Abstract
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.
Highlights
The current development of robotics has been influenced by the growth of NICT (New Information and Communication Technologies), which has provided the perfect scenario for the confronting of new challenges
OpenCV is a library of functions for real-time computer vision that was developed by Intel
The results are represented from the detection of basic signals to more complex signals, which are involved in the tests of the designed classifiers, as well as traffic signals and new scenarios
Summary
The current development of robotics has been influenced by the growth of NICT (New Information and Communication Technologies), which has provided the perfect scenario for the confronting of new challenges. The main contribution of this work is to propose the use of advanced computer vision algorithms to perform much more sophisticated and engaging experiments with practical mobile robot laboratories for pedagogical purposes The motivation of this is to provide much more challenging experiments for the students to improve the quality of the teaching-learning process in this field. A summarized list of contributions of this work is the following: (1) the incorporation of new computer vision capabilities to the practical mobile robot laboratories; (2) to provide much more challenging experiments in an interactive and engaging environment; (3) introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots; and (4) the experiments can be tested in simulation firstly, and after that, they can be implemented in a real and easy-to-use environment in a relatively fast and straightforward way. The remainder of the paper is organized as follows: Section 2 presents the fundamental concepts related to this article; Section 3 describes the implementation of the experiments in the laboratory; Section 4 shows the results of some test experiments developed in simulation and with the platform; and Section 5 presents the main conclusions and future work
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