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Autonomous Mobile Robotics

This special issue belongs to the section “Mechanical Engineering“.

Special Issue Information

Dear Colleagues,

Autonomous and self-navigating robots have been rising in popularity due to a push for a more technologically aided future. The applications of autonomous robots are vast and span many different fields as evidenced by recent developments such as smart mobility for intelligent transport systems, advanced manufacturing technologies for future industry, mini-UAVs for missions such as monitoring large infrastructures and search and rescue applications, and applications in the fields of agriculture and ocean engineering. These autonomous robots, often heterogeneous in terms of shape, energy autonomy, and computing capabilities, are increasingly evolving in open, complex, dynamic environments, and they are interacting with humans. In addition, the convergence of massive databases, important embedded computing capabilities and new paradigms of artificial intelligence have given robotics systems greater autonomy and reasoning capabilities.

This Special Issue aims to collect the most recent studies and applications on deep learning techniques and mobile robotics. Topics will include, but are not strictly limited to

  • Advanced machine learning techniques for SLAM;
  • Deep fusion architectures for robotic perception sensors;
  • Emergent sensing capabilities for mobile robotics;
  • Deep reinforcement learning for mobile robots' navigation;
  • Dynamic scene analysis and multi-object detection and tracking;
  • Real-time inference and hardware implementation;
  • Data-driven navigation and control;
  • Autonomous mobile robots’ applications on ground, air, and water…

Prof. Vincent Frémont
Dr. Sergio Alberto Rodriguez Florez
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 2400 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.

Keywords

  • Advanced machine learning techniques for SLAM
  • Deep fusion architectures for robotic perception sensors
  • Emergent sensing capabilities for mobile robotics
  • Deep reinforcement learning for mobile robots' navigation
  • Dynamic scene analysis and multi-object detection and tracking
  • Real-time inference and hardware implementation
  • Data-driven navigation and control
  • Autonomous mobile robots’ applications on ground, air, and water…

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Appl. Sci. - ISSN 2076-3417