A Khepera IV library for robotic control education using V-REP

Farías, Gonzalo; Fábregas, Ernesto; Peralta, Emmanuel; Torres, Enrique; Dormido, Sebastián

doi:10.1016/j.ifacol.2017.08.1721

Cited by 24 publications

(20 citation statements)

References 5 publications

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“…The V-REP software is connected to Spyder via Remote API, making it compatible for Python programming. Simulation tests are carried out in V-REP using the KH4VREP library, which has been developed by the authors [33]- [35]. Figure 6 shows a view of the GUI of the library.…”

Section: A Simulation Resultsmentioning

confidence: 99%

Reinforcement Learning for Position Control Problem of a Mobile Robot

et al. 2020

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Due to the increase in complexity in autonomous vehicles, most of the existing control systems are proving to be inadequate. Reinforcement Learning is gaining traction as it is posed to overcome these difficulties in a natural way. This approach allows an agent that interacts with the environment to get rewards for appropriate actions, learning to improve its performance continuously. The article describes the design and development of an algorithm to control the position of a wheeled mobile robot using Reinforcement Learning. One main advantage of this approach concerning traditional control algorithms is that the learning process is carried out automatically with a recursive procedure forward in time. Moreover, given the fidelity of the model for the particular implementation described in this work, the whole learning process can be carried out in simulation. This fact avoids damages to the actual robot during the learning stage. It shows that the position control of the robot (or similar specific tasks) can be done without the need to know the dynamic model of the system explicitly. Its main drawback is that the learning stage can take a long time to finish and that it depends on the complexity of the task and the availability of adequate hardware resources. This work provides a comparison between the proposed approach and traditional existing control laws in simulation and real environments. The article also discusses the main effects of using different controlled variables in the performance of the developed control law.

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Section: A Simulation Resultsmentioning

confidence: 99%

Reinforcement Learning for Position Control Problem of a Mobile Robot

et al. 2020

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“…To initiate the proposed algorithm, a simulation of a simple scenario was performed using the V-REP simulator and the Khepera IV robot model previously developed by the authors [ 36 , 37 ]. The experimental concept is that the robot can navigate through the arena, detecting the arrows.…”

Section: Resultsmentioning

confidence: 99%

“…In which students can experiment with their own controllers in order to incorporate these concepts in the teaching of robot control. In this sense, experiments on position control, obstacle avoidance, among others, have been implemented with very good results, for example [ 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

Farías

Fábregas

Torres

et al. 2020

Sensors

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This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.

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Section: Experiments 1: Object Detection By Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

Farías¹,

Fábregas²,

Torres³

et al. 2020

Preprint

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This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

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A Khepera IV library for robotic control education using V-REP

Cited by 24 publications

References 5 publications

Reinforcement Learning for Position Control Problem of a Mobile Robot

Reinforcement Learning for Position Control Problem of a Mobile Robot

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

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