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Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve t...

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Autores principales: Zhao, Jianwei, Fang, Jianhua, Wang, Shouzhong, Wang, Kun, Liu, Chengxiang, Han, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537580/
https://www.ncbi.nlm.nih.gov/pubmed/34695990
http://dx.doi.org/10.3390/s21206777
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author Zhao, Jianwei
Fang, Jianhua
Wang, Shouzhong
Wang, Kun
Liu, Chengxiang
Han, Tao
author_facet Zhao, Jianwei
Fang, Jianhua
Wang, Shouzhong
Wang, Kun
Liu, Chengxiang
Han, Tao
author_sort Zhao, Jianwei
collection PubMed
description The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.
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spelling pubmed-85375802021-10-24 Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection Zhao, Jianwei Fang, Jianhua Wang, Shouzhong Wang, Kun Liu, Chengxiang Han, Tao Sensors (Basel) Article The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually. MDPI 2021-10-12 /pmc/articles/PMC8537580/ /pubmed/34695990 http://dx.doi.org/10.3390/s21206777 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Jianwei
Fang, Jianhua
Wang, Shouzhong
Wang, Kun
Liu, Chengxiang
Han, Tao
Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
title Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
title_full Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
title_fullStr Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
title_full_unstemmed Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
title_short Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
title_sort obstacle avoidance of multi-sensor intelligent robot based on road sign detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537580/
https://www.ncbi.nlm.nih.gov/pubmed/34695990
http://dx.doi.org/10.3390/s21206777
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