Virtual Robot Experimentation Platform Research Articles

Project-Based Teaching in Control Theory Education Based on V-REP: A Cart Inverted Pendulum Case

This paper provides a teaching concept for control theory education based on Virtual Robot Experimentation Platform (V-REP). A cart inverted pendulum virtual physical model is developed on V-REP. Students must analyze, design, and implement a suitable controller for the cart inverted pendulum system using their knowledge of the control theory. Different from traditional experiment and numerical simulation, virtual experiment is safe and less constrained. Moreover, the experiment results are more intuitive and obvious. This study can improve students’ interest in learning the control theory and help students understand the relevant content better.

Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players' objective functions. Through Lyapunov stability analysis, it is proven that the players' actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.

Robust attitude estimation for an unmanned aerial vehicle using multiple GPS receivers

Estimation of the system state is an important task in the field of control, localization, and navigation. Many sensors are used to extract the attitude information. GPS (Global Positioning System) is widely used to provide position and velocity for ground and aerial vehicles. However, only a few research works have addressed the problem of attitude estimation using multiple GPS receivers. The main contribution of this paper consists on proposing a robust algorithm for attitude estimation of an unmanned aerial vehicle using INS/3GPS integration. First, we propose two alternative methods for attitude determination with three GPS receivers: Direct Computing Method (DCM) and Least Square Method (LSM) based on Singular Value Decomposition (SVD). Second, the attitude given by GPS receivers is fused with the measurement of a three-axis gyroscope using a robust Smooth Variable Structure Filter (SVSF). The optimal geometric configuration of GPS antennas is determined based on a deep performance analysis of the proposed system. Then, robustness analysis is assessed against measurement noise and parameters uncertainties. Simulation results are presented to demonstrate the robustness of the proposed approach. A more realistic evaluation of the proposed solution is obtained using an aerial vehicle type “Helicopter” from the Virtual Robot Experimentation Platform (VREP simulator). The proposed filter is compared with an Extended Kalman Filter (EKF). The obtained results have confirmed how much the SVSF is robust for attitude estimation in presence of the considered disturbances.

Design of mobile robot navigation controller using neuro-fuzzy logic system

To be successful, autonomous navigation must overcome challenges associated with the functions such as detecting perception, current location, planning, and regulating movements. It is possible to have a purposeful and reactive navigational approach. Robots and humans working in the same area must comply with safety laws regarding navigation. As a result, mobile robots in organized environments now have a navigation controller that can operate in reactive and deliberative approaches. Therefore, a neuro-fuzzy system is introduced to explore the benefits of both deliberative and reactive navigation control. It combines neural networks with a fuzzy logic controller and is used to investigate various strategies and give the robot corrective decisional commands. An amigo Bot equipped with a Kinect sensor is used to test the proposed approach in conjunction with the Virtual Robot Experimentation Platform (V-REP) and ROS Groovy Galapagos.

Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example.

Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of human arms, it is believed that the application scope of robotic arms in assisting people’s daily life can be greatly increased. The purpose of this study is to build a general system that can use intelligent techniques to assist in the construction of a personalized rehabilitation system. More importantly, this research hopes to establish an intelligent system that can be adjusted according to the needs of the problem domain, that is, the system can move toward the direction of problem-solving through autonomous learning. The artificial neural molecular system (ANM system), developed early in our laboratory, which captured the close structure/function relationship of biological systems, was used. The system was operated on the V-REP (Virtual Robot Experimentation Platform). The results show that the ANM system can use self-learning methods to adjust the start-up time, rotation angle, and the sequence of the motor operation of different motors in order to complete the designated task assignment.

Fuzzy logic controlled autonomous quadruped robot

Researchers have taken several steps to build an appropriate 4-point based quadruped robot controller. These efforts can be categorized into analytical and intelligent methods into two main fields. The study of an analytical method is based on the robot’s primarily complex movement equations. On the other hand, an intelligent method requires less knowledge of kinematic and dynamic equations to control the motion and orientation of the quadruped robot autonomously in any environment. In this study, we have chosen the Fuzzy Controller (FC) as an intelligent sensor-actuator control system for the quadruped robot. This robot includes sensors to sense the physical quantities of the environment. And the outputs of the sensors are used as inputs to the FC. According to the sensor inputs and defined rule-based system, the controller gives the actuator control commands to the robot's leg. Further on, the fuzzification and defuzzification modules make decisions that control the robot's motion and allow the robot to navigate automatically. The two-dimensional and three-dimensional models of the quadruped robot have been designed through the SolidWorks software, and the fuzzy controlled simulations of the quadruped robot are presented in the Virtual Robot Experimentation Platform (V-REP) software.

