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

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

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

A Multi-modal Hybrid Robot with Enhanced Traversal Performance*

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

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

Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

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Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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

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

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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

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

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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

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

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

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

Physical Human-Aerial Robot Interaction and Collaboration: Exploratory Results and Lessons Learned

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Physical Human-Aerial Robot Interaction and Collaboration: E...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Amr Afifi Gianluca Corsini Quentin Sable Youssef Aboudorra Daniel Sidobre Antonio Franchi Robotics and Mechatronics lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands LAAS-CNRS UPS Université de Toulouse Toulouse France Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

In this work, we present, a first of its kind, physical human-aerial robot interaction (pHARI) experiment, with an articulated aerial manipulator (AM). The robotic platform is a fully-actuated multi-rotor aerial vehicle (MRAV) with fixedly-tilted propellers endowed with a 3degree of freedom (DoF) robotic arm. We implemented a state-of-the-art control architecture composed of a feedback linearization motion controller, an admittance filter and a hybrid wrench observer. The experiments prove the viability of a new use case in aerial robotics, namely pHARI. The experimental results also shed light on the limitations of the current state-of-the-art and provide insights into possible research directions. The video of the experiments, which is available at https://***/LrQxXbQ5IHc, shows an experiment simulating work at height, where a human manually guides an AM and then attaches a tool to its end effector (EE).

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LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation

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arXiv 2024年

作者： Jiao, Jianhao He, Jinhao Liu, Changkun Aegidius, Sebastian Hu, Xiangcheng Braud, Tristan Kanoulas, Dimitrios The Robot Perception and Learning Lab Intelligent Robotics Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Nansha District Guangzhou China The Department of Computer Science and Engineering HKUST Hong Kong The Department of Electronic an Computer Engineering HKUST Hong Kong The AI Centre Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Archimedes/Athena RC Greece

This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available at https://***/LiteVLoc. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

AirShot: Efficient Few-Shot Detection for Autonomous Exploration

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arXiv 2024年

作者： Wang, Zihan Li, Bowen Wang, Chen Scherer, Sebastian Institute for Imaging Data and Communications School of Engineering The University of Edinburgh United Kingdom AirLab Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Lab Institute for Artificial Intelligence and Data Science Department of Computer Science and Engineering University at Buffalo NY14260 United States

Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples. Despite recent efforts have been made to yield online processing capabilities, slow inference speeds of low-powered robots fail to meet the demands of real-time detection-making them impractical for autonomous exploration. Existing methods still face performance and efficiency challenges, mainly due to unreliable features and exhaustive class loops. In this work, we propose a new paradigm AirShot, and discover that, by fully exploiting the valuable correlation map, AirShot can result in a more robust and faster few-shot object detection system, which is more applicable to robotics community. The core module Top Prediction Filter (TPF) can operate on multi-scale correlation maps in both the training and inference stages. During training, TPF supervises the generation of a more representative correlation map, while during inference, it reduces looping iterations by selecting top-ranked classes, thus cutting down on computational costs with better performance. Surprisingly, this dual functionality exhibits general effectiveness and efficiency on various off-the-shelf models. Exhaustive experiments on COCO2017, VOC2014, and SubT datasets demonstrate that TPF can significantly boost the efficacy and efficiency of most off-the-shelf models, achieving up to 36.4% precision improvements along with 56.3% faster inference speed. Code and Data are at: https://***/ImNotPrepared/AirShot. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency