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Article

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

1
Department of Computer Science, CONACYT-INAOE (Instituto Nacional de Astrofísica, Óptica y Electrónica), Santa María Tonanzintla, Puebla 72840, Mexico
2
The CIDIT (Center for Research, Development and Innovation Tecnolo), Universidad de Ciencia y Tecnología Descartes, CIDIT-Posgrado, Tuxtla Gutiérrez 29065, Mexico
3
Faculty of Civil Engineering, CONACYT-Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico
4
CONACYT-BUAP, Physical-Mathematical Science Department, Puebla 72570, Mexico
5
Technological Institute of Tuxtla Gutierrez/TECNM, Tuxtla Gutiérrez 29000, Mexico
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Federico Cuesta
Appl. Sci. 2021, 11(14), 6468; https://doi.org/10.3390/app11146468
Received: 28 May 2021 / Revised: 30 June 2021 / Accepted: 2 July 2021 / Published: 13 July 2021
(This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation)
Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow for comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies a fuzzy inference system (FIS) to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on a multilayer perceptron (MLP) architecture, which is a class of feedforward artificial neural network (ANN), and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive Obstacle–Avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “experience” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task. View Full-Text
Keywords: artificial intelligence; motion control; reactive obstacle–avoidance; wheeled mobile robots artificial intelligence; motion control; reactive obstacle–avoidance; wheeled mobile robots
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MDPI and ACS Style

Medina-Santiago, A.; Morales-Rosales, L.A.; Hernández-Gracidas, C.A.; Algredo-Badillo, I.; Pano-Azucena, A.D.; Orozco Torres, J.A. Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence. Appl. Sci. 2021, 11, 6468. https://doi.org/10.3390/app11146468

AMA Style

Medina-Santiago A, Morales-Rosales LA, Hernández-Gracidas CA, Algredo-Badillo I, Pano-Azucena AD, Orozco Torres JA. Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence. Applied Sciences. 2021; 11(14):6468. https://doi.org/10.3390/app11146468

Chicago/Turabian Style

Medina-Santiago, A., Luis A. Morales-Rosales, Carlos A. Hernández-Gracidas, Ignacio Algredo-Badillo, Ana D. Pano-Azucena, and Jorge A. Orozco Torres. 2021. "Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence" Applied Sciences 11, no. 14: 6468. https://doi.org/10.3390/app11146468

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