E-puck motion control using multi-objective particle swarm optimization

This article describes the velocity-based motion and orientation control method for a differential-driven two-wheeled E-puck Robot (DDER) using the Multi-Objective Particle Swarm Optimization (MPSO) algorithm in the Virtual Robot Experimentation Platform (V-REP) software environment. The wheel velocities data and Infra-Red (IR) sensors reading make the multi-objective fitness functions for MPSO. We use front, left, and right IR sensors reading and right wheel velocity data to design the first fitness function for MPSO. Similarly, the front, left, and right IR sensors reading, and left wheel velocity data have been taken for making the second fitness function for MPSO. The multi-objective fitness functions of MPSO minimize the motion and orientation of the DDER during navigation. Due to the minimization of motion and orientation, the DDER covers less distance to reach the goal and takes less time. The Two-Dimensional (2D) and Three-Dimensional (3D) navigation results of the DDER among the scattered obstacles have been presented in the V-REP software environment. The comparative analysis with previously developed Invasive Weed Optimization (IWO) algorithm has also been performed to show the effectiveness and efficiency of the proposed MPSO algorithm.

Real-time cooperative kinematic control for multiple robots in distributed scenarios with dynamic neural networks

This work investigates the real-time cooperative kinematic control for multiple robots in distributed scenarios. The information interaction among robots is described as a directed topology in which each robotic manipulator is connected to specified nodes. Besides, a performance index of minimum joint velocity norm is introduced to regulate the robots’ motion. Combining with the performance index and integrating the related constraints, a control scheme is developed to guide the behavior of robots with an error feedback term leveraged to improve the real-time execution accuracy of tasks. Moreover, considering that the involved parameters are time-dependent, a dynamic neural-network-assisted solver is constructed to solve the control scheme online. In the end, simulations and illustrative experiments on the virtual robot experiment platform (V-REP) are conducted by a set of UR5 robots to execute tasks in a switchable directed topology, where the corresponding results demonstrate the effectiveness of the proposed scheme.

Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform

Abstract This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage of this GRNN over the feedforward neural network is that it provides accurate results in a short period with minimal error. First, the designed GRNN architecture receives real-time obstacle information from the Infra-Red (IR) sensors of an E-puck robot. According to IR sensor data interpretation, this architecture sends the left and right wheel velocities command to the E-puck robot in the V-REP software platform. In the present study, the GRNN architecture includes the MIMO system, i.e., multiple inputs (IR sensors data) and multiple outputs (left and right wheel velocities). The three-dimensional (3D) motion and orientation results of the GRNN architecture-controlled E-puck robot are carried out in the V-REP software platform among scattered and wall-type obstacles. Further on, compared with the feedforward neural network, the proposed GRNN architecture obtains better navigation path length with minimum error results.

Adaptive control for networked uncertain cooperative dual-arm manipulators: an event-triggered approach

AbstractIn this paper, an improved adaptive motion-force control approach is introduced to control the cooperative manipulators transporting a shared object under limited communication. The adaptive controller is designed based on the backstepping approach to control the motion of the handled object in the presence of uncertainties and external disturbances. Moreover, the force controller is established to maintain constant internal forces. An event-triggered (ET) mechanism is derived based on the Lyapunov analysis to deal with the bandwidth restrictions and maintain the system stability during the cooperative manipulation. The effectiveness of the proposed control scheme is investigated by comparing it with the existing variations of adaptive backstepping control (i.e., traditional and state augmented schemes). Moreover, the designed triggering mechanism is compared with different triggering conditions presented in the literature. The proposed control approach is further validated in a more realistic virtual robot experimentation platform (i.e., V-REP) using two SCORBOT-ER VPlus manipulators. From the TrueTime-based simulation runs, the proposed control scheme exhibits superior performance in tandem with efficient utilization of the network resources during the transportation task.

Distributed Formation Control for Multiagent Systems Using a Fractional-Order Proportional–Integral Structure

This work presents the development of a distributed formation control for multiagent systems (MASs) using a fractional-order proportional–integral (FOPI) structure. The fractional control protocol proposed is evaluated on agents modeled with single- and double-integrator dynamics, in which it enhances the convergence speed of the formation and improves the robustness of the system. In addition, a novel method to study the stability and convergence of the MASs in the frequency domain is designed. This allows deriving constraints on the control parameters to guarantee the stability and ensure the formation when the system topology is modeled by a time-invariant undirected graph. Finally, realistic simulations through MATLAB and a Virtual Robot Experimentation Platform (V-REP) validate the effectiveness of the theoretical results.

