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Human-collective visualization transparency

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arXiv 2020年

作者： Roundtree, Karina A. Cody, Jason R. Leaf, Jennifer Onan Demirel, H. Adams, Julie A. School of Mechanical Industrial and Manufacturing Engineering Oregon State University Department of Electrical Engineering and Computer Science United States Military Academy Collaborative Robotics and Intelligent Systems Institute Oregon State University

Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective transparency research has focused on how the design of the algorithms, visualizations, and control mechanisms influence human-collective behavior. Traditional collective visualizations have shown all of the individual entities composing a collective, which may become problematic as collectives scale in size and heterogeneity, and tasks become more demanding. Human operators can become overloaded with information, which will negatively affect their understanding of the collectives current state and overall behaviors, which can cause poor teaming performance. An analysis of visualization transparency and the derived visualization design guidance, based on remote supervision of collectives, are the primary contributions of this manuscript. The individual agent and abstract visualizations were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. The abstract visualization provided better transparency by enabling operators with different individual differences and capabilities to perform relatively the same and promoted higher human-collective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Transparency

Markerless Multi-View-based Multi-User Head Tracking System for Virtual Reality Applications

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Markerless Multi-View-based Multi-User Head Tracking System ...

IEEE International Conference on systems, Man and Cybernetics

作者： Dylan Bicho Pedro Girão João Paulo Luís Garrote Urbano J. Nunes Paulo Peixoto Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra

In this paper, we propose a markerless multi-view-based multi-user head tracking system for VR/AR/MR applications. In the Virtual Reality domain, the user typically interacts with the virtual world through his movements. The point-of-view of the user's head mounted display (HMD) is based on the user's head pose. Most systems track the head of the user based on the tracking of the HMD itself and are typically constrained to a single user. Aiming to improve the user's experience in complex dynamic environments, we propose a head tracking method based on a multi-camera scenario, able to deal with the interaction of multiple users while coping with the existence of dynamic objects. The method relies on the voxelization of dense point cloud data, which is segmented to detect elements in the scene. Each element is holistically tracked along time and each detected user has his head's position tracked using a dedicated Particle Filter. The system was evaluated using an annotated ground-truth, according to several metrics, and demonstrated a mean 3D positioning error below 18 mm. Furthermore, tests with multiple users were performed that successfully validate the proposed head tracking approach. The aforementioned tests also showed that the proposed approach is computationally efficient (running time below 20 ms for 4 users).

关键词： Three-dimensional displays Head Resists Sensors Cameras Virtual reality Tracking

Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System

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arXiv 2021年

作者： Ma, Jun Cheng, Zilong Zhu, Haiyue Li, Xiaocong Tomizuka, Masayoshi Lee, Tong Heng Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Singapore Institute of Manufacturing Technology Singapore138634 Singapore John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Mechanical Engineering University of California BerkeleyCA94720 United States

Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features. The technology offers high-precision, contactless, dust/lubricant-free, multi-axis, and large-stroke positioning. In this work, we focus on the accurate and smooth tracking problem of a multi-axis magnetically levitated (maglev) planar positioning system for a specific S-curve reference trajectory. The floating characteristics and the multi-axis coupling make accurate identification of the system dynamics difficult, which lead to a challenge to design a high performance control system. Here, the tracking task is achieved by a 2-Degree of Freedom (DoF) controller consisting of a feedforward controller and a robust stabilizing feedback controller with a prescribed sparsity pattern. The approach proposed in this paper utilizes the basis of an H∞ controller formulation and a suitably established convex inner approximation. Particularly, a subset of robust stabilizable controllers with prescribed structural constraints is characterized in the parameter space, and so thus the re-formulated convex optimization problem can be easily solved by several powerful numerical algorithms and solvers. With this approach, the robust stability of the overall system is ensured with a satisfactory system performance despite the presence of parametric uncertainties. Furthermore, experimental results clearly demonstrate the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

Human intention recognition for human aware planning in integrated warehouse systems

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arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned;however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity

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arXiv 2020年

作者： Bird, Jordan J. Faria, Diego R. Ekárt, Anikó Premebida, Cristiano Ayrosa, Pedro P.S. ARVIS Lab Aston University Birmingham United Kingdom School of Engineering and Applied Science Aston University United Kingdom Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Department of Computer Science & LABTED State University of Londrina Londrina Brazil

