Two-wheeled Self-balancing Robot Research Articles

Design, Manufacturing and Testing of a Two-Wheeled Self-Balancing Robot

This work presents a self-balancing robot's modeling, manufacturing, and testing. This study aimed to design and construct a robot capable of maintaining balance while stationary and in motion, employing an effective control strategy. The robot's design incorporates sensors, microcontrollers, actuators, and control algorithms. The control strategy involved implementing an LQR (Linear Quadratic Regulator) controller and a Kalman filter for state estimation. The results demonstrate the robot's ability to effectively maintain balance and navigate flat terrain while controlled by a joystick. This study provides valuable insights into the design and control of self-balancing robots.

Structural Analysis of Self-Balancing Robot

Abstract: A two-wheeled self-balancing robot is a classic example of inverted pendulum model. Exploring the realm of inverted pendulum systems, the two-wheeled self-balancing robot stands as a quintessential example, showcasing its inherent natural instability. In contemporary times, this system has emerged as a focal point of research, drawing attention for its extensive potential applications, ranging from Segway vehicles to enhanced wheelchairs and more efficient robotic designs. Within the scope of this project, a deliberate effort has been undertaken to confront and mitigate the inherent instability of the two-wheeled self-balancing robot through the implementation of a PID control system. This paper aims to carry out static structural analysis on the robot carrying maximum weight and subjected to road conditions. The robot model used in analysis is designed in SolidWorks and includes primary assembly. Analysis was performed in the Static Structural Suite of ANSYS Workbench.

STUDY THE PERFORMANCE OF TWO-WHEELED BALANCING MOBILE ROBOT USING FUZZY PD CONTROLLER

Nowadays, the research on a two-wheeled self-balancing robot is an active area of research especially in terms of design as well as control to continue the innovation applications of robots in the future. Most of the two-wheeled self-balancing robots are designed based on an inverted pendulum system for stability and maneuverability. The aim of this paper is to propose the fuzzy PD controller to control and maintain its balance on the two wheels. A sensor of the Inertial Measurement Unit (IMU) was used as an input to evaluate and obtain the position and orientation of the robot. The control algorithms for the robot also are designed to keep the pendulum upright. Then, the fuzzy PD concept was applied to correct the error between the desired set point and the actual tilt angle position to adjust the speed of the motor accordingly. The results obtained from this controller were capable of maintaining the balancing of the robot by using an experimental method of PID tuning. The prototype of the two-wheeled self-balancing robot was implemented with Arduino Uno and a fuzzy PD controller. However, the limitation of the project is the longer size and heavier weight of the robot are less stable, then a better controller is needed to balance the robot.

Self-balancing robot: modeling and comparative analysis between PID and linear quadratic regulator

<p>A two-wheeled self-balancing robot (TWSBR) is an underactuated system that is inherently nonlinear and unstable. While many control methods have been introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase in maximum initial tilt angle and a reduction in settling time.</p>

Research on wheel-legged robot based on LQR and ADRC

The traditional two-wheeled self-balancing robot can travel quickly in a flat road environment, and it is easy to destabilize and capsize when passing through a bumpy road. To improve the passing ability of a two-wheeled robot, a new wheel-legged two-wheeled robot is developed. A seven-link leg structure is proposed through the comprehensive design of mechanism configuration, which decouples the balanced motion and leg motion of the robot. Based on the Euler–Lagrange method, the dynamic model of the system is obtained by applying the nonholonomic dynamic Routh equation in the generalized coordinate system. The robot’s state space is divided according to the robot’s height, and the Riccati equation is solved in real-time by the linear quadratic regulator (LQR) method to complete the balance and motion control of the robot. The robot leg motion control is achieved based on the active disturbance rejection control (ADRC) way. A robot simulation model is built on Recurdyn to verify the algorithm’s feasibility, and then an experimental prototype is built to demonstrate the algorithm’s effectiveness. The experimental results show that the control method based on LQR and ADRC can make the robot pass through the bumpy road.

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

In this article, a controller based on a Robot Operating System (ROS) for a two-wheeled self-balancing robot is designed. The proposed ROS architecture is open, allowing the integration of different sensors, actuators, and processing units. The low-cost robot was designed for educational purposes. It used an ESP32 microcontroller as the central unit, an MPU6050 Inertial Measurement Unit sensor, DC motors with encoders, and an L298N integrated circuit as a power stage. The mathematical model is analyzed through Newton-Euler and linearized around an equilibrium point. The control objective is to self-balance the robot to the vertical axis in the presence of disturbances. The proposed control is based on a bounded saturation, which is lightweight and easy to implement in embedded systems with low computational resources. Experimental results are performed in real-time under regulation, conditions far from the equilibrium point, and rejection of external disturbances. The results show a good performance, thus validating the mechanical design, the embedded system, and the control scheme. The proposed ROS architecture allows the incorporation of different modules, such as mapping, autonomous navigation, and manipulation, which contribute to studying robotics, control, and embedded systems.

