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Low-Light Enhancement Visual SLAM via Deep Deformable Power Curve Estimation

IEEE Sensors Journal 2025年第10期25卷 17404-17412页

作者： Liu, Yanjie Wu, Heng Zhang, Weiwu Wang, Chao Wei, Yanlong Ren, Meixuan Harbin Institute of Technology State Key Laboratory of Robotics and System Department of Mechatronics Engineering Harbin150001 China

The unevenness and darkness of lighting in real environments are one of the main issues that affect the practical application of visual simultaneous localization and mapping (SLAM). To address this issue, we propose a visual SLAM algorithm based on low-light enhancement networks. First, we designed a lightweight low-light enhancement network based on deformable power curves, DPCE-Net, to adjust the pixels’ dynamic range of a low-light image. The curve is derived from power function, which considers the range, monotonicity, and differentiability of pixel values, and has a high dynamic range. DPCE-Net is trained and evaluated without reference images, effectively expanding the application scenarios of low-light enhancement. Then, combine the DPCE-Net with ORB-SLAM2 to construct a low-light enhancement SLAM (DPCE-SLAM). In the low-light image enhancement experiment, we compared with the state-of-the-art method, Enlighten-GAN, and Zero-DEC, achieving the good performance in structural similarity (SSIM), mean square error (mse), peak signal to noise ratio (PSNR), and natural image quality evaluator (NIQE) indicators. In state estimation evaluation, DPCE-SLAM can effectively increase the number of correctly matched feature points in dark environments, while also improving the robustness of state estimation. © 2001-2012 IEEE.

关键词： Gallium nitride

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Generalized Dynamics of Non-Grounded Systems with Series Elastic Actuators and Effect of Actuator Base Fixation on Force Control Robustness

Generalized Dynamics of Non-Grounded Systems with Series Ela...

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2025 IEEE International Conference on mechatronics, ICM 2025

作者： Choi, Wonhyeok Oh, Sehoon DGIST Department of Robotics and Mechatronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798331533892

This paper proposes a generalized dynamics model for a Non-Grounded System (NGS) mounted with a Reaction Force Sensing Series Elastic Actuator (RFSEA), focusing on scenarios where the actuator's base is unfixed and interacts with the ground. In RFSEA-based force control, the actuator base, which provides the necessary reaction force as dictated by Newton's third law, plays a critical role. This study examines how the interaction between the actuator base and the ground it is attached to influences the response of the series elasticity that generates force in the RFSEA. The investigation involves two steps. First, a generalized dynamics model that considers the dynamics between the actuator base and the ground is derived. Second, the Small Gain Theorem (SGT) is applied to evaluate the robustness of force control under load-side model uncertainties when a Force-based Disturbance Observer (FDOB), originally designed for GS, is adapted to NGS. The analysis reveals that force control utilizing FDOB can become unstable when the actuator base is unfixed, and simulations validate these findings. © 2025 IEEE.

关键词： Electric grounding

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Neurotechnology in Gaming: A Systematic Review of Visual Evoked Potential-Based Brain-Computer Interfaces

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IEEE Access 2025年 13卷 74940-74962页

作者： Keutayeva, Aigerim Jesse Nwachukwu, China Alaran, Muslim Otarbay, Zhenis Abibullaev, Berdakh Astana010000 Kazakhstan Nazarbayev University Department of Biomedical Engineering Astana010000 Kazakhstan Nazarbayev University Department of Robotics and Mechatronics Astana010000 Kazakhstan

