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Control Technology and Applications (CCTA),

作者： Niklas Dahlquist Björn Lindqvist Akshit Saradagi George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as ’pick and place’, whose arrival times are not known a priori. In such a scenario, a coordinating architecture is expected to be reactive to newly arrived tasks and the resulting rerouting of agents should be dependent on the stage of completion of their current multi-stage tasks. In the novel architecture proposed in this article, a central auctioning system gathers bids (cost-estimates for completing currently available tasks) from all agents, and solves a combinatorial problem to optimally assign tasks to agents. For every agent, it’s participation in the auctioning system and execution of an assigned multi-stage task is managed using behavior trees, which switch among several well-defined behaviors in response to changing scenarios. The auctioning system is run at a fixed rate, allowing for newly added tasks to be incorporated into the auctioning system, which makes the solution reactive and allows for rerouting of some agents (subject to the states of the behavior trees). We demonstrate that the proposed architecture is especially well-suited for multistage tasks, where high costs are incurred when rerouting agents who have completed one or more stages of their current tasks. The proposed framework is experimentally validated in multiple scenarios in a lab environment. A video of a demonstration can be found at: https://***/ZdEkoOOlB2g.

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Temporally Robust Multi-Agent STL Motion Planning in Continuous Time

Temporally Robust Multi-Agent STL Motion Planning in Continu...

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American Control Conference (ACC)

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Signal Temporal Logic (STL) is a formal language over continuous-time signals (such as trajectories of a multiagent system) that allows for the specification of complex spatial and temporal system requirements (such as staying sufficiently close to each other within certain time intervals). To promote robustness in multi-agent motion planning with such complex requirements, we consider motion planning with the goal of maximizing the temporal robustness of their joint STL specification, i.e. maximizing the permissible time shifts of each agent's trajectory while still satisfying the STL specification. Previous methods presented temporally robust motion planning and control in a discrete-time optimization scheme. In contrast, we parameterize the trajectory by continuous Bézier curves, where the curvature and the time-traversal of the trajectory are parameterized individually. We show an algorithm generating continuous-time temporally robust trajectories and prove soundness of our approach. Moreover, we empirically show that our parametrization realizes this with a considerable speed-up compared to state-of-the-art methods based on constant interval time discretization.

关键词： Redundancy Formal languages Robustness Encoding Trajectory Planning Logic

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TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation

TAGIC: Task-Guided Image Communication Framework for Seamles...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Yufeng Diao Yichi Zhang Philip G. Zhao Daniele De Martini School of Engineering University of Glasgow UK Department of Computer Science University of Manchester UK Oxford Robotics Institute (ORI) University of Oxford UK

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Image-based teleoperation offers significant flexibility and efficiency in several applications, such as teleoperated driving; still, it highly depends on reliable communication band-width and high Signal-to-Noise Ratio (SNR), which is hard to guarantee in uncontrolled environments. This poster tackles the challenge of reliable communication under limited bandwidth. We propose to leverage the context and task knowledge to guide the compression to favor task performance rather than image fidelity. In particular, we jointly designed source-channel coding with a task performer to present an end-to-end TAsk-Guided Image Communication (TAGIC) framework, which uses Soft Introspective Variational Autoencoder (S-IntroVAE) and prioritizes the task-critical image information with limited communication bandwidth in the low SNR region. We demonstrate the effectiveness of TAGIC in a teleoperated driving scenario through the CARLA simulation platform - a widely used simulator in the autonomous driving community. Given the equivalent value of bandwidth compression ratio, TAGIC achieves a 202.6% improvement in the driving score over existing methods at low SNR.

关键词： Resistance Knowledge engineering Image coding Image communication Conferences Bandwidth Reliability

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HREyes: Design, Development, and Evaluation of a Novel Method for AUVs to Communicate Information and Gaze Direction*

HREyes: Design, Development, and Evaluation of a Novel Metho...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Michael Fulton Aditya Prabhu Junaed Sattar Department of Computer Science and Engineering Minnesota Robotics Institute University of Minnesota Twin Cities Minneapolis MN USA

We present the design, development, and evaluation of HREyes: biomimetic communication devices which use light to communicate information and, for the first time, gaze direction from AUVs to humans. First, we introduce two types of information displays using the HREye devices: active lucemes and ocular lucemes. Active lucemes communicate information explicitly through animations, while ocular lucemes communicate gaze direction implicitly by mimicking human eyes. We present a human study in which our system is compared to the use of an embedded digital display that explicitly communicates information to a diver by displaying text. Our results demonstrate accurate recognition of active lucemes for trained interactants, limited intuitive understanding of these lucemes for untrained interactants, and relatively accurate perception of gaze direction for all interactants. The results on active luceme recognition demonstrate more accurate recognition than previous light-based communication systems for AUVs (albeit with different phrase sets). Additionally, the ocular lucemes we introduce in this work represent the first method for communicating gaze direction from an AUV, a critical aspect of nonverbal communication used in collabo-rative work. With readily available hardware as well as open-source and easily re-configurable programming, HREyes can be easily integrated into any AUV with the physical space for the devices and used to communicate effectively with divers in any underwater environment with appropriate visibility.

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Multiplanar Self-Calibration for Mobile Cobot 3D Object Manipulation Using 2D Detectors and Depth Estimation

Multiplanar Self-Calibration for Mobile Cobot 3D Object Mani...

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

作者： Tuan Dang Khang Nguyen Manfred Huber Department of Computer Science and Engineering Learning and Adaptive Robotics Laboratory University of Texas at Arlington Arlington TX USA

Calibration is the first and foremost step in dealing with sensor displacement errors that can appear during extended operation and off-time periods to enable robot object manipulation with precision. In this paper, we present a novel multiplanar self-calibration between the camera system and the robot's end-effector for 3D object manipulation. Our approach first takes the robot end-effector as ground truth to calibrate the camera's position and orientation while the robot arm moves the object in multiple planes in 3D space, and a 2D state-of-the-art vision detector identifies the object's center in the image coordinates system. The transformation between world coordinates and image coordinates is then computed using 2D pixels from the detector and 3D known points obtained by robot kinematics. Next, an integrated stereo-vision system estimates the distance between the camera and the object, resulting in 3D object localization. We test our proposed method on the Baxter robot with two 7-DOF arms and a 2D detector that can run in real time on an onboard GPU. After self-calibrating, our robot can localize objects in 3D using an RGB camera and depth image. The source code is available at https://***/tuantdang/calib_cobot.

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Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Chou, Ka Seng Wong, Kei Long Aguiari, Davide Tse, Rita Tang, Su-Kit Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna Italy Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates UCLA Samueli Computer Science University of California Los Angeles Los Angeles United States

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more than 2.3 million discharging samples via a custom Internet of Thing device for more than one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Lithium-ion batteries

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Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation

arXiv

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arXiv 2023年

作者： Carrazco, Julio Ivan Davila Morerio, Pietro Del Bue, Alessio Murino, Vittorio Italian Institute of Technology Genoa Italy Department of Computer Science University of Verona Verona Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genoa Italy Department of Marine Electrical Electronic and Telecommunications Engineering Robotics and Systems Engineering University of Genova Genoa Italy

This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as only one single unlabeled target sample is assumed to be available for model adaptation. Driven by such single sample, our method LearnAug-UDA learns how to augment source data, making it perceptually similar to the target. As a result, a classifier trained on such augmented data will generalize well for the target domain. To achieve this, we designed an encoder-decoder architecture that exploits a perceptual loss and style transfer strategies to augment the source data. Our method achieves state-of-the-art performance on two well-known Domain Adaptation benchmarks, DomainNet and VisDA. The project code is available at https://***/IIT-PAVIS/LearnAug-UDA. © 2023, CC BY.

关键词： Benchmarking

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Recurrent Neural Network-based Modeling Approach for 4D Printing Design : Long sequence prediction based on short sequences

Recurrent Neural Network-based Modeling Approach for 4D Prin...

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2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Xu, Yifan Wang, Mengtao Wang, Zhongkui Meng, Lin Ritsumeikan University Graduate School of Science and Engineering 1-1-1 Noji-higashi Kusatsu Shiga525-8577 Japan Ritsumeikan University Department of Robotics 1-1-1 Noji-higashi Kusatsu Shiga525-8577 Japan Ritsumeikan University Department of Electronic and Computer Engineering 1-1-1 Noji-higashi Kusatsu Shiga525-8577 Japan

ISBN: (纸本)9798350366204

This study presents a method for predicting the deformation of hydrogel models fabricated by four-dimensional printing technology using deep learning. In this method, a large number of hydrogel models with the same shape but different distributions of length and expansion ratio are first created using the modeling software Abaqus. Then, deformation simulations are performed based on finite element analysis using the thermal expansion method, and the simulation results are processed and organized to create a data set. Afterward, a recurrent neural network (RNN) was created and trained using a composite dataset consisting of multiple hydrogel model sequences of different lengths to enable the RNN to learn model deformation features and, accordingly, predict the deformation of various longer sequences of hydrogels in a very short period of time while recording the change in accuracy of the prediction for each length. This research provides a new approach to modeling and inverse design for 4D printing technology. © 2024 IEEE.

关键词： Prediction models

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Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severity Level of Parkinson's Disease from Spontaneous Speech

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wai, Thiri Liao, Yu-Shan Liao, Ting-Yun Lin, Chin-Hsien Hung, Chi-Sheng Fu, Li-Chen National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Biomedical Engineering College of Medical Science and Technology Taipei Medical University National Taiwan University Hospital Bei-Hu Branch Department of Neurology Taipei Taiwan Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan National Taiwan University Hospital and National Taiwan University College of Medicine Department of Internal Medicine Taipei Taiwan and Advanced Robotics Taiwan

ISBN: (纸本)9781665410205

Parkinson's disease (PD) is the cause of the gradual decline of nerve cells that control movement disorder disease, which is most common among the elderly in the US after Alzheimer's disease. There are several studies on detecting Parkinson's disease from speech using machine learning techniques;however, most of them focus on classifying healthy patients (HC) against Parkinson's disease (PD). This paper focuses on developing a screening system that could detect healthy (HC) vs. mild Parkinson's (MP) vs. severe Parkinson's (SP) from spontaneous speech. Four acoustic feature sets were compared for the screening system. Our proposed AeXGB was also compared with six different classifying approaches. The result has shown that extracting the phonation features and using AeXGB could achieve an accuracy of 92% for classifying HC vs. MP vs. SP, which outperforms the traditional machine learning approach for three class classifications of Parkinson's severity level from spontaneous speech. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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