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Distributed Monitoring of Moving Thermal Targets Using Unmanned Aerial Vehicles and Gaussian Mixture Models
Autonomous, Collaborative, and Confined Infrastructure Assessment with Purpose-Built Mega-Joey Robots
Model-Based Predictive Control for Position and Orientation Tracking in a Multilayer Architecture for a Three-Wheeled Omnidirectional Mobile Robot

Journal Description

Robotics

Robotics is an international, peer-reviewed, open access journal on robotics published monthly online by MDPI. The International Federation for the Promotion of Mechanism and Machine Science (IFToMM) and Robotic Global Surgical Society (TROGSS) are 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 21.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
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.
Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.

Impact Factor: 3.3 (2024); 5-Year Impact Factor: 3.8 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2218-6581

Latest Articles

24 pages, 19432 KiB

Open AccessArticle

Robot Learning from Teleoperated Demonstrations: A Pilot Study Towards Automating Mastic Deposition in Construction Sites

by Irati Rasines, Erlantz Loizaga, Rebecca Erlebach, Anurag Bansal, Sara Sillaurren, Patricia Rosen, Sascha Wischniewski, Arantxa Renteria and Itziar Cabanes

Robotics 2025, 14(8), 114; https://doi.org/10.3390/robotics14080114 - 19 Aug 2025

The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a [...] Read more.

The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a constantly changing environment that requires adaptive control strategies to ensure consistent application quality. This pilot study focuses on testing a new human–robot collaborative approach for automating the mastic application in concrete expansion joints. The system learns the task from demonstrations performed by expert construction operators teleoperating the robot. This study evaluates the usability, efficiency, and adoption of robotic assistance in joint-filling tasks compared to traditional manual methods. The study analyzes execution time and joint quality measurements, psychophysiological signal analysis, and post-task user feedback. This multi-source approach enables a comprehensive assessment of task performance and both objective and subjective evaluations of technology acceptance. The findings underscore the effectiveness of automated systems in improving safety and productivity on construction sites, while also identifying key areas for technological improvement. Full article

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

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23 pages, 21704 KiB

Open AccessArticle

Autonomous Grasping of Deformable Objects with Deep Reinforcement Learning: A Study on Spaghetti Manipulation

by Prem Gamolped, Nattapat Koomklang, Abbe Mowshowitz and Eiji Hayashi

Robotics 2025, 14(8), 113; https://doi.org/10.3390/robotics14080113 - 18 Aug 2025

Packing food into lunch boxes requires the correct portion to be selected. Food items such as fried chicken, eggs, and sausages are straightforward to manipulate when packing. In contrast, deformable objects like spaghetti can give challenges to lunch box packing due to their [...] Read more.

Packing food into lunch boxes requires the correct portion to be selected. Food items such as fried chicken, eggs, and sausages are straightforward to manipulate when packing. In contrast, deformable objects like spaghetti can give challenges to lunch box packing due to their fragility and tendency to break apart, and the fluctuating weight of noodles. Furthermore, achieving the correct amount is crucial for lunch box packing. This research focuses on self-learned grasping by a robotic arm to enable the ability to autonomously predict and grasp deformable objects, specifically spaghetti, to achieve the correct amount within specified ranges. We utilize deep reinforcement learning as the core learning. We developed a custom environment and policy network along a real-world scenario that was simplified as in a food factory, incorporating multi-sensors to observe the environment and pipeline to work with a real robotic arm. Through the study and experiments, our results show that the robot can grasp the spaghetti within the desired ranges, although occasional failures were caused by the nature of the deformable object. Addressing the problem under varying environmental conditions such as data augmentation can partially help model prediction. The study highlights the potential of combining deep learning with robotic manipulation for complex deformable object tasks, offering insight for applications in automated food handling and other industries. Full article

(This article belongs to the Section Humanoid and Human Robotics)

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15 pages, 252 KiB

Open AccessArticle

Mortal vs. Machine: A Compact Two-Factor Model for Comparing Trust in Humans and Robots

by Andrew Prahl

Robotics 2025, 14(8), 112; https://doi.org/10.3390/robotics14080112 - 16 Aug 2025

Trust in robots is often analyzed with scales built for either humans or automation, making cross-species comparisons imprecise. Addressing that gap, this paper distils decades of trust scholarship, from clinical vs. actuarial judgement to modern human–robot teaming, into a lean two-factor framework: Mortal [...] Read more.

Trust in robots is often analyzed with scales built for either humans or automation, making cross-species comparisons imprecise. Addressing that gap, this paper distils decades of trust scholarship, from clinical vs. actuarial judgement to modern human–robot teaming, into a lean two-factor framework: Mortal vs. Machine (MvM). We first surveyed classic technology-acceptance and automation-reliance research and then integrated empirical findings in human–robot interaction to identify diagnostic cues that can be instantiated by both human and machine agents. The model includes (i) ability—perceived task competence and reliability—and (ii) value congruence—alignment of decision weights and trade-off priorities. Benevolence, oft-included in trust studies, was excluded because current robots cannot manifest genuine goodwill and existing items elicit high dropout. The resulting scale travels across contexts, allowing for researchers to benchmark a robot against a human co-worker on identical terms and enabling practitioners to pinpoint whether performance deficits or priority clashes drive acceptance. By reconciling anthropocentric and technocentric trust literature in a deployable diagnostic, MvM offers a field-ready tool and a conceptual bridge for future studies of AI-empowered robotics. Full article

(This article belongs to the Section Humanoid and Human Robotics)

13 pages, 854 KiB

Open AccessArticle

Physical Reinforcement Learning with Integral Temporal Difference Error for Constrained Robots

by Luis Pantoja-Garcia, Vicente Parra-Vega and Rodolfo Garcia-Rodriguez

Robotics 2025, 14(8), 111; https://doi.org/10.3390/robotics14080111 - 14 Aug 2025

The paradigm of reinforcement learning (RL) refers to agents that learn iteratively through continuous interactions with their environment. However, when the value function is unknown, a neural network is used, which is typically encoded into an unknown temporal difference equation. When RL is [...] Read more.

The paradigm of reinforcement learning (RL) refers to agents that learn iteratively through continuous interactions with their environment. However, when the value function is unknown, a neural network is used, which is typically encoded into an unknown temporal difference equation. When RL is implemented in physical systems, explicit convergence and stability analyses are required to guarantee the worst-case operations for any trial, even when the initial conditions are set to zero. In this paper, physical RL (p-RL) refers to the application of RL in dynamical systems that interact with their environments, such as robot manipulators in contact tasks and humanoid robots in cooperation or interaction tasks. Unfortunately, most p-RL schemes lack stability properties, which can even be dangerous for specific robot applications, such as those involving contact (constrained) tasks or interaction tasks. Considering an unknown and disturbed DAE2 robot, in this paper a p-RL approach is developed to guaranteeing robust stability throughout a continuous-time-adaptive actor–critic, with local exponential convergence of force–position tracking error. The novel adaptive mechanisms lead to robustness, while an integral sliding mode enforces tracking. Simulations are presented and discussed to show our proposal’s effectiveness, and some final remarks are addressed concerning the structural aspects. Full article

(This article belongs to the Section AI in Robotics)

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31 pages, 6032 KiB

Open AccessArticle

Event-Based Closed-Loop Control for Path Following of a Purcell’s Three-Link Swimmer

by Cristina Nuevo-Gallardo, Luis Mérida-Calvo, Inés Tejado, Blas M. Vinagre and Vicente Feliu-Batlle

Robotics 2025, 14(8), 110; https://doi.org/10.3390/robotics14080110 - 14 Aug 2025

Purcell’s three-link swimmers, characterised by segments connected through one-degree-of-freedom joints, exhibit a difficulty in following precise paths. This is attributed to their motion primitives, which do not inherently generate displacement in a singular, predictable direction. To overcome this limitation, this paper proposes a [...] Read more.

Purcell’s three-link swimmers, characterised by segments connected through one-degree-of-freedom joints, exhibit a difficulty in following precise paths. This is attributed to their motion primitives, which do not inherently generate displacement in a singular, predictable direction. To overcome this limitation, this paper proposes a closed-loop control strategy on the basis of event-based control. This control approach enables the robot to perform a specific motion primitive when the deviation from the desired trajectory exceeds a predefined threshold. In other words, an asynchronous strategy is used to adjust the motion of the swimmer, thereby ensuring tracking of the desired path with a limited error. The effectiveness of this closed-loop control strategy is demonstrated through experiments with a motor-driven 30 cm-length prototype. These tests show that this event-based control strategy allows the swimmer to follow specific paths with a tracking error of less than 30% of its length. Full article

(This article belongs to the Special Issue Adaptive and Nonlinear Control of Robotics)

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49 pages, 15124 KiB

Open AccessArticle

Flexible Constraint-Based Controller Framework for Ros_Control

by Miguel Prada, Asier Fernandez, Anthony Remazeilles and Joseph McIntyre

Robotics 2025, 14(8), 109; https://doi.org/10.3390/robotics14080109 - 11 Aug 2025

Generating robot behaviors in dynamic real-world situations generally requires the programming of multiple, often redundant degrees of freedom to meet multiple goals governing the desired motions. In this work, we propose a constraint-based controller specification methodology. A novel declarative language is used to [...] Read more.

Generating robot behaviors in dynamic real-world situations generally requires the programming of multiple, often redundant degrees of freedom to meet multiple goals governing the desired motions. In this work, we propose a constraint-based controller specification methodology. A novel declarative language is used to combine semantically specialized building blocks into composite controllers. This description is automatically transformed at runtime into an executable form, which can automatically leverage multiple threads to parallelize computations whenever possible. Enabling runtime definition of controller topologies out of declarative descriptions not only reduces the work required to develop such controllers, but it also allows one to dynamically synthesize new controllers based on higher-level task planners or by user interaction through Graphical User Interfaces (GUIs). Our solution adds new functionality to the Robot Operating System (ROS)/ros_control ecosystem, where robot behaviors are typically achieved by deploying single-objective, off-the-shelf controllers for tasks like following joint trajectories, executing interpolated point-to-point motions in Cartesian space, or for basic compliant behaviors. Our proposed constraint-based framework enhances ros_control by providing the means to easily construct composite controllers from existing primary elements using our design language. Building on top of the ros_control infrastructure facilitates the usage of our controller with a wide range of supported robots and enables quick integration with the existing ROS ecosystem. Full article

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

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14 pages, 18726 KiB

Open AccessArticle

Safe Autonomous UAV Target-Tracking Under External Disturbance, Through Learned Control Barrier Functions

by Promit Panja, Madan Mohan Rayguru and Sabur Baidya

Robotics 2025, 14(8), 108; https://doi.org/10.3390/robotics14080108 - 3 Aug 2025

Ensuring the safe operation of Unmanned Aerial Vehicles (UAVs) is crucial for both mission-critical and safety-critical tasks. In scenarios where UAVs must track airborne targets, they need to follow the target’s path while maintaining a safe distance, even in the presence of unmodeled [...] Read more.

Ensuring the safe operation of Unmanned Aerial Vehicles (UAVs) is crucial for both mission-critical and safety-critical tasks. In scenarios where UAVs must track airborne targets, they need to follow the target’s path while maintaining a safe distance, even in the presence of unmodeled dynamics and environmental disturbances. This paper presents a novel collision avoidance strategy for dynamic quadrotor UAVs during target-tracking missions. We propose a safety controller that combines a learning-based Control Barrier Function (CBF) with standard sliding mode feedback. Our approach employs a neural network that learns the true CBF constraint, accounting for wind disturbances, while the sliding mode controller addresses unmodeled dynamics. This unified control law ensures safe leader-following behavior and precise trajectory tracking. By leveraging a learned CBF, the controller offers improved adaptability to complex and unpredictable environments, enhancing both the safety and robustness of the system. The effectiveness of our proposed method is demonstrated through the AirSim platform using the PX4 flight controller. Full article

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

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16 pages, 2448 KiB

Open AccessArticle

A Body-Powered Underactuated Prosthetic Finger Driven by MCP Joint Motion

by Worathris Chungsangsatiporn, Chaiwuth Sithiwichankit, Ratchatin Chancharoen, Ronnapee Chaichaowarat, Nopdanai Ajavakom and Gridsada Phanomchoeng

Robotics 2025, 14(8), 107; https://doi.org/10.3390/robotics14080107 - 31 Jul 2025

This study presents the design, fabrication, and clinical validation of a lightweight, body-powered prosthetic index finger actuated via metacarpophalangeal (MCP) joint motion. The proposed system incorporates an underactuated, cable-driven mechanism combining rigid and compliant elements to achieve passive adaptability and embodied intelligence, supporting [...] Read more.

This study presents the design, fabrication, and clinical validation of a lightweight, body-powered prosthetic index finger actuated via metacarpophalangeal (MCP) joint motion. The proposed system incorporates an underactuated, cable-driven mechanism combining rigid and compliant elements to achieve passive adaptability and embodied intelligence, supporting intuitive user interaction. Results indicate that the prosthesis successfully mimics natural finger flexion and adapts effectively to a variety of grasping tasks with minimal effort. This study was conducted in accordance with ethical standards and approved by the Institutional Review Board (IRB), Project No. 670161, titled “Biologically-Inspired Synthetic Finger: Design, Fabrication, and Application.” The findings suggest that the device offers a viable and practical solution for individuals with partial hand loss, particularly in settings where electrically powered systems are unsuitable or inaccessible. Full article

(This article belongs to the Section Neurorobotics)

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26 pages, 6831 KiB

Open AccessArticle

Human–Robot Interaction and Tracking System Based on Mixed Reality Disassembly Tasks

by Raúl Calderón-Sesmero, Adrián Lozano-Hernández, Fernando Frontela-Encinas, Guillermo Cabezas-López and Mireya De-Diego-Moro

Robotics 2025, 14(8), 106; https://doi.org/10.3390/robotics14080106 - 30 Jul 2025

Disassembly is a crucial process in industrial operations, especially in tasks requiring high precision and strict safety standards when handling components with collaborative robots. However, traditional methods often rely on rigid and sequential task planning, which makes it difficult to adapt to unforeseen [...] Read more.

Disassembly is a crucial process in industrial operations, especially in tasks requiring high precision and strict safety standards when handling components with collaborative robots. However, traditional methods often rely on rigid and sequential task planning, which makes it difficult to adapt to unforeseen changes or dynamic environments. This rigidity not only limits flexibility but also leads to prolonged execution times, as operators must follow predefined steps that do not allow for real-time adjustments. Although techniques like teleoperation have attempted to address these limitations, they often hinder direct human–robot collaboration within the same workspace, reducing effectiveness in dynamic environments. In response to these challenges, this research introduces an advanced human–robot interaction (HRI) system leveraging a mixed-reality (MR) interface embedded in a head-mounted device (HMD). The system enables operators to issue real-time control commands using multimodal inputs, including voice, gestures, and gaze tracking. These inputs are synchronized and processed via the Robot Operating System (ROS2), enabling dynamic and flexible task execution. Additionally, the integration of deep learning algorithms ensures precise detection and validation of disassembly components, enhancing accuracy. Experimental evaluations demonstrate significant improvements, including reduced task completion times, enhanced operator experience, and compliance with strict adherence to safety standards. This scalable solution offers broad applicability for general-purpose disassembly tasks, making it well-suited for complex industrial scenarios. Full article

(This article belongs to the Special Issue Robot Teleoperation Integrating with Augmented Reality)

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18 pages, 16316 KiB

Open AccessArticle

AntGrip—Boosting Parallel Plate Gripper Performance Inspired by the Internal Hairs of Ant Mandibles

by Mohamed Sorour and Barbara Webb

Robotics 2025, 14(8), 105; https://doi.org/10.3390/robotics14080105 - 30 Jul 2025

Ants use their mandibles—effectively a two-finger gripper—for a wide range of grasping activities. Here, we investigate whether mimicking the internal hairs found on ant mandibles can improve performance of a two-finger parallel plate robot gripper. With bin-picking applications in mind, the gripper fingers [...] Read more.

Ants use their mandibles—effectively a two-finger gripper—for a wide range of grasping activities. Here, we investigate whether mimicking the internal hairs found on ant mandibles can improve performance of a two-finger parallel plate robot gripper. With bin-picking applications in mind, the gripper fingers are long and slim, with interchangeable soft gripping pads that can be hairy or hairless. A total of 2400 video-documented experiments have been conducted, comparing hairless to hairy pads with different hair patterns. Simply by adding hairs, the grasp success rate was increased by at least

29 %

, and the number of objects that remain securely gripped during manipulation more than doubled. This result not only advances the state of the art in grasping technology, but also provides novel insight into the mechanical role of mandible hairs in ant biology. Full article

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

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20 pages, 2108 KiB

Open AccessReview

Underwater Polarized Light Navigation: Current Progress, Key Challenges, and Future Perspectives

by Mingzhi Chen, Yuan Liu, Daqi Zhu, Wen Pang and Jianmin Zhu

Robotics 2025, 14(8), 104; https://doi.org/10.3390/robotics14080104 - 29 Jul 2025

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, [...] Read more.

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries. Full article

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

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21 pages, 6018 KiB

Open AccessArticle

Model-Based Design of the 5-DoF Light Industrial Robot

by Yongping Shi, Tianbing Ma, Hao Wang, Tao Zhang, Xin Zhang, Huapeng Wu and Ming Li

Robotics 2025, 14(8), 103; https://doi.org/10.3390/robotics14080103 - 29 Jul 2025

With the application and rapid development of light industrial robots, it is vital to accelerate the prototype design to fulfill the demands of shortening the robot’s production cycle, owing to rapid update iterations. Since the traditional design method cannot intuitively and efficiently check [...] Read more.

With the application and rapid development of light industrial robots, it is vital to accelerate the prototype design to fulfill the demands of shortening the robot’s production cycle, owing to rapid update iterations. Since the traditional design method cannot intuitively and efficiently check the deficiencies in the design preparation, the secondary design iterations will result in higher equipment costs, longer design cycles, and lower development efficiency. The MBD (model-based design), a full 3D (three-dimensional) design and manufacturing method, is proposed to swiftly finish the prototype design for solving the above problems. Firstly, the robot design preparation is completed with the design requirements to generate a robot 3D model. Secondly, several design methods are used: (i) the rapid prototyping, which includes the joint component verification and selection to further optimize the 3D model; (ii) the robot kinematics algorithm, which provides a theoretical foundation for the 3D model design; (iii) the robot kinematics simulation, which verifies the correctness of the kinematics algorithm. Finally, the feasibility of the MBD is verified by the robot prototype and the motion control system test. Taking the MBD to design a 5-DoF (five-degrees-of-freedom) robot as an example, the joint verification and selection are finished quickly and accurately to build the robot prototype without the need for secondary design processing, and the kinematic algorithm verified by the co-simulation platform can be used directly in the actual motion control of the robot prototype, which accelerates the development of the robot motion control system. Full article

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

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18 pages, 2335 KiB

Open AccessArticle

MLLM-Search: A Zero-Shot Approach to Finding People Using Multimodal Large Language Models

by Angus Fung, Aaron Hao Tan, Haitong Wang, Bensiyon Benhabib and Goldie Nejat

Robotics 2025, 14(8), 102; https://doi.org/10.3390/robotics14080102 - 28 Jul 2025

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that [...] Read more.

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments. Full article

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

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32 pages, 5721 KiB

Open AccessReview

Control Strategies for Two-Wheeled Self-Balancing Robotic Systems: A Comprehensive Review

by Huaqiang Zhang and Norzalilah Mohamad Nor

Robotics 2025, 14(8), 101; https://doi.org/10.3390/robotics14080101 - 26 Jul 2025

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review [...] Read more.

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development. Full article

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

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16 pages, 10372 KiB

Open AccessArticle

PRONOBIS: A Robotic System for Automated Ultrasound-Based Prostate Reconstruction and Biopsy Planning

by Matija Markulin, Luka Matijević, Janko Jurdana, Luka Šiktar, Branimir Ćaran, Toni Zekulić, Filip Šuligoj, Bojan Šekoranja, Tvrtko Hudolin, Tomislav Kuliš, Bojan Jerbić and Marko Švaco

Robotics 2025, 14(8), 100; https://doi.org/10.3390/robotics14080100 - 22 Jul 2025

This paper presents the PRONOBIS project, an ultrasound-only, robotically assisted, deep learning-based system for prostate scanning and biopsy treatment planning. The proposed system addresses the challenges of precise prostate segmentation, reconstruction and inter-operator variability by performing fully automated prostate scanning, real-time CNN-transformer-based image [...] Read more.

