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Action Recognition via Multi-View Perception Feature Tracking for Human–Robot Interaction
Exploiting a Variable-Sized Map and Vicinity-Based Memory for Dynamic Real-Time Planning of Autonomous Robots
DUnE: A Versatile Dynamic Unstructured Environment for Off-Road Navigation
Robotic Motion Intelligence Using Vector Symbolic Architectures and Blockchain-Based Smart Contracts
Non-Holonomic Mobile Manipulator Obstacle Avoidance with Adaptive Prioritization

Journal Description

Robotics

Robotics is an international, peer-reviewed, open access journal on robotics published monthly online by MDPI. The IFToMM is affiliated with Robotics and its members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Robotics) / CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.9 (2023); 5-Year Impact Factor: 3.1 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2218-6581

Latest Articles

28 pages, 7611 KiB

Open AccessArticle

Design and Experimental Study of a Robotic System for Target Point Manipulation in Breast Procedures

by Bing Li, Hafiz Muhammad Muzzammil, Junwu Zhu and Lipeng Yuan

Robotics 2025, 14(6), 78; https://doi.org/10.3390/robotics14060078 - 2 Jun 2025

To achieve obstacle-avoiding puncture in breast interventional surgery, a robotics system based on three-fingered breast target-point manipulation is proposed and designed. Firstly, based on the minimum number of control points required for three-dimensional breast deformation control and the bionic structure of the human [...] Read more.

To achieve obstacle-avoiding puncture in breast interventional surgery, a robotics system based on three-fingered breast target-point manipulation is proposed and designed. Firstly, based on the minimum number of control points required for three-dimensional breast deformation control and the bionic structure of the human hand, the structure and control scheme of the robotics system based on breast target-point manipulation are proposed. Additionally, the workspace of the robotics system is analyzed. Then, an optimal control point selection method based on the minimum resultant force principle is proposed to achieve precise manipulation of the breast target point. Concurrently, a breast soft tissue manipulation framework incorporating a Model Reference Adaptive Control (MRAC) system is developed to enhance operational accuracy. A dynamic model of breast soft tissue is developed by using the manipulative force–displacement data obtained during the process of manipulating breast soft tissue with mechanical fingers to realize the manipulative force control of breast tissue. Finally, through simulation and experiments on breast target-point manipulation tasks, the results show that this robotic system can achieve spatial control of breast positioning at arbitrary points. Meanwhile, the robotic system proposed in this study demonstrates high-precision control with an accuracy of approximately 1.158 mm (standard deviation: 0.119 mm), fulfilling the requirements for clinical interventional surgery in target point manipulation. Full article

(This article belongs to the Section Medical Robotics and Service Robotics)

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16 pages, 605 KiB

Open AccessArticle

Kriging-Variance-Informed Multi-Robot Path Planning and Task Allocation for Efficient Mapping of Soil Properties

by Laurence Roberts-Elliott, Gautham P. Das and Grzegorz Cielniak

Robotics 2025, 14(6), 77; https://doi.org/10.3390/robotics14060077 - 31 May 2025

One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low [...] Read more.

One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates the sampling, coupled with a dynamic sampling strategy informed by Kriging variance from interpolation. This strategy aims to reduce the number of samples needed for accurate mapping by exploring and sampling areas that maximise information gained per sample. The key innovative contributions include (1) a novel Distance Over Variance (DOV) bid calculation for auction-based multi-robot task allocation, which incentivises sampling in high-uncertainty, nearby areas; (2) integration of the DOV bid calculation into the cheapest insertion heuristic for task queuing; and (3) thresholding of newly created tasks at locations with low Kriging variance to drop those unlikely to offer significant information gain. The proposed methods were evaluated through comparative simulated experiments using historical soil compaction data. Evaluation trials demonstrate the suitability of the DOV bid calculation combined with task dropping, resulting in substantial improvements in key performance metrics, including mapping accuracy. While the experiments were conducted in simulation, the system is compatible with ROS and the ‘move_base’ action client to allow real-world deployment. The results from these simulations indicate that the Kriging-variance-informed approach can be applied to the exploration and mapping of other soil properties (e.g., pH, soil organic carbon, etc.) and environmental data. Full article

