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Machines
Special Issues
Sensing to Cognition: The Evolution of Robotic Vision
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Sensing to Cognition: The Evolution of Robotic Vision

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1132

Special Issue Editors

Prof. Dr. Xiangjun Zou

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Guest Editor
College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
Interests: precision agriculture; field robotics; machine vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaojuan Li

E-Mail Website
Guest Editor
College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
Interests: agricultural robotics
Dr. Junfeng Gao

E-Mail Website
Guest Editor
Department of Computer Science, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: computer vision; robotics perception; particularly their applications in the agri-food and healthcare domains
Dr. Fengyun Wu

E-Mail Website
Guest Editor
School of Modern Information Industry, Biovision Robotics Joint Research Laboratory, Guangzhou College of Commerce, Guangzhou 510000, China
Interests: precision agriculture; computer vision

Special Issue Information

Dear Colleagues,

Robotic vision has evolved from basic sensing to advanced cognitive functions, such as object recognition, scene understanding, and autonomous decision-making. Powered by deep learning and visual reasoning, robots are now integral to tasks in autonomous navigation, industrial automation, and human–robot interaction. This Special Issue focuses on recent advances that bridge low-level perception and high-level cognition, especially in dynamic, unstructured environments. Particular attention is given to embodied visual perception, where perception is shaped by physical interaction with the environment, and to multi-robot visual collaboration, enabling distributed robotic systems to jointly interpret and respond to their surroundings. We invite original research and reviews in areas including deep/reinforcement learning for robotic perception; 3D vision and spatial reasoning; visual understanding in human–robot interaction; vision-based scene analysis and decision-making; embodied perception; and multi-robot coordination. This Special Issue seeks to foster interdisciplinary exchange and highlight innovations that drive intelligent visual systems from perception to cognition.

Prof. Dr. Xiangjun Zou
Prof. Dr. Xiaojuan Li
Dr. Junfeng Gao
Dr. Fengyun Wu
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 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. Machines is an international peer-reviewed open access monthly 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

  • robotic vision
  • deep learning for visual perception
  • reinforcement learning in robotics
  • 3D perception and spatial reasoning
  • scene understanding
  • visual reasoning and cognition
  • autonomous navigation
  • embodied perception
  • multi-robot visual collaboration

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Published Papers (1 paper)

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Research

18 pages, 3663 KB  
Article
Trajectory Tracking Control of a Six-Axis Robotic Manipulator Based on an Extended Kalman Filter-Based State Observer
by Jianxuan Liu, Tao Chen, Zhen Dou, Xiaojuan Li and Xiangjun Zou
Machines 2026, 14(1), 78; https://doi.org/10.3390/machines14010078 - 8 Jan 2026
Viewed by 597
Abstract
To achieve high-precision trajectory tracking for multi-joint robotic manipulators in the presence of model uncertainties, external disturbances, and strong coupling effects, this paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme incorporating an extended Kalman filter-based disturbance observer. First, the Kalman [...] Read more.
To achieve high-precision trajectory tracking for multi-joint robotic manipulators in the presence of model uncertainties, external disturbances, and strong coupling effects, this paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme incorporating an extended Kalman filter-based disturbance observer. First, the Kalman filter is combined with an extended state observer to perform the real-time observation of both internal and external disturbances in the system, accurately estimating system uncertainty and external disturbances. This approach reduces noise interference while significantly improving the correction accuracy of position and tracking errors. Second, an improved nonsingular fast terminal sliding mode controller with an optimized convergence law is introduced to ensure stability during the tracking process, effectively mitigate oscillation phenomena, and accelerate the system’s convergence speed. Finally, the convergence of the proposed method is analyzed by constructing an appropriate Lyapunov function. Simulation and experimental results strongly validate the superior performance of the proposed control strategy, demonstrating that the system can achieve high-precision trajectory tracking under the complex coupled effects of a six-axis robotic manipulator, and exhibits significant advantages in terms of accuracy and robustness. Full article
(This article belongs to the Special Issue Sensing to Cognition: The Evolution of Robotic Vision)
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