An Efficient Method to Recover Relative Pose for Vehicle-Mounted Cameras Under Planar Motion

In this paper, a 2-point algorithm is proposed to estimate the relative pose as well as the absolute scale between two vehicle-mounted cameras efficiently. The system model is deduced by combining a two-view geometric model and the planar motion constraint to reduce the degrees of freedom. 2-point correspondences are utilized to calculate the rotation information independently from the translation information, which indicates that the proposed algorithm can deal with pure rotation scenes. Besides, provided that the camera's configuration satisfies a certain condition, the absolute scale can be recovered. An approximation algorithm is developed and combined with the random sample and consensus scheme to deal with the uneven ground surfaces in practice. As only 2-point correspondences are required, less iterations are demanded in the estimating procedure compared with many other existing related algorithms. Both simulation and experiments are implemented to evaluate the proposed algorithm, in which the synthetic data, virtual robot experimentation platform, KITTI Vision Benchmark, and SUMMIT-XL platform are acquired. According to the results, the proposed algorithm performs better than many related algorithms including the well-known 5-point algorithm in many cases, especially when the camera's trajectory contains sharp corners.

LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

This paper presents the application of different approaches of intelligent controllers based on LAMDA (Learning Algorithm for Multivariate Data Analysis) to perform the trajectory tracking task of a mobile robot Pioneer 3-DX simulated in the Virtual Robot Experimentation Platform V-REP environment. The paper proposes the design of a cascade control strategy composed of two controllers, the external controller based on a feedback linearization for the robot kinematics and the internal controller applied to the robot dynamics where the LAMDA approaches are applied. In this work, it is considered an uncertain dynamic model. Thus, its identification and control are based on a reduced-order model for designing purposes. Finally, the dynamic controller is designed using different controllers based on LAMDA, such as LAMDA-PID, LAMDA-Sliding Mode Control, and Adaptive LAMDA. To perform a comparative analysis between them that includes a conventional PID, SMC, and Fuzzy-PID, different trajectories are examined to evaluate the methods. The obtained results are compared qualitatively and quantitatively to determine which proposal performs the most accurate control and presents the best results in terms of performance.

Separated Sonar Localization System for Indoor Robot Navigation

This article addresses the task of mobile robot localization for indoor navigation. In this article, we propose a novel indoor localization system based on separated sonar sensors, which can be deployed in large-scale indoor environments conveniently. In our approach, the separated sonar receivers deploy on the top ceiling, and the mobile robot equipped with the separated sonar transmitters navigates in the indoor environment. The distance measurements between the receivers and the transmitters can be obtained in real time from the control board of receivers with the infrared synchronization. The positions of the mobile robot can be computed without accumulative error. And the proposed localization method can achieve high precision in the indoor localization tasks at a very low cost. We also present a calibration method based on the simultaneous localization and mapping to initialize the positions of our system. To evaluate the feasibility and the dynamic accuracy of the proposed system, we construct our localization system in the Virtual Robot Experimentation Platform simulation platform and deploy this system in a real-world environment. Both the simulation and real-world experiments have demonstrated that our system can achieve centimeter-level accuracy. The localization accuracy of the proposed system is sufficient for robot indoor navigation.

V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network

Abstract This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes the mean square error between the actual and predicted values of the FNN. In this work, 2 separate DC motors and 16 ultrasonic sensors have been used for making differential drive steering angle and for collecting the distance from obstacles, respectively. The proposed without tuned FNN and PSO-tuned FNN receives obstacle's distance as inputs form ultrasonic sensors and control the steering angle of a differential drive of automated Pioneer P3-DX wheeled robot as output. We have compared the results between without tuned FNN and PSO-tuned FNN, and it has been found that PSO-tuned FNN gives a better trajectory and takes less distance to reach the target. Virtual Robot Experimentation Platform software has been used to design the real-time simulation results. A comparative study between without tuned FNN and PSO-tuned FNN verifies the effectiveness of PSO-tuned FNN for automated Pioneer P3-DX wheeled robot navigation. Also, we have compared this winner PSO-tuned FNN to the previously developed PSO-optimized Fuzzy Logic Controller navigational technique to show the authenticity and real-time implementation of PSO-tuned FNN.

Nonlinear predictive control applied to a biped walker with adjustable step length using a passive walking‐based reference generator

SummaryThis paper presents the use of passive dynamics to generate the sagittal plane references for a biped walker. In order to achieve an adjustable gait width, a neural network reference generator, trained with passive walking data on different slopes, was applied. The control algorithm was implemented with an Nonlinear Model Predictive Control (NMPC) strategy, in order to perform multiple‐input multiple‐output reference tracking while maintaining balance. The main contributions of the present work are using a kneed passive walker model for reference generation, which creates gait profiles that are more natural than the compass gait model, presenting a low computational cost, since only the forward kinematic model is used, and enabling adjustable gait width, due to the use of a neural network trained with data from the passive walker. Simulation results using V‐REP (Virtual Robot Experimentation Platform) are presented, using a sagittal kneed walker with a torso and arms, demonstrating the effectiveness of the proposed method.