(This article belongs to the Special Issue Autonomous Robotics for Exploration)

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28 pages, 6914 KiB

Open AccessArticle

Guided Reinforcement Learning with Twin Delayed Deep Deterministic Policy Gradient for a Rotary Flexible-Link System

by Carlos Saldaña Enderica, José Ramon Llata and Carlos Torre-Ferrero

Robotics 2025, 14(6), 76; https://doi.org/10.3390/robotics14060076 - 31 May 2025

This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear quadratic regulator (LQR) acting as a [...] Read more.

This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a linear quadratic regulator (LQR) acting as a guiding controller during training. Flexible-link mechanisms common in advanced robotics and aerospace systems exhibit oscillatory behavior that complicates precise control. To address this, the system is first identified using experimental input-output data from a Quanser^® virtual plant, generating an accurate state-space representation suitable for simulation-based policy learning. The hybrid control strategy enhances sample efficiency and accelerates convergence by incorporating LQR-generated trajectories during TD3 training. Internally, the TD3 agent benefits from architectural features such as twin critics, delayed policy updates, and target action smoothing, which collectively improve learning stability and reduce overestimation bias. Comparative results show that the guided TD3 controller achieves superior performance in terms of vibration damping, transient response, and robustness, when compared to conventional LQR, fuzzy logic, neural networks, and GA-LQR approaches. Although the controller was validated using a high-fidelity digital twin, it has not yet been deployed on the physical plant. Future work will focus on real-time implementation and structural robustness testing under parameter uncertainty. Overall, this research demonstrates that guided reinforcement learning can yield stable and interpretable policies that comply with classical control criteria, offering a scalable and generalizable framework for intelligent control of flexible mechanical systems. Full article

(This article belongs to the Section Industrial Robots and Automation)

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44 pages, 3893 KiB

Open AccessSystematic Review

Task Scheduling with Mobile Robots—A Systematic Literature Review

by Catarina Rema, Pedro Costa, Manuel Silva and Eduardo J. Solteiro Pires

Robotics 2025, 14(6), 75; https://doi.org/10.3390/robotics14060075 - 30 May 2025

The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also [...] Read more.

The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots. Full article

(This article belongs to the Section Industrial Robots and Automation)

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37 pages, 13864 KiB

Open AccessArticle

LSTM-Enhanced Deep Reinforcement Learning for Robust Trajectory Tracking Control of Skid-Steer Mobile Robots Under Terra-Mechanical Constraints

by Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez and Alvaro Javier Prado Romo

Robotics 2025, 14(6), 74; https://doi.org/10.3390/robotics14060074 - 29 May 2025

Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical [...] Read more.

Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical constraints. Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor–Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. To address the inherent partial observability in real-world navigation, this study presents an original approach that integrates Long Short-Term Memory (LSTM) networks into DRL-based controllers. This allows control agents to retain and leverage temporal dependencies to infer unobservable system states. The developed agents were trained and tested in simulations and then assessed in field experiments under uneven terrain and dynamic model parameter changes that lead to traction losses in mining environments, targeting various trajectory tracking tasks, including lemniscate and squared-type reference trajectories. This contribution strengthens the robustness and adaptability of DRL agents by enabling better generalization of learned policies compared with their baseline counterparts, while also significantly improving trajectory tracking performance. In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. Furthermore, DDPG-LSTM and TD3-LSTM reduced their control effort through the total variation in control input by 15% and 20% compared with their respective baseline controllers, respectively. Findings from this work provide valuable insights into the role of memory-augmented reinforcement learning for robust motion control in unstructured and high-uncertainty environments. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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18 pages, 2621 KiB

Open AccessArticle

May I Assist You?—Exploring the Impact of Telepresence System Design on the Social Perception of Remote Assistants in Collaborative Assembly Tasks

by Jennifer Brade, Sarah Mandl, Franziska Klimant, Anja Strobel, Philipp Klimant and Martin Dix

Robotics 2025, 14(6), 73; https://doi.org/10.3390/robotics14060073 - 28 May 2025

Remote support in general is a method that saves time and resources. A relatively new and promising technology for remote support that combines video conferencing and physical mobility is that of telepresence systems. The remote assistant, that is, the user of said technology, [...] Read more.

