Robotics

29 pages, 166576 KB

Open AccessArticle

A Decentralized Potential Field-Based Self-Organizing Control Framework for Trajectory, Formation, and Obstacle Avoidance of Fully Autonomous Swarm Robots

by Mohammed Abdel-Nasser, Sami El-Ferik, Ramy Rashad and Abdul-Wahid A. Saif

Robotics 2025, 14(12), 192; https://doi.org/10.3390/robotics14120192 - 18 Dec 2025

Viewed by 322

Abstract

In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without [...] Read more.

In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without centralized coordination. Each robot autonomously computes attractive, repulsive, and formation forces to navigate toward target positions while maintaining inter-robot spacing and avoiding both static and dynamic obstacles. Inspired by biological swarm behavior, the controller emphasizes robustness, scalability, and flexibility. The proposed method has been successfully validated in the ARGoS simulator, which provides realistic physics, sensor modeling, and a robust environment that closely approximates real-world conditions. The system was tested with up to 15 robots and is designed to scale to larger swarms (e.g., 100 robots), demonstrating stable performance across a range of scenarios. Results obtained using ARGoS confirm the swarm’s ability to maintain formation, avoid collisions, and reach a predefined goal area within a configurable 1 m radius. This zone serves as a spatial convergence region suitable for multi-robot formation, even in the presence of unknown fixed obstacles and movable agents. The framework can seamlessly handle the addition or removal of swarm members without reconfiguration. Full article

(This article belongs to the Special Issue Advanced Control and Optimization for Robotic Systems)

► Show Figures

Figure 1

20 pages, 6418 KB

Open AccessArticle

Workspace and Singularity Analysis of 4-DOF 3R1T Parallel Mechanism with a Circular Rail

by Pavel Laryushkin, Ilya Brem, Alexey Fomin and Anton Antonov

Robotics 2025, 14(12), 191; https://doi.org/10.3390/robotics14120191 - 17 Dec 2025

Viewed by 235

Abstract

Limited workspace and singularities are major challenges for parallel mechanisms. This article addresses these issues for a 4-DOF 1-SPS/3-RRRRR parallel mechanism with a circular rail, proposed in our prior work. The mechanism has a 3R1T motion type with a movable center of spherical [...] Read more.

Limited workspace and singularities are major challenges for parallel mechanisms. This article addresses these issues for a 4-DOF 1-SPS/3-RRRRR parallel mechanism with a circular rail, proposed in our prior work. The mechanism has a 3R1T motion type with a movable center of spherical motion. The paper begins with a detailed description of the mechanism design. A closed-form solution of the inverse kinematics follows next, which computes the active joint coordinates and determines the spatial positions of all joints and links. Based on this solution, an iterative approach is applied to analyze the workspace for three different heights of the spherical motion center. The analysis reveals the regions of a full twist about the platform symmetry axis, bounded by maximum tilt angles of 51°, 38°, and 23°, respectively. Introducing joint constraints significantly reduces the workspace, limiting the tilt angles to 21°, 26°, and 0° at the same heights. Subsequently, screw theory is applied to identify serial, parallel, and constraint singularities, and an iterative approach is used to find the boundary of the singularity-free workspace. The analysis shows that the full-twist tilt angles are limited to 33°, a value determined solely on the platform geometry and independent of the spherical motion center height. These results establish a foundation for the design optimization and prototyping of the mechanism. Full article

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

► Show Figures

Figure 1

22 pages, 18926 KB

Open AccessArticle

Fixed-Time and Prescribed-Time Image-Based Visual Servoing with Asymmetric Time-Varying Output Constraint

by Jianfei Lin, Lei Ma, Deqing Huang, Na Qin, Yilin Chen, Yutao Wang and Dongrui Wang

Robotics 2025, 14(12), 190; https://doi.org/10.3390/robotics14120190 - 16 Dec 2025

Viewed by 281

Abstract

This paper addresses image-based visual servoing with the field-of-view limitation of the camera. A novel control method is proposed with dual constraints based on fixed-time and prescribed-time convergence. With the introduction of a prescribed-time performance function and an asymmetric barrier Lyapunov function, asymmetric [...] Read more.

