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HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

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

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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

Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science CambridgeCB1 1PT United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory BristolBS16 1QY United Kingdom Hartpury University Department of Animal and Agriculture GloucesterGL19 3BE United Kingdom Aston University College of Engineering and Physical Sciences BirminghamB4 7ET United Kingdom Royal Military College of Canada Department of Mathematics and Computer Science KingstonONK7K 7B4 Canada Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2014 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation

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arXiv 2024年

作者： Peng, Qucheng Zheng, Ce Ding, Zhengming Wang, Pu Chen, Chen Center for Research in Computer Vision University of Central Florida Orlando United States Robotics Institute Carnegie Mellon University Pittsburgh United States Department of Computer Science Tulane University New Orleans United States University of North Carolina at Charlotte Charlotte United States

Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc. However, it suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation. To cope with the label deficiency issue, one common solution is to train the HPE models with easily available synthetic datasets (source) and apply them to real-world data (target) through domain adaptation (DA). Unfortunately, prevailing domain adaptation techniques within the HPE domain remain predominantly fixated on effecting alignment and aggregation between source and target features, often sidestepping the crucial task of excluding domain-specific representations. To rectify this, we introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation. Within this framework, we address the network architecture aspect by disentangling representations into distinct domain-invariant and domain-specific components, facilitating aggregation of domain-invariant features while simultaneously segregating domain-specific ones. Moreover, we tackle the discrepancy measurement facet by delving into various keypoint relationships and applying separate aggregation or segregation mechanisms to enhance alignment. Extensive experiments on various benchmarks, e.g., Human3.6M, LSP, H3D, and FreiHand, show that our method consistently achieves state-of-the-art performance. The project is available at https: //***/davidpengucf/EPIC. © 2024, CC BY.

关键词： Virtual reality

Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

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Enhanced Detection of Demagnetization in Direct-Drive Wind T...

IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the Electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

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arXiv 2023年

作者： Tang, Chao Huang, Dehao Ge, Wenqi Liu, Weiyu Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://***/view/graspgpt. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

Lidar Panoptic Segmentation in an Open World

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arXiv 2024年

作者： Chakravarthy, Anirudh S. Ganesina, Meghana Reddy Hu, Peiyun Leal-Taixé, Laura Kong, Shu Ramanan, Deva Osep, Aljosa Robotics Institute Carnegie Mellon University United States Dynamic Vision and Learning TU Munich Germany Faculty of Science and Technology University of Macau China Department of Computer Science and Engineering Texas A&M University United States

Addressing Lidar Panoptic Segmentation (LPS) is crucial for safe deployment of autnomous vehicles. LPS aims to recognize and segment lidar points w.r.t. a pre-defined vocabulary of semantic classes, including thing classes of countable objects (e.g., pedestrians and vehicles) and stuff classes of amorphous regions (e.g., vegetation and road). Importantly, LPS requires segmenting individual thing instances (e.g., every single vehicle). Current LPS methods make an unrealistic assumption that the semantic class vocabulary is fixed in the real open world, but in fact, class ontologies usually evolve over time as robots encounter instances of novel classes that are considered to be unknowns w.r.t. thepre-defined class vocabulary. To address this unrealistic assumption, we study LPS in the Open World (LiPSOW): we train models on a dataset with a pre-defined semantic class vocabulary and study their generalization to a larger dataset where novel instances of thing and stuff classes can appear. This experimental setting leads to interesting conclusions. While prior art train class-specific instance segmentation methods and obtain state-of-the-art results on known classes, methods based on classagnostic bottom-up grouping perform favorably on classes outside of the initial class vocabulary (i.e., unknown classes). Unfortunately, these methods do not perform on-par with fully data-driven methods on known classes. Our work suggests a middle ground: we perform class-agnostic point clustering and over-segment the input cloud in a hierarchical fashion, followed by binary point segment classification, akin to Region Proposal Network [1]. We obtain the final point cloud segmentation by computing a cut in the weighted hierarchical tree of point segments, independently of semantic classification. Remarkably, this unified approach leads to strong performance on both known and unknown classes. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Combining Scene Coordinate Regression and Absolute Pose Regression for Visual Relocalization

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Combining Scene Coordinate Regression and Absolute Pose Regr...

IEEE International Conference on robotics and Automation (ICRA)

作者： Jiahao Ruan Li He Yisheng Guan Hong Zhang School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering Shenzhen Key - Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China

Visual relocalization is a fundamental problem in computer vision and robotics. Recently, regression-based methods become popular and they can be categorized into two classes: absolute pose regression and scene coordinate regression. In this work, we present a combined regression network that jointly learns scene coordinate regression and absolute pose regression for single-image visual relocalization. The proposed network composes of a feature encoder and two regression branches with uncertainty modeling. In particular, we design a deep feature conditioning module, aiming at propagating the coarse pose information in absolute pose regression to inform the predictions in scene coordinate regression. The proposed network is trained in an end-to-end fashion to learn both regression tasks. Moreover, we propose an uncertainty-driven RANSAC algorithm that incorporates the predicted scene coordinates and their uncertainties to solve the camera pose during inference. To the best of our knowledge, this work is the first to combine scene coordinate regression and pose regression in a hierarchical framework for visual relocalization. Experiments on indoor and outdoor benchmarks demonstrate the effectiveness and the superiority of the proposed method over the state-of-the-art methods.

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A tethered quadrotor UAV-buoy system for marine locomotion

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arXiv 2021年

作者： Kourani, Ahmad Daher, Naseem Vision and Robotics Lab Department of Mechanical Engineering American University of Beirut Beirut Lebanon Vision and Robotics Lab Department of Electrical and Computer Engineering

Unmanned aerial vehicles (UAVs) are finding their way into offshore applications. In this work, we postulate an original system that entails a marine locomotive quadrotor UAV that manipulates the velocity of a floating buoy by means of a cable. By leveraging the advantages of UAVs relative to high speed, maneuverability, ease of deployment, and wide field of vision, the proposed UAV-buoy system paves the way in front of a variety of novel applications. The dynamic model that couples the buoy, UAV, cable, and water environment is presented using the Euler-Lagrange method. A stable control system design is proposed to manipulate the forward-surge speed of the buoy under two constraints: maintaining the cable in a taut state, and keeping the buoy in contact with the water surface. Polar coordinates are used in the controller design process to attain correlated effects on the tracking performance, whereby each control channel independently affects one control parameter. This results in improved performance over traditional Cartesian-based velocity controllers, as demonstrated via numerical simulations in wave-free and wavy seas. Copyright © 2021, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)