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31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Kerber, Jonas Wagner, Sebastian Groh, Korbinian Notz, Dominik Kuhbeck, Thomas Watzenig, Daniel Knoll, Alois Department of Robotics Artificial Intelligence and Embedded Systems Technische Universität München Department of Informatics Munich Germany Bmw Na Mountain View United States Institute of Automation and Control Graz University of Technology Graz Austria

Assessment and testing are among the biggest challenges for the release of automated driving. Up to this date, the exact procedure to achieve homologation is not settled. Current research focuses on scenario-based approaches that represent driving scenarios as test cases within a scenario space. This avoids redundancies in testing, enables the inclusion of virtual testing into the process, and makes a statement about test coverage possible. However, it is unclear how to define such a scenario space and the coverage criterion. This work presents a novel approach to the definition of the scenario space. Spatiotemporal filtering on naturalistic highway driving data provides a large amount of driving scenarios as a foundation. A custom distance measure between scenarios enables hierarchical agglomerative clustering, categorizing the scenarios into subspaces. The members of a resulting cluster found through this approach reveal a common structure that is visually observable. We discuss a data-driven solution to define the necessary test coverage for the assessment of automated driving. Finally, the contribution of the findings to achieve homologation is elaborated. © 2020 IEEE.

关键词： Risk analysis

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Let me join you! Real-time F-formation recognition by a socially aware robot

Let me join you! Real-time F-formation recognition by a soci...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Hrishav Bakul Barua Pradip Pramanick Chayan Sarkar Theint Haythi Mg Embedded Systems and Robotics (Cognitive Robotics) group of TCS Research & Innovation Kolkata India Myanmar Institute of Information Technology Mandalay Myanmar

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

This paper presents a novel architecture to detect social groups in real-time from a continuous image stream of an ego-vision camera. F-formation defines social orientations in space where two or more person tends to communicate in a social place. Thus, essentially, we detect F-formations in social gatherings such as meetings, discussions, etc. and predict the robot's approach angle if it wants to join the social group. Additionally, we also detect outliers, i.e., the persons who are not part of the group under consideration. Our proposed pipeline consists of - a) a skeletal key points estimator (a total of 17) for the detected human in the scene, b) a learning model (using a feature vector based on the skeletal points) using CRF to detect groups of people and outlier person in a scene, and c) a separate learning model using a multi-class Support Vector Machine (SVM) to predict the exact F-formation of the group of people in the current scene and the angle of approach for the viewing robot. The system is evaluated using two data-sets. The results show that the group and outlier detection in a scene using our method establishes an accuracy of 91%. We have made rigorous comparisons of our systems with a state-of-the-art F-formation detection system and found that it outperforms the state-of-the-art by 29% for formation detection and 55% for combined detection of the formation and approach angle.

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Estimation of Arterial Blood Pressure Waveform from Photoplethysmogram Signal using Linear Transfer Function Approach

Estimation of Arterial Blood Pressure Waveform from Photople...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ashutosh Dash Nirmalya Ghosh Amit Patra Anirban Dutta Choudhury Department of Electrical Engineering Indian Institute of Technology Kharagpur West Bengal Embedded Systems and Robotics TCS Research and Innovation India

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The primary risk factor of hypertension, is the lack of awareness caused by the unavailability of ubiquitous blood pressure (BP) measurement. In this study, we have investigated the BP estimation using the photoplethysmogram (PPG) signal and a suitable subject-specific mathematical model. The linear transfer function (LTF) technique was used to identify the subject-specific model. Firstly, we tried to identify the model considering arterial blood pressure (ABP) as input and PPG as output, and we achieved an average estimation accuracy (normalized root mean square, NRMSE) of 84.4%. Next, we fitted an inverse model, where ABP is the output, and PPG is the input, and we achieved an average estimation accuracy (NRMSE) of 84.7%. Finallly, We verified that the two identified models mentioned above are inverse of each other. In this study, we have used ABP and PPG signals of 10 (male = 7, female = 3) subjets from the MIMIC II database. The results are quite promising for the use of the PPG in the detection and diagnosis of cardiovascular diseases.

关键词： Mathematical model Estimation Biomedical monitoring Transfer functions Standards Delay effects Arteries

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Correction to: Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

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Multimedia Tools and Applications 2022年第6期82卷 9551-9552页

作者： Garg, Gaurav Prasad, Girijesh Garg, Lalit Miyakoshi, Makoto Nakai, Toshiharu Coyle, Damien Embedded & Robotics BrainAlive Research Pvt. Ltd Kanpur India School of Computing and Intelligent Systems Magee Campus Ulster University Londonderry UK Faculty of Information & Communication Technology University of Malta Msida Malta Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego San Diego USA Neuroimaging & Informatics Lab (Niinf) National Centre for Geriatrics and Gerontology (NCGG) Obu Japan

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Mobile 3D printing robot simulation with viscoelastic fluids

arXiv

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arXiv 2021年

作者： Berdica, Uljad Fu, Yuewei Liu, Yuchen Angelidis, Emmanouil Feng, Chen New York University BrooklynNY11201 United States New York University Abu Dhabi United Arab Emirates Department of Neuromorphic Computing Fortiss - Research Institute of the Free State of Bavaria Robotics Artificial Intelligence and Embedded Systems Technical University of Munich

The system design and algorithm development of mobile 3D printing robots need a realistic simulation. They require a mobile robot simulation platform to interoperate with a physics-based material simulation for handling interactions between the time-variant deformable 3D printing materials and other simulated rigid bodies in the environment, which is not available for roboticists yet. To bridge this gap and enable the real-time simulation of mobile 3D printing processes, we develop a simulation framework that includes particle-based viscoelastic fluid simulation and particle-to-mesh conversion in the widely adopted Gazebo robotics simulator, avoiding the bottlenecks of traditional additive manufacturing simulation approaches. This framework is the first of its kind that enables the simulation of robot arms or mobile manipulators together with viscoelastic fluids. The method is tested using various material properties and multiple collaborating robots to demonstrate its simulation ability for the robots to plan and control the printhead trajectories and to visually sense at the same time the printed fluid materials as a free-form mesh. The scalability as a function of available material particles in the simulation was also studied. A simulation with an average of 5 FPS was achieved on a regular desktop computer. © 2021, CC BY.

关键词： 3D printers

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Learning to Prevent Monocular SLAM Failure using Reinforcement Learning 18

Learning to Prevent Monocular SLAM Failure using Reinforceme...

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Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing

作者： Vignesh Prasad Karmesh Yadav Rohitashva Singh Saurabh Swapnil Daga Nahas Pareekutty K. Madhava Krishna Balaraman Ravindran Brojeshwar Bhowmick Embedded Systems & Robotics TCS Research & Innovation Kolkata India Robotics Institute Carnegie Mellon University Pittsburgh USA Dept. of Robotics Engineering John Hopkins University Baltimore USA Robotics Research Center KCIS IIIT Hyderabad India Dept. of Computer Science Indian Institute of Technology Madras India

ISBN: (纸本)9781450366151

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

关键词： Monocular SLAM

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State-Based Transcription of Components of Carnatic Music

State-Based Transcription of Components of Carnatic Music

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Venkata Subramanian Viraraghavan Arpan Pal Hema Murthy R Aravind Embedded Systems and Robotics TCS Research and Innovation Tata Consultancy Services Department of Computer Science and Engineering Indian Institute of Technology Madras Department of Electrical Engineering Indian Institute of Technology Madras

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Automatic Carnatic Music (CM) transcription is an open problem in need of a standardized descriptive notation. The level of detail needed in a descriptive transcription makes it tedious to obtain ground truth by manual means. In this paper, we propose a novel state-based representation of the pitch curve motivated by CM components called constant-pitch notes and stationary points. We also propose a novel transcription technique that uses the Viterbi algorithm to estimate the states and quantized pitch-values. The proposed technique adheres best to raga-notes compared to the existing critical-points technique and uniform quantization. In a listening test, clips synthesized from the proposed notation were rated significantly better (324 ratings, p <;0.001) than those from critical-points. Further, speed-halving based on state information best matches the actual, observed CM component-duration ratios without losing raga-characteristics. Thus, the proposed transcription can be corrected manually to obtain ground truth and can enhance learning tools.