This paper addresses image-based visual servoing with the field-of-view limitation of the camera. A novel control method is proposed with dual constraints based on fixed-time and prescribed-time convergence. With the introduction of a prescribed-time performance function and an asymmetric barrier Lyapunov function, asymmetric time-varying output constraints are achieved. This ensures that the image features remain within the predefined range, thereby addressing the field-of-view constraint problem in visual servoing applications. The combination of the prescribed-time performance function and the fixed-time stability theory ensures that the tracking error converges to a predetermined range within a prescribed time. Furthermore, it can converge to zero in fixed time, thus significantly improving the error convergence rates. The effectiveness and superiority of the method are demonstrated through physical experiments. Moreover, a case study of a contact network component bolt alignment task, aiming at automatically aligning a sleeve to a bolt, is carried out to demonstrate the applicability of the proposed method in practice. Full article

(This article belongs to the Topic Advances in Robot Vision Perception and Control Technology)

► Show Figures

Figure 1

21 pages, 4133 KB

Open AccessArticle

PGTI: Pose-Graph Topological Integrity for Map Quality Assessment in SLAM

by Shuxiang Xie, Ken Sakurada, Ryoichi Ishikawa, Masaki Onishi and Takeshi Oishi

Robotics 2025, 14(12), 189; https://doi.org/10.3390/robotics14120189 - 15 Dec 2025

Viewed by 277

Abstract

We introduce the pose-graph topological integrity, an approach designed to assess the correctness of pose graphs in simultaneous localization and mapping. Traditional methods assessed map quality according to the optimality criteria based on pose graphs, which often rely on heuristically defined edge information [...] Read more.

We introduce the pose-graph topological integrity, an approach designed to assess the correctness of pose graphs in simultaneous localization and mapping. Traditional methods assessed map quality according to the optimality criteria based on pose graphs, which often rely on heuristically defined edge information matrices. These methods cannot capture the inconsistencies between the constructed map and the actual environment. In contrast, the proposed approach utilizes heat kernel signatures to directly quantify topological inconsistencies between the pose graph and support graph derived from free-space constraints. This enables a multi-scale and per-vertex evaluation of topological integrity. The experiments on real-world datasets demonstrate that the proposed metric can detect topological errors and distinguish between serious ones and harmless ones. Full article

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

► Show Figures

Figure 1

31 pages, 4381 KB

Open AccessArticle

Real-Time Forecasting of a Fire-Extinguishing Agent Jet Trajectory from a Robotic Fire Monitor Under Disturbances

by Irina Pozharkova and Sergey Chentsov

Robotics 2025, 14(12), 188; https://doi.org/10.3390/robotics14120188 - 14 Dec 2025

Viewed by 283

Abstract

This article presents a methodology for real-time forecasting of a fire-extinguishing agent jet trajectory from a robotic fire monitor under wind influence, which can significantly displace the impact area position and complicate targeting. The proposed methodology is designed for controlling firefighting robots in [...] Read more.

This article presents a methodology for real-time forecasting of a fire-extinguishing agent jet trajectory from a robotic fire monitor under wind influence, which can significantly displace the impact area position and complicate targeting. The proposed methodology is designed for controlling firefighting robots in conditions where visual monitoring of the impact area is impeded by factors such as: obscuration of the fire-extinguishing agent flow by smoke, low visibility of its fragmented particles against the background environment, and long-range jet discharge. Trajectory forecasting is implemented using a neural network model. The training and verification of this model are performed with datasets constructed from the results of numerical simulations of fire-extinguishing agent motion under wind influence, based on Computational Fluid Dynamics (CFD) methods. Experimentally obtained data are used for the validation of the trained neural network model and the selected CFD models. The paper describes the methodology for conducting full-scale tests of fire monitors; a photogrammetric algorithm for generating validation datasets from the test results; an algorithm for calculating target characteristics, which describe the jet trajectory and are consistent with experimental data, used for forming training and verification datasets based on simulation; and a procedure for selecting Computational Fluid Dynamics models and their parameters to ensure the required accuracy. The article also presents the results of an experimental evaluation of the developed methodology’s effectiveness for real-time prediction of the water jet trajectory from a fire monitor under various control and disturbance parameters. Full article

(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)

► Show Figures

Figure 1

17 pages, 4175 KB

Open AccessArticle

Contrastive and Domain-Adaptive Evaluation of Control Laws Using Surface Electromyography During Exoskeleton-Assisted Walking

by Zhen Ding, Yanlong Li, Pengyu Jin, Chunzhi Yi and Chifu Yang

Robotics 2025, 14(12), 187; https://doi.org/10.3390/robotics14120187 - 12 Dec 2025

Viewed by 292

Abstract

Accurate and real-time evaluation of energy expenditure is crucial for optimizing exoskeleton control laws. Conventional regression-based prediction approaches are strongly affected by inter-individual variability in surface electromyography (sEMG) signals, limiting their generalization across subjects. To address this limitation, we reformulate the evaluation task [...] Read more.

Accurate and real-time evaluation of energy expenditure is crucial for optimizing exoskeleton control laws. Conventional regression-based prediction approaches are strongly affected by inter-individual variability in surface electromyography (sEMG) signals, limiting their generalization across subjects. To address this limitation, we reformulate the evaluation task as a comparative classification problem, instead of predicting absolute metabolic values, the proposed method directly judges which of two control strategies induces lower energy expenditure. We design a Control Laws Evaluation Network (CLEN) based on a Siamese architecture, which captures pairwise sEMG representations to compare assistance strategies. To further mitigate subject-specific variability, we introduce a Dual Adversarial Adaptive Optimization Strategy (DAAOS) that aligns feature distributions across domains using maximum classifier discrepancy and domain confusion. Experimental results on both public and local datasets demonstrate that the proposed domain-adaptive framework significantly outperforms regression-based approaches, achieving accuracies of

77.6 \pm 3.1 %

on the public dataset and

73.3 \pm 4.7 %

on the local dataset across unseen subjects. The findings indicate that the proposed framework provides an effective and generalizable metric for optimizing exoskeleton control, with potential applications in mobility assistance. Full article

(This article belongs to the Section Neurorobotics)

► Show Figures

Figure 1

17 pages, 2669 KB

Open AccessArticle

Extensible Heterogeneous Collaborative Perception in Autonomous Vehicles with Codebook Compression

by Babak Ebrahimi Soorchaei, Arash Raftari and Yaser Pourmohammadi Fallah

Robotics 2025, 14(12), 186; https://doi.org/10.3390/robotics14120186 - 10 Dec 2025

Viewed by 346

Abstract

Collaborative perception can mitigate occlusion and range limitations in autonomous driving, but deployment remains constrained by strict bandwidth budgets and heterogeneous agent stacks. We propose a communication-efficient and backbone-agnostic framework in which each agent’s encoder is treated as a black box, and a [...] Read more.

Collaborative perception can mitigate occlusion and range limitations in autonomous driving, but deployment remains constrained by strict bandwidth budgets and heterogeneous agent stacks. We propose a communication-efficient and backbone-agnostic framework in which each agent’s encoder is treated as a black box, and a lightweight interpreter maps its intermediate features into a canonical space. To reduce transmission cost, we integrate codebook-based compression that sends only compact discrete indices, while a prompt-guided decoder reconstructs semantically aligned features on the ego vehicle for downstream fusion. Training follows a two-phase strategy: Phase 1 jointly optimizes interpreters, prompts, and fusion components for a fixed set of agents; Phase 2 enables plug-and-play onboarding of new agents by tuning only their specific prompts. Experiments on OPV2V and OPV2VH+ show that our method consistently outperformed early-, intermediate-, and late-fusion baselines under equal or lower communication budgets. With a codebook of size 128, the proposed pipeline preserved over 95% of the uncompressed detection accuracy while reducing communication cost by more than two orders of magnitude. The model also maintained strong performance under bandwidth throttling, missing-agent scenarios, and heterogeneous sensor combinations. Compared to recent state-of-the-art methods such as PolyInter, MPDA, and PnPDA, our framework achieved higher AP while using significantly smaller message sizes. Overall, the combination of prompt-guided decoding and discrete Codebook compression provides a scalable, bandwidth-aware, and heterogeneity-resilient foundation for next-generation collaborative perception in connected autonomous vehicles. Full article

(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

► Show Figures

Figure 1

18 pages, 3433 KB

Open AccessArticle

Modeling and Energy Expenditure Comparison of RRR and PRR Planar Robotic Manipulators for Pick-and-Place Operations

by Chiara Nezzi, Veit Gufler and Renato Vidoni

Robotics 2025, 14(12), 185; https://doi.org/10.3390/robotics14120185 - 8 Dec 2025

Viewed by 285

Abstract

Energy efficiency represents a fundamental aspect of sustainable industrial automation, where minimizing energy expenditure supports both environmental and economic goals. This work presents the modeling and comparative analysis of the energy consumption of three planar robotic manipulators performing pick-and-place operations: a serial RRR [...] Read more.

Energy efficiency represents a fundamental aspect of sustainable industrial automation, where minimizing energy expenditure supports both environmental and economic goals. This work presents the modeling and comparative analysis of the energy consumption of three planar robotic manipulators performing pick-and-place operations: a serial RRR configuration (RRR-D2) and two alternative PRR architectures (PRR90 and PRR45) featuring linear prismatic guides. For each manipulator, kinematic and dynamic models are derived, and actuator electro-mechanical effects are incorporated to obtain realistic energy evaluations. The analysis is carried out over four representative trajectories and two design variables, enabling a consistent comparison in terms of both total and recoverable energy through regenerative braking. Results show that geometric and actuation parameters significantly influence energy performance and that specific PRR configurations can achieve comparable motion capabilities to the RRR structure with reduced energy demand. The proposed framework supports energy-aware robot selection and design, contributing to the development of efficient and sustainable planar manipulators for repetitive industrial operations. Full article

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

► Show Figures

Figure 1

20 pages, 14885 KB

Open AccessArticle

MultiPhysio-HRC: A Multimodal Physiological Signals Dataset for Industrial Human–Robot Collaboration

by Andrea Bussolan, Stefano Baraldo, Oliver Avram, Pablo Urcola, Luis Montesano, Luca Maria Gambardella and Anna Valente

Robotics 2025, 14(12), 184; https://doi.org/10.3390/robotics14120184 - 5 Dec 2025

Viewed by 695

Abstract

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a [...] Read more.

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems. Full article

(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)

► Show Figures

Figure 1

14 pages, 1621 KB

Open AccessArticle

Synthetic Hamiltonian Energy Prediction for Motor Performance Assessment in Neurorehabilitation Procedures: A Machine Learning Approach with TimeGAN-Augmented Data

by Henry P. Paz-Arias, Omar A. Dominguez-Ramirez, Raúl Villafuerte-Segura, Jeimmy Y. Eche-Salazar and Jose F. Lucio-Naranjo

Robotics 2025, 14(12), 183; https://doi.org/10.3390/robotics14120183 - 4 Dec 2025

Viewed by 308

Abstract

This study presents an assessment scheme for haptic interaction systems based on Hamiltonian energy prediction, which contributes to procedures applied to neurorehabilitation. It focuses on robotic systems involving human participation in the control loop, where uncertainty may compromise both stability and task performance. [...] Read more.

This study presents an assessment scheme for haptic interaction systems based on Hamiltonian energy prediction, which contributes to procedures applied to neurorehabilitation. It focuses on robotic systems involving human participation in the control loop, where uncertainty may compromise both stability and task performance. To address this, a regression-based model is proposed to predict total mechanical energy using the robot’s position and velocity signals during active interaction. Synthetic data generated via TimeGAN are used to enhance model generalization. Advanced machine learning techniques—particularly Gradient Boosting—demonstrate outstanding accuracy, achieving an MSE of

0.628 \times 10^{- 10}

and

R^{2} = 0.999976

. These results validate the use of synthetic data and passive-mode-trained models for assessing motor performance in active settings. The method is applied to a patient diagnosed with Guillain-Barré Syndrome, using the Hamiltonian function to estimate energy during interaction and objectively assess motor performance changes. The results obtained show that our proposal is of great relevance since it solves a current field of opportunity in the area. Full article

(This article belongs to the Special Issue Development of Biomedical Robotics)

► Show Figures

Figure 1

13 pages, 4347 KB

Open AccessArticle

Haptic-Based Threaded Insertion: Insights from Human Demonstrations

by Gautami Golani, Kenzhi Iskandar Wong, Suhas Raghavendra Kulkarni, Sugandhana Shanmuganathan, Sri Harsha Turlapati, Yongjun Wee and Domenico Campolo

Robotics 2025, 14(12), 182; https://doi.org/10.3390/robotics14120182 - 3 Dec 2025

Viewed by 261

Abstract

This work presents a method to utilise only haptic information in successfully completing a threaded insertion. We derive the insights for this task from human demonstrations and highlight the sufficiency of haptic data in this application, without the use of vision-based feedback or [...] Read more.

This work presents a method to utilise only haptic information in successfully completing a threaded insertion. We derive the insights for this task from human demonstrations and highlight the sufficiency of haptic data in this application, without the use of vision-based feedback or complex geometric models. We begin with human demonstrations to characterize the haptic artefacts that arise while employing backspinning motion. Force and motion data reveal a repeatable axial force transient (a spike); this signature repeats periodically for each revolution and appears for both internally and externally threaded parts of varying sizes. We then validate the same haptic cue on a robot arm. Finally, we use this insight in an end-to-end bulb insertion pipeline. A custom mechanical adapter ensures a secure grasp to enable autonomous threaded insertion of the bulb. Experimental results confirm that the force-based approach enables robust and repeatable insertion, demonstrating that haptic cues alone are sufficient for everyday threaded assemblies. Full article

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

► Show Figures

Figure 1

19 pages, 3355 KB

Open AccessArticle

Effectiveness of Unsupervised Training for Applications in Deep Neural Network Robot Navigation

by Quan Wu and John Hedley

Robotics 2025, 14(12), 181; https://doi.org/10.3390/robotics14120181 - 2 Dec 2025

Viewed by 459

Abstract

Supervised training of neural networks is time consuming, and the scenarios required for obtaining a representative dataset must be carefully considered for each task. Applying an unsupervised training approach can greatly simplify this data collection aspect. This paper explores options for the unsupervised [...] Read more.

Supervised training of neural networks is time consuming, and the scenarios required for obtaining a representative dataset must be carefully considered for each task. Applying an unsupervised training approach can greatly simplify this data collection aspect. This paper explores options for the unsupervised training of a convolutional neural network for the navigation of a mobile robot and compares its benefits with respect to a supervised training approach. A simulated training environment was created, in which the robot, through random motion, gathered the required data needed for training. Two approaches to training were investigated: either selectively choosing the training data from the random set acquired or considering modifying the network output to favor improved navigation. Both methods proved successful at obtaining an optimum value of 80% efficiency of directional travel whilst maintaining a collision avoidance performance of 97.7%. The results proved our approach was comparable in performance with respect to supervised training approaches, whilst it demonstrated superiority in terms of training-data collection. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

► Show Figures

Figure 1

18 pages, 1653 KB

Open AccessArticle

Sim2Real Transfer of Imitation Learning of Motion Control for Car-like Mobile Robots Using Digital Twin Testbed

by Narges Mohaghegh, Hai Wang and Amirmehdi Yazdani

Robotics 2025, 14(12), 180; https://doi.org/10.3390/robotics14120180 - 30 Nov 2025

Viewed by 770

Abstract

Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real [...] Read more.

Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real environments. In this study, a DT-based testbed is developed to train and evaluate an imitation learning (IL) control framework in which a neural network policy learns to replicate the behavior of a hybrid Model Predictive Control (MPC)–Backstepping expert controller. The DT framework ensures consistent benchmarking between simulated and physical execution, supporting a structured and safe process for policy validation and deployment. Experimental analysis demonstrates that the learned policy effectively reproduces expert behavior, achieving bounded trajectory-tracking errors and stable performance across simulation and real-world tests. The results confirm that DT-enabled IL provides a viable pathway for Sim2Real transfer, accelerating controller development and deployment in autonomous mobile robotics. Full article

(This article belongs to the Section AI in Robotics)

► Show Figures

Figure 1

39 pages, 1887 KB

Open AccessReview

A Comprehensive Review of Autonomous Mobile Robots in Healthcare: Implications for Patient-Transporting Human-Following Robots

by Hyojin Shin, Seohee Kim, Eunseo Jung, Youjung Han, Nayoung Lee, Sangchan Park, Changhoon Jeon and Jiyoung Woo

Robotics 2025, 14(12), 179; https://doi.org/10.3390/robotics14120179 - 30 Nov 2025

Viewed by 1782

Abstract

Robots and artificial intelligence have revolutionized the healthcare sector. Patient transportation within hospitals is emerging as a critical application; however, reducing errors and inefficiencies caused by human intervention and ensuring safe, efficient, and reliable movement of patients are necessary. This study provides a [...] Read more.

