Next Article in Journal
Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4
Next Article in Special Issue
Linguistic Patterns for Code Word Resilient Hate Speech Identification
Previous Article in Journal
ECG-Based Identification of Sudden Cardiac Death through Sparse Representations
Article

Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments

1
Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City 07700, Mexico
2
Tecnológico Nacional de México/IT de Tlalnepantla, Research and Postgraduate Division, Estado de México 54070, Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: Pau-Choo Chung, Gary G. Yen, De-Nian Yang and Meng-Hsun Tsai
Sensors 2021, 21(22), 7667; https://doi.org/10.3390/s21227667
Received: 16 October 2021 / Revised: 8 November 2021 / Accepted: 16 November 2021 / Published: 18 November 2021
(This article belongs to the Special Issue AI Drives Our Future Life)
Increasingly, robotic systems require a level of perception of the scenario to interact in real-time, but they also require specialized equipment such as sensors to reach high performance standards adequately. Therefore, it is essential to explore alternatives to reduce the costs for these systems. For example, a common problem attempted by intelligent robotic systems is path planning. This problem contains different subsystems such as perception, location, control, and planning, and demands a quick response time. Consequently, the design of the solutions is limited and requires specialized elements, increasing the cost and time development. Secondly, virtual reality is employed to train and evaluate algorithms, generating virtual data. For this reason, the virtual dataset can be connected with the authentic world through Generative Adversarial Networks (GANs), reducing time development and employing limited samples of the physical world. To describe the performance, metadata information details the properties of the agents in an environment. The metadata approach is tested with an augmented reality system and a micro aerial vehicle (MAV), where both systems are executed in an authentic environment and implemented in embedded devices. This development helps to guide alternatives to reduce resources and costs, but external factors limit these implementations, such as the illumination variation, because the system depends on only a conventional camera. View Full-Text
Keywords: autonomous driving; machine learning; computer vision; virtual training autonomous driving; machine learning; computer vision; virtual training
Show Figures

Figure 1

MDPI and ACS Style

Maldonado-Romo, J.; Aldape-Pérez, M.; Rodríguez-Molina, A. Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments. Sensors 2021, 21, 7667. https://doi.org/10.3390/s21227667

AMA Style

Maldonado-Romo J, Aldape-Pérez M, Rodríguez-Molina A. Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments. Sensors. 2021; 21(22):7667. https://doi.org/10.3390/s21227667

Chicago/Turabian Style

Maldonado-Romo, Javier, Mario Aldape-Pérez, and Alejandro Rodríguez-Molina. 2021. "Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments" Sensors 21, no. 22: 7667. https://doi.org/10.3390/s21227667

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop