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New Trends in Robot Vision Sensors and System
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New Trends in Robot Vision Sensors and System

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1882

Special Issue Editor

Dr. Tao Ye

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing 100083, China
Interests: artificial intelligence; computer vision; pattern recognition; robot vision navigation

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "New Trends in Robot Vision Sensors and System", aims to showcase the latest advancements and innovative applications of robot vision technologies, with a particular focus on their impact in challenging environments such as coal mining. It seeks to explore how novel vision sensors, advanced perception algorithms, and integrated system designs are enhancing robotic capabilities in object detection, scene understanding, and autonomous navigation. Topics of interest include intelligent sensing for specialized robots, multi-sensor fusion, 3D vision, SLAM in complex and unstructured environments, and the integration of AI for robust visual perception. We welcome contributions that present new methodologies, system architectures, and case studies that push the boundaries of what is possible with robot vision.

Dr. Tao Ye
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • robot vision sensors
  • intelligent sensing
  • multi-sensor information fusion
  • object detection
  • SLAM
  • artificial intelligence
  • autonomous systems
  • perception
  • 3D vision
  • mining robotics

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

Published Papers (2 papers)

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Research

22 pages, 6854 KB  
Article
Vision-Based Detection of Large Coal Fragments in Fully Mechanized Mining Faces Using Adaptive Weighted Attention and Transfer Learning
by Yuan Wang, Jian Lei, Leping Li, Zhengxiong Lu, Lele Xu and Shuanfeng Zhao
Sensors 2026, 26(4), 1167; https://doi.org/10.3390/s26041167 - 11 Feb 2026
Viewed by 415
Abstract
The unloading port of a scraper conveyor is a critical component in fully mechanized mining operations and is prone to blockages caused by large coal fragments. These blockages primarily result from the limited accuracy and insufficient real-time performance of existing visual perception methods [...] Read more.
The unloading port of a scraper conveyor is a critical component in fully mechanized mining operations and is prone to blockages caused by large coal fragments. These blockages primarily result from the limited accuracy and insufficient real-time performance of existing visual perception methods used by crushing robots to identify large coal pieces in complex mining environments. To address this issue, this paper proposes a visual inspection method for coal mine crushing robots based on transfer learning and an adaptive weighted attention mechanism, termed LCDet. First, a lightweight backbone network incorporating grouped convolution is designed to enhance feature representation while significantly reducing model complexity, thereby meeting deployment requirements. Second, an adaptive weighted attention mechanism is introduced to suppress background interference and emphasize regions containing large coal fragments, particularly enhancing blurred edge textures. In addition, a transfer learning-based training strategy is adopted to improve generalization performance and reduce dependence on large-scale training data. The experimental results on the public DsLMF+ dataset demonstrate that LCDet achieves accuracy, recall, mAP50, and mAP50–95 values of 79.3%, 75.1%, 84.5%, and 56.2%, respectively, achieving a favorable balance between detection accuracy and model complexity. On a self-constructed large coal dataset, LCDet attains accuracy, recall, mAP50, and mAP50–95 of 90.4%, 91.3%, 96.5%, and 69.3%, respectively, outperforming the baseline YOLOv8n model. Compared with other detection methods, LCDet exhibits superior performance while maintaining a relatively low parameter count. These results indicate that LCDet enables lightweight and accurate detection of large coal fragments, supporting real-time deployment on crushing robots in fully mechanized mining environments. Full article
(This article belongs to the Special Issue New Trends in Robot Vision Sensors and System)
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29 pages, 13843 KB  
Article
Approach and Fork Insertion to Target Pallet Based on Image Measurement Method
by Nobuyuki Kita and Takuro Kato
Sensors 2026, 26(1), 154; https://doi.org/10.3390/s26010154 - 25 Dec 2025
Viewed by 977
Abstract
Previously, we proposed a navigation method based on image processing to move to the front of a pallet that can be detected in the field of view. We also proposed an image-processing method to estimate the inclination angle when the pallet is inclined [...] Read more.
Previously, we proposed a navigation method based on image processing to move to the front of a pallet that can be detected in the field of view. We also proposed an image-processing method to estimate the inclination angle when the pallet is inclined at a pitch with respect to the running surface, such as a pallet loaded on a truck. In this study, we improved the robustness of the existing method so that the series of operations from approach to fork insertion can be realized stably without being affected by the environment. Furthermore, a series of operations from approach to fork insertion was realized by an automatic forklift (Autonomous Guided Forklift, AGF) in an indoor laboratory space simulating a warehouse. Full article
(This article belongs to the Special Issue New Trends in Robot Vision Sensors and System)
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