Robots and artificial intelligence have revolutionized the healthcare sector. Patient transportation within hospitals is emerging as a critical application; however, reducing errors and inefficiencies caused by human intervention and ensuring safe, efficient, and reliable movement of patients are necessary. This study provides a comprehensive review of research and technological trends in healthcare robotics, focusing on the design, algorithms, and applications of following robots for patient transportation. We examine foundational robotic algorithms, sensor and navigation technologies, and human–robot interaction techniques that enable robots to safely follow patients and assist medical personnel. Additionally, we survey the evolution of transportation robots across industries and highlight current research on hospital-specific following robots. Finally, we survey the current research and trends in following robots and robots for patient transportation to assist medical personnel in real-world healthcare applications. A comprehensive and in-depth understanding of the design and application of following robots can be pivotal to guiding future research on following robots and laying the foundation for their commercialization in healthcare. Full article

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

► Show Figures

Figure 1

23 pages, 15360 KB

Open AccessArticle

A Mobile Robotic System Design and Approach for Autonomous Targeted Disinfection

by Mohammed Z. Shaqura, Linyan Han, Mohammadali Javaheri Koopaee, Wissem Haouas, Moustafa Motawei, Peter Mooney, Nick Fry, Tony Wiese, Bilal Kaddouh and Robert C. Richardson

Robotics 2025, 14(12), 178; https://doi.org/10.3390/robotics14120178 - 30 Nov 2025

Viewed by 367

Abstract

The recent global pandemic has posed unprecedented challenges to public health systems and has highlighted the critical need for effective, contactless disinfection strategies in shared environments. This study investigates the use of autonomous robotics to enhance disinfection efficiency and safety in public spaces [...] Read more.

The recent global pandemic has posed unprecedented challenges to public health systems and has highlighted the critical need for effective, contactless disinfection strategies in shared environments. This study investigates the use of autonomous robotics to enhance disinfection efficiency and safety in public spaces through the development of a custom-built mobile spraying platform. The proposed robotic system is equipped with an integrated 3D object pose estimation framework that fuses RGB-based object detection with point cloud segmentation to accurately identify and localize high-contact surfaces. To facilitate autonomous operation, both local and global motion planning algorithms are implemented, enabling the robot to navigate complex environments and execute disinfection tasks with minimal human intervention. Experimental results demonstrate the feasibility of the proposed disinfection robotic system. Full article

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

► Show Figures

Figure 1

22 pages, 25352 KB

Open AccessArticle

Open-Loop Characterisation of Soft Actuator Pressure Regulated by Pulse-Driven Solenoid Valve

by Andrés J. Serrano-Balbontín, Inés Tejado, Blas M. Vinagre, Sumeet S. Aphale and Andres San-Millan

Robotics 2025, 14(12), 177; https://doi.org/10.3390/robotics14120177 - 28 Nov 2025

Viewed by 367

Abstract

Solenoid valves are widely used for pressure regulation in soft pneumatic robots, but their inherent electromechanical nonlinearities—such as dead zones, saturation, and pressure-dependent dynamics—pose significant challenges for accurate control. Conventional pulse modulation techniques, including pulse-width modulation (PWM), often exacerbate these effects by neglecting [...] Read more.

Solenoid valves are widely used for pressure regulation in soft pneumatic robots, but their inherent electromechanical nonlinearities—such as dead zones, saturation, and pressure-dependent dynamics—pose significant challenges for accurate control. Conventional pulse modulation techniques, including pulse-width modulation (PWM), often exacerbate these effects by neglecting valve-switching transients. This paper presents a physics-informed dynamic modelling framework that captures transient and pressure-dependent behaviours in solenoid valve-driven soft pneumatic systems operating under pulse modulation. The model is experimentally validated on a soft pneumatic actuator (SPA) platform using four modulation schemes: PWM, integral pulse frequency modulation (IPFM), its inverted variant (IIPFM), and

Δ

Σ

modulation. Results demonstrate that only the IIPFM scheme produces near-linear input–pressure characteristics, in close agreement with model predictions. The proposed framework provides new physical insights into valve-induced nonlinearities and establishes a systematic basis for high-fidelity modelling and control of soft pneumatic robotic systems. Full article

(This article belongs to the Special Issue Dynamic Modeling and Model-Based Control of Soft Robots)

► Show Figures

Figure 1

12 pages, 3628 KB

Open AccessArticle

A Dataset of Standard and Abrupt Industrial Gestures Recorded Through MIMUs

by Elisa Digo, Michele Polito, Elena Caselli, Laura Gastaldi and Stefano Pastorelli

Robotics 2025, 14(12), 176; https://doi.org/10.3390/robotics14120176 - 28 Nov 2025

Viewed by 477

Abstract

Considering the human-centric approach promoted by Industry 5.0, safety becomes a crucial aspect in scenarios of human–robot interaction, especially when abrupt human movements occur due to inattention or unexpected circumstances. To this end, human motion tracking is necessary to promote a safe and [...] Read more.

