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arXiv 2025年

作者： Alharbat, Ayham Ruscelli, Gabriele Diversi, Roberto Mersha, Abeje Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a first-principles model and a neural network. This framework addresses one of the limitations of the state-of-the-art model-based wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind);in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a first-principles model. © 2025, CC BY-NC-ND.

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Applying Radio Frequency Attenuators to Reduce the Impact of U-NII-4 Interference on DSRC Channel 180

Applying Radio Frequency Attenuators to Reduce the Impact of...

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IEEE Southeastcon

作者： Grayson Hatcher Marco Tello Nathan Kirkwood Billy Kihei Department of Electrical and Computer Engineering Kennesaw State University Marietta Georgia Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta Georgia

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

In recent years, competition within Radio Frequency (RF) communication bands has led to an overlap between the U-NII-4 band used by low-cost consumer devices, and the Dedicated Short Communication Band (DSRC) used by the Georgia Department of Transportation. Past research confirmed that this interference leads to drastic reduction in Packet Reception Rate (PRR) for Roadside Units (RSUs) and On-Board Units (OBUs) throughout the country, resulting in a reduction of stability between different components of the Intelligent Transportation System (ITS). Our research team set a goal to prove that a low-cost and simple modification can be made to RSUs currently in use that could increase PRR, by developing and testing RF technologies, primarily attenuators available on the market currently. These solutions could be easily adapted to infrastructure systems, and only require they run in-line with the stock antennas already in use. We were able to accomplish this goal, as well as made findings that may be beneficial within the world of RF communications for RSU devices.

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Self-Aware AI-Based Anomaly Detection for AI IoT Enabled Smart Healthcare System

Self-Aware AI-Based Anomaly Detection for AI IoT Enabled Sma...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Yusra Shereen Muhammad Farrukh Shahid Muhammad Hassan Tanveer School of Computing FAST - NUCES Karachi Pakistan Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The Internet of Things (IoT) based smart healthcare systems can significantly enhance the quality of patient care. These systems can enable healthcare providers to monitor patient conditions in real time. IoT-based smart healthcare systems have the potential to revolutionize the way we provide care. Anomaly detection is crucial in IoT-based smart healthcare systems due to numerous reasons. It plays a dominant role in proactive health-care analysis, especially in brain diseases. In brain Magnetic Res-onance Imaging (MRI), anomaly detection helps in identifying several abnormalities, such as cysts, strokes, infections, vascular and developmental malformations, and neurological disorders like Alzheimer's and multiple sclerosis. The existing approaches of brain MRI anomaly detection, and anomaly detection in IoT-based smart healthcare systems utilizes static AI models. Conventional AI based models can be trained once and cannot tackle the changes in real time. The population health and the nature of disease consistently change, and conventional AI based models can't adapt to those changes. Conventional AI based system cannot tackle the changes caused by variations like diseases and public health and become ineffective over the time. Self-aware AI models can address these issues as they can learn and adapt to their environment. Self-aware AI-based systems have the potential to change lives through their ability to learn, adapt, and improve over time. In this paper, we proposed a novel approach to anomaly detection based on self-aware AI in IoT-based smart healthcare systems. Additionally, an algorithm for anomaly detection in MRI images, namely Self-Aware AI based Anomaly Detection (SAAD) is proposed. Our proposed system can detect anomalies in brain MRI images. We achieved 98% accuracy and 0.99 precision and recall, outperforming the existing approaches. The proposed self-aware AI based anomaly detection framework for IoT-based smart healthcare systems has the potential

关键词： Adaptation models Magnetic resonance imaging Medical services Brain modeling Real-time systems Internet of Things Artificial intelligence Public healthcare Anomaly detection Self-aware

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Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis with Stereo Cameras in Unpredictable Environments

Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis...