Brain-computer interfaces (BCIs) have received considerable attention in gaming, enabling innovative interactions with digital environments. Visual Evoked Potentials (VEPs)—robust, noninvasive neural responses to visual stimuli—offer high information transfer rates, making them particularly promising. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, examines VEP-based BCIs in gaming. We searched the Web of Science and Google Scholar, identifying 16 347 studies from the past decade, with 46 selected for in-depth analysis after rigorous screening. The review explores VEP response modeling, electroencephalography (EEG) signal acquisition and processing, stimulation paradigms, and their gaming applications. These systems enhance accessibility for players with physical or cognitive impairments, support adaptive difficulty scaling, personalize gameplay, aid neurorehabilitation, and enable multiplayer interactions. However, challenges remain, including technical limitations, complex data interpretation, user adaptability, and ergonomic issues. Advances in signal processing, personalized calibration, and hybrid multimodal approaches could improve usability. Future research should focus on integrating VEP-based BCIs with emerging technologies, optimizing user comfort, and developing adaptive interaction models to enhance immersion and accessibility. By addressing these challenges and utilizing neuroscience and computational advancements, VEP-based BCIs promise to transform gaming into a more inclusive and immersive experience for diverse users. © 2013 IEEE.

关键词： Augmented reality

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An Integrated Setup for Sequential Hybrid Laser Beam Micromachining (LBMM) and Micro-EDM for Machining Micro-hole Arrays with Automated Tool Localization

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International Journal of Precision engineering and Manufacturing 2025年 1-18页

作者： Azhar, Wan Ahmad Bin Wan Saleh, Tanveer Autonomous Systems and Robotics Research Unit Department of Mechatronics Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia

Micro holes array has significant applications in the field of aerospace and turbo machinery, industrial filtration, microfluidic devices, biomedical engineering, and so on. There are various ways to manufacture micro holes arrays. In order to have a cost-effective, yet precise and efficient process for the fabrication of micro holes array, an integrated setup has been introduced in this paper to combine Laser Beam Micro Machining (LBMM) and micro-EDM (μEDM). This approach uses LBMM for quick material removal and µEDM for precise finishing. However, one of the main challenges in hybrid LBMM-µEDM is the localization of LBMM micro holes for accurate positioning of μEDM electrode. In this present study, an integrated setup with automated hole localization has been presented to improve the hybrid LBMM-μEDM process in terms of alignment accuracy and machining rate. The microscope camera that is mounted on the μEDM stage scans and captures images of LBMM holes. Then, the images were stitched and processed using image processing techniques to get the center coordinate of the LBMM micro holes. Finally, the operator validated the coordinates and G-code was automatically generated for the µEDM process to perform the fine finishing. The result indicated that the alignment accuracy is within 15 μm. Furthermore, the newly developed automation strategies for the hybrid LBMM-μEDM process show significant improvement in machining time with 5 × faster than the pure μEDM process, and better performances in overcut, taper angle, and electrode wear. Furthermore, the hybrid process was found to be 1.6 × more energy efficient as compared to the pure µEDM process. Overall, the newly developed integrated system was found to be highly effective for producing micro holes array. © The Author(s), under exclusive licence to Korean Society for Precision engineering 2025.

关键词： Precision engineering

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ChildACT: Child Action Recognition Dataset in RGB Data 20

ChildACT: Child Action Recognition Dataset in RGB Data

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Sandygulova, Anara Yershov, Andrey Zhanatkyzy, Aida Telisheva, Zhansaule School of Engineering and Digital Sciences Nazarbayev University Department of Robotics and Mechatronics Astana Kazakhstan

ISBN: (纸本)9798350378931

Human action recognition is concerned with analysing, understanding and synthesizing kinematic and kinetic data. Data-driven computer vision solutions have advanced the progress in the field of action recognition while it would not be possible without large and publicly available datasets. While there are numerous datasets available for adult action recognition, child-centered action recognition efforts are still scarce. This paper presents a dataset that consists of video recordings of 200 children of different age and gender groups performing seven actions (boxing, waving, clapping, running, jogging, walking towards the camera, and walking from side to side). Two camera perspectives are provided, with a top view and a frontal view in RGB format. The dataset provides an opportunity for kinematic analysis of children motion while it also aims to bridge the performance gap between activity recognition systems for adults and children. © 2025 IEEE.

