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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

IEEE/SICE International Symposium on System Integration

作者： Chuong Phuoc Le Charles Ellison Steven Bunkley Hung La Anton Netchaev 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

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack.

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ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English 7

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ArzEn-ST: A Three-way Speech Translation Corpus for Code-Swi...

7th Arabic Natural Language Processing Workshop, WANLP 2022 held with EMNLP 2022

作者： Hamed, Injy Habash, Nizar Abdennadher, Slim Vu, Ngoc Thang Computational Approaches to Modeling Language Lab New York University Abu Dhabi United Arab Emirates Institute for Natural Language Processing University of Stuttgart Germany Computer Science Department The German University in Cairo Egypt

ISBN: (纸本)9781959429272

We present our work on collecting ArzEnST, a code-switched Egyptian Arabic - English Speech Translation Corpus. This corpus is an extension of the ArzEn speech corpus, which was collected through informal interviews with bilingual speakers. In this work, we collect translations in both directions, monolingual Egyptian Arabic and monolingual English, forming a three-way speech translation corpus. We make the translation guidelines and corpus publicly available. We also report results for baseline systems for machine translation and speech translation tasks. We believe this is a valuable resource that can motivate and facilitate further research studying the codeswitching phenomenon from a linguistic perspective and can be used to train and evaluate NLP systems. © 2022 Association for computational Linguistics.

关键词： computational linguistics

FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation

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arXiv 2024年

作者： Gode, Samiran Nayak, Abhijeet Oliveira, Débora N.P. Krawez, Michael Schmid, Cordelia Burgard, Wolfram Artificial Intelligence and Robotics Lab Department of Computer Science and Artificial Intelligence University of Technology Nuremberg Germany Inria Ecole Normale Supérieure CNRS PSL Research University France

Effective robot navigation in unseen environments is a challenging task that requires precise control actions at high frequencies. Recent advances have framed it as an image-goal-conditioned control problem, where the robot generates navigation actions using frontal RGB images. Current state-of-the-art methods in this area use diffusion policies to generate these control actions. Despite their promising results, these models are computationally expensive and suffer from weak perception. To address these limitations, we present FlowNav, a novel approach that uses a combination of Conditional Flow Matching (CFM) and depth priors from off-the-shelf foundation models to learn action policies for robot navigation. FlowNav is significantly more accurate at navigation and exploration than state-of-the-art methods. We validate our contributions using real robot experiments in multiple unseen environments, demonstrating improved navigation reliability and accuracy. We make the code and trained models publicly available† Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent robots

Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

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arXiv 2023年

作者： Rysbek, Zhanibek Oh, Ki Hwan Shervedani, Afagh Mehri Klemenčič, Timotej Žefran, Miloš Di Eugenio, Barbara Robotics Lab Department of Electrical and Computer Engineering The University of Illinois at Chicago ChicagoIL60607 United States Laboratory of Robotics Faculty of Electrical Engineering University of Ljubljana Slovenia Natural Language Processing Lab Department of Computer Science The University of Illinois at Chicago ChicagoIL60607 United States

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established, but communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, and how the robot can communicate task goals, comprehend human intent, and take the lead as needed. To achieve this, we formulate a task that requires active force communication and propose a taxonomy that extends existing literature. Also, we conducted a study to observe how humans behave during collaborative manipulation tasks. An important contribution of this work is the novel features based on force-kinematic signals that demonstrate predictive power to recognize symbolic human intent. Further, we show the feasibility of developing a real-time intent classifier based on the novel features and speculate the role it plays in high-level robot controllers for physical Human-Robot Interaction (pHRI). This work provides important steps to achieve more human-like fluid interaction in physical co-manipulation tasks that are applicable and not limited to humanoid, assistive robots, and human-in-the-loop automation. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

Fuzzy Logic Based Brake Failure Detection & Collision Avoidance Model for Automated Driving System

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Fuzzy Logic Based Brake Failure Detection & Collision Avoida...

