-tolerant control scheme in the discrete-time domain subject to periodic disturbances, consisting of a fault estimator, dynamic disturbance compensation loop, and fault-tolerant controller. The fault estimator simultaneously estimates both the system states and actuator/sensor faults. The existence and stability conditions of the proposed estimator are given, and a robust design method is proposed to make the state estimates robust to disturbances. To further reduce the estimation errors caused by periodic disturbances, a novel disturbance compensation loop is introduced and is optimized by a joint zero-assignment and pole-optimization method to delicately compensate for the adverse impacts of periodic input disturbances. The proposed robust fault-tolerant controller uses fault estimation to ensure fast recovery in the event of bounded actuator/sensor faults. The proposed scheme is evaluated through simulations of a two-wheeled mobile robot subject to periodic disturbances and simultaneous abrupt inclination angular sensor and ramp actuator faults, where its performance is shown to exceed that of existing methods.

Adaptive tracking control of two-wheeled mobile robots under Denial-of-Service attacks

This paper focuses on the tracking control problem for the two-wheeled mobile robot (TWMR) with unknown parameters. The robot collects its own states from the networked positioning system, which is subject to Denial-of-Service (DoS) attacks. To handle the uncertainties in the robot model and mitigate the attack effects on the system performance, parameter estimators with the projection operator technique are introduced. Then, an adaptive tracking controller is designed by adopting the backstepping technique. Correspondingly, a stability condition is derived, which guarantees that all the closed-loop signals are semi-globally uniformly bounded and tracking errors can converge to an adjustable compact set. The stability condition also reveals the relationships among the attack durations, design parameters and tracking errors, which can be utilized to guide the choice of design parameters. Experimental results are provided to validate the effectiveness of the proposed control scheme.

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

<span lang="EN-US">This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling.</span>

Identification of Differential Drive Robot Dynamic Model Parameters.

The paper presents the identification process of the mathematical model parameters of a differential-drive two-wheeled mobile robot. The values of the unknown parameters of the dynamics model were determined by carrying out their identification offline with the Levenberg-Marguardt method and identification online with the Recursive least-squares method. The authors compared the parameters identified by offline and online methods and proposed to support the recursive least squares method with the results obtained by offline identification. The correctness of the identification process of the robot dynamics model parameters, and the operation of the control system was verified by comparing the desired trajectories and those obtained through simulation studies and laboratory tests. Then an analysis of errors defined as the difference between the values of reference position, orientation and velocity, and those obtained from simulations and laboratory tests was carried out. On itd basis, the quality of regulation in the proposed algorithm was determined.