Considering the human-centric approach promoted by Industry 5.0, safety becomes a crucial aspect in scenarios of human–robot interaction, especially when abrupt human movements occur due to inattention or unexpected circumstances. To this end, human motion tracking is necessary to promote a safe and efficient human–machine interaction. Literature datasets related to the industrial context generally contain controlled and repetitive gestures tracked with visual systems or magneto-inertial measurement units (MIMUs), without considering the occurrence of unexpected events that might cause operators’ abrupt movements. Accordingly, the aim of this paper is to present the dataset DASIG (Dataset of Standard and Abrupt Industrial Gestures) related to both standard typical industrial movements and abrupt movements registered through MIMUs. Sixty healthy working-age participants were asked to perform standard pick-and-place gestures interspersed with unexpected abrupt movements triggered by visual or acoustic alarms. The dataset contains MIMUs signals collected during the execution of the task, data related to the temporal generation of alarms, anthropometric data of all participants, and a script for demonstrating DASIG usability. All raw data are provided, and the collected dataset is suitable for several analyses related to the industrial context (gesture recognition, motion planning, ergonomics, safety, statistics, etc.). Full article

(This article belongs to the Special Issue Human–Robot Collaboration in Industry 5.0)

► Show Figures

Figure 1

36 pages, 5387 KB

Open AccessArticle

SCARA Assembly AI: The Synthetic Learning-Based Method of Component-to-Slot Assignment with Permutation-Invariant Transformers for SCARA Robot Assembly

by Tibor Péter Kapusi, Timotei István Erdei, Masuk Abdullah, Géza Husi and András Hajdu

Robotics 2025, 14(12), 175; https://doi.org/10.3390/robotics14120175 - 27 Nov 2025

Viewed by 565

Abstract

This paper presents a novel synthetic learning-based approach for solving the component-to-slot assignment problem in robotics using a SCARA robot. The method uses a fully simulated environment that generates and annotates scenes based on rules and visual features. Within this environment, we train [...] Read more.

This paper presents a novel synthetic learning-based approach for solving the component-to-slot assignment problem in robotics using a SCARA robot. The method uses a fully simulated environment that generates and annotates scenes based on rules and visual features. Within this environment, we train a permutation-invariant neural model to predict correct assignments between detected components and predefined target slots. Set Transformer-based encoders are combined with a self-attention MLP scoring head. Assignment prediction is optimized using an improved soft Hungarian loss function. To increase data realism and generalizability, we implement a synthetic dataset generation module on the NVIDIA Omniverse platform. This setup enables precise control over scene composition and component placement. The resulting model achieves high matching accuracy on complex layouts with variable numbers of components and demonstrates strong generalization across multiple configurations. Our results validate the feasibility of learning bijective mappings in simulated assembly scenarios, providing a foundation for scalable real-world robotic pick-and-place tasks. Tests were also conducted on actual robot units. Full article

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

► Show Figures

Figure 1

38 pages, 2219 KB

Open AccessReview

A Review of Human Intention Recognition Frameworks in Industrial Collaborative Robotics

by Mokone Kekana, Shengzhi Du, Nico Steyn, Abderraouf Benali and Halim Djerroud

Robotics 2025, 14(12), 174; https://doi.org/10.3390/robotics14120174 - 24 Nov 2025

Viewed by 1432

Abstract

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic [...] Read more.

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic assistance and promoting seamless human–robot collaboration, particularly in enhancing safety, improving operational efficiency, and enabling natural interactions. The paper discusses learning techniques such as rule-based, probabilistic, machine learning, and deep learning models. These technologies empower robots with human-like adaptability and decision-making skills. It also explores cues for intention recognition, categorising them into physical, physiological, and contextual cues. It highlights how implementing these various sensory inputs sharpen the interpretation of human intentions. Additionally, the discussion assesses the limitations of current research, including the need for usability, robustness, industrial readiness, real-time processing, and generalisability across various industrial applications. This evaluation identifies future research gaps that could improve the effectiveness of these systems in industrial settings. This work contributes to the ongoing conversation about the future of collaborative robotics, laying the foundation for advancements that can bridge the gap between human and robotic interactions. The key findings point out the significance of predictive understanding in promoting safer and more efficient human–robot interactions in industrial environments and provide recommendations for its use. Full article

