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Intelligent Sensing for Robotic Control and Visual Perception
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Intelligent Sensing for Robotic Control and Visual Perception

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 560

Special Issue Editor

Prof. Dr. Adrian Burlacu

E-Mail Website
Guest Editor
Department of Automatic Control and Applied Informatics, Gheorghe Asachi Technical University of Iasi, 70050 Iasi, Romania
Interests: intelligent robotic systems; optimisation; modelling; computer vision; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vision is one of the most powerful awareness extensions that can be integrated into a system. When coupled with modern intelligent techniques, artificial vision systems achieve far greater robustness and adaptability.

This Special Issue, ‘Intelligent Sensing for Robotic Control and Visual Perception’, will highlight recent advances in sensor technologies and perception algorithms that enable robots to understand and interact with complex, dynamic environments. We seek contributions on novel sensors, multimodal fusion, event-based and vision-based perception, learning-based control, and system-level integration for real-time navigation, manipulation, human–robot interaction, and mixed reality applications. Submissions may include theoretical developments, experimental systems, and application-driven demonstrations that advance robust, adaptive robotic sensing and control.

Prof. Dr. Adrian Burlacu
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

  • intelligent sensing
  • sensor fusion
  • visual perception/computer vision/mixed reality
  • event-based vision
  • deep
  • learning for perception and control
  • SLAM, localization, and mapping
  • tactile and proprioceptive sensing
  • real-time perception and control
  • human–robot interaction

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Published Papers (2 papers)

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Research

23 pages, 32417 KB  
Article
Vision-Based Person-Following Algorithm for Assistive Elderly-Care Quadruped Robots
by Vishnudev Kurumbaparambil, Subashkumar Rajanayagam and Stefan Twieg
Sensors 2026, 26(10), 3263; https://doi.org/10.3390/s26103263 - 21 May 2026
Abstract
The demographic shift towards an aging population necessitates innovative solutions for care and mobility support. While commercial quadruped robots like the Unitree Go1 offer dynamic stability, their native following modes often lack the safety margins and predictability required, and they do not consistently [...] Read more.
The demographic shift towards an aging population necessitates innovative solutions for care and mobility support. While commercial quadruped robots like the Unitree Go1 offer dynamic stability, their native following modes often lack the safety margins and predictability required, and they do not consistently follow the user, at times deviating and navigating independently. This paper presents a robust, vision-based, person-following algorithm designed to address these limitations. Utilizing a ZED 2 stereo camera and Robot Operating System (ROS), the system employs a finite state machine to ensure deterministic target tracking. A velocity control strategy partitions the robot’s motion into distinct stability, proportional, and braking zones based on depth data to ensure fluid interaction. The framework was validated on a Unitree Go1 quadruped platform in an outdoor environment involving 90-degree turns to evaluate tracking robustness. By operating in a headless mode, the system achieved a mean processing latency of 66.5±4.3 ms. Experimental results demonstrated consistent operational stability, 0.0% intrusion into the intimate safety zone, and effective velocity synchronization between 0.47 and 0.54 m/s. While this study establishes a robust technical baseline using healthy subjects, it serves as a preliminary development platform; further iterative testing with elderly users in clinical settings is required to move toward deployment. Beyond the evaluated trials, the framework maintained reliable functional performance across various care facility workshops, successfully following the target in all deployment scenarios. These findings establish a stable technical foundation for the future development of robotic walking partners. Full article
(This article belongs to the Special Issue Intelligent Sensing for Robotic Control and Visual Perception)
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29 pages, 11849 KB  
Article
Hi-RAGrasp: A Human-in-the-Loop Experience-Augmented Method for Task-Oriented Grasping
by Yaxin Liu, Yue Hu, Yan Liu and Ming Zhong
Sensors 2026, 26(10), 3221; https://doi.org/10.3390/s26103221 - 19 May 2026
Abstract
With the growing demand for assistive robots in aging societies, task-oriented grasping in household environments has become increasingly important. Compared with structured industrial settings, household scenarios are characterized by diverse objects, unstructured layouts, and strong variability in task semantics. However, traditional methods focus [...] Read more.
With the growing demand for assistive robots in aging societies, task-oriented grasping in household environments has become increasingly important. Compared with structured industrial settings, household scenarios are characterized by diverse objects, unstructured layouts, and strong variability in task semantics. However, traditional methods focus on geometric stability and fail to capture task-relevant semantic constraints on manipulation regions, while existing approaches suffer from unstable reasoning and lack effective mechanisms for incorporating human intervention into the reasoning process. To address these challenges, we propose Hi-RAGrasp, a task-oriented grasping framework that integrates progressive multi-stage reasoning, Human-in-the-Loop (HITL) interaction, and Retrieval-Augmented Generation (RAG). A coarse-to-fine pipeline progressively refines predictions from object-level localization to part-level grounding, enabling robust mapping from human instructions to fine-grained task-relevant regions. Meanwhile, a HITL correction mechanism and a structured human experience database are introduced and combined with RAG to form a unified paradigm that aligns with prior experience when available and falls back to reasoning otherwise, enabling experience reuse and future experience accumulation without retraining. In addition, a Geometric Heuristic Segmentation (GHS) method is proposed to improve task-relevant region localization for textureless objects. Experiments show that our method achieves a segmentation success rate of 77.73% on the evaluation dataset and a grasp success rate of 75% in real-world scenarios, significantly outperforming existing methods and demonstrating strong effectiveness and practicality in open environments. Full article
(This article belongs to the Special Issue Intelligent Sensing for Robotic Control and Visual Perception)
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