Winder Adilio Matamoros, Jose Luis Ordoñez Avila, Jose Luis Ordoñez Fernandez
This document developed the experimental design process of the mechanisms for a zoomorphic robot in CAD software, with three degrees of freedom on each limb. The mechanisms, subjected to a virtual simulation environment, tested for resistance, kinematics, and dynamics. The realization of the project was based on a spiral methodology. As the main results, the best material to build the robot structure was iron ductile, and the displacement speed three cm/s. Finally, the authors conclude that a spiral methodology and CAD software is an effective method to design zoomorphic robots.
{"title":"Spiral Method for Experimental Design of Mechanisms for Zoomorphic Robot in CAD Software","authors":"Winder Adilio Matamoros, Jose Luis Ordoñez Avila, Jose Luis Ordoñez Fernandez","doi":"10.1145/3449301.3449346","DOIUrl":"https://doi.org/10.1145/3449301.3449346","url":null,"abstract":"This document developed the experimental design process of the mechanisms for a zoomorphic robot in CAD software, with three degrees of freedom on each limb. The mechanisms, subjected to a virtual simulation environment, tested for resistance, kinematics, and dynamics. The realization of the project was based on a spiral methodology. As the main results, the best material to build the robot structure was iron ductile, and the displacement speed three cm/s. Finally, the authors conclude that a spiral methodology and CAD software is an effective method to design zoomorphic robots.","PeriodicalId":137428,"journal":{"name":"International Conference on Robotics and Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114490396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this work is to expose the design of an electronic RF communication system that allows movement control of an all-terrain robot. For the development of this device, a spiral methodology is used, allowing the development and analysis of four stages of the robot. This study starts with RF communication with a microcontroller, implementation of PID control, 2-degree-of-freedom arm control, and field testing. Significant results are obtained in the development of a PI to control the movement of the robot at a maximum distance of 450 meters and autonomy of 2 hours. Finally, it is concluded that the spiral methodology facilitated the planning and execution of the electronic design for the robot. The PID minimized the error between the communication of the user and the robot. The robot could be used to monitor agro-industrial farms in Honduras.
{"title":"Spiral Cycle Implementation for Designing an All-Terrain Teleoperated Robot in Honduras","authors":"Jose Luis Ordoñez Avila, H. Jimenez, A. Carrasco","doi":"10.1145/3449301.3449344","DOIUrl":"https://doi.org/10.1145/3449301.3449344","url":null,"abstract":"The purpose of this work is to expose the design of an electronic RF communication system that allows movement control of an all-terrain robot. For the development of this device, a spiral methodology is used, allowing the development and analysis of four stages of the robot. This study starts with RF communication with a microcontroller, implementation of PID control, 2-degree-of-freedom arm control, and field testing. Significant results are obtained in the development of a PI to control the movement of the robot at a maximum distance of 450 meters and autonomy of 2 hours. Finally, it is concluded that the spiral methodology facilitated the planning and execution of the electronic design for the robot. The PID minimized the error between the communication of the user and the robot. The robot could be used to monitor agro-industrial farms in Honduras.","PeriodicalId":137428,"journal":{"name":"International Conference on Robotics and Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116015694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recognition of people’s expression has been a very difficult task for computers from the time of its invention and still continues to pose a lot of challenges to the modern day generation of computers. To solve this problem, Convolutional Neural Network (CNN) is used which involves the application of preprocessing, feature extraction, training technique, and testing modules/methods to determine facial expression recognition. These methods were tested on the Oulu-CASIA VIS dataset [1]. The results obtained classified images of people’s facial expressions into six (6) distinct emotional classes, viz (anger, disgust, fear, happiness, sadness and surprise) showing an average accuracy of 98.99% and thus affirming that the application of the convolutional neural network (CNN) in facial expression recognition is efficient.
{"title":"Application of Convolutional Neural Network for Facial Expression Recognition","authors":"C. C. Atabansi","doi":"10.1145/3449301.3449307","DOIUrl":"https://doi.org/10.1145/3449301.3449307","url":null,"abstract":"The recognition of people’s expression has been a very difficult task for computers from the time of its invention and still continues to pose a lot of challenges to the modern day generation of computers. To solve this problem, Convolutional Neural Network (CNN) is used which involves the application of preprocessing, feature extraction, training technique, and testing modules/methods to determine facial expression recognition. These methods were tested on the Oulu-CASIA VIS dataset [1]. The results obtained classified images of people’s facial expressions into six (6) distinct emotional classes, viz (anger, disgust, fear, happiness, sadness and surprise) showing an average accuracy of 98.99% and thus affirming that the application of the convolutional neural network (CNN) in facial expression recognition is efficient.","PeriodicalId":137428,"journal":{"name":"International Conference on Robotics and Artificial Intelligence","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122019549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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