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Electronics
Special Issues
Advancements in Robotics: Perception, Manipulation, and Interaction
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Advancements in Robotics: Perception, Manipulation, and Interaction

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 1830

Special Issue Editors

Dr. Peng Zhou

E-Mail Website
Guest Editor
Computer Science Department, The University of Hong Kong, Hong Kong 999077, China
Interests: robot manipulation; robot learning; task and motion planning
Dr. Anqing Duan

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: learning by demonstration; reinforcement learning; rehabilitation robotics
Dr. Liang Lu

E-Mail Website
Guest Editor
Computer Science Department, The University of Hong Kong, Hong Kong 999077, China
Interests: active perception; scene understanding; field robotics
Dr. Jiajun Xu

E-Mail Website
Guest Editor
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: rehabilitation robotics; biomimetic robotics; human-robot interaction control; machine learning
Dr. Wanyu Ma

E-Mail Website
Guest Editor
Faculty of Engineering, The Chinese University of Hong Kong, The Chinese University of Hong Kong, Hong Kong 999077, China
Interests: robot manipulation; machine intelligence; human-robot interaction
Dr. David Navarro-Alarcon

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: robotics; control systems

Special Issue Information

Dear Colleagues,

The realm of robotics has witnessed profound advancements, expanding its reach into numerous fields where the synergy between human and machine is paramount. Innovations in robotic perception, manipulation, and interaction have not only propelled the capabilities of robots but have also revolutionized the way they collaborate with humans in diverse environments. To capture and disseminate the latest breakthroughs in this dynamic domain, we are organizing a Special Issue titled "Advancements in Robotics: Perception, Manipulation, and Interaction" with Electronics.

This Special Issue aims to explore the cutting-edge developments in robotic systems that enhance sensory perception, refine manipulation skills, and enrich interactive experiences. We are calling for contributions that shed light on the theoretical underpinnings, practical applications, and transformative impacts of these advancements. We welcome submissions that delve into the implementation, deployment, and real-world outcomes of novel robotic technologies.

Authors are encouraged to submit original research that encapsulates a broad spectrum of topics within the scope of robotic advancements. The areas of interest for this Special Issue include, but are not limited to, the following:      

  • Sensor technologies and perception algorithms for robotics;
  • Robotic manipulation in unstructured or dynamic environments;
  • Human–robot interaction and collaboration;
  • Machine learning and AI applications in robotic systems;
  • Autonomous decision making and control in robotics;
  • Robotic assistance in healthcare, manufacturing, and service sectors;
  • Ethical considerations and social impacts of robotic interactions;
  • Integration of robotics with IoT and smart infrastructure;
  • Advances in robotic mobility and dexterity;
  • Haptic feedback and teleoperation in robotic systems;
  • Robotics in extreme or hazardous environments;
  • Personalized and adaptive robotic assistance.

We are looking forward to your valuable contributions to this Special Issue, which we believe will serve as a pivotal platform for academics, researchers, industrial experts, and practitioners to exchange insights, foster collaborations, and advance the state of the art in robotics.

Dr. Peng Zhou
Dr. Anqing Duan
Dr. Liang Lu
Dr. Jiajun Xu
Dr. Wanyu Ma
Dr. David Navarro-Alarcon
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. Electronics 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

  • robotics and AI robot perception
  • active perception
  • multi-modal perception
  • scene understanding
  • field robotics
  • robot grasping
  • robot manipulation
  • robot learning
  • task and motion planning
  • rehabilitation robotics
  • biomimetic robotics
  • human–robot interaction control

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Published Papers (2 papers)

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Research

31 pages, 12314 KiB  
Article
Utilizing Attention-Enhanced Deep Neural Networks for Large-Scale Preliminary Diabetes Screening in Population Health Data
by Hongwei Hu, Wenbo Dong, Jianming Yu, Shiyan Guan and Xiaofei Zhu
Electronics 2024, 13(21), 4177; https://doi.org/10.3390/electronics13214177 - 24 Oct 2024
Viewed by 598
Abstract
Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an [...] Read more.
Early screening for diabetes can promptly identify potential early stage patients, possibly delaying complications and reducing mortality rates. This paper presents a novel technique for early diabetes screening and prediction, called the Attention-Enhanced Deep Neural Network (AEDNN). The proposed AEDNN model incorporates an Attention-based Feature Weighting Layer combined with deep neural network layers to achieve precise diabetes prediction. In this study, we utilized the Diabetes-NHANES dataset and the Pima Indians Diabetes dataset. To handle significant missing values and outliers, group median imputation was applied. Oversampling techniques were used to balance the diabetes and non-diabetes groups. The data were processed through an Attention-based Feature Weighting Layer for feature extraction, producing a feature matrix. This matrix was subjected to Hadamard product operations with the raw data to obtain weighted data, which were subsequently input into deep neural network layers for training. The parameters were fine-tuned and the L2 regularization and dropout layers were added to enhance the generalization performance of the model. The model’s reliability was thoroughly assessed through various metrics, including the accuracy, precision, recall, F1 score, mean squared error (MSE), and R2 score, as well as the ROC and AUC curves. The proposed model achieved a prediction accuracy of 98.4% in the Pima Indians Diabetes dataset. When the test dataset was expanded to the large-scale Diabetes-NHANES dataset, which contains 52,390 samples, the test precision of the model improved further to 99.82%, with an AUC of 0.9995. A comparative analysis was conducted using multiple models, including logistic regression with L1 regularization, support vector machine (SVM), random forest, K-nearest neighbors (KNNs), AdaBoost, XGBoost, and the latest semi-supervised XGBoost. The feature extraction method using attention mechanisms was compared with the classical feature selection methods, Lasso and Ridge. The experiments were performed on the same dataset, and the conclusion was that the Attention-based Ensemble Deep Neural Network (AEDNN) outperformed all the aforementioned methods. These results indicate that the model not only performs well on smaller datasets but also fully leverages its advantages on larger datasets, demonstrating strong generalization ability and robustness. The proposed model can effectively assist clinicians in the early screening of diabetes patients. This is particularly beneficial for the preliminary screening of high-risk individuals in large-scale, extensive healthcare datasets, followed by detailed examination and diagnosis. Compared to the existing methods, our AEDNN model showed an overall performance improvement of 1.75%. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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19 pages, 4224 KiB  
Article
A Rigid–Flexible Supernumerary Robotic Arm/Leg: Design, Modeling, and Control
by Jiajun Xu, Mengcheng Zhao, Tianyi Zhang and Aihong Ji
Electronics 2024, 13(20), 4106; https://doi.org/10.3390/electronics13204106 - 18 Oct 2024
Viewed by 703
Abstract
As humans’ additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed [...] Read more.
As humans’ additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed to develop SRLs simultaneously realizing flexible operation and stable support. In this paper, an SRL with FRAs was designed and implemented. The analytic model of the FRA was established to formulate the movement trajectory and stiffness profile of the SRL. A hierarchical hidden Markov model (HHMM) was proposed to recognize the wearer’s motion intention and control the SRL to complete the specific working mode and motion type. Experiments were conducted to exhibit the feasibility and superiority of the proposed robot. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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