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Robotics
Special Issues
Visual Servoing-Based Robotic Manipulation
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Visual Servoing-Based Robotic Manipulation

A Special Issue of Robotics (ISSN 2218-6581) belonging to the section "Intelligent Robots and Mechatronics".

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 11924

Editor

Dr. Naresh Marturi

E-Mail Website
Guest Editor
Extreme Robotics Laboratory (ERL), University of Birmingham, Birmingham, UK
Interests: visual servoing; robotic manipulation; grasping; telemanipulation

Special Issue Information

Dear Colleagues,

Visual servoing is an important area of research in robotics, driven by advancements in computer vision, machine learning and sensor technologies. As robots become increasingly autonomous and capable of executing complex tasks, the ability to accurately perceive and interact with their environment becomes indispensable. Visual servoing provides a powerful framework for achieving precise control and manipulation in dynamic and uncertain environments. Robots equipped with visual servoing capabilities excel in tasks such as pick-and-place operations, assembly and manipulation, and are applied across various domains, including industrial automation, healthcare and service robotics. Despite the development of general-purpose visual servoing methods, the specific requirements of different applications often necessitate specialized approaches. These requirements are influenced by variations in tasks, environmental conditions and the unique challenges inherent in each application domain. While numerous visual servoing techniques and control strategies have been proposed, there remain significant opportunities for further innovation in creating solutions that meet the specific needs of various applications. Moreover, the integration of visual feedback systems with control strategies has the potential to enhance the performance and adaptability of robotic systems, contributing to the advancement of robotic manipulation technologies.

This Special Issue aims to consolidate the latest research and advancements in utilizing visual feedback for robotic manipulation tasks. Particularly, this Special Issue will explore the state-of-the-art visual servoing technologies, their practical applications in robotic manipulation, and the challenges and opportunities these developments present for future research.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Adaptive and robust visual servoing control methods;
  • Deep learning and computer vision techniques for robotic manipulation;
  • Sensor fusion and multi-modal approaches for enhanced manipulation;
  • Applications of visual servoing in industrial automation;
  • Simulation, modeling and real-world implementation of visual servoing systems;
  • Human–robot interaction and collaboration using visual feedback.

Dr. Naresh Marturi
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics 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 1800 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

  • visual servoing
  • image-based visual servoing (IBVS)
  • position-based visual servoing (PBVS)
  • 3D visual servoing
  • control algorithms
  • robotic manipulation
  • machine vision
  • adaptive visual servoing
  • robust control methods
  • sensor fusion
  • multi-sensor integration
  • deep learning for visual servoing
  • dynamic environments
  • uncalibrated visual servoing

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Published Papers (4 papers)

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Research

17 pages, 6269 KB  
Article
Robust Graph-Based Spatial Coupling of Dynamic Movement Primitives for Multi-Robot Manipulation
by Zhenxi Cui, Jiacong Chen, Xin Xu and Henry K. Chu
Robotics 2026, 15(1), 29; https://doi.org/10.3390/robotics15010029 - 22 Jan 2026
Viewed by 1399
Abstract
Dynamic Movement Primitives (DMPs) provide a flexible framework for robotic trajectory generation, offering adaptability, robustness to disturbances, and modulation of predefined motions. Yet achieving reliable spatial coupling among multiple DMPs in cooperative manipulation tasks remains a challenge. This paper introduces a graph-based trajectory [...] Read more.
Dynamic Movement Primitives (DMPs) provide a flexible framework for robotic trajectory generation, offering adaptability, robustness to disturbances, and modulation of predefined motions. Yet achieving reliable spatial coupling among multiple DMPs in cooperative manipulation tasks remains a challenge. This paper introduces a graph-based trajectory planning framework that designs dynamic controllers to couple multiple DMPs while preserving formation. The proposed method is validated in both simulation and real-world experiments on a dual-arm UR5 robot performing tasks such as soft cloth folding and object transportation. Results show faster convergence and improved noise resilience compared to conventional approaches. These findings demonstrate the potential of the proposed framework for rapid deployment and effective trajectory planning in multi-robot manipulation. Full article
(This article belongs to the Special Issue Visual Servoing-Based Robotic Manipulation)
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23 pages, 17893 KB  
Article
Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations
by Shifa Sulaiman, Amarnath Harikumar, Simon Bøgh and Naresh Marturi
Robotics 2026, 15(1), 17; https://doi.org/10.3390/robotics15010017 - 9 Jan 2026
Viewed by 1504
Abstract
Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and [...] Read more.
Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures. Full article
(This article belongs to the Special Issue Visual Servoing-Based Robotic Manipulation)
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22 pages, 4481 KB  
Article
Hybrid Deep Learning Framework for Eye-in-Hand Visual Control Systems
by Adrian-Paul Botezatu, Andrei-Iulian Iancu and Adrian Burlacu
Robotics 2025, 14(5), 66; https://doi.org/10.3390/robotics14050066 - 19 May 2025
Cited by 4 | Viewed by 4590
Abstract
This work proposes a hybrid deep learning-based framework for visual feedback control in an eye-in-hand robotic system. The framework uses an early fusion approach in which real and synthetic images define the training data. The first layer of a ResNet-18 backbone is augmented [...] Read more.
This work proposes a hybrid deep learning-based framework for visual feedback control in an eye-in-hand robotic system. The framework uses an early fusion approach in which real and synthetic images define the training data. The first layer of a ResNet-18 backbone is augmented to fuse interest-point maps with RGB channels, enabling the network to capture scene geometry better. A manipulator robot with an eye-in-hand configuration provides a reference image, while subsequent poses and images are generated synthetically, removing the need for extensive real data collection. The experimental results reveal that this enriched input representation significantly improves convergence accuracy and velocity smoothness compared to a baseline that processes real images alone. Specifically, including feature point maps allows the network to discriminate crucial elements in the scene, resulting in more precise velocity commands and stable end-effector trajectories. Thus, integrating additional, synthetically generated map data into convolutional architectures can enhance the robustness and performance of the visual servoing system, particularly when real-world data gathering is challenging. Unlike existing visual servoing methods, our early fusion strategy integrates feature maps directly into the network’s initial convolutional layer, allowing the model to learn critical geometric details from the very first stage of training. This approach yields superior velocity predictions and smoother servoing compared to conventional frameworks. Full article
(This article belongs to the Special Issue Visual Servoing-Based Robotic Manipulation)
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34 pages, 20595 KB  
Article
Collision-Free Path Planning in Dynamic Environment Using High-Speed Skeleton Tracking and Geometry-Informed Potential Field Method
by Yuki Kawawaki, Kenichi Murakami and Yuji Yamakawa
Robotics 2025, 14(5), 65; https://doi.org/10.3390/robotics14050065 - 17 May 2025
Cited by 2 | Viewed by 2627
Abstract
In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task [...] Read more.
In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments. Full article
(This article belongs to the Special Issue Visual Servoing-Based Robotic Manipulation)
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