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Applied Sciences
Special Issues
Intelligent Robotic Systems: New Trends and Challenges
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Intelligent Robotic Systems: New Trends and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 614

Special Issue Editors

Dr. Félix Escalona Moncholí

E-Mail Website
Guest Editor
University Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, Spain
Interests: computer vision; deep learning; 3D object recognition; mapping; navigation; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Gomez-Donoso

E-Mail Website
Guest Editor
University Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, Spain
Interests: computer vision; deep learning; 3D object recognition; mapping; navigation; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Miguel Angel Cazorla

E-Mail Website
Guest Editor
University Institute for Computer Research, University of Alicante, P.O. Box 99, 03080 Alicante, Spain
Interests: computer vision; deep learning; 3D object recognition; mapping; navigation; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the cutting-edge advancements, emerging trends, and persistent challenges in the field of intelligent robotic systems. With rapid technological advancements, the integration of artificial intelligence, machine learning, and advanced sensing capabilities has transformed the landscape of robotic systems. This proposal seeks to bring together leading researchers and practitioners to contribute original research articles, reviews, and case studies that delve into the diverse facets of intelligent robotic systems.

This Special Issue will cover a broad spectrum of topics, including but not limited to:

  • Cognitive Robotics.
  • Human–Robot Interaction.
  • Autonomous Systems.
  • Machine Learning in Robotics.
  • Swarm Robotics.
  • Robotic Sensing and Perception.

Dr. Félix Escalona Moncholí
Dr. Francisco Gomez-Donoso
Prof. Dr. Miguel Angel Cazorla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • cognitive robotics
  • human-robot interaction
  • autonomous systems
  • machine learning
  • artificial intelligence
  • swarm robotics
  • robotic sensing
  • robotic perception

Published Papers (1 paper)

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26 pages, 6602 KiB  
Article
FAGD-Net: Feature-Augmented Grasp Detection Network Based on Efficient Multi-Scale Attention and Fusion Mechanisms
by Xungao Zhong, Xianghui Liu, Tao Gong, Yuan Sun, Huosheng Hu and Qiang Liu
Appl. Sci. 2024, 14(12), 5097; https://doi.org/10.3390/app14125097 - 12 Jun 2024
Viewed by 375
Abstract
Grasping robots always confront challenges such as uncertainties in object size, orientation, and type, necessitating effective feature augmentation to improve grasping detection performance. However, many prior studies inadequately emphasize grasp-related features, resulting in suboptimal grasping performance. To address this limitation, this paper proposes [...] Read more.
Grasping robots always confront challenges such as uncertainties in object size, orientation, and type, necessitating effective feature augmentation to improve grasping detection performance. However, many prior studies inadequately emphasize grasp-related features, resulting in suboptimal grasping performance. To address this limitation, this paper proposes a new grasping approach termed the Feature-Augmented Grasp Detection Network (FAGD-Net). The proposed network incorporates two modules designed to enhance spatial information features and multi-scale features. Firstly, we introduce the Residual Efficient Multi-Scale Attention (Res-EMA) module, which effectively adjusts the importance of feature channels while preserving precise spatial information within those channels. Additionally, we present a Feature Fusion Pyramidal Module (FFPM) that serves as an intermediary between the encoder and decoder, effectively addressing potential oversights or losses of grasp-related features as the encoder network deepens. As a result, FAGD-Net achieved advanced levels of grasping accuracy, with 98.9% and 96.5% on the Cornell and Jacquard datasets, respectively. The grasp detection model was deployed on a physical robot for real-world grasping experiments, where we conducted a series of trials in diverse scenarios. In these experiments, we randomly selected various unknown household items and adversarial objects. Remarkably, we achieved high success rates, with a 95.0% success rate for single-object household items, 93.3% for multi-object scenarios, and 91.0% for cluttered scenes. Full article
(This article belongs to the Special Issue Intelligent Robotic Systems: New Trends and Challenges)
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