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IEEE Southeastcon

作者： Shrey Patel Aiden Kovarovics Aaditya More Carrington Chun Montreal Fletcher M. Hasan Tanveer Sumit Chakravarty Razvan Voicu Dept. of Robotics & Mechatronics Engineering Kennesaw State University Marietta USA Dept. of Electrical and Computer Engineering Kennesaw State University Marietta USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Current robotic arm systems typically depend on pretrained models or function within controlled environments using known positions or fiducial markers for object manipulation. Although these methods achieve high accuracy, they face limitations due to lengthy training processes and the need for carefully controlled setups. This research introduces a methodology that leverages stereo cameras to develop a generalized algorithm aimed at overcoming these constraints and enhancing robotic manipulation in uncontrolled environments. The proposed solution utilizes an Intel RealSense stereo camera in conjunction with YOLO segmentation models for initial object identification. It combines segmentation meshes with the camera's depth values to create a detailed three-dimensional point cloud of the target object's surface. This point cloud is analyzed through Random Sample Consensus (RANSAC) and geometry-based feature extraction techniques, which allow the algorithm to approximate the object's shape as a combination of fundamental 3D forms, such as cuboids, cylinders, and spheres. A grasp point is then estimated by identifying suitable surfaces for grasping with a two-pronged gripper, followed by calculating an optimal approach angle and position kinematics for the robotic arm. This innovative approach enables robotic arm systems to autonomously adapt to various objects in uncontrolled environments, significantly expanding their practical applications. For example, the methodology could support modular robotic systems designed for sample collection in extraterrestrial missions, where adaptability and autonomous operations are essential. Ultimately, this research aims to enhance the efficiency and capabilities of robotic systems in dynamic and unpredictable settings.

关键词： Point cloud compression YOLO Training Three-dimensional displays Robot vision systems Grasping Manipulators Cameras Feature extraction Object recognition

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OFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-Adjustment

arXiv

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arXiv 2025年

作者： Park, Junhyun Moon, Chanyu Lee, Donghwan Kim, Kyungsu Hwang, Minho Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Electrical Engineering and Computer Science DGIST Korea Republic of Seoul National University Korea Republic of

Contrastive Language-Image Pre-Training (CLIP) has enabled zero-shot classification in radiology, reducing reliance on manual annotations. However, conventional contrastive learning struggles with normal case detection due to its strict intra-sample alignment, which disrupts normal sample clustering and leads to high false positives (FPs) and false negatives (FNs). To address these issues, we propose OFF-CLIP, a contrastive learning refinement that improves normal detection by introducing an off-diagonal term loss to enhance normal sample clustering and applying sentence-level text filtering to mitigate FNs by removing misaligned normal statements from abnormal reports. OFF-CLIP can be applied to radiology CLIP models without requiring any architectural modifications. Experimental results show that OFF-CLIP significantly improves normal classification, achieving a 0.61 Area under the curve (AUC) increase on VinDr-CXR over CARZero, the state-of-the-art zero-shot classification baseline, while maintaining or improving abnormal classification performance. Additionally, OFF-CLIP enhances zero-shot grounding by improving pointing game accuracy, confirming better anomaly localization. These results demonstrate OFF-CLIP’s effectiveness as a robust and efficient enhancement for medical vision-language models. Copyright © 2025, The Authors. All rights reserved.

关键词： Radiology

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A Single Scale Doesn’t Fit All: Adaptive Motion Scaling for Efficient and Precise Teleoperation

arXiv

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arXiv 2025年

作者： Yoon, Jeonghyeon Park, Sanghyeok Park, Hyojae Kim, Cholin Park, Sihyeoung Hwang, Minho Department of Robotics and Mechatronics Engineering DGIST Daegu42988 Korea Republic of School of Undergraduate Studies DGIST Daegu42988 Korea Republic of

Teleoperation is increasingly employed in environments where direct human access is difficult, such as hazardous exploration or surgical field. However, if the motion scale factor(MSF) intended to compensate for workspace-size differences is set inappropriately, repeated clutching operations and reduced precision can significantly raise cognitive load. This paper presents a shared controller that dynamically applies the MSF based on the user’s intended motion scale. Inspired by human motor skills, the leader arm trajectory is divided into coarse(fast, large-range movements) and fine(precise, small-range movements), with three features extracted to train a fuzzy C-means(FCM) clustering model that probabilistically classifies the user’s motion scale. Scaling the robot’s motion accordingly reduces unnecessary repetition for large-scale movements and enables more precise control for fine operations. Incorporating recent trajectory data into model updates and offering user feedback for adjusting the MSF range and response speed allows mutual adaptation between user and system. In peg transfer experiments, compared to using a fixed single scale, the proposed approach demonstrated improved task efficiency(number of clutching and task completion time decreased 38.46% and 11.96% respectively), while NASA-TLX scores confirmed a meaningful reduction(58.01% decreased) in cognitive load. This outcome suggests that a user-intent-based motion scale adjustment can effectively enhance both efficiency and precision in teleoperation. Copyright © 2025, The Authors. All rights reserved.

