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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chuong Phuoc Le Pratik Walunj An Duy Nguyen Yongyi Zhou Binh Nguyen Thang Nguyen Anton Netchaev Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA Department of Engineering Texas A&M University–Corpus Christi Corpus Christi TX USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Culverts, essential components of drainage systems, require regular inspection to ensure optimal functionality. However, culvert inspections pose numerous challenges, including accessibility, manpower, defect localization, and reliance on superficial assessments. To address these challenges, we propose a novel Culvert Autonomous Inspection Robotic System (CAIS) equipped with advanced sensing and evaluation capabilities. Our solution integrates an RGBD camera, deep learning, lighting systems, and non-destructive evaluation (NDE) techniques to enable accurate and comprehensive condition assessments. We present a pioneering Partially Observable Markov Decision Process (POMDP) framework to resolve uncertainty in autonomous inspections, especially in confined and unstructured environments like culverts or tunnels. The framework outputs detailed 3D maps highlighting visual defects and NDE condition assessments, demonstrating consistent and reliable performance in both indoor and outdoor scenarios. Additionally, we provide an open-source implementation of our framework on GitHub, contributing to the advancement of autonomous inspection technology and fostering collaboration within the research community. Source codes are available * .

关键词： Visualization Uncertainty Three-dimensional displays Source coding Markov decision processes Robot vision systems Inspection Sensors Reliability Software development management

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fatima Khalid Muhammad Hanif Qurat Ul Ain Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Topi KPK Pakistan Aerial Robotics and Vision Lab Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Topi KPK Pakistan Department of Computing Shifa Tameer-E-Millat University Islamabad Pakistan

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management.

关键词： Training Adaptation models Accuracy Computational modeling Crops Lighting computer architecture Feature extraction Robustness Diseases

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AirLine: Efficient Learnable Line Detection with Local Edge Voting

AirLine: Efficient Learnable Line Detection with Local Edge ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiao Lin Chen Wang College of Computing Georgia Institute of Technology Atlanta GA USA Department of Computer Science and Engineering Spatial AI & Robotics Lab State University of New York at Buffalo NY USA

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-Ievel precision, yet with a $3-80\times$ runtime acceleration compared to other learning-based methods, which is critical for low-power robots.

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A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

A Comparison of Approximate Analytic and Neural Network Solu...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

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Analog Magnetic Sensor-Robotic System for Steel Structure Inspection

Analog Magnetic Sensor-Robotic System for Steel Structure In...

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IEEE/SICE International Symposium on System Integration

作者： Anh Quyen Pham Duc La Hung Manh La AIR-VIET Corporation in Vietnam Kahlert School of Computing University of Utah Salt Lake City UT USA Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk

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A Multi-modal Hybrid Robot with Enhanced Traversal Performance*

A Multi-modal Hybrid Robot with Enhanced Traversal Performan...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhipeng He Na Zhao Yudong Luo Sian Long Xi Luo Hongbin Deng School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Computer Science and Technology Morphing Robotics Lab Dalian Maritime University Dalian China Yichang Testing Tech. Research Institution Yichang China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Current multi-modal hybrid robots with flight and wheeled modes have fallen into the dilemma that they can only avoid obstacles by re-taking off when encountering obstacles due to the poor performance of wheeled obstacle-crossing. To tackle this problem, this paper presents a novel multi-modal hybrid robot with the ability to actively adjust the wheel’s size, which is inspired by the behavior of the turtle’s legs when it encounters obstacles, to enhance the traversal performance. In detail, we describe the hardware design that allows the robot to achieve a modal switch between flight and wheeled modes through foldable structures and variable wheel diameters; then, we present the architecture to control these two morphing mechanisms. After that, we establish the theoretical kinematic models for both the foldable arm and variable wheel and carry out extensive experiments to test the performance of the foldable arm, the variable-diameter wheel, as well as the traversal performance of the robot. Experimental results show that the proposed multimodal robot can realize the function of a quadrotor, respond quickly with full-scale folding within 0.9 s, climb a maximum slope of 36°, and traverse narrow passageways, which exhibit superior mobility and environmental adaptability.

关键词： Legged locomotion Wheels Prototypes Manipulators Sensors Mobile robots Collision avoidance

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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Bio-Skin: A Cost-Effective Thermostatic Tactile Sensor with Multi-Modal Force and Temperature Detection

arXiv

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arXiv 2025年

作者： Guo, Haoran Wang, Haoyang Li, Zhengxiong Tao, Lingfeng Oklahoma State University Mechatronics and Intelligent Robotics Lab 563 Engineering North StillwaterOK74075 United States University of Colorado Denver Department of Computer Science and Engineering 1380 Lawrence St. Center LW-834 DenverCO80217 United States

Tactile sensors can significantly enhance the perception of humanoid robotics systems by providing contact information that facilitates human-like interactions. However, existing commercial tactile sensors focus on improving the resolution and sensitivity of single-modal detection with high-cost components and densely integrated design, incurring complex manufacturing processes and unaffordable prices. In this work, we present Bio-Skin, a cost-effective multi-modal tactile sensor that utilizes single-axis Hall-effect sensors for planar normal force measurement and bar-shape piezo resistors for 2D shear force measurement. A thermistor coupling with a heating wire is integrated into a silicone body to achieve temperature sensation and thermostatic function analogous to human skin. We also present a cross-reference framework to validate the two modalities of the force sensing signal, improving the sensing fidelity in a complex electromagnetic environment. Bio-Skin has a multi-layer design, and each layer is manufactured sequentially and subsequently integrated, thereby offering a fast production pathway. After calibration, Bio-Skin demonstrates performance metrics—including signal-to-range ratio, sampling rate, and measurement range—comparable to current commercial products, with one-tenth of the cost. The sensor’s real-world performance is evaluated using an Allegro hand in object grasping tasks, while its temperature regulation functionality was assessed in a material detection task. Copyright © 2025, The Authors. All rights reserved.

关键词： Tactile sensors

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IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning

arXiv

引用

arXiv 2025年

作者： Moon, Brady Suvarna, Nayana Jong, Andrew Chatterjee, Satrajit Yuan, Junbin Scherer, Sebastian Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States GRASP Lab the University of Pennsylvania PhiladelphiaPA United States Mechanical Engineering Department Carnegie Mellon University PittsburghPA United States

Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS, an incremental and adaptive sampling-based informative path planner that can be run efficiently with onboard computation. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated world beliefs. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor UAV and a fixed-wing UAV, each having unique motion models and configuration spaces. Our results show up to a 41% improvement in information gain compared to baseline methods, suggesting significant potential for deployment in real-world applications. Project website and video at ***. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

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A Multi-model Fusion of LiDAR-inertial Odometry via Localization and Mapping

A Multi-model Fusion of LiDAR-inertial Odometry via Localiza...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： An Duy Nguyen Chuong Phuoc Le Pratik Walunj Anton Netchaev Thanh Nho Do Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA Graduate School of Biomedical Engineering UNSW Sydney Kensington Campus NSW Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a comprehensive LiDAR-inertial odometry framework featuring robust smoothing and mapping capabilities, effectively correcting LiDAR feature point skewness using an inertial measurement unit (IMU). While the Extended Kalman Filter (EKF) is a common choice for nonlinear motion estimation, its complexity grows when handling maneuvering targets. To overcome this challenge, a new framework that incorporates the Iterated Interactive Multiple Models of Kalman Filter (IMMKF) is given, providing a solution for reliable navigation in dynamic motion and noisy conditions. To ensure map consistency, an ikd-tree that facilitates continuous updates and adaptive rebalance is employed, preserving the map’s integrity. To guarantee the robustness of our approach, it undergoes extensive testing across diverse scales of indoor and outdoor environments. This testing scenario simulates absolute GPS denial. In terms of estimated motion, the new algorithm demonstrates superior accuracy compared to existing approaches. The implementation is openly accessible on GitHub 4 for further exploration.

关键词： Location awareness Technological innovation Laser radar Accuracy Smoothing methods Computational modeling Feature extraction Odometry Kalman filters Testing

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