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Safety Evaluation of Robot systems via Uncertainty Quantification

Safety Evaluation of Robot Systems via Uncertainty Quantific...

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ieee international conference on Robotics and Automation (ICRA)

作者： Baek, Woo-Jeong Kroeger, Torsten Karlsruhe Inst Technol Inst Anthropomat & Robot IAR IPR Karlsruhe Germany

ISBN: (纸本)9798350323658

In this paper, we present an approach for quantifying the propagated uncertainty of robot systems in an online and data-driven manner. Especially in Human-Robot Collaboration, keeping track of the safety compliance during run time is essential: Misclassifying dangerous situations as safe might result in severe accidents. According to official regulations (e. g., ISO standards), safety in industrial robot applications depends on critical parameters, such as the distance and relative velocity between humans and robots. However, safety can only be assured given a measure for the reliability of these parameters. While different risk detection and mitigation approaches exist in literature, a measure that can be used to evaluate safety limits online, and succinctly implies whether a situation is safe or dangerous, is missing to date. Motivated by this, we introduce a generalizable method for calculating the propagated measurement uncertainty of arbitrary parameters, that captures the accumulated uncertainty originating from sensory devices and environmental disturbances of the system. To show that our approach delivers correct results, we perform validation experiments in simulation. In addition, we employ our method in two real-world settings and demonstrate how quantifying the propagated uncertainty of critical parameters facilitates assessing safety online in Human-Robot Collaboration.

关键词： Robot applications

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EgpuIP: An embedded GPU Accelerated Library for Image Processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 ieee.

关键词： Graphics processing unit

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A Comparative Analysis of Deep Learning-Based Object Detectors for embedded systems

A Comparative Analysis of Deep Learning-Based Object Detecto...

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ieee international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Yehor Boltov Inna Skarga-Bandurova Maryna Derkach G.E. Pukhov Institute for Modelling in Energy Engineering Kyiv Ukraine Oxford Brookes University Oxford UK Ternopil Ivan Puluj National Technical University Ternopil Ukraine

The paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. The objective was to identify the optimal algorithm that can efficiently operate within limited computational resources and memory constraints of embedded systems while minimizing energy consumption. We tested a set of popular detectors, such as Faster R-CNN, YOLO, and SSD MobileNet and evaluated their performance on the testing platform with CUDA cores. A faster variant of bounding box suppression, blazing fast Non-Maximum Suppression (NMS), was employed to enhance efficiency. Additionally, the generalized intersection over union (GIoU) metric was adopted as the optimal testing method, allowing for more accurate object localization and detection. Results demonstrate that the SSD MobileNet-V2 and Tiny YOLO detectors exhibit promising real-time performance on embedded platforms, although their accuracy is diminished compared to larger models.

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Hardware-in-the-loop Simulation of a Programmable Logic Controller, Industrial Robots and Conveyor systems using RoboGuide 22

Hardware-in-the-loop Simulation of a Programmable Logic Cont...

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22nd international conference on Research and Education in Mechatronics

作者： Rawashdeh, Nathir Bondalapati, Rahul Patil, Punyokti Ajmani, GurveetSingh Akki, Shivayogi Michigan Technol Univ Dept Appl Comp Houghton MI 49931 USA

ISBN: (纸本)9798331505981;9798331505974

This paper presents an evaluation method for accessing a RoboGuide virtual robot cell and establishing control over a PLC-integrated robot system, with the primary objective of enhancing industrial automation capabilities. The experimental setup encompasses the simulation of a virtual robot cell featuring four Fanucs robots and two part conveyor belts, all controlled through Allen-Bradley PLC-based systems and a Human Machine Interface (HMI). This technological framework enables the testing and validation of control code on simulated industrial systems, ultimately contributing to increased operational efficiency and productivity. The study not only outlines the essential components involved in various industrial sectors but also assesses their efficacy, offering valuable insights into practical applications. Emphasizing the critical role of quality control and assurance in the automation sector for ensuring product standards and safety, the paper describes a simulation created within RoboGuide. In this simulation, the robot and PLC establish full-duplex communication over an Ethernet TCP/IP protocol. A custom control panel is developed, connecting the HMI to the PLC, thereby enabling the execution of robot actions or teaching pendant programs within the simulation environment. The core objective of this research is to assess the functionality of an assembly line using hardware-in-the-loop (HIL) simulation prior to the implementation of Factory Acceptance Tests (FAT) and Site Acceptance Tests (SAT) in a real-world industrial setting.