关键词： NASA

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Ultimate Boundedness and Output Convergence of Prioritized Output Tracking Control Under Nonsmooth and Imperfect Feedback Linearization

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IEEE Transactions on Automatic Control 2025年

作者： An, Sang-Ik Lee, Dongheui Park, Gyunghoon University of Seoul School of Electrical and Computer Engineering Korea Republic of Tu Wien Autonomous Systems at Institute of Computer Technology Faculty of Electrical Engineering and Information Technology Austria Institute of Robotics and Mechatronics Germany

The concept of priority has been introduced to the robotic systems in 1980 s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input-output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we firstly prioritize the input-output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. Additionally, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method. © 1963-2012 IEEE.

关键词： Feedback linearization

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RWD-DOF: a dual-degree-of-freedom reconfigurable wheel design for improved robotic mobility

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Scientific Reports 2025年第1期15卷 1-24页

作者： Liu, Yanjie Wei, Yanlong Feng, Tong Wang, Chao Wu, Heng State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institule of Technology Harbin 150001 China

Wheeled mobile robots are efficient on flat surfaces but face limitations in overcoming obstacles like steps due to their fixed wheel radius. This paper presents a novel modular reconfigurable wheel with a dual-degree-of-freedom active reconfigurable mechanism, designed to adapt dynamically to varying step sizes. The wheel’s structure includes three curved segments, whose synchronized motion is driven by five-bar linkages and two planetary gear systems. By analyzing the kinematic constraints and both forward and inverse kinematics of the reconfigurable wheel, we established the range of dimensions it can surmount over steps, specifically focusing on height and depth, as well as the relationship with the output angles q1 and q2 of the two motors driving the reconfiguration. Simulations using ADAMS software demonstrates the wheel’s capability to maintain smooth, continuous center-of-mass movement over steps and stairs. Moreover, physical prototype testing further confirms the feasibility and advantages of the proposed wheel structure. This adaptive design significantly enhances the performance of wheeled robots in navigating complex environments, offering promising potential for applications in challenging terrain. © The Author(s) 2025.

关键词： ADAMS simulation Dual-Degree-of-Freedom mechanism Kinematic analysis Modular reconfigurable wheel Step-like obstacles navigation

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MPPI-DBaS: Safe Trajectory Optimization with Adaptive Exploration

arXiv

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arXiv 2025年

作者： Wang, Fanxin Cheng, Yikun Tao, Chuyuan Department of Mechatronics and Robotics Xi’an Jiaotong-Liverpool University China Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States

In trajectory optimization, Model Predictive Path Integral (MPPI) control is a sampling-based Model Predictive Control (MPC) framework that generates optimal inputs by efficiently simulating numerous trajectories. In practice, however, MPPI often struggles to guarantee safety assurance and balance efficient sampling in open spaces with the need for more extensive exploration under tight constraints. To address this challenge, we incorporate discrete barrier states (DBaS) into MPPI and propose a novel MPPI-DBaS algorithm that ensures system safety and enables adaptive exploration across diverse scenarios. We evaluate our method in simulation experiments where the vehicle navigates through closely placed obstacles. The results demonstrate that the proposed algorithm significantly outperforms standard MPPI, achieving a higher success rate and lower tracking errors. © 2025, CC BY.

关键词： Stochastic systems

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Biologically Inspired Networking (BIN): Leveraging Neurobiological Principles for Adaptive, Scalable, and Efficient Communication

Biologically Inspired Networking (BIN): Leveraging Neurobiol...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Razvan Cristian Voicu Amanda Frischmann Muhammad Hassan Tanveer Amir Ali Amiri Moghadam Yannique Tello Gabriela Oprea-Ilies Yusun Chang Department of Robotics & Mechatronics Engineering Kennesaw State University Atlanta Georgia Windship Cancer Institute Emory University School of Medicine Atlanta Georgia

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

The surge in interconnected applications, particularly in AI, IoT, and intelligent infrastructure, imposes new demands on communication networks, requiring them to handle high data volumes, support real-time exchanges, and dynamically adapt to fluctuating conditions. Traditional networking models, reliant on centralized processing and static protocols, are insufficient to meet these evolving needs, resulting in inefficiencies, latency, and scalability issues. This paper introduces a biologically inspired networking paradigm that mimics neurobiological principles such as event-driven communication, distributed intelligence, and adaptive resource management. By conceptualizing data types as analogous to neurotransmitters and infrastructure learning as synaptic plasticity, the framework provides a dynamic, scalable solution that enhances efficiency and resilience. The proposed model is particularly suited for resource-constrained environments, dynamically optimizing resource allocation and communication pathways in real time. Simulations demonstrate significant improvements in latency, resource utilization, scalability, and reliability, proving its potential as a foundation for next-generation networks.

关键词： Adaptation models Adaptive systems Biological system modeling Scalability Real-time systems Resource management Internet of Things Artificial intelligence Surges Resilience

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