关键词： Pattern recognition systems

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AREPO: Uncertainty-Aware Robot Ensemble Learning under Extreme Partial Observability

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IEEE robotics and Automation Letters 2025年第6期10卷 5737-5744页

作者： Du, Yurui Hanut, Louis Bruyninckx, Herman Detry, Renaud Research Unit Processing Speech and Images KU Leuven Department of Electrical Engineering LeuvenB-3000 Belgium Research Unit Robotics Automation and Mechatronics KU Leuven Department of Mechanical Engineering LeuvenB-3000 Belgium

Real-world applications of vision-based robot learning face two major challenges: extreme partial observability and effective simulation-to-reality (sim-to-real) transfer. This letter introduces a robust robot learning framework that enhances uncertainty awareness to address these challenges. We reinterpret variational-autoencoder-based visual reinforcement learning (RL) from an uncertainty-quantification perspective, enabling resilience to high sensory noise and severe visual occlusions-common in industrial robotic tasks. To further improve sim-to-real transfer, we propose an uncertainty-aware ensemble RL algorithm. We validate our methods on a laboratory task designed as a proxy for real-world industrial applications characterized by harsh environments with low visibility and physical occlusions. Both simulation and real-world results demonstrate significant improvements in task accuracy and efficiency over various baselines, highlighting the benefits of uncertainty-aware robot learning for complex operational contexts. © 2016 IEEE.

关键词： Adversarial machine learning

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AI-Driven 3D Printing: Generative Gcode for Quality and Efficiency

AI-Driven 3D Printing: Generative Gcode for Quality and Effi...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Haque, Sean Tanveer, Muhammad Hassan Voicu, Razvan Cristian Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Artificial Intelligence (AI) is transforming the landscape of three-dimensional (3D) printing by enabling generative approaches that extend beyond conventional, parameter-tuning strategies. Rather than merely adjusting nozzle temperatures or speeds, modern AI models can create or refine Gcode paths themselves, seeking to balance objectives such as print strength, dimensional accuracy, and defect minimization. This paper introduces a holistic AI-driven workflow for automated Gcode generation. We demonstrate that generative Large Language Models (LLMs) can parse, synthesize, and optimize printing instructions, thus offering an alternative to human-engineered slicing parameters. Our approach incorporates a continuous feedback loop in which a predictive model evaluates each generated Gcode script for potential print outcomes. Experimental results illustrate strong correlations between the number of lines in generated Gcode and key performance metrics (tensile strength, defect rate, and dimensional accuracy). Notably, some scripts with fewer lines reduced material defects yet slightly degraded dimensional accuracy, while more complex scripts improved measurement fidelity but also introduced minor flaws. These findings illuminate the trade-offs in AI-driven 3D printing and highlight the method's capacity for ongoing improvement. By exploring future expansions, such as continuous-function printing trajectories and multi-material prints, this study shows that AI-based Gcode generation can significantly advance 3D printing quality and customization across diverse applications. © 2025 IEEE.

关键词： Model predictive control

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Seat-Focused Multi-DOF Exoskeleton Wheelchair Synergy for Continuous Lower-Limb Rehabilitation

Seat-Focused Multi-DOF Exoskeleton Wheelchair Synergy for Co...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Voicu, Razvan Cristian Freeman, Charles F. Garretson, Christopher E. Sun, Zoe M. Bonus, Ryan H. Slutzky, Zachary A. Mukesh, Niranjan Tanveer, Muhammad Hassan Tello, Yannique Chang, Yusun Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Wheelchair users with limited or nonexistent lower-limb mobility often struggle with significant muscle atrophy, circulatory complications, and other health issues. Although exoskeletons for gait training have shown promise, many are optimized for upright ambulation and overlook individuals remaining fully seated due to clinical or practical constraints. This article presents a seat-focused exoskeleton synergy integrated with a wheelchair to facilitate multi-DOF lower-limb mobilization without requiring transfers or standing maneuvers. The system addresses a critical gap by providing repeated, lower-intensity knee and ankle exercises throughout the day-either passively, actively with assistance, or in a resistive training mode-thus promoting muscle engagement and circulation from a seated position. The exoskeleton leverages the wheelchair's power source and employs modular, adjustable rails to accommodate diverse leg lengths. Emphasis is placed on biomechanical alignment, safe torque control, and user comfort, supported by a model-based controller accounting for inertia, Coriolis effects, and gravity in real time. Bench tests and preliminary trials confirm stable torque output, accurate tracking of desired motion profiles, and minimal burden when attaching or detaching the device. The article introduces modeling for a two-DOF prototype (knee flexion/extension and ankle dorsiflexion/plantarflexion), expandable to include additional joints or degrees of freedom, enabling broader rehabilitation targets such as hip abduction/adduction. By embedding rehabilitative capabilities directly into the wheelchair environment, the proposed approach seeks to close the gap between infrequent clinic appointments and continuous therapy aimed at mitigating muscle ailments. A seat-focused exoskeleton designed for every-day use can facilitate accessible, on-demand rehabilitation and substantially improve long-term health outcomes for mobility impaired users. © 2025 IEEE.