关键词： Industrial automation Robot simulation Programmable Logic Controller (PLCs) Hardware-in-the-loop (HIL) RoboGuide

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AI-Driven Security Enhancements in Wireless Sensor Networks 2

AI-Driven Security Enhancements in Wireless Sensor Networks

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2nd ieee international conference on Contemporary computing and Communications, InC4 2024

作者： Singh, Kamred Udham Kumar, Ankit Varshney, Neeraj Raja, Linesh Singh, Teekam Dewangan, Amit Kumar School of Computing Graphic Era Hill University Dehradun India Guru Ghasidas Vishwavidyalaya Department of Information Technology Bilaspur India GLA University Department of Computer Engineering & Applications UP Mathura India Manipal University Jaipur Department of Computer Applications Jaipur India Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383652

Wireless sensor networks have garnered significant adoption as a technology for collecting and analyzing data from the environment. Their versatility and wide-ranging applications, encompassing critical military operations, forest fire detection, healthcare systems, and civilian uses, have made them a focal point of extensive research. The potential of WSNs to provide valuable insights and address various real-world challenges has spurred significant interest and exploration in the scientific community. Nevertheless, Wireless Sensor Networks (WSNs) are susceptible to security threats, especially in unattended environments. To address these threats, the popular Low-Energy Adaptive Clustering Hierarchy protocol is deployed with machine learning techniques. This paper makes an analysis of different machine learning algorithms with multiple class of parameters and evaluates it with criterion as such favorable for a WSN. The Random Forest technique, which allows for the classification of assaults such as blackhole attacks, grayhole attacks, TDMA attacks, and flooding attacks, is the most suitable algorithm for intrusion detection. It comprises six extracted characteristics. The accuracy score is 99.12% with prediction accuracy for attacks: 87%, 89%, 92% and 93% respectively. Unlike detecting the anomaly alone, this mechanism when given to a centralized management system become a smart governance model. This prevents the network from different intrusion methodologies and enhance the characteristics such as energy efficiency, network lifetime, throughput, performance and security issue of the wireless sensor network. © 2024 ieee.

关键词： Random forests

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Recursive Estimation Library For Education

Recursive Estimation Library For Education

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2023 international conference Automatics and Informatics, ICAI 2023

作者： Grigorov, Ivan Atanasov, Nasko Nikolov, Nikola Technical University of Varna Faculty of Computer Sciences and Automation Varna9010 Bulgaria

ISBN: (纸本)9798350312911

Adaptive control, a critical component of real-time automated control systems, employs various strategies to ensure effective operation. timely execution of identification algorithms is paramount, given the continuous influx of new data and the necessity for rapid control action adjustments within specific timeframes. This paper introduces a comprehensive recursive estimation library, offering a solution to the challenges of real-time adaptive control. The library's functionalities, applications, and contributions to the field of control systems are discussed in detail. © 2023 ieee.

关键词： Adaptive control systems

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The Cost of Flexibility: embedded versus Discrete Routers in CGRAs for HPC

The Cost of Flexibility: Embedded versus Discrete Routers in...

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ieee international conference on Cluster computing (CLUSTER)

作者： Adhi, Boma Cortes, Carlos Tan, Yiyu Kojima, Takuya Podobas, Artur Sano, Kentaro RIKEN Ctr Computat Sci R CCS Kobe Hyogo Japan Iwate Univ Dept Syst Innovat Engn Sci & Engn Morioka Iwate Japan Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan KTH Royal Inst Technol Stockholm Sweden

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Coarse-Grained Reconfigurable Arrays (CGRAs) are a class of reconfigurable architectures that inherit the performance and usability properties of Central Processing Units (CPUs) and the reconfigurability aspects of Field-Programmable Gate Arrays (FPGAs). Historically, CGRAs have been successfully used to accelerate embedded applications and are today also being considered to accelerate High-Performance computing (HPC) applications in future supercomputers. However, embedded systems and supercomputers are two vastly different domains with different applications and constraints, and it is today not fully understood what CGRA design decisions adequately cater to the HPC market. One such unknown design decision is regarding the interconnect that facilitates intra-CGRA communication. Today, intra-CGRA communication comes in two flavors: using routers closely embedded into the compute units or using discrete routers outside the compute units. The former trades flexibility for a reduction in hardware cost, while the latter has greater flexibility but is more resource hungry. In this paper, we aspire to understand which of both designs best suits the CGRA HPC segment. We extend our previous methodology, which consists of both a parameterized CGRA design and an OpenMP-capable compiler, to accommodate both types of routing designs, including verification tests using RTL simulation. Our results show that the discrete router design can facilitate better use of processing elements (PEs) compared to