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Amil Roohani Dar Faisal Riaz Department of CS&IT University of Kotli (AJ&K) Kotli AJ&K Pakistan Control Automotive and Robotics Lab Affiliated Lab of National Centre of Robotics and Automation (NCRA) HEC Islamabad Pakistan Department of Computer Science & Information Technology Mirpur University of Sceince & Technology (MUST) Mirpur Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

The manual and automated braking system is intended to prevent rear-end collisions. However, unforeseen malfunctions within the vehicle's system during braking could potentially cause accidents. Collision Warning (CW) systems are safety technologies in vehicles that aid drivers in preventing collisions. This paper introduces a fuzzy-assisted pre-warning system for brake testing that considers multiple factors contributing to brake failure, along with a post-failure CW algorithm tailored for heavy commercial vehicles. Simulations conducted using NetLogo demonstrate that accidents can be prevented during brake failure through appropriate adjustments in vehicle speed. The proposed collision avoidance system has been validated for its effectiveness, and the results are encouraging and enhance road user safety.

关键词： Robot sensing systems Software Road safety Safety Brakes Collision avoidance robotics and automation Accidents Vehicles Testing

A Stable Guidance Method for Teleoperation-based Robot Learning from Demonstration

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A Stable Guidance Method for Teleoperation-based Robot Learn...

IEEE International Conference on Automation science and Engineering (CASE)

作者： Weiyong Si Zhehao Jin Zhenyu Lu Ning Wang Chenguang Yang School of Computer Science and Electronic Engineering at the University of Essex Colchester UK College of Information Engineering Zhejiang University of Technology Hangzhou China Bristol Robotics Lab at the University of the West of England Bristol UK Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Robot learning from teleoperation-based demonstration has been proven to be an effective method for robot skills learning. However, the human demonstration process is mentally and physically challenging due to the dexterous and continuous interaction control. Especially, for contact-rich tasks human operators need to control the pose and contact force between the end-effector and the environment, only based on visual and haptic feedback. To tackle the problem, this work studies a stable guidance method for teleoperation-based demonstration to improve the experience and performance of human demonstration and reduce the workload of human operators. The guidance design is based on a dynamic system-based imitation learning model, which is achieved by human demonstration. The virtual guidance force will decrease as the virtual guidance’s iterative learning. The efficiency of human demonstration is improved, and human operators’ physical and mental workload is reduced. Two typical experiments, robot-assisted polishing and robot-assisted ultrasound scanning, are conducted to validate the performance of the guidance method.

关键词： Visualization Ultrasonic imaging Imitation learning Force Dynamics Process control Robot learning End effectors Haptic interfaces Iterative methods

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving

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arXiv 2024年

作者： Zhao, Zhipeng Li, Bowen Du, Yi Fu, Taimeng Wang, Chen Lab Institute for Artificial Intelligence and Data Science Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neural networks require extensive datasets and struggle with explicitly capturing the fundamental physical laws, which can easily lead to poor generalization. By merging the advantages of both methods, neuro-symbolic approaches present a promising direction. These methods embed physical laws into neural models, potentially significantly improving generalization capabilities. However, no prior works were evaluated in real-world settings for off-road driving. To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i.e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving. Our experiments showed that PhysORD can accurately predict vehicle motion and tolerate external disturbance by modeling uncertainties. The learned dynamics model achieves 46.7% higher accuracy using only 3.1% of the parameters compared to data-driven methods, demonstrating the data efficiency and superior generalization ability of our neural-symbolic method. Copyright © 2024, The Authors. All rights reserved.

关键词： Off road vehicles

Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval

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arXiv 2024年

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Li, Tiefeng Research Center for Intelligent Robotics Zhejiang Lab China Department of Engineering Mechanics Center for X-Mechanics Zhejiang University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Diffusion-Based Generative Modeling With Discriminative Guidance for Streamable Speech Enhancement

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Diffusion-Based Generative Modeling With Discriminative Guid...

IEEE Spoken Language Technology Workshop

作者： Chenda Li Samuele Cornell Shinji Watanabe Yanmin Qian Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Carnegie Mellon University USA

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Diffusion-based generative models (DGMs) have recently attracted attention in speech enhancement (SE) research as previous works showed a remarkable generalization capability. However, DGMs are also computationally intensive, since they usually require many iterations in the reverse diffusion process (RDP), making them impractical for streaming SE systems. In this paper, we propose to use scores estimated from discriminative models in the first steps of the RDP. These discriminative-based scores require only one forward pass with the discriminative model for multiple RDP steps, thus greatly reducing computations. This approach also allows for performance improvements. We show that choosing an appropriate number of discriminative guidance steps can result in an overall model with better performance than generative and discriminative models. Furthermore, we propose a novel streamable time-domain generative model with an algorithmic latency of 50 ms, which has no significant performance degradation compared to offline models.

关键词： Degradation computational modeling Conferences Diffusion processes Speech enhancement Time-domain analysis