Remote support in general is a method that saves time and resources. A relatively new and promising technology for remote support that combines video conferencing and physical mobility is that of telepresence systems. The remote assistant, that is, the user of said technology, gains both presence and maneuverability in the distant location. As telepresence systems vary greatly in their design, the question arises as to whether the design influences the perception of the remote assistant. Unlike pure design studies, the present work focuses not only on the design and evaluation of the telepresence system itself, but especially on its perception during a collaborative task involving a human partner visible through the telepresence system. This paper presents two studies in which participants performed an assembly task under the guidance of a remote assistant. The remote assistant was visible through differently designed telepresence systems that were evaluated in terms of social perception and trustworthiness. Four telepresence systems were evaluated in study 1 (N = 32) and five different systems in study 2 (N = 34). The results indicated that similarly designed systems showed only marginal differences, but a system that was designed to transport additional loads and was therefore less agile and rather bulky was rated significantly less positively regarding competence than the other systems. It is particularly noteworthy that it was not the height of the communication medium that was decisive for the rating, but above all, the agility and mobility of the system. These results provide evidence that the design of a telepresence system can influence the social perception of the remote assistant and therefore has implications for the acceptance and use of telepresence systems. Full article

(This article belongs to the Special Issue Extended Reality and AI Empowered Robots)

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20 pages, 579 KiB

Open AccessArticle

Model-Based Predictive Control for Position and Orientation Tracking in a Multilayer Architecture for a Three-Wheeled Omnidirectional Mobile Robot

by Elena Villalba-Aguilera, Joaquim Blesa and Pere Ponsa

Robotics 2025, 14(6), 72; https://doi.org/10.3390/robotics14060072 - 28 May 2025

This paper presents the design and implementation of a Model-based Predictive Control (MPC) strategy integrated within a modular multilayer architecture for a three-wheeled omnidirectional mobile robot, the Robotino 4 from Festo. The implemented architecture is organized into three hierarchical layers to support modularity [...] Read more.

This paper presents the design and implementation of a Model-based Predictive Control (MPC) strategy integrated within a modular multilayer architecture for a three-wheeled omnidirectional mobile robot, the Robotino 4 from Festo. The implemented architecture is organized into three hierarchical layers to support modularity and system scalability. The upper layer is responsible for trajectory planning. This planned trajectory is forwarded to the intermediate layer, where the MPC computes the optimal velocity commands to follow the reference path, taking into account the kinematic model and actuator constraints of the robot. Finally, these velocity commands are processed by the lower layer, which uses three independent PID controllers to regulate the individual wheel speeds. To evaluate the proposed control scheme, it was implemented in MATLAB R2024a using a lemniscate trajectory as the reference. The MPC problem was formulated as a quadratic optimization problem that considered the three states: the global position coordinates and orientation angle. The simulation included state estimation errors and motor dynamics, which were experimentally identified to closely match real-world behavior. The simulation and experimental results demonstrate the capability of the MPC to track the lemniscate trajectory efficiently. Notably, the close agreement between the simulated and experimental results validated the fidelity of the simulation model. In a real-world scenario, the MPC controller enabled simultaneous regulation of both the position and orientation, which offered a greater performance compared with approaches that assume a constant orientation. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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Graphical abstract

17 pages, 1922 KiB

Open AccessArticle

Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments

by Alessandro Minervini, Adrian Carrio and Giorgio Guglieri

Robotics 2025, 14(6), 71; https://doi.org/10.3390/robotics14060071 - 27 May 2025

Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and [...] Read more.

Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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20 pages, 10201 KiB

Open AccessArticle

On First-Principle Robot Building in Undergraduate Robotics Education in the Robotic System Levels Model

by Bryan Van Scoy, Peter Jamieson and Veena Chidurala

Robotics 2025, 14(6), 70; https://doi.org/10.3390/robotics14060070 - 27 May 2025

Robotics has widespread applications throughout industrial automation, autonomous vehicles, agriculture, and more. For these reasons, undergraduate education has begun to focus on preparing engineering students to directly contribute to the design and use of such systems. However, robotics is inherently multi-disciplinary and requires [...] Read more.

Robotics has widespread applications throughout industrial automation, autonomous vehicles, agriculture, and more. For these reasons, undergraduate education has begun to focus on preparing engineering students to directly contribute to the design and use of such systems. However, robotics is inherently multi-disciplinary and requires knowledge of controls and automation, embedded systems, sensors, signal processing, algorithms, and artificial intelligence. This makes training the future robotics workforce a challenge. In this paper, we evaluate our experiences with project-based learning approaches to teaching robotics at the undergraduate level at Miami University. Specifically, we analyze three consecutive years of capstone design projects on increasingly complex robotics design problems for multi-robot systems. We also evaluate the laboratories taught in our course “ECE 314: Elements of Robotics”. We have chosen these four experiences since they focus on the use of “cheap” first-principled robots, meaning that these robots sit on the fringe of embedded system design in that much of the student time is spent on working with a micro-controller interfacing with simple and cheap actuators and sensors. To contextualize our results, we propose the Robotic System Levels (RSL) model as a structured way to understand the levels of abstraction in robotic systems. Our main conclusion from these case studies is that, in each experience, students are exposed primarily to a subset of levels in the RSL model. Therefore, the curriculum should be designed to emphasize levels that align with educational objectives and the skills required by local industries. Full article

(This article belongs to the Section Educational Robotics)

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21 pages, 5038 KiB

Open AccessArticle

Design of a Lifting Robot for Repetitive Inter-Floor Material Transport with Adjustable Gravity Compensation

by Byungseo Kwak, Seungbum Lim and Jungwook Suh

Robotics 2025, 14(6), 69; https://doi.org/10.3390/robotics14060069 - 26 May 2025

The construction of high-rise buildings necessitates efficient and reliable material transport systems to improve productivity and reduce labor-intensive tasks. Traditional methods such as cranes and elevators are widely used but are often constrained by high costs and spatial limitations. Manipulator-based robotic systems have [...] Read more.

The construction of high-rise buildings necessitates efficient and reliable material transport systems to improve productivity and reduce labor-intensive tasks. Traditional methods such as cranes and elevators are widely used but are often constrained by high costs and spatial limitations. Manipulator-based robotic systems have been explored as alternatives; however, they require complex control algorithms and struggle with confined construction environments. To address these challenges, we propose a lifting robot designed for repetitive inter-floor material transport in construction sites. The proposed system integrates a gear-connected double parallelogram linkage with a crank-rocker mechanism, enabling one-degree of freedom (1-DOF) operation for simplified control and precise positioning. Additionally, a spring-cable-based gravity compensation mechanism is implemented to reduce actuator torque, enhancing energy efficiency and structural stability. A prototype was fabricated, and experimental validation was conducted to evaluate torque reduction, positioning accuracy, and structural performance. Results demonstrate that the proposed system effectively minimizes driving torque, improves load-handling stability, and enhances overall operational efficiency. This study provides a foundation for developing automated lifting solutions in construction, contributing to reduced worker strain and increased productivity. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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29 pages, 14562 KiB

Open AccessArticle

Communicating the Automatic Control Principles in Smart Agriculture Education: The Interactive Water Pump Example

by Dimitrios Loukatos, Ioannis Glykos and Konstantinos G. Arvanitis

Robotics 2025, 14(6), 68; https://doi.org/10.3390/robotics14060068 - 26 May 2025

The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping students in agriculture, agronomy, and engineering with essential skills. The implementation of targeted [...] Read more.