Toward Design and Implementation of Self-Balancing Robot Using Deep Learning

In the Internet of Things (IoT) era, an immense amount of sensing devices are obtained and produce various sensory data over time for a wide range of disciplines and applications. These devices will result in significant, fast, and real-time data streams based on the utilization characteristics. Utilizing analytics over such data streams to identify new information, model future insights, and make control decisions is a necessary process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. This paper presents a study of digital agriculture and its significance in terms of the application of an IoT-based device — a two-wheeled self-balancing robot — followed by a thorough procedural explanation of the development of the device, which begins with the mathematical modeling of the system through the Euler–Lagrange method to obtain the equations of motion for the same and linearize the equation to define the control method to be used to balance the robot structure, all based on the concept of the inverted pendulum. Then paper discusses the suitable and the most efficient control method, which is the linear quadratic regulator (LQR), for these robots. Then deep learning-based LQR (DL-LQR) method is implemented in the robots performing the algorithm to balance it successfully.

Matlab/Simulink를 이용한 이륜 자율 균형 로봇의 3D 모델링과 제어

The two-wheeled self-balancing robot (TWSBR) based on an inverted pendulum is an attractive research topic that requires not only maneuvering but also postural balancing control. As TWSBRs have secured platforms with flexible movement and ease of operation, they have been rapidly spreading in everyday life rather than industrial sites. Meanwhile, 3D CAD design and analysis tools enable designers and engineers to realize innovative products while they reduce development time and costs. In this paper, we designed the components of TWSBR in 3D using SOLIDWORKS and assembled a 3D model of TWSBR using function blocks in Simscape. In addition, we derived a linearization model of TWSBR at the upright balancing position and designed an LQR controller for this model. Through the integrated simulation and animation environment in Matlab/Simulink using the 3D model of TWSBR, we presented that the LQR controller can effectively perform upright-balancing and maneuvering control.

A METHOD OF PD CONTROL FOR BALANCING A UNICYCLE ROBOT

Unicycle is a robot that imitates a performance of a circus artist on a one-wheeled self-balancing bicycle. This research assumes that this model is equivalent to two separated popular models: a two-wheeled self-balancing robot and reaction wheeled inverted pendulum. On each model, we build a PD controller. Thence, we present a structure of PD controllers to balance this model at the equilibrium point. We also build an experimental unicycle robot for the laboratory. Our method is proven to work well based on both simulation and experiment.

Research on Two-Round Self-Balancing Robot SLAM Based on the Gmapping Algorithm

Aiming at the inconvenience of inspection and monitoring of coal mine pump room equipment in a narrow and complex environment, this paper proposes and designs a two-wheel self-balancing inspection robot based on laser SLAM. Using SolidWorks, the three-dimensional mechanical structure of the robot is designed, and the overall structure of the robot is analyzed by finite element statics. The kinematics model of the two-wheel self-balancing robot was established, and the multi-closed-loop PID controller was used to design the two-wheel self-balancing control algorithm of the robot. The 2D LiDAR-based Gmapping algorithm was used to locate the robot and construct the map. Through the self-balancing test and anti-jamming test, it is verified that the self-balancing algorithm designed in this paper has a certain anti-jamming ability and good robustness. By using Gazebo to build a simulation comparison experiment, it is verified that the selection of the particle number is of great significance for improving the map accuracy. The actual test results show that the constructed map has high accuracy.

Optimum Design for a New Reconfigurable Two-Wheeled Self-Balancing Robot Based on Virtual Equivalent Parallel Mechanism

Abstract Two-wheeled self-balancing robot (TWSBR) is a mobile robot with a wide application in security, rescue, entertainment, and other fields. To make the robot obtain a larger range of the controllable inclination angle, a reconfigurable mechanism of the moment of inertia is designed for the TWSBR, and the energy consumption of the reconfigurable mechanism is reduced by a gravity compensation mechanism. This paper constructs a virtual equivalent parallel mechanism (VEPM) to model the robot-ground system combining the robot and the ground. The kinematics, dynamic model, and performance indexes of the VEPM are solved based on the vector method, the Lagrangian dynamics, and the screw theory. Then, the dimensions of the mechanism are optimized based on the comprehensive performance analysis. Finally, the effectiveness of the optimization algorithm and gravity compensation mechanism is verified through simulation and motion experiments. The reconfigurable mechanism enables the TWSBR to stand up, step up, and surmount obstacles. The performance analysis and optimal design approaches proposed in this paper have positive significance for the systematic modeling and optimal design of two-wheeled and two-legged robots.

REMOTE-CONTROLLED TWO-WHEELED SELF-BALANCING ROBOT

The article presents the design and fabrication of a remotely controlled two-wheeled self-balancing robot, along with its mobile control by means of an application on a phone displaying basic parameters in a terminal. A 3D model made in SolidEdge software, the electronic components used to build the robot and electronic schematics and software are presented. Real-world tests were conducted that demonstrate the robot's operation. The robot can be used for educational and commercial purposes, as well as in the human home environment.

