Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2019

Special Issue Editors

Dr. Chongkun Xia

E-Mail Website
Guest Editor
School of advanced manufacturing,Sun Yat-sen University, Shenzhen 518107, China
Interests: cable-driven flexible robotic arm; visual–tactile sensors; multimodal perception and manipulation; robotic dexterous hand
Prof. Dr. Xueqian Wang

E-Mail Website
Guest Editor
Shenzhen Graduate School of Tsinghua University, Shenzhen 518055, China
Interests: intelligent robots; including dynamics and control; teleoperation; deep reinforcement learning; fault diagnosis
Dr. Yajing Shen

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, HK 999077, Hong Kong
Interests: artificial intelligence; biomedical engineering (BME); motion control and manufacturing; robotics; sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of sensors ( vision, tactile sensing, etc.) is revolutionizing embodied intelligence in robotics, particularly in achieving human-level dexterous manipulation. For example, vision provides essential spatial cognition, while ​tactile sensing offers physical interaction intelligence that is fundamentally inaccessible to optical systems. In addition, recent breakthroughs in the AI-driven multimodal fusion of advanced sensors are overcoming longstanding challenges in adaptive grasping, deformable object manipulation, and real-time interaction with dynamic environments.

This Special Issue seeks research investigating ​transformative sensing technologies that bridge the perception–action gap, with emphasis on vision–touch co-design frameworks and their applications in closed-loop manipulation. We welcome contributions spanning from fundamental sensor innovations to applications in medical robotics, industrial automation, and collaborative robots. Priority will be given to solutions demonstrating both theoretical advancement and practical validation, including the following：

  • Tactile sensing technologies and applications;
  • Advanced sensor design and perception methods;
  • Sensor fusion for tactile and visual feedback;
  • Tactile sensors in dexterous manipulation;
  • Advances in perception and control technologies for robotic systems;
  • Sensor fusion in healthcare, service, and industrial robotics;
  • AI and LLM in sensor fusion;
  • Multi-modal sensor fusion for grasping and manipulation.

Dr. Chongkun Xia
Prof. Dr. Xueqian Wang
Dr. Yajing Shen
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. Sensors 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 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.

Keywords

  • intelligent robotics
  • robotic sensing
  • sensor fusion
  • AI manipulation
  • grasping control
  • visual sensing
  • tactile sensing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 2934 KB  
Article
A Universal Tool Interaction Force Estimation Approach for Robotic Tool Manipulation
by Diyun Wen, Jiangtao Xiao, Yu Xie, Tao Luo, Jinhui Zhang and Wei Zhou
Sensors 2025, 25(21), 6619; https://doi.org/10.3390/s25216619 (registering DOI) - 28 Oct 2025
Abstract
The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge [...] Read more.
The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with x, y, and z axes and 0.03 Nm with τx, τy, and τz axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly. Full article
(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)
►▼ Show Figures

Figure 1

20 pages, 8723 KB  
Article
A Sensor Based Waste Rock Detection Method in Copper Mining Under Low Light Environment
by Jianing Ding, Fuming Qu, Weihua Zhou, Jiajun Xu, Lingyu Zhao and Yaming Ji
Sensors 2025, 25(19), 5961; https://doi.org/10.3390/s25195961 - 25 Sep 2025
Viewed by 553
Abstract
During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine [...] Read more.
During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine waste rock is inherently complex, particularly within the conveyor belt section of the sorting machine, where insufficient and uneven lighting significantly impairs the performance of vision-based detection systems. To address the above challenges, a deep-learning-based copper mine waste rock detection algorithm under low-light environments is proposed. Firstly, an Illumination Adaptive Transformer (IAT) module is added as a preprocessing layer at the beginning of the Backbone to enhance the brightness of the images acquired by the vision sensor. Secondly, a Local Enhancement-Global Modulation (LEGM) module is integrated after the A2C2f and C3k2 modules in the Neck to enhance the detection accuracy. Finally, to further improve the model performance, MPDIoU is introduced to optimize the original loss function CIoU. As a result, the proposed algorithm achieved an mAP@0.5 of 0.957 and an mAP@0.5:0.95 of 0.689, outperforming advanced methods by 1.9% and 8.6%, respectively. Full article
(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)
►▼ Show Figures

Figure 1

Review

Jump to: Research

43 pages, 3753 KB  
Review
Comprehensive Review of Deep Learning Approaches for Single-Image Super-Resolution
by Zirun Liu, Shijie Jiang, Shuhan Feng, Qirui Song and Ji Zhang
Sensors 2025, 25(18), 5768; https://doi.org/10.3390/s25185768 - 16 Sep 2025
Viewed by 1009
Abstract
Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on deep learning, proposes a method-oriented classification framework, and [...] Read more.
Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on deep learning, proposes a method-oriented classification framework, and explores it from three aspects: theoretical basis, technological evolution, and domain-specific applications. Firstly, the basic concepts, development trajectory, and practical value of SISR are introduced. Secondly, in-depth research is conducted on key technical components, including benchmark dataset construction, a multi-scale upsampling strategy, objective function optimization, and quality assessment indicators. Thirdly, some classic SISR model reconstruction results are listed and compared. Finally, the limitations of SISR research are pointed out, and some prospective research directions are proposed. This article provides a systematic knowledge framework for researchers and offers important reference value for the future development of SISR. Full article
(This article belongs to the Special Issue Advanced Sensors for Intelligent Robotic Systems: Vision, Touch, and Dexterous Manipulation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop