Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots based on Artificial Intelligence

A. Medina-Santiago
* ORCID logo ,
Luis Alberto Morales-Rosales
,
Carlos Arturo Hernández-Gracidas
,
Ignacio Algredo-Badillo
ORCID logo ,
Ana Dalia Pano-Azucena
,
Jorge Antonio Orozco Torres
Version 1 : Received: 28 May 2021 / Approved: 31 May 2021 / Online: 31 May 2021 (12:13:07 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

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 comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron 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.

Keywords

Artificial intelligence; Motion Control; Reactive Obstacle-Avoidance; Wheeled Mobile Robots

Subject

Computer Science and Mathematics, Robotics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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