Search Results (1,007)

To meet the development requirements of the path planning unit for unmanned underwater vehicles (UUVs), research is conducted on UUV 3D obstacle avoidance path planning algorithms and software design. Firstly, aiming at the problem of underwater 3D obstacle avoidance path planning for UUVs, a global path planning algorithm based on the improved D* Lite is designed, and a local path planning algorithm combining the 3D obstacle avoidance strategy and the improved artificial potential field (APF) algorithm is designed. Secondly, based on the above path planning algorithms, a UUV 3D obstacle avoidance path planning software is developed under the Robot Operating System 2 (ROS2) framework and deployed on an Orange Pi 5B. To test the algorithms and the developed software, a UUV autonomous navigation hardware-in-the-loop (HIL) simulation system is constructed. Finally, based on this system, three types of HIL simulation experiments are conducted, including global path planning, local path planning, and comprehensive obstacle avoidance path planning. The simulation experiments show that the improved D* Lite-APF algorithm has better comprehensive performance than the traditional D* Lite-APF algorithm; the path planning software can guide the UUV to reach the goal point safely and runs stably and reliably. The designed UUV 3D obstacle avoidance path planning algorithm and software exhibit good obstacle avoidance performance and can be applied to the rapid development of actual UUV path planning units. Full article

In emergency scenarios where visibility is compromised, rapid and accurate object detection becomes critical. This study addresses this challenge by proposing an IoT-enabled robotic solution capable of operating in low-visibility environments, with a focus on supporting search and rescue missions through autonomous sensing and real-time data communication. This research presents the development and implementation of an IoT-based sensorized system designed to detect objects in low-visibility environments. The system aims to enhance search and rescue operations by identifying potential human presence in areas with limited access due to smoke, darkness, or hazardous conditions. The platform integrates distance sensors, a thermal camera (AMG8833), a PIR motion sensor, and wireless communication through the Arduino MKR1000 and ESP32-CAM boards. The mobile robot is equipped with obstacle avoidance, person detection, and IoT communication modules, allowing data to be sent to the cloud via ThingSpeak and enabling remote commands through TalkBack. A structured methodology was followed, including technology selection, hardware/software design, and testing under various lighting and opacity conditions. Experimental results showed the effectiveness of the system in identifying obstacles and detecting heat signatures representing human body, with optimal performance observed at a 15 cm detection threshold. The system demonstrated robust operation in simulated rescue environments, providing real-time data transmission and remote-control capabilities. Full article

As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean methods with technologies such as simulation, robotics, and virtual reality in a modular educational environment. At the University Centre Pitești, six hands-on projects were implemented to guide students through key concepts, including production system layout, digital assistance, sustainability, and human–robot collaboration. Through experiential learning, students engage in iterative design, data analysis, and practical validation using real equipment and software platforms. The results indicate that the LLF effectively supports the development of technical, digital, transversal, and human-centred competencies aligned with EUR-ACE^® standards. Students acquire skills in process optimisation, ergonomics, and sustainable production, while also reflecting on the ethical and social implications of automation. The study concludes that the LLF model provides a scalable and adaptable framework for engineering education. It fosters competence-based learning and prepares students for the demands of Industry 5.0. This paper contributes a replicable educational approach that blends Lean efficiency, digital transformation, and human-centred values into a cohesive learning ecosystem. Full article

The emergence of highly complex generative AI and large language models represents both a significant challenge and an opportunity for multiple engineering domains. Under the Industry 4.0 paradigm, various connected automation and industrial engineering applications can leverage the inference and generative design capabilities of these models to improve control algorithms and systems. In particular, widespread deployment of mobile robotic platforms in modern industry, enhanced with LLM capabilities, can provide a substantial increase in the efficiency and cost-effectiveness of such solutions. In this study, we investigate the suitability of current-generation LLM systems for industrial mobile robot control. We present a systematic, end-to-end methodology for benchmarking four GenAI/LLMs, SmolLM2, Llama 3.2, Gemma3, and Gemma3-qat, for a typical mobile robot platform configuration. The approach is two-staged, based on both assessing the specific domain knowledge of the models in an industrial context and their integration with a robotic simulation environment based on ROS2. Reported results focus on quantitative assessment of multiple metrics (quality, coverage, speed, and reliability) and their integration in aggregated scoring mechanisms, which can help developers select and adapt the best model for a particular application, together with custom software implementation. Full article

This paper presents an open-architecture mechatronic drive platform for operating a three-phase BLDC servomotor in an industrial robotic axis. A sequential and iterative mechatronic design methodology is adopted, integrating electronic design, digital control, mechanical development, and experimental prototyping, with emphasis on open-loop operation. The electronic circuit was designed using schematics and a PCB and validated in Proteus Design Suite 8.15 (Labcenter Electronics Ltd., London, UK) to verify switching sequences and inverter behavior. The power stage is based on a six-switch insulated-gate bipolar transistor (IGBT) inverter module, complemented by an independent snubber protection board and a dedicated digital gate-drive control board. The mechanical enclosure was designed using computer-aided design (CAD), CAD software tools (Shapr3D, version 5.911.0 (9224), Shapr3D Zrt., Budapest, Hungary), and fabricated via 3D printing. Switching behavior was simulated in Octave using parameters from a real industrial BLDC servomotor (Yaskawa SGMAH series) extracted from a Motoman robotic axis. The contribution is design-oriented in a mechatronic engineering sense, emphasizing accessibility, openness, and experimental enablement of industrial drive hardware rather than control-performance optimization. An industrial Yaskawa BLDC servomotor from the Motoman robot is used to determine switching sequences and safe operating parameters. Experimental open-loop tests were conducted by directly commanding the six inverter switching sectors, resulting in the stable synchronous rotation of the motor on the developed electromechanical platform. Full article

This paper presents the development and experimental evaluation of the Athena parallel robot, a novel system designed for robot-assisted pancreatic surgery. The development of the experimental model based on the kinematic scheme, including the command and control system (hardware and software), the calibration procedure, and the performance measurements of the experimental model based on finite element analyses of the 3D model, are also detailed in this paper. Based on these finite element analyses, a region of the robot that introduces clearance during the operation of the experimental model is found. The paper also presents the methodology used for mapping the robot’s workspace with an optical system, which enabled improvements to ensure coverage of the entire pancreas area. The results obtained before and after the mechanical improvements are presented, demonstrating a reduction in clearance by up to 4.1 times following part replacement, as well as a workspace extension that enables the active instrument to reach the entire pancreatic region. Full article

Advances in artificial intelligence (AI), soft robotics, and miniaturized imaging technologies have accelerated the development of endorobotic platforms that aim to enhance detection accuracy and improve patient experience. In this narrative review, we synthesize evidence on AI-assisted detection and characterization systems (CADe/CADx), robotic locomotion mechanisms, adhesion strategies, imaging modalities, and material and power constraints relating to next-generation CRC screening technologies. Reported performance metrics are interpreted within their original methodological contexts, acknowledging the heterogeneity of datasets, limited representation of diverse populations, underreporting of negative findings, and scarcity of large, real-world comparative trials. We introduce a conceptual translational framework that links engineering design principles with validation needs across in silico, in vitro, preclinical, and clinical stages, and we outline safety considerations, workflow integration challenges, and sterility requirements that influence real-world deployability. Regulatory alignment is discussed using the U.S. FDA Total Product Life Cycle (TPLC) and Good Machine Learning Practice (GMLP) frameworks to highlight expectations for data quality, model robustness, device–software interoperability, and post-market monitoring. Collectively, the evidence demonstrates promising technological innovation but also highlights substantial gaps that must be addressed before AI-enabled endorobotic systems can be safely and effectively integrated into routine CRC screening. Continued interdisciplinary work, supported by rigorous validation and transparent reporting, will be essential to advance these technologies toward meaningful clinical impact. Full article

This paper presents the design and implementation of a novel autonomous underwater robot with fully vectored propulsion based on model predictive control (MPC) to rapidly respond to the position and attitude required for autonomous operation. Specifically, the mechatronic design of the eight vector-distributed thruster layout for the robot’s fully vectored propulsion is detailed, and the software architecture based on the robot operating system (ROS) is constructed. Then, the corresponding dynamics model is established by adopting the Fossen approach for the prediction and optimization of the control process. To achieve autonomous control, an MPC-based controller is designed and implemented to calculate the control input for the specified control objective. Finally, way-point tracking and trajectory-tracking experiments are carried out in an indoor tank equipped with a motion-capture system to validate the feasibility and effectiveness of the robot’s design and control framework. In addition, the robustness of the robot system is verified by artificially perturbing the robot in the hovering state. Full article

Stroke continues to impose an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet, is an emerging resource in neurorehabilitation for stroke survivors. Technologies such as asynchronous transmission to handle intermittent connectivity, edge computing to conserve bandwidth and lengthen device life, functional interoperability across platforms, security mechanisms scalable to resource constraints, and hybrid architectures that combine local processing with cloud synchronization help bridge the digital divide and infrastructure limitations in low-resource environments. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain–computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients. Full article

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

Autonomous UAV/UGV swarms increasingly operate in contested environments where purely digital control architectures are vulnerable to cyber compromise, communication denial, and timing faults. This paper presents Guarded Swarms, a hybrid framework that combines digital coordination with hardware-level analog safety enforcement. The architecture builds on Topic-Based Communication Space Petri Nets (TB-CSPN) for structured multi-agent coordination, extending this digital foundation with independent analog guard channels—thrust clamps, attitude limiters, proximity sensors, and emergency stops—that operate in parallel at the actuator interface. Each channel can unilaterally veto unsafe commands within microseconds, independently of software state. The digital–analog interface is formalized via timing contracts that specify sensor-consistency windows and actuation latency bounds. A two-robot case study demonstrates token-based arbitration at the digital level and OR-style inhibition at the analog level. The framework ensures local safety deterministically while maintaining global coordination as a best-effort property. This paper presents an architectural contribution establishing design principles and interface contracts. Empirical validation remains future work. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment. Full article

This study investigates the perceptions of Greek sixth-grade students regarding Artificial Intelligence (AI). Understanding students’ pre-instructional conceptions is essential for developing targeted interventions that build on existing knowledge rather than assuming conceptual deficits. A qualitative design was employed with 229 students from seven elementary schools in Athens, Greece. Data were collected through open-ended questions and word association tasks, then analyzed using Walan’s AI perceptions framework as an integrated set of analytical lenses (cognitive, affective, behavioral/use, and ethical considerations). Findings revealed that students hold multifaceted conceptions of AI. Cognitively, they described AI as robots, computational systems, software tools, and autonomous learning programs. Affectively, they expressed ambivalence, balancing appreciation of AI’s usefulness with concerns over potential risks. Behaviorally, they identified interactive question–answer functions, creative applications, and everyday assistance roles. Ethically, students raised issues of responsible use, societal implications, and human–AI relationships. This study contributes to international research, highlighting that primary students’ understandings of AI are more nuanced than is sometimes assumed, and offer empirical insights for designing culturally responsive, ethically informed AI literacy curricula. Full article