The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping students in agriculture, agronomy, and engineering with essential skills. The implementation of targeted STEM activities has the potential to enhance the teaching of Agriculture 4.0 through the utilisation of practical applications that stimulate student interest, thereby facilitating more accessible and effective teaching. In this context, this study presents a system comprising retrofitted real-scale components that facilitate the understanding of digital technologies and automations in agriculture. The specific system utilises a typical centrifugal electric pump and a water tank and adds logic to it, so that its flow follows various user-defined setpoints, given and monitored via a smartphone application, despite the in-purpose disturbances invoked via intermediating valves. This setup aims for students to gain familiarity with concepts such as closed-loop systems and PID controllers. Going further, fertile ground is provided for experimentation on the efficiency of the PID controller via testing different algorithmic variants incorporating non-linear methods as well. Feedback collected from the participating students via a corresponding survey highlights the importance of integrating similar hands-on interdisciplinary activities into university curricula to foster engineering education. Full article

(This article belongs to the Special Issue Intelligent Robotic and Mechatronic Systems in Agricultural and Environmental Education)

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18 pages, 1837 KiB

Open AccessArticle

Real-Time Dolphin Whistle Detection on Raspberry Pi Zero 2 W with a TFLite Convolutional Neural Network

by Rocco De Marco, Francesco Di Nardo, Alessandro Rongoni, Laura Screpanti and David Scaradozzi

Robotics 2025, 14(5), 67; https://doi.org/10.3390/robotics14050067 - 19 May 2025

The escalating conflict between cetaceans and fisheries underscores the need for efficient mitigation strategies that balance conservation priorities with economic viability. This study presents a TinyML-driven approach deploying an optimized Convolutional Neural Network (CNN) on a Raspberry Pi Zero 2 W for real-time [...] Read more.

The escalating conflict between cetaceans and fisheries underscores the need for efficient mitigation strategies that balance conservation priorities with economic viability. This study presents a TinyML-driven approach deploying an optimized Convolutional Neural Network (CNN) on a Raspberry Pi Zero 2 W for real-time detection of bottlenose dolphin whistles, leveraging spectrogram analysis to address acoustic monitoring challenges. Specifically, a CNN model previously developed for classifying dolphins’ vocalizations and originally implemented with TensorFlow was converted to TensorFlow Lite (TFLite) with architectural optimizations, reducing the model size by 76%. Both TensorFlow and TFLite models were trained on 22 h of underwater recordings taken in controlled environments and processed into 0.8 s spectrogram segments (300 × 150 pixels). Despite reducing model size, TFLite models maintained the same accuracy as the original TensorFlow model (87.8% vs. 87.0%). Throughput and latency were evaluated by varying the thread allocation (1–8 threads), revealing the best performance at 4 threads (quad-core alignment), achieving an inference latency of 120 ms and sustained throughput of 8 spectrograms/second. The system demonstrated robustness in 120 h of continuous stress tests without failure, underscoring its reliability in marine environments. This work achieved a critical balance between computational efficiency and detection fidelity (F1-score: 86.9%) by leveraging quantized, multithreaded inference. These advancements enable low-cost devices for real-time cetacean presence detection, offering transformative potential for bycatch reduction and adaptive deterrence systems. This study bridges artificial intelligence innovation with ecological stewardship, providing a scalable framework for deploying machine learning in resource-constrained settings while addressing urgent conservation challenges. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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Graphical abstract

22 pages, 4481 KiB

Open AccessArticle

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

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 KiB

Open AccessArticle

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

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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20 pages, 5640 KiB

Open AccessArticle

H_∞ Control for Systems with Mechanical Constraints Based on Orthogonal Decomposition

by Ahmad Aldaher and Sergei Savin

Robotics 2025, 14(5), 64; https://doi.org/10.3390/robotics14050064 - 16 May 2025

In this paper, we study

H_{\infty}

control for systems with explicit mechanical constraints and a lack of state information, such as walking robots. This paper proposes an

H_{\infty}

control design scheme based on solving an optimization problem with linear matrix inequality [...] Read more.