关键词： Physical therapy

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Humanoid robotics Powered by Vision-Based LLM Zero Shot Interactions

Humanoid Robotics Powered by Vision-Based LLM Zero Shot Inte...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Smith, Gabriel David Debrah-Pinamang, Kwasi Voicu, Razvan Cristian Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Advancements in artificial intelligence (AI) have transformed robotics by enabling systems to autonomously execute complex tasks with minimal human involvement. Traditional methods, however, often depend on costly hardware, continuous monitoring, and intricate software integration, which constrain scalability and widespread implementation. This research presents an innovative approach that combines vision-based large language models (LLMs) with zero-shot prompting to autonomously program robotic systems, including a humanoid robot equipped with dual manipulators. The proposed system harnesses contextual image data to efficiently generate task-specific code, eliminating the need for iterative corrections. Training is conducted through OpenAI's Assistant feature, utilizing documents predominantly comprising images, while continuous operation is facilitated by a self-looping mechanism. Experimental results highlight the system's capability to perform manipulator tasks with notable accuracy, paving the way for scalable, adaptive, and dynamic automation. This study addresses both practical and theoretical challenges in automation, providing a cost-effective framework for next-generation robotic systems. © 2025 IEEE.

关键词： Anthropomorphic robots

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Self-supervised Deformable Registration of Digital Tomosynthesis and 3D CT Images for Surgical Navigation

Self-supervised Deformable Registration of Digital Tomosynth...

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Medical Imaging 2025: Image Processing

作者： Park, Muyul So, Jeongtae Jung, Young-Jun Ito, Mikiko Lee, Byung Kee Lee, Gyeongmin Park, Sang Hyun LG Electronics Seoul Korea Republic of Department of Robotics and Mechatronics Engineering DGIST Daegu Korea Republic of

ISBN: (纸本)9781510685901

To enhance the precision and efficiency of surgical procedures, there is an increasing demand for integrating medical imaging systems into the surgical environment. For example, a mobile C-Arm image has been widely applied to intraoperative imaging system. However, the projective nature of C-Arm image limits its effectiveness for accurate surgical guidance. Recently, a mobile digital tomosynthesis (DTS) system has emerged as an attractive alternative, providing quasi-3D images with a radiation dose comparable to that of conventional 2D radiography and ease of use in the intraoperative scene. To enhance the utility of mobile DTS, it is required to register DTS with preoperative CT. However, the previously proposed registration methods have shown insufficient results due to the limited depth resolution and the streaking artifact inherent to DTS. Therefore, we propose a novel self-supervised learning-based 3D deformable registration between CT and mobile DTS by introducing a dataset generation technique to train the deep learning network with an additional constraint. The proposed registration algorithm is composed of a sequential process involving an affine registration based on iterative optimization and a deformable registration based on self-supervised learning. To evaluate the registration accuracy, we compared the distances between matching points. We observed that the 3D distance decreased from 14.07±1.79 voxels to 3.73±0.53 voxels after affine registration and to 2.55±0.50 voxels after the self-supervised deformable registration. © 2025 SPIE.

关键词： Image registration

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