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International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Mario Schulz Ivan Velickovic Sasa Andelkovic Volker Zerbe Goran S. Dordevic Computer Science and Automation Faculty System and Control Theory Department Technical University Ilmenau Ilmenau Germany Faculty of Electronics engineering Robotics Lab University of Nis Nis Serbia

Robots are complex systems that require multidisciplinary approach to development. Hence, both research and production of a robotic system require a tool that would provide means for coordination between teams with different areas of expertise as well as the ability to integrate, simulate and debug the system in a comprehensive way. Also, with the growing popularity of service robotics, research and modeling of human-machine interaction gains the attention. From a simulation tool, it is expected to support the means for describing this interaction. For that purpose we are investigating the ML Designer TM , not just as a possible simulation tool, but also as a modeling and design paradigm. For verification purposes we will compare it against MATlab Simulink TM . A pneumatic system will be modeled with both tools and a comparison will be performed.

关键词： Mathematical model Robot kinematics Cleaning system testing Power system modeling MATlab Pneumatic systems Control systems Level control Costs

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AUV dead-reckoning navigation based on neural network using a single accelerometer 11

AUV dead-reckoning navigation based on neural network using ...

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11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Xie, Yan-Xin Liu, Jun Hu, Cheng-Quan Cui, Jun-Hong Xu, Hongli State Key Laboratory of Robotics CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9781450346375

The accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and accelerometer. A major challenge for DR navigation is from measurement error of the inertial sensors (gyroscope, accelerometer, etc.), especially when the AUV is near or at the ocean surface. The AUV's motion is affected by ocean waves, and its pitch angle changes rapidly with the waves. This rapid change and the measurement errors will cause great noise to the direction measured by gyroscopes, and then lead to a large error to the DR navigation. To address this problem, a novel DR method based on neural network (DR-N) is proposed to explore the time-varying relationship between acceleration measurement and orientation measurement, which leverages acoustic localization and neural network estimate timely pitch angle through the explored time-varying relationship. This method enables AUV's DR navigation with a single acceleration, without relying on both acceleration and gyroscope. Most importantly, we can improve the accuracy of AUV navigation through avoiding DR errors caused by gyroscope noise at the sea surface. Simulations show DR-N significantly improves navigation accuracy.

关键词： Accelerometers

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Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

Analyzing Data Efficiency and Performance of Machine Learnin...

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European Conference on Mobile Robots (ECMR)

作者： Aleksa Marusic Louis Annabi Sao Mai Nguyen Adriana Tapus Autonomous Systems and Robotics Lab Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris Palaiseau France Dep. Informatique IMT Atlantique

Physical rehabilitation focuses on the improvement of body functions, usually after injury or surgery. Patients undergoing rehabilitation often need to perform exercises at home without the presence of a physiotherapist. computer-aided assessment of physical rehabilitation can improve patients' performance and help in completing prescribed rehabilitation exercises. In this work, we focus on human motion analysis in the context of physical rehabilitation for Low Back Pain (LBP). As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data acquired from RGB videos and human pose estimation from those. In this work, we provide an analysis of two types of algorithms on a Low Back Pain rehabilitation datasets. One is based on a Gaussian Mixture Model (GMM), with performance metrics based on the log-Likelihood values from GMM. Furthermore, with the recent development of Deep Learning and Graph Neural Networks, algorithms based on Spatio-Temporal Graph Convolutional Networks (STGCN) are taken as a novel approach. We compared the algorithms in terms of data efficiency and performance, with evaluation performed on two LBP rehabilitation datasets: KIMORE and Keraal. Our study confirms that Kinect, OpenPose, and BlazePose data yield similar evaluation scores, and shows that STGCN outperforms GMM in most configurations.

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HIERARCHICAL ACTIVITY DISCOVERY WITHIN SPATIO-TEMPORAL CONTEXT FOR VIDEO ANOMALY DETECTION

HIERARCHICAL ACTIVITY DISCOVERY WITHIN SPATIO-TEMPORAL CONTE...

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IEEE International Conference on Image Processing

作者： Dan Xu Xinyu Wu Dezhen Song Nannan L Yen-Lun Chen Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Computer Science and Engineering Texas A&M University USA

ISBN: (纸本)9781479923427

In this paper, we present a novel approach for video anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity pattern discovery framework comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised ways for automatically constructing normal activity patterns at different levels. An unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the efficiency of the proposed method on the UCSD anomaly detection datasets (Ped1 and Ped2) and compare the performance with existing work.

