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Situationally Aware Mobile Robots

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1352

Special Issue Editors


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Guest Editor
Automation and Robotics Research Group (ARG), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: SLAM; situational awareness; semantic perception; aerial robots; mobile robots; autonomous architectures; deep learning applied to robots
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: artificial intelligence; computer security and reliability; computing in mathematics; natural science; engineering and medicine control systems; engineering; electrical engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: machine learning; computer vision; motion control; slam; multi-robot systems; state estimation; situational awareness; aerial robots; trajectory planning; software architectures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotics plays a pivotal role in expanding the competitiveness of all sectors of the economy, as well as offering new solutions to societal challenges. While industrial robots typically perform repetitive tasks, operating in industrial settings where the world is designed around them, service robots assist humans at work in non-industrial settings or at home, operating in complex dynamic unstructured environments. Robots need to continuously acquire complete situational awareness in such environments (i.e., perceiving the environment within time and space, comprehending its meaning, and responding to it for future adaption) to enable intelligent decision-making and autonomous task execution. Although the robotics research community has been investigating topics such as perception, mapping, and localization for many years, there is a strong need for a more advanced understanding of robotic situational awareness. This Special Issue of Sensors aims to present cutting-edge research proposing intelligent localization, mapping, and navigation techniques for mobile robots, harnessing metric, semantic, and topological relations while ensuring scalability in large-scale, real-world environments The topics of interest for this Special Issue are (but are not limited to):

  • SLAM;
  • Localization;
  • Mapping;
  • Perception;
  • Planning and Navigation;
  • Multi-Agent SLAM;
  • Multi-Agent Planning and Navigation.

Dr. Hriday Bavle
Prof. Dr. Holger Voos
Dr. Jose Luis Sanchez-Lopez
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Research

14 pages, 10568 KiB  
Article
Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs
by Javier Rodriguez-Vazquez, Inés Prieto-Centeno, Miguel Fernandez-Cortizas, David Perez-Saura, Martin Molina and Pascual Campoy
Sensors 2024, 24(3), 777; https://doi.org/10.3390/s24030777 - 25 Jan 2024
Cited by 1 | Viewed by 969
Abstract
Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses [...] Read more.
Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses on detecting the vertices of solar panels, which provides a richer granularity than traditional approaches. Drawing inspiration from CenterNet, our architecture is optimized for embedded platforms like the NVIDIA AGX Jetson Orin, achieving close to 60 FPS at a resolution of 1024 ×1376 pixels, thus outperforming the camera’s operational frequency. Such a real-time capability is essential for efficient robotic operations in time-critical industrial asset inspection environments. The design of our model emphasizes reduced computational demand, positioning it as a practical solution for real-world deployment. Additionally, the integration of active learning strategies promises a considerable reduction in annotation efforts and strengthens the model’s operational feasibility. In summary, our research emphasizes the advantages of keypoint-based object detection, offering a practical and effective approach for real-time solar farm inspections with UAVs. Full article
(This article belongs to the Special Issue Situationally Aware Mobile Robots)
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