This paper presents the PRONOBIS project, an ultrasound-only, robotically assisted, deep learning-based system for prostate scanning and biopsy treatment planning. The proposed system addresses the challenges of precise prostate segmentation, reconstruction and inter-operator variability by performing fully automated prostate scanning, real-time CNN-transformer-based image processing, 3D prostate reconstruction, and biopsy needle position planning. Fully automated prostate scanning is achieved by using a robotic arm equipped with an ultrasound system. Real-time ultrasound image processing utilizes state-of-the-art deep learning algorithms with intelligent post-processing techniques for precise prostate segmentation. To create a high-quality prostate segmentation dataset, this paper proposes a deep learning-based medical annotation platform, MedAP. For precise segmentation of the entire prostate sweep, DAF3D and MicroSegNet models are evaluated, and additional image post-processing methods are proposed. Three-dimensional visualization and prostate reconstruction are performed by utilizing the segmentation results and robotic positional data, enabling robust, user-friendly biopsy treatment planning. The real-time sweep scanning and segmentation operate at 30 Hz, which enable complete scan in 15 to 20 s, depending on the size of the prostate. The system is evaluated on prostate phantoms by reconstructing the sweep and by performing dimensional analysis, which indicates 92% and 98% volumetric accuracy on the tested phantoms. Three-dimansional prostate reconstruction takes approximately 3 s and enables fast and detailed insight for precise biopsy needle position planning. Full article

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

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13 pages, 2559 KiB

Open AccessArticle

An AI Approach to Markerless Augmented Reality in Surgical Robots

by Abhishek Shankar, Luay Jawad and Abhilash Pandya

Robotics 2025, 14(7), 99; https://doi.org/10.3390/robotics14070099 - 19 Jul 2025

This paper examines the integration of markerless augmented reality (AR) within the da Vinci Surgical Robot, utilizing artificial intelligence (AI) for improved precision. The main challenge in creating AR for these systems is the small size (5 mm diameter) of the cameras used. [...] Read more.

This paper examines the integration of markerless augmented reality (AR) within the da Vinci Surgical Robot, utilizing artificial intelligence (AI) for improved precision. The main challenge in creating AR for these systems is the small size (5 mm diameter) of the cameras used. Traditional camera-calibration approaches produce significant errors when used for miniature cameras. Further, the use of external markers can be obstructive and inaccurate in dynamic surgical environments. The study focuses on overcoming these limitations of traditional AR methods by employing advanced neural networks for camera calibration and real-time image processing. We demonstrate the use of a dense neural network to reduce the total projection error by directly learning the mapping of a 3D point to a 2D image plane. The results show a median error of 7 pixels (1.4 mm) when using a neural network, as compared to an error of 50 pixels (10 mm) when using a more traditional approach involving camera calibration and robot kinematics. This approach not only enhances the accuracy of AR for surgical procedures but also offers a more seamless integration with existing robotic platforms. These research findings underscore the potential of AI in revolutionizing AR applications in medical robotics and other teleoperated systems, promising efficient and safer interventions. Full article

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

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15 pages, 1795 KiB

Open AccessArticle

Minimum-Energy Trajectory Planning for an Underactuated Serial Planar Manipulator

by Domenico Dona’, Jason Bettega, Iacopo Tamellin, Paolo Boscariol and Roberto Caracciolo

Robotics 2025, 14(7), 98; https://doi.org/10.3390/robotics14070098 - 18 Jul 2025

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null [...] Read more.