In this paper, we study

H_{\infty}

control for systems with explicit mechanical constraints and a lack of state information, such as walking robots. This paper proposes an

H_{\infty}

control design scheme based on solving an optimization problem with linear matrix inequality constraints. Our method is based on the orthogonal decomposition of the state variables and the use of two linear controllers and a Luenberger observer, tuned to achieve the desired properties of the closed-loop system. The method takes into account static linear additive disturbance, which appears due to the uncertainties associated with the mechanical constraints. We propose a dynamics linearization procedure for systems with mechanical constraints, taking into account the inevitable lack of information about the environment; this procedure allows a nonlinear system to be transformed into a form suitable for the application of the proposed control design method. The method is tested on a constrained underactuated three-link robot and a flat quadruped robot, showing the desired behavior in both cases. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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44 pages, 823 KiB

Open AccessReview

A Systematic Literature Review of DDS Middleware in Robotic Systems

by Muhammad Liman Gambo, Abubakar Danasabe, Basem Almadani, Farouq Aliyu, Abdulrahman Aliyu and Esam Al-Nahari

Robotics 2025, 14(5), 63; https://doi.org/10.3390/robotics14050063 - 14 May 2025

The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to [...] Read more.

The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions. Full article

(This article belongs to the Special Issue Innovations in the Internet of Robotic Things (IoRT))

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25 pages, 5822 KiB

Open AccessReview

Analytical Modeling, Virtual Prototyping, and Performance Optimization of Cartesian Robots: A Comprehensive Review

by Yasir Mehmood, Ferdinando Cannella and Silvio Cocuzza

Robotics 2025, 14(5), 62; https://doi.org/10.3390/robotics14050062 - 3 May 2025

A comprehensive literature review on the kinematics and dynamics modeling and virtual prototyping (V.P) of the Cartesian robots with a flexible configuration is presented in this paper. Different modeling approaches of the main components of the Cartesian robot, which includes linear belt drives [...] Read more.

A comprehensive literature review on the kinematics and dynamics modeling and virtual prototyping (V.P) of the Cartesian robots with a flexible configuration is presented in this paper. Different modeling approaches of the main components of the Cartesian robot, which includes linear belt drives and structural components, are presented and discussed in this paper. Furthermore, the vibrations modeling, trajectory planning, and control strategies of the Cartesian robot are also presented. The performance optimization of the Cartesian robot is discussed here, which is affected by the highly flexible configuration of the robot incurred due to high-mix, low-volume production. The importance of virtual prototyping techniques, like finite element analysis and multi-body dynamics, for modeling Cartesian robots or its components is presented. Design and performance optimization methods for robots with a flexible configuration are discussed, although their application to Cartesian robots is rare in the literature and it presents an exciting opportunity for future research in this area. This review paper focuses on the importance of further research on the virtual prototyping tools for flexibly configured robots and their integration with experimental validation. The findings offer useful insights to industries looking to maximize their production processes while keeping the customization, reliability, and efficiency. Full article

(This article belongs to the Special Issue Integrating Robotics into High-Accuracy Industrial Operations)

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25 pages, 3258 KiB

Open AccessArticle

Singular Configuration Analysis of Modular-Driven 4- and 6-DoF Parallel Topology Robots

by Zoltán Forgó, Ferenc Tolvaly-Roșca and Attila Csobán

Robotics 2025, 14(5), 61; https://doi.org/10.3390/robotics14050061 - 2 May 2025

The number of applications of parallel topology robots in industry is growing, and the interest of academics in finding new solutions and applications to implement such mechanisms is present all over the world. Industrywide, the most commonly used motion types need four- and [...] Read more.

