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Framework for Fast Experimental Testing of Autonomous Navigation Algorithms

Group of Automation, Robotics and Computer Vision (AUROVA), University of Alicante, San Vicente del Raspeig S/N, 03690 Alicante, Spain
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These authors contributed equally to this work.
Appl. Sci. 2019, 9(10), 1997; https://doi.org/10.3390/app9101997
Received: 12 April 2019 / Revised: 9 May 2019 / Accepted: 10 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Mobile Robots Navigation)
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Abstract

Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository. View Full-Text
Keywords: autonomous navigation; mobile robots; Monte Carlo localization; SLAM; GNSS; planning; control; Kalman filter autonomous navigation; mobile robots; Monte Carlo localization; SLAM; GNSS; planning; control; Kalman filter
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Muñoz–Bañón, M.Á.; del Pino, I.; Candelas, F.A.; Torres, F. Framework for Fast Experimental Testing of Autonomous Navigation Algorithms. Appl. Sci. 2019, 9, 1997.

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