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null residual (and yet practical low) vibration in underactuated systems. The trajectory planning problem is cast as a constrained optimal control problem (OCP) for a two-degree-of-freedom revolute–revolute planar manipulator. The proposed method produces energy-efficient motion while limiting residual vibrations under motor torque limitations. Experiments compare the proposed trajectories to input shaping techniques (ZV, ZVD, NZV, NZVD). Results show energy savings that range from 12% to 69% with comparable and negligible residual oscillations. Full article

(This article belongs to the Special Issue Adaptive and Nonlinear Control of Robotics)

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21 pages, 6802 KiB

Open AccessArticle

Digital Twin Driven Four-Dimensional Path Planning of Collaborative Robots for Assembly Tasks in Industry 5.0

by Ilias Chouridis, Gabriel Mansour, Asterios Chouridis, Vasileios Papageorgiou, Michel Theodor Mansour and Apostolos Tsagaris

Robotics 2025, 14(7), 97; https://doi.org/10.3390/robotics14070097 - 15 Jul 2025

Collaborative robots are vital in Industry 5.0 operations. They are utilized to perform tasks in collaboration with humans or other robots to increase overall production efficiency and execute complex tasks. Aiming at a comprehensive approach to assembly processes and highlighting new applications of [...] Read more.

Collaborative robots are vital in Industry 5.0 operations. They are utilized to perform tasks in collaboration with humans or other robots to increase overall production efficiency and execute complex tasks. Aiming at a comprehensive approach to assembly processes and highlighting new applications of collaborative robots, this paper presents the development of a digital twin (DT) for the design, monitoring, optimization and simulation of robots’ deployment in assembly cells. The DT integrates information from both the physical and virtual worlds to design the trajectory of collaborative robots. The physical information about the industrial environment is replicated within the DT in a computationally efficient way that aligns with the requirements of the path planning algorithm and the DT’s objectives. An enhanced artificial fish swarm algorithm (AFSA) is utilized for the 4D path planning optimization, taking into account dynamic and static obstacles. Finally, the proposed framework is utilized for the examination of a case in which four industrial robotic arms are collaborating for the assembly of an industrial component. Full article

(This article belongs to the Special Issue Robot Teleoperation Integrating with Augmented Reality)

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22 pages, 4827 KiB

Open AccessArticle

Development of a Multifunctional Mobile Manipulation Robot Based on Hierarchical Motion Planning Strategy and Hybrid Grasping

by Yuning Cao, Xianli Wang, Zehao Wu and Qingsong Xu

Robotics 2025, 14(7), 96; https://doi.org/10.3390/robotics14070096 - 15 Jul 2025

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a [...] Read more.

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

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

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27 pages, 6183 KiB

Open AccessArticle

A Cartesian Parallel Mechanism for Initial Sonography Training

by Mykhailo Riabtsev, Jean-Michel Guilhem, Victor Petuya, Mónica Urizar and Med Amine Laribi

Robotics 2025, 14(7), 95; https://doi.org/10.3390/robotics14070095 - 10 Jul 2025

This paper presents the development and analysis of a novel 6-DOF Cartesian parallel mechanism intended for use as a haptic device for initial sonography training. The system integrates a manipulator designed for delivering force feedback in five degrees of freedom; however, in the [...] Read more.

This paper presents the development and analysis of a novel 6-DOF Cartesian parallel mechanism intended for use as a haptic device for initial sonography training. The system integrates a manipulator designed for delivering force feedback in five degrees of freedom; however, in the current stage, only mechanical architecture and kinematic validation have been conducted. Future enhancements will focus on implementing and evaluating closed-loop force control to enable complete haptic feedback. To assess the kinematic performance of the mechanism, a detailed kinematic model was developed, and both the Kinematic Conditioning Index (KCI) and Global Conditioning Index (GCI) were computed to evaluate the system’s dexterity. A trajectory simulation was conducted to validate the mechanism’s movement, using motion patterns typical in sonography procedures. Quasi-static analysis was performed to study the transmission of force and torque for generating realistic haptic feedback, critical for simulating real-life sonography. The simulation results showed consistent performance, with dexterity and torque distribution confirming the suitability of the mechanism for haptic applications in sonography training. Additionally, structural analysis verified the robustness of key components under expected loads. In order to validate the proposed design, the prototype was constructed using a combination of aluminum components and 3D-printed ABS parts, with Igus^® linear guides for precise motion. The outcomes of this study provide a foundation for the further development of a low-cost, effective sonography training system. Full article

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

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