In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification. In this work, we take a set of 5 spoken Harvard sentences from 7 subjects and consider their MFCC attributes. Using character level LSTMs (supervised learning) and OpenAI’s attention-based GPT-2 models, synthetic MFCCs are generated by learning from the data provided on a per-subject basis. A neural network is trained to classify the data against a large dataset of Flickr8k speakers and is then compared to a transfer learning network performing the same task but with an initial weight distribution dictated by learning from the synthetic data generated by the two models. The best result for all of the 7 subjects were networks that had been exposed to synthetic data, the model pre-trained with LSTM-produced data achieved the best result 3 times and the GPT-2 equivalent 5 times (since one subject had their best result from both models at a draw). Through these results, we argue that speaker classification can be improved by utilising a small amount of user data but with exposure to synthetically-generated MFCCs which then allow the networks to achieve near maximum classification scores. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

Passive Quadrupedal Gait Synchronization for Extra Robotic Legs Using a Dynamically Coupled Double Rimless Wheel Model

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Passive Quadrupedal Gait Synchronization for Extra Robotic L...

IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel J. Gonzalez H. Harry Asada Robotics Research Center in the Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA d’Arbeloff Laboratory for Information Systems and Technology in the Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human's limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincaré return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone.

关键词： Legged locomotion Wheels Synchronization Couplers Robot kinematics

Lecture Notes in Networks and Systems

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Preface

Lecture Notes in Networks and systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

Classification of FACS-Action Units with CNN Trained from Emotion Labelled Data Sets

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Classification of FACS-Action Units with CNN Trained from Em...

IEEE International Conference on systems, Man and Cybernetics

作者： Pedro Carvalho Gerardo Paulo Menezes Institute of Systems and Robotics University of Coimbra Institute of Systems and Robotics and with the Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra Portugal

This paper explores the adaptation of a convolutional neural network (CNN) designed for emotion classification to the extraction of human expressions action units. An adaptation of the network structure transforms a single label into a multi-label classifier which supports the simultaneous recognition of multiple action units that compose human expressions, according to Ekman's FACS. The dataset used for this work was FER-2013 that includes exemplars of seven basic expressions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). In order to enhance the quality of the results, the dataset was augmented with random perturbations from a wide set including: translation, scale and horizontal flip. The results obtained demonstrate that it is possible to train a CNN for multi-label expression classification from an emotion-labelled dataset.

关键词： Emotion recognition Face recognition Training Feature extraction Convolutional neural nets Gold

A fault diagnosis benchmark of technical systems with incomplete data — six solutions

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Control engineering Practice 2025年 164卷

作者： Daniel Jung Erik Frisk Mattias Krysander Anna Sztyber-Betley Francesco Corrini Andrea Arici Nicolas Anselmi Mirko Mazzoleni Jiamin Xu Siwen Mo Zixuan Xu Chongpan Yang Zhile Du Hossein Safaeipour Mehdi Forouzanfar Vahid Mirahi Anna Pinnarelli Vicenç Puig Qiao Deng Yufei Liu Homayoun Najjaran Vehicular Systems Department of Electrical Engineering Linköping University Linköping SE-581 83 Sweden Faculty of Mechatronics Warsaw University of Technology Warsaw 02-525 Poland Department of Management Information and Production Engineering University of Bergamo Via G. Marconi 5 Dalmine (BG) 24044 Italy School of Automation Central South University Changsha 410083 China Department of Electrical Engineering Ahv.C. Islamic Azad University Ahvaz Iran Department of Mechanical Energy and Management Engineering (DIMEG) University of Calabria Rende Italy Institute of Robotics and Industrial Informatics (CSIC-UPC) University of Catalonia Barcelona Spain Department of Electrical and Computer Engineering University of Victoria Victoria V8W 2Y2 BC Canada

This paper presents a benchmark problem for fault diagnosis of an internal combustion engine that has been formulated and solved. The objective is to design a diagnosis system using and incomplete model information training data that only contains a limited set of fault realizations. Six different solutions to the benchmark, that were presented at the IFAC Safeprocess symposium 2024, are described and evaluated. The contribution of this paper is the benchmark and the presentation of six different solutions in one paper. The paper is intended to provide a starting point for engineers and researchers who work with fault diagnosis and monitoring of technical systems.

关键词： Fault detection and isolation Model-based diagnosis Data-driven fault diagnosis