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Control Architecture Model in Mobile Robots for the Development of Navigation Routes in Structured Environments

Control Architecture Model in Mobile Robots for the Development of Navigation Routes in Structured Environments

Alejandro Hossian, Gustavo Monte, Verónica Olivera
Copyright: © 2019 |Pages: 19
ISBN13: 9781522580607|ISBN10: 1522580603|EISBN13: 9781522580614
DOI: 10.4018/978-1-5225-8060-7.ch013
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MLA

Hossian, Alejandro, et al. "Control Architecture Model in Mobile Robots for the Development of Navigation Routes in Structured Environments." Rapid Automation: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 276-294. https://doi.org/10.4018/978-1-5225-8060-7.ch013

APA

Hossian, A., Monte, G., & Olivera, V. (2019). Control Architecture Model in Mobile Robots for the Development of Navigation Routes in Structured Environments. In I. Management Association (Ed.), Rapid Automation: Concepts, Methodologies, Tools, and Applications (pp. 276-294). IGI Global. https://doi.org/10.4018/978-1-5225-8060-7.ch013

Chicago

Hossian, Alejandro, Gustavo Monte, and Verónica Olivera. "Control Architecture Model in Mobile Robots for the Development of Navigation Routes in Structured Environments." In Rapid Automation: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 276-294. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8060-7.ch013

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Abstract

Robotic navigation applies to multiple disciplines and industrial environments. Coupled with the application of Artificial Intelligence (AI) with intelligent technologies, it has become significant in the field of cognitive robotics. The capacity of reaction of a robot in unexpected situations is one of the main qualities needed to function effectively in the environment where it should operate, indicating its degree of autonomy. This leads to improved performance in structured environments with obstacles identified by evaluating the performance of the reactive paradigm under the application of the technology of neural networks with supervised learning. The methodology implemented a simulation environment to train different robot trajectories and analyze its behavior in navigation and performance in the operation phase, highlighting the characteristics of the trajectories of training used and its operating environment, the scope and limitations of paradigm applied, and future research.

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