Teaching control in mobile robotics with V-REP and a Khepera IV library

Fábregas, Ernesto; Farías, Gonzalo; Peralta, Emmanuel; Vargas, Héctor; Dormido, Sebastián

doi:10.1109/cca.2016.7587920

Cited by 18 publications

(17 citation statements)

References 8 publications

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“…The architecture of the platform is divided into two main parts: a virtual environment (simulation of the system) and an experimental environment (robots in a real scenario). The virtual environment is based on [15], which is a library developed by the authors, to include the Khepera IV robot into V-REP simulator [16]. This simulator is a versatile and scalable framework for creating 3D simulations in a relatively short period of time.…”

Section: Introductionmentioning

confidence: 99%

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

et al. 2019

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The use of mobile robots for teaching automatic control is becoming more popular in engineering curricula. Currently, many robot simulators with high-graphical capabilities can be easily used by instructors to teach control engineering. However, the use of real robots is not as straightforward as simulations. There are many hardware and software details that must be considered before applying control. This paper presents the development of an easy-to-use platform for teaching control of mobile robots. The laboratory has been carefully designed to conceal all technical issues, such as communications or the localization that do not address the fundamental concepts of control engineering. To this end, a position sensor based on computer vision has been developed to provide the positions of the robots on the platform in real time. The Khepera IV robot has been selected for this platform because of its flexibility and advanced built-in sensors but the laboratory could be easily adapted for similar robots. The platform offers the opportunity to perform laboratory practices to test many different control strategies within a real experimental multi-agent environment. A methodology for using the platform in the lab is also provided.INDEX TERMS Robotics education, mobile robot laboratory, vision-based indoor positioning sensor.

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Section: Introductionmentioning

confidence: 99%

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

et al. 2019

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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%

“…This matrix is a two-dimensional array with the number of columns corresponding to the number of obstacle sensors (8) and the number of rows corresponding to the number of motors (2). The weights of the matrix are determined empirically depending on the location of the sensors in the robot [33]. Note that the obstacles avoidance algorithm can be considered as a disturbance in the motors speeds for the control position problem (see figure 3).…”

Section: A Simulation Resultsmentioning

confidence: 99%

“…Figure 3 shows the block diagram of the control algorithm for this experiment, where the inner dashed square represents the controller and the outer dashed square represents the robot. Note that the Position Sensor is an IPS (Indoor Positioning System), which provides the absolute position and orientation of the robot [5], [33]. Different solutions for this problem can be found in the bibliography, [13], [14], [17] and last one is represented by…”

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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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“…Robots that can navigate autonomously are complex systems involving several components that have a common denominator: communication capabilities and the speed of processing and operation. These capabilities are very important because an autonomous robot must have all the necessary information available to make the right decisions at every sampling time [ 10 , 11 ].…”

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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Teaching control in mobile robotics with V-REP and a Khepera IV library

Cited by 18 publications

References 8 publications

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

Reinforcement Learning for Position Control Problem of a Mobile Robot

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

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