Terminal Traction Control of Teleoperation Manipulator With Random Jitter Disturbance Based on Active Disturbance Rejection Sliding Mode Control

The present study proposes an active disturbance rejection sliding mode traction control system to deal with the dramatic change of joint angular acceleration and jitter attributed to non-uniform traction during terminal traction of the manipulator based on the use of human-computer interaction (HCI) device, as well as achieve the high-precision and high-speed terminal traction control. First, the traction trajectory generated by the HCI device is mapped into the motion space of the manipulator, and a low-pass filter is employed to filter out the high-frequency noises attributed to non-uniform traction. Second, the corresponding joint solution of the traction trajectory is solved by using the inverse kinematics algorithm. Then the nonlinear and strongly coupled manipulator is linearly decoupled. Lastly, the effect of non-uniform traction on the manipulator’s joints is considered as interference, and an active disturbance rejection sliding mode controller is introduced for each joint. To reduce the chattering, the output torque of the controller is processed with a low-pass filter. The stability of the controller is proved with the Lyapunov method. To verify the traction effect of the traction control system and its anti-interference ability for non-uniform traction, the trajectory tracking experiment with random jitter and the real terminal traction control experiment is performed on the UR5 manipulator in the virtual robot experiment platform. As revealed from the experimentally achieved results, the proposed controller can significantly limit the effect of non-uniform traction on each joint of the manipulator and achieve the fast, stable, and high-precision terminal traction control of the manipulator by the HCI device.

Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study

Abstract Multi-Robot System (MRS) is composed of a group of robots that work cooperatively. However, Multi-Agent System (MAS) is computational systems consisting of a group of agents that interact with each other to solve a problem. The central difference between MRS and MAS is that in the first case, the agent is a robot, and in the second, it is a software. Analyzing the scientific literature, it is possible to notice that few studies address the integration between MAS and MRS. In order to achieve the interdisciplinary integration, the theoretical background of these areas must be considered in this paper, so that the integration can be applied using a case study of decentralized MRS. The objective of this MRS is to track and surround a stationary target. Also, it has been implemented and validated in the robot simulator called Virtual Robot Experimentation Platform (V-REP). In the validation of the proposed MRS, a scenario with three robots and a stationary target were defined. In the tracking task, the robot can detect the target whose position is not known a priori. When the detection occurs, the V-REP informs the target position to the robot because the environment is discretized into a grid of rectangular cells. After that, all the robots are directed to the target, and the surround task is realized. In this task, a mathematical model with direct communication between the robots was used to keep the robots equidistant therefrom and from each other.

A teaching method for the theory and application of robot kinematics based on MATLAB and V‐REP

AbstractWith the continuous development of the world, robots are necessary for achieving high production rates and competitiveness. With an increasing robot demand, many universities offer robotics courses. These courses teach students the mathematical principles of robot‐related algorithms. However, it is difficult for students to apply pure mathematical algorithms in robotics to real robot development in a traditional classroom. This paper's motivation is to present a new robotics teaching method: Algorithm, Virtual experiment, Programming and Controller (AVPC), which shows students the steps of complete robot algorithm development, verification, and application process, taking the kinematics of an educational desktop 6‐DOF robotic arm as a typical teaching case. For the low‐cost educational desktop six‐axis robotic arm, an inverse kinematics algorithm is proposed. The inverse solution algorithm is realized in MATLAB, and then the dynamic model of the desktop robotic arm is established in Virtual Robot Experimentation Platform (V‐REP). The inverse kinematics algorithm in MATLAB is used to control the virtual robot arm movement in V‐REP to the specified position and orientation through the application programming interface (API) interface, thus verifying the correctness of the inverse kinematics algorithm. The inverse algorithm is implemented by using C language function in the microcontroller, and the running time of the algorithm is compared with that of the iterative solution method. In the present study, we investigated the students’ feelings and exam scores of using the new teaching method. The results show that the new teaching method has a significant impact on the improvement of students’ learning effect of robotics.

A method for robotic grasping based on improved Gaussian mixture model.

The present research envisages a method for the robotic grasping based on the improved Gaussian mixture model. The improved Gaussian mixture model is a method proposed by incorporating Bayesian ideas into the Gaussian model. It will use the Gaussian model to perform grasping training in a certain area which we called trained area. The improved Gaussian models utilized the trained Gaussian models as prior models. The proposed method improved the cumulative updates and the evaluation results of the improved models to make robots more adaptable to grasp in the untrained areas. The self-taught learning ability of the robot about grasping was semi-supervised. Firstly, the observable variables of objects were determined by a camera. Then, we dragged the robot to grasp object. The relationship between the variables and robot's joint angles were mapped. We obtained new samples in the close untrained area to improve the Gaussian model. With these new observable variables, the robot grasped it successfully. Finally, the effectiveness of the method was verified by experiments and comparative tests on grasping of real objects and grasping simulation of the improved Gaussian models through the virtual robot experimentation platform.