Optimal Control Approach for Robot System Using LQG Technique

A two-wheeled self-balancing robot system bases on the physical problem of an inverted pendulum. Stabilization of this type of mobile robot requires applying an active control approach. This paper proposes an efficient Linear Quadratic Gaussian (LQG) optimal control for the two-wheeled robot system. The LQG (a combination of a Kalman Filter (KF) and Linear Quadratic Regulator (LQR)) controller is designed to stabilize the robot while reducing the effect of the process and measurement noises on its performance. The LQG controller parameters (elements of state and control weighting matrices of the LQR and KF) are optimally tuned using the Particle Swarm Optimization (PSO) optimization method. The robot stabilization scheme is simulated utilizing MATLAB software to validate the proposed PSO-LQG controller system. The effectiveness of the proposed controller is validated based on the control criteria parameters, which are rise time, settling time, maximum overshoot, and steady-state error. The results prove that the proposed PSO-LQG controller can give very good movement performance in terms of both transient and steady-state responses.

Using Bio-Realistic Gaussian-Shaped Population and Dopamine-Modulated STDP for Training a Self-Balancing System

Human body balance is a gradual formation through repetition of actions, trial and error, and improving the mechanism of muscular-skeletal architecture for adapting to the demands of the environment. In the learning process, sensory receptors continuously send signals to the brain, then the brain to muscles and make a new signals pathway. Each time the body performs an action, millions of new synaptic connections are formed, and repetitive actions strengthen connections. So, a balanced body reuses the learned mechanism without performing any complex calculations. In contrast, the balance problem of a self-balancing robot has been solved by many different control algorithms. In this work, we propose a novel way to balance a two-wheeled self-balancing robot using bio-realistic Spiking Neural Networks (SNNs) to learn self-balancing, which is closely related to the way babies learn. To accomplish this, the gaussian shaped sensory neuronal population is connected with motor neurons through Spike-Timing-Dependent Plasticity (STDP) based synapses, further controlled with dopamine neurons. The key aspects of this approach are its bio-realistic nature and zero dependencies on data for adopting a new behavior compared to Deep Reinforcement Learning. Furthermore, this biologically-inspired mechanism can be used to improve the methodology for programming the robots to mimic Biological Intelligence.

State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

Abstract The two-wheeled self-balancing robot (TWSBR) is based on the axletree and inverted pendulum. Its balancing problem requires a control action. To speed up the response of the robot and minimize the steady state error, in this article, a grey wolf optimizer (GWO) method is proposed for TWSBR control based on state space feedback control technique. The controller stabilizes the balancing robot and minimizes the overshoot value of the system. The dynamic model of the system is derived based on Euler formula and linearized to state space representation to enhance the control technique. Then, the GWO optimizes the state feedback controller parameters. Simulation results show that the system reaches the zero steady-state error with less than 2 ms, which proves the effectiveness of the proposed controller over the classical state feedback controller in terms of fast response, very small overall error, and minimum overshoot.

PID Controller for Balancing One-Wheeled Self-Balancing Robot

One-wheeled self-balancing robot is a high-order SIMO system. It is developed from bicycle balancing and two-wheeled self-balancing robot by using only one wheel instead of two wheels. In this paper, we consider this model as two combined SIMO systems. Thence, a PID control structure is designed to balance this model not falling on a plane. Simulations are shown to prove again the ability of PID controllers in balancing this robot is two directions. Besides, we present a hardware platform of one-wheel self-balancing robot. Through the real model, PID control algorithm is proved to balance this object well at equilibrium point.

An Obstacle Avoidance Two-Wheeled Self-Balancing Robot

An Obstacle Avoidance Two-Wheeled Self-Balancing Robot

Comprehensive Performance Optimization of a New Reconfigurable Two-Wheeled Self-Balancing Robot Based on Virtual Equivalent Parallel Mechanism

Comprehensive Performance Optimization of a New Reconfigurable Two-Wheeled Self-Balancing Robot Based on Virtual Equivalent Parallel Mechanism

Fuzzy fractional-order PID control for two-wheeled self-balancing robots on inclined road surface

Two-wheeled self-balancing robots (TWSBR) is a highly nonlinear and inherently unstable under-driving system. When controlling its movement on an inclined surface, it is more difficult than when it is on a level road. This paper proposes a fuzzy fractional-order PID (FFOPID) controller for the motion control of a TWSBR system in an inclined environment. The control goal of TWSBR is to realize the wheel position control and to stabilize the non-vertical direction of intermediate body (IB). Finally, we compare the control effect of the proposed FFOPID controller with that of the integer-order PID controller, the fuzzy PID (FPID) controller, and the fractional-order PID (FOPID) controller when TWSBR moving on the inclined plane. The simulation results show that the FFOPID controller has better control performance and anti-interference ability.

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.