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A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards

arXiv

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arXiv 2021年

作者： Angelidis, Emmanouil Buchholz, Emanuel Arreguit O’Neil, Jonathan Patrick Rougé, Alexis Stewart, Terrence von Arnim, Axel Knoll, Alois Ijspeert, Auke Department of Neuromorphic Computing fortiss - Research Institute MunichBavaria Germany Biorobotics Laboratory Institute of Bioengineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland Computational Neuroscience Research Group University of Waterloo Centre for Theoretical Neuroscience Waterloo Canada Robotics Artificial Intelligence and Embedded Systems Technical University of Munich Munich Germany

Central Pattern Generators (CPGs) models have been long used to investigate both the neural mechanisms that underlie animal locomotion as well as a tool for robotic research. In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our CPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the Neural Engineering Framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled abstract oscillators modulated by high-level signals, capable of producing a variety of output gaits. We show that with this mathematical formulation of the Central Pattern Generator model, the model can be turned into a Spiking Neural Network (SNN) that can be easily simulated with Nengo, an SNN simulator. The spiking CPG model is then used to produce the swimming gaits of a simulated lamprey robot model in various scenarios. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. The proposed methodology can be generalized to other types of CPGs suitable for both engineering applications and scientific research. We test our system on two neuromorphic platforms, SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms shows a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed. © 2021, CC BY-SA.

关键词： Dynamical systems

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A Systematic Modelling Framework for Commercial Unmanned Hexacopter Considering Fractional Order System Theory

A Systematic Modelling Framework for Commercial Unmanned Hex...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Nithya Sridhar N. Sai Abhinay B. Chaithanya Krishna Kaushik Das Arnab Maity Embedded Systems and Robotics Research Group of Tata Consultancy Services Bangalore India Arnab Maity is with the Faculty of Department of Aerospace Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

This research work proposes a systematic methodology to model Commercial-Off-The-Shelf Unmanned Aerial Vehicle (UAVs) that belong to the class of multicopters. The modelling framework consists of meticulous analysis of frequency domain and time domain data acquired from the hexacopter. The aim of this work is to model a commercially available UAV to enable the development of an efficient outer loop controller, specific to the intended application. This framework makes use of sine and step response data of the hexacopter's velocities in all the three planar axes corresponding to the global frame of reference. The framework considers the fractional-order model as a potential candidate for the UAV. Hence, it consists of a systematic method to determine the order of the model in integers or fractions, and its parameters. In the modelling process, control theory and fractional order theory have been sufficiently used to best correlate theoretical proposition with experimental outcomes. Also, real world factors and system constraints have been explicated and considered appropriately. The model obtained using the proposed framework has an average error of less than 5% when compared to the real system's response. Additionally, the ill effect of considering a near integer order model for a fractional order system is demonstrated to show the importance of this modelling framework. Further, a linear controller is implemented for the system using the estimated model, and corroborated with real world results.

关键词： Data models Mathematical model Unmanned aerial vehicles Analytical models Frequency-domain analysis Time-domain analysis Control theory

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A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained systems

A C Code Generator for Fast Inference and Simple Deployment ...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Oliver Urbann Simon Camphausen Arne Moos Ingmar Schwarz Sören Kerner Maximilian Otten Automation and Embedded Systems Fraunhofer IML Dortmund Germany Robotics Research Institute TU Dortmund University Dortmund Germany Software and Information Engineering Fraunhofer IML Dortmund Germany

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Inference of Convolutional Neural Networks in time critical applications usually requires a GPU. In robotics or embedded devices these are often not available due to energy, space and cost constraints. Furthermore, installation of a deep learning framework or even a native compiler on the target platform is not possible. This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function. It can easily be included in existing projects and due to lack of dependencies, cross compilation is usually possible. Additionally, the code generation is optimized based on the known trained CNN and target platform following four design principles. The system is evaluated utilizing small CNN designed for this application. Compared to TensorFlow XLA and Glow speed-ups of up to 11.81 can be shown and even GPUs are outperformed regarding latency.

关键词： Robots Artificial neural networks Machine learning Libraries Generators embedded systems Optimization

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