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

► Show Figures

Figure 1

30 pages, 40146 KB

Open AccessArticle

Blast Hole Seeking and Dipping: Navigation and Perception Framework in a Mine Site Inspection Robot

by Liyang Liu, Ehsan Mihankhah, Nathan D. Wallace, Javier Martinez and Andrew J. Hill

Robotics 2025, 14(12), 173; https://doi.org/10.3390/robotics14120173 - 21 Nov 2025

Viewed by 588

Abstract

In open-pit mining, holes are drilled into the surface of the excavation site and are then detonated with explosives to facilitate digging. These blast holes need to be inspected internally for quality assurance, as well as for operational and geological reasons. Manual hole [...] Read more.

In open-pit mining, holes are drilled into the surface of the excavation site and are then detonated with explosives to facilitate digging. These blast holes need to be inspected internally for quality assurance, as well as for operational and geological reasons. Manual hole inspection is slow and expensive, limited in its ability to capture the geometric and geological characteristics of holes. This is the motivation behind the development of our autonomous mine site inspection robot—“DIPPeR”. In this paper, the automation aspect of the project is explained. We present a robust navigation and perception framework that provides streamlined blasthole detection, tracking, and precise down-hole sensor insertion during repetitive inspection tasks. To mitigate the effects of noisy GPS and odometry data typical of surface mining environments, we employ a proximity-based adaptive navigation system that enables the vehicle to dynamically adjust its operations according to target detectability and localisation accuracy. For perception, we process LiDAR data to extract the cone-shaped volume of drill waste above ground, and then project the 3D cone points into a virtual depth image to form accurate 2D segmentation of hole regions. To ensure continuous target tracking as the robot approaches the goal, our system automatically adjusts the projection parameters to ensure consistent appearance of the hole in the image. In the vicinity of the hole, we apply least squares circle fitting combined with non-maximum candidate suppression to achieve accurate hole localisation and collision-free down-hole sensor insertion. We demonstrate the effectiveness and robustness of our framework through dedicated perception and navigation feature tests, as well as streamlined mission trials conducted in high-fidelity simulations and real mine-site field experiments. Full article

(This article belongs to the Section Agricultural and Field Robotics)

► Show Figures

Graphical abstract

25 pages, 23143 KB

Open AccessArticle

Experimental Characterization of Miniature DC Motors for Robotics in High Magnetic Field Environments

by Francesco Mazzei, Luca Bernardi, Paolo Francesco Scaramuzzino, Corrado Gargiulo and Fabio Curti

Robotics 2025, 14(12), 172; https://doi.org/10.3390/robotics14120172 - 21 Nov 2025

Viewed by 737

Abstract

The deployment of robotic systems in hazardous and magnetically intense environments requires careful assessment of their performance under external disturbances. In particular, electromagnetic motors used for actuation may interact with strong magnetic fields, potentially impairing their functionality. This study investigates the behaviour of [...] Read more.

The deployment of robotic systems in hazardous and magnetically intense environments requires careful assessment of their performance under external disturbances. In particular, electromagnetic motors used for actuation may interact with strong magnetic fields, potentially impairing their functionality. This study investigates the behaviour of miniature brushed coreless Direct Current (DC) motors for small Unmanned Aerial Vehicle (UAV) applications in magnetically harsh environments, such as underground accelerator facilities like the Large Hadron Collider (LHC) at CERN. Experimental tests were conducted measuring four main physical quantities: the torque components acting along the axes orthogonal to the shaft, the torque about the shaft axis, variations in angular speed, and electrical current consumption. The results showed that the motors were able to operate under external magnetic field intensities up to 0.4 T, although measurable torques acted on the internal permanent magnet and on the ferromagnetic housing material. Some discrepancies and speed fluctuations were observed during operation and were attributed to mobility of the internal permanent magnet. Overall, the findings demonstrate that the tested miniature motors exhibit resilience in high magnetic fields but suffer from manufacturing variability, suggesting that higher-quality motors with more consistent characteristics would be preferable for reliable robotic operation in harsh environments. Full article

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

► Show Figures

Figure 1

Journal Menu

Journal Browser

Robotics, Volume 14, Issue 12 (December 2025) – 21 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI