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The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93 % . The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a spherical robot with an internal mechanism based on a pendulum. The design is based on significant improvements made, including an electronics upgrade, to a previous robot prototype developed in our laboratory. Such modifications do not significantly impact its corresponding simulation model previously developed in CoppeliaSim, so it can be used with minor modifications. The robot is incorporated into a real test platform designed and built for this purpose. As part of the incorporation of the robot into the platform, software codes are made to detect its position and orientation, using the system SwisTrack, to control its position and speed. This implementation allows successful testing of control algorithms previously developed by the authors for other robots such as Villela, the Integral Proportional Controller, and Reinforcement Learning. [ABSTRACT FROM AUTHOR]

This article proposes the use of reinforcement learning (RL) algorithms to control the position of a simulated Kephera IV mobile robot in a virtual environment. The simulated environment uses the OpenAI Gym library in conjunction with CoppeliaSim, a 3D simulation platform, to perform the experiments and control the position of the robot. The RL agents used correspond to the deep deterministic policy gradient (DDPG) and deep Q network (DQN), and their results are compared with two control algorithms called Villela and IPC. The results obtained from the experiments in environments with and without obstacles show that DDPG and DQN manage to learn and infer the best actions in the environment, allowing us to effectively perform the position control of different target points and obtain the best results based on different metrics and indices. [ABSTRACT FROM AUTHOR]

This article presents a closed-loop position control of a mobile robot, which is capable of moving from its current position to a target point by manipulating its linear and angular velocities. The main objective of this article is to modify an existing control law based on the kinematic model to improve the response when the robot is backwards oriented and to reach the destination point in less time and with a shorter trajectory. Stability of the proposed control law is validated by Lyapunov Criterion. Some procedures are implemented to test this approach both in simulation with MATLAB, and experimentally with the Khepera IV robot. [ABSTRACT FROM AUTHOR]

In this paper, a new event-based control strategy for mobile robots is presented. It has been designed to work in wireless environments where a centralized controller has to interchange information with the robots over an RF (radio frequency) interface. The event-based architectures have been developed for differential wheeled robots, although they can be applied to other kinds of robots in a simple way. The solution has been checked over classical navigation algorithms, like wall following and obstacle avoidance, using scenarios with a unique or multiple robots. A comparison between the proposed architectures and the classical discrete-time strategy is also carried out. The experimental results shows that the proposed solution has a higher efficiency in communication resource usage than the classical discrete-time strategy with the same accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the development, structure, implementation, and some applications of a remote laboratory for teaching automatic control concepts to engineering students. There are two applications: formation control of mobile robots and a ball-plate system. In teaching control engineering, there are two main approaches to control design: model-based control and non-model-based control. Students are given insight into: 1) for model-based control: identification of real processes (i.e., dealing with noise, choosing the sampling time, observing nonlinear effects at startup, pairing input–output variables); and 2) for non-model-based control: the advantages and disadvantages of auto-tuning techniques. The paper concludes by presenting an evaluation of these remote labs and discussing the advantages of using them as complementary tools for teaching control engineering at the Bachelor's and Master's level. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]