Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence
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 mode...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6468 |
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| author | A. Medina-Santiago Luis Alberto Morales-Rosales Carlos Arturo Hernández-Gracidas Ignacio Algredo-Badillo Ana Dalia Pano-Azucena Jorge Antonio Orozco Torres |
| author_facet | A. Medina-Santiago Luis Alberto Morales-Rosales Carlos Arturo Hernández-Gracidas Ignacio Algredo-Badillo Ana Dalia Pano-Azucena Jorge Antonio Orozco Torres |
| author_sort | A. Medina-Santiago |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:46:48Z |
| format | Article |
| id | doaj.art-44c505ed733a4d89ba0af4633adc280e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:46:48Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-44c505ed733a4d89ba0af4633adc280e2023-11-22T03:10:11ZengMDPI AGApplied Sciences2076-34172021-07-011114646810.3390/app11146468Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial IntelligenceA. Medina-Santiago0Luis Alberto Morales-Rosales1Carlos Arturo Hernández-Gracidas2Ignacio Algredo-Badillo3Ana Dalia Pano-Azucena4Jorge Antonio Orozco Torres5Department of Computer Science, CONACYT-INAOE (Instituto Nacional de Astrofísica, Óptica y Electrónica), Santa María Tonanzintla, Puebla 72840, MexicoFaculty of Civil Engineering, CONACYT-Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, MexicoCONACYT-BUAP, Physical-Mathematical Science Department, Puebla 72570, MexicoDepartment of Computer Science, CONACYT-INAOE (Instituto Nacional de Astrofísica, Óptica y Electrónica), Santa María Tonanzintla, Puebla 72840, MexicoDepartment of Computer Science, CONACYT-INAOE (Instituto Nacional de Astrofísica, Óptica y Electrónica), Santa María Tonanzintla, Puebla 72840, MexicoTechnological Institute of Tuxtla Gutierrez/TECNM, Tuxtla Gutiérrez 29000, MexicoObstacle–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.https://www.mdpi.com/2076-3417/11/14/6468artificial intelligencemotion controlreactive obstacle–avoidancewheeled mobile robots |
| spellingShingle | A. Medina-Santiago Luis Alberto Morales-Rosales Carlos Arturo Hernández-Gracidas Ignacio Algredo-Badillo Ana Dalia Pano-Azucena Jorge Antonio Orozco Torres Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence Applied Sciences artificial intelligence motion control reactive obstacle–avoidance wheeled mobile robots |
| title | Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence |
| title_full | Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence |
| title_fullStr | Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence |
| title_full_unstemmed | Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence |
| title_short | Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence |
| title_sort | reactive obstacle avoidance systems for wheeled mobile robots based on artificial intelligence |
| topic | artificial intelligence motion control reactive obstacle–avoidance wheeled mobile robots |
| url | https://www.mdpi.com/2076-3417/11/14/6468 |
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