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Intelligent Sensing for Robotic Control and Visual Perception

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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 2899

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Prof. Dr. Adrian Burlacu

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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
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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

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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 (4 papers)

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Research

24 pages, 5556 KB

Open AccessArticle

MVO: A Magneto-Visual Odometry System for Indoor Positioning

by Tongxing Peng, Chao Ming, Zhengpeng Yang, Huaiyan Wang, Jiyan Yu and Xiaoming Wang

Sensors 2026, 26(14), 4555; https://doi.org/10.3390/s26144555 - 17 Jul 2026

Viewed by 523

Abstract

High-precision and resilient indoor positioning is a fundamental requirement for the autonomous operation of mobile robots in GNSS-denied environments. While visual sensors are commonly used for odometry, their operational reliability can be compromised in challenging scenarios such as drastic illumination fluctuations and sparse-textured environments. To address these sensor limitations, this study presents MVO, a magneto-visual odometry framework that explores indoor magnetic field anomalies as complementary constraints for visual odometry. By integrating a 30-magnetometer planar array model with a stereo camera, the proposed system establishes a multi-modal perception framework for indoor spaces. In the frontend, magnetic field gradient information is utilized to provide relative-pose constraints, which assist in the matching of image feature points and help maintain tracking continuity under visual degradation. In the backend, a factor graph optimization (FGO) framework incorporates magnetic relative-pose factors and visual reprojection factors into a unified optimization objective, which is then solved using the incremental smoothing and mapping 2 (iSAM2) algorithm. Frontend-level simulations are conducted to analyze the effects of magnetometer spatial configuration, sensor number, and calibration-error sensitivity on magnetic relative-pose estimation and covariance. Trajectory-level evaluations are further performed on the EuRoC dataset augmented with high-fidelity synthesized magnetic field data, including localization accuracy and computational load. Under this synthesized magnetic field setting, MVO shows improved localization accuracy and moderate computational load compared with the selected MSCKF-Stereo and VINS-Fusion reference baselines. These results provide a simulation-based feasibility validation of integrating magnetic field constraints with visual information for indoor odometry, while validation with real magnetometer array measurements remains future work. Full article

(This article belongs to the Special Issue Intelligent Sensing for Robotic Control and Visual Perception)

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15 pages, 4825 KB

Open AccessArticle

Integrating Visual Perception and Control Strategies in Custom Omnidirectional Mobile Robots

by Radu-Laurențiu Roșca, Andrei-Iulian Iancu, Adrian Burlacu and Cătălin Dosoftei

Sensors 2026, 26(12), 3918; https://doi.org/10.3390/s26123918 - 20 Jun 2026

Viewed by 388

Abstract

Autonomous mobile robots are used in optimizing warehouse logistics, yet achieving precise positioning during docking maneuvers and autonomous planning remains a technical challenge. This study presents a custom vision-based control system designed for an autonomous omnidirectional wheeled robot. The proposed methodology acquires visual feedback using a stereo camera integrated within the Robot Operating System framework. Two visual feedback control laws are formulated and rigorously evaluated: a Classic Position-Based Visual Servoing algorithm, which minimizes pose error using a quaternion-based approach, and a second solution that utilizes Dual Lie Algebra to compute the 3D visual sensor’s velocities, ensuring convergence towards the desired point-feature configuration. Experimental validation reveals that while both methods achieve docking, the dual pose-free approach enables more robust, effortless movement of the robot platform than Classic Position-Based Visual Servoing. Consequently, these findings indicate that integrating depth-based feature recovery with advanced algebraic strategies offers a stable control strategy for automated industrial scenarios. Full article

(This article belongs to the Special Issue Intelligent Sensing for Robotic Control and Visual Perception)

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23 pages, 32417 KB

Open AccessArticle

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

Viewed by 724

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 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 \pm 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

Open AccessArticle

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

Viewed by 651

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 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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