The number of applications of parallel topology robots in industry is growing, and the interest of academics in finding new solutions and applications to implement such mechanisms is present all over the world. Industrywide, the most commonly used motion types need four- and six-degrees-of-freedom (DoF) robots. While there are commercial variants from different robot vendors, this study offers new alternatives to these. Based on Lie algebra synthesis, symmetrical parallel structures are identified, according to certain rules. Implementing 2-DoF actuation modules, the number of robot limbs is reduced compared to existing commercial robot structures. In terms of the applicability of a parallel mechanism (also concerning the control algorithm), it is important to determine singular configurations. Therefore, in addition to the kinematic schematics of the newly proposed mechanisms, their singular configurations are also discussed. Based on some dimensional simplifications (without a loss of generality), the conditions for the singular configurations are enumerated for the presented parallel topology robots with symmetrical kinematic chains. Finally, a comparison of the proposed mechanism is presented, considering its singular configurations. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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25 pages, 3077 KiB

Open AccessEditor’s ChoiceArticle

A Partitioned Operational Space Approach for Singularity Handling in Six-Axis Manipulators

by Craig Carignan and Giacomo Marani

Robotics 2025, 14(5), 60; https://doi.org/10.3390/robotics14050060 - 30 Apr 2025

Task prioritization for inverse kinematics can be a powerful tool for realizing objectives in robot manipulation. This is particularly true for robots with redundant degrees of freedom, but it can also help address a debilitating singularity in six-axis robots. A roll-pitch-roll wrist is [...] Read more.

Task prioritization for inverse kinematics can be a powerful tool for realizing objectives in robot manipulation. This is particularly true for robots with redundant degrees of freedom, but it can also help address a debilitating singularity in six-axis robots. A roll-pitch-roll wrist is especially problematic for any six-axis robot because it produces a “gimbal-lock” singularity in the middle of the wrist workspace when the roll axes align. A task priority methodology can be used to realize only the achievable components of the commanded motion in the reduced operational space of a manipulator near singularities while phasing out the uncontrollable direction. In addition, this approach allows the operator to prioritize translation and rotation in the region of singularities. This methodology overcomes a significant drawback to the damped least-squares method, which can produce tool motion that deviates significantly from the desired path even in directions that are controllable. The approach used here reduces the operational space near the wrist singularity while maintaining full command authority over tool translation. The methodology is demonstrated in simulations conducted on a six degree-of-freedom Motoman MH250 manipulator. Full article

(This article belongs to the Section Industrial Robots and Automation)

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32 pages, 2540 KiB

Open AccessArticle

Formation Control of Wheeled Mobile Robots with Fault-Tolerance Capabilities

by Muhammad Shahab, Ali Nasir and Nezar M. Alyazidi

Robotics 2025, 14(5), 59; https://doi.org/10.3390/robotics14050059 - 27 Apr 2025

This research investigates the impact of actuator faults on the formation control of multiple-wheeled mobile robots—a critical aspect in coordinating multi-robot systems for applications such as surveillance, exploration, and transportation. When a fault occurs in any of the robots, it can disrupt the [...] Read more.

This research investigates the impact of actuator faults on the formation control of multiple-wheeled mobile robots—a critical aspect in coordinating multi-robot systems for applications such as surveillance, exploration, and transportation. When a fault occurs in any of the robots, it can disrupt the formation and adversely affect the system’s performance, thereby compromising system efficiency and reliability. While numerous studies have focused on fault-tolerant control strategies to maintain formation integrity, there is a notable gap in the literature regarding the relationship between controller gains and settling time under varying degrees of actuator loss. In this paper, we develop a kinematic model of wheeled mobile robots and implement a leader–follower-based formation control strategy. Actuator faults are systematically introduced with varying levels of effectiveness (e.g., 80%, 60%, and 40% of full capacity) to observe their effects on formation maintenance. We generate data correlating controller gains with settling time under different actuator loss conditions and fit a polynomial curve to derive an equation describing this relationship. Comprehensive MATLAB simulations are conducted to evaluate the proposed methodology. The results demonstrate the influence of actuator faults on the formation control system and provide valuable insights into optimizing controller gains for improved fault tolerance. These findings contribute to the development of more robust multi-robot systems capable of maintaining formation and performance despite the presence of actuator failures. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

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