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

作者： Jonas le Fevre Sejersen Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A *) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners.

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PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking

PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Micha Heiß Jakob Grimm Hansen Dengyun Li Michał Kozłowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.

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A Music-Therapy Robotic Platform for Children with Autism: A Pilot Study

arXiv

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arXiv 2022年

作者： Fengr, Huanghao Mahoor, Mohammad H. Dino, Francesca Changshu Institute of Technology Suzhou Changshu China Computer Vision and Social Robotics Labarotory Department of Electrical and Computer Engineering University of Denver United States

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills including motor control, turn-taking, and emotion recognition. Innovative technology, such as socially assistive robots, has shown to be a viable method for Autism therapy. This paper presents a novel robot-based music-therapy platform for modeling and improving the social responses and behaviors of children with ASD. Our autonomous social interactive system consists of three modules. Module 1 provides an autonomous initiative positioning system for the robot, NAO, to properly localize and play the instrument (Xylophone) using the robot's arms. Module 2 allows NAO to play customized songs composed by individuals. Module 3 provides a real-life music therapy experience to the users. We adopted Short-time Fourier Transform and Levenshtein distance to fulfill the design requirements: a)"music detection" and b)"smart scoring and feedback", which allows NAO to understand music and provide additional practice and oral feedback to the users as applicable. We designed and implemented six Human-Robot-Interaction (HRI) sessions including four intervention sessions. Nine children with ASD and seven Typically Developing participated in a total of fifty HRI experimental sessions. Using our platform, we collected and analyzed data on social behavioral changes and emotion recognition using Electrodermal Activity (EDA) signals. The results of our experiments demonstrate most of the participants were able to complete motor control tasks with 70% accuracy. Six out of the 9 ASD participants showed stable turn-taking behavior when playing music. The results of automated emotion classification using Support Vector Machines illustrates that emotional arousal in the ASD group can be detected and well recognized via EDA bio-signals. In summary, the results of our data analyses, including emotion classification using EDA signals, indicate that the proposed robot-music based therapy p

关键词： Diseases

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A short review on infrared thermography: history, philosophy, approaches, standards and application examples 45

A short review on infrared thermography: history, philosophy...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Plastropoulos, Angelos Adiuku, Ndidiamaka Avdelidis, Nicolas P. Ibarra-Castanedo, Clemente Maldague, Xavier P.V. Servais, Pierre Integrated Vehicle Health Management Centre School of Aerospace Transport and Manufacturing Cranfield University United Kingdom Computer Vision & Systems Laboratory Department of Electrical & Computer Engineering Laval University Quebec CityG1V 0A6 Canada NDT PRO Srl Le grand enclos 18 LibramontB6800 Belgium

ISBN: (纸本)9781510661868

Infrared thermography is a condition monitoring technique that, from a measurement of the radiant heat pattern emitted by a material, is able to determine regions or points of increased or reduced heat emission that can indicate the presence of an imperfection in the investigated material. The result of an infrared thermographic investigation is a sequence of thermograms or thermal images, in other words a picture of temperature, that can be further processed for qualitative and quantitative purposes. Such images can be presented in either false color or black and white format. In the present work, the philosophy and history of thermal–infrared imaging are reviewed. Moreover, the different evaluation approaches (passive and active), as well as many standards related to infrared thermography are discussed. Finally, various applications of the transient thermography approach are briefly presented. © 2023 SPIE.

关键词： Thermography (imaging)

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FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field∗

arXiv

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arXiv 2024年

作者： Stathoulopoulos, Nikolaos Lindqvist, Björn Koval, Anton Agha-Mohammadi, Ali-Akbar Nikolakopoulos, George Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA911 09 United States

In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Distributed Inverse Dynamics Control for Quadruped Robots using Geometric Optimization

arXiv

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arXiv 2024年

作者： Khandelwal, Nimesh Manu, Amritanshu Gupta, Shakti S. Kothari, Mangal Krishnamurthy, Prashanth Khorrami, Farshad Deptartment of Mechanical Engineering IIT Kanpur UP Kanpur India Department of Electrical and Computer Engineering New York University BrooklynNY United States Mobile Robotics Laboratory Department of Mechanical Engineering IIT Kanpur UP Kanpur208016 India

This paper presents a distributed inverse dynamics controller (DIDC) for quadruped robots that addresses the limitations of existing reactive controllers: simplified dynamical models, the inability to handle exact friction cone constraints, and the high computational requirements of whole-body controllers. Current methods either ignore friction constraints entirely or use linear approximations, leading to potential slip and instability, while comprehensive whole-body controllers demand significant computational resources. Our approach uses full rigid-body dynamics and enforces exact friction cone constraints through a novel geometric optimization-based solver. DIDC combines the required generalized forces corresponding to the actuated and unactuated spaces by projecting them onto the actuated space while satisfying the physical constraints and maintaining orthogonality between the base and joint tracking objectives. Experimental validation shows that our approach reduces foot slippage, improves orientation tracking, and converges at least two times faster than existing reactive controllers with generic QP-based implementations. The controller enables stable omnidirectional trotting at various speeds and consumes less power than comparable methods while running efficiently on embedded processors. © 2024, CC BY-SA.

关键词： Multipurpose robots

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control

Uncertainty Quantification for Recursive Estimation in Adapt...

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American Control Conference (ACC)

作者： Max H. Cohen Makai Mann Kevin Leahy Calin Belta Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA Massachusetts Institute of Technology Lincoln Laboratory Lexington MA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA Department of Electrical and Computer Engineering University of Maryland College Park MD

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling our approach is that the parameter estimate generated by the continuous-time recursive least squares (RLS) algorithm at any point in time is an affine transformation of the initial parameter estimate. This property allows for parameterizing such estimates using objects that are closed under affine transformation, such as zonotopes, and enables the efficient propagation of such set-based estimates as time progresses. We illustrate how such an approach facilitates the synthesis of safety-critical controllers for systems with parametric uncertainty and additive disturbances using control barrier functions, and demonstrate the utility of our approach through illustrative examples.

关键词： Uncertainty Parameter estimation Additives Control systems Recursive estimation

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A Joint Tracking System: Robot is Online to Access Surveillance Views

A Joint Tracking System: Robot is Online to Access Surveilla...

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IEEE International Conference on robotics and Biomimetics

作者： Zheyuan Lin Shanshan Ji Wen Wang Mengjie Qin Rong Yang Minhong Wan Jason Gu Te Li Chunlong Zhang Research Center for Intelligent Robotics Zhejiang Laboratory Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot’s field of vision and heavy occlusion by people or objects.

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Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Keyhan Rayati Amirhossein Feizi Alireza Beigy Pourya Shahverdi Mehdi Tale Masouleh Ahmad Kalhor Wing-Yue Geoffrey Louie Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan USA

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs.

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