关键词： Visual surveillance video anomaly detection hierarchical discovery energy function Visual surveillance Activity patterns anomaly Videotapes anomaly detection Crowding

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Prevalence of Surgical Flow Disruptions Across Intra-operative High- and Low-Workload Phases in Cardiac Surgery

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2021年第1期10卷 263 - 266页

作者： Lauren R. Kennedy-Metz Roger D. Dias Annette M. Phillips Alexander Shapeton Suzana Zorca Kay B. Leissner Marco A. Zenati Medical Robotics and Computer Assisted Surgery Lab Veterans Affairs Boston Healthcare System Harvard Medical School Boston MA USA Human Factors & Cognitive Engineering Lab Brigham & Women’s Hospital Harvard Medical School Boston MA USA Veterans Affairs Boston Healthcare System Boston MA USA Veterans Affairs Boston Healthcare System Boston MA USA

While the influence of surgical flow disruptions (SFDs) on surgical performance has been observed in live cardiovascular surgery and their influence on cognitive workload has been evaluated in high-fidelity simulations, the relationship between SFDs and cognitive workload of surgical providers in the live cardiac surgery operating room has yet to be explored. Recent evidence suggests that preventable surgical errors often have a cognitive basis, warranting investigation into the association between SFDs and workload. This study aimed to characterize SFDs according to auditory and cognitive domains and further to compare the frequency and nature of SFDs within periods of high team cognitive workload and low team cognitive workload. Overall, the presence of cognitive distractions was associated more with high team cognitive workload states, while the frequency of auditory distractions was significantly higher during periods of low team cognitive workload states. Future work should consider the types, frequency, and sources of SFDs as well as their impact on surgical procedures and outcomes in order to devise appropriate methods to mitigate or manage potential disruptions to surgical workflow.

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Optimization-Based Control Allocation for Driving/Braking Torque Vectoring in a Race Car

Optimization-Based Control Allocation for Driving/Braking To...

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American Control Conference

作者： Moad Kissai Bruno Monsuez Xavier Mouton Adriana Tapus Department of Computer and System Engineering (U2IS) Institut Polytechnique de Paris ENSTA Paris Autonomous Systems and Robotics Lab Palaiseau France Chassis Systems Department Group Renault Guyancourt France

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Most of recent researches on the automotive field focus on autonomous vehicles. These vehicles are equipped with conventional chassis systems. The goal is to control the vehicle's traction, brakes, and front steering. This paper discusses the importance of advanced chassis systems, as driving/braking torque vectoring, for both autonomous and non-autonomous vehicles, especially in a race mode. Reliable co-simulation results show that expanding the vehicle's potential leads to high performances and safety with respect to severe situations when optimal control allocation is ensured. Therefore, future passenger cars shall not only be equipped with additional sensors, but also by advanced systems along with adequate control algorithms.

关键词： Tires Torque Mathematical model Force Automobiles Wheels Resource management

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A holistic visual place recognition approach using lightweight CNNs for significant viewpoint and appearance changes

arXiv

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arXiv 2018年

作者： Khaliq, Ahmad Ehsan, Shoaib Chen, Zetao Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Vision for Robotics Lab Eth Zurich Zurich8092 Switzerland Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot applications

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Domain-invariant similarity activation map contrastive learning for retrieval-based long-term visual localization

arXiv

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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on RobotCar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

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Why and how to avoid the flipped quaternion multiplication

arXiv

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arXiv 2018年

作者： Sommer, Hannes Gilitschenski, Igor Bloesch, Michael Weiss, Stephan Siegwart, Roland Nieto, Juan Autonomous Systems Lab ETH Zurich Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Dyson Robotics Lab Imperial College London United Kingdom Institute of Smart System Technologies Alpen-Adria-Universitat Klagenfurt Austria

Over the last decades quaternions have become a crucial and very successful tool for attitude representation in robotics and aerospace. However, there is a major problem that is continuously causing trouble in practice when it comes to exchanging formulas or implementations: there are two quaternion multiplications in common use, Hamilton's multiplication and its flipped version, which is often associated with NASA's Jet Propulsion laboratory. We believe that this particular issue is completely avoidable and only exists today due to a lack of understanding. This paper explains the underlying problem for the popular passive world to body usage of rotation quaternions, and derives an alternative solution compatible with Hamilton's multiplication. Furthermore, it argues for entirely discontinuing the flipped multiplication. Additionally, it provides recipes for efficiently detecting relevant conventions and migrating formulas or algorithms between them. Copyright © 2018, The Authors. All rights reserved.

关键词： NASA

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Genetic programming of process decomposition strategies for evolvable hardware

Genetic programming of process decomposition strategies for ...

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NASA/DoD Workshop on Evolvable Hardware

作者： Ho-Sik Seok Kwang-Ju Lee Byoung-Tak Zhang Dong-Wook Lee Kwee-Bo Sim Artificial Intelligence Laboratory SCAI School of Computer Science and Engineering Seoul National University Seoul South Korea Robotics and Intelligent Information System Lab Electrical and Electronic Engineering Chung-Ang University Seoul South Korea

Evolvable hardware is able to offer considerably higher performance than general-purpose processors and significantly more flexibility than ASICs. In order to take the advantages of general-purpose processors and ASICs, dividing a complex process into subprocesses is essential. In this paper, we propose a evolutionary method called context switching that splits a task into a set of subtasks whose complexity is manageable on the given hardware. The method is based on genetic programming. Due to its expressive power generic program can represent flexible strategies for decomposing complex tasks. The effectiveness of context switching is demonstrated on the design of adaptive controllers for a team of autonomous mobile robots.

关键词： Genetic programming Hardware Genetic algorithms Artificial intelligence Intelligent robots Mobile robots Programmable control Adaptive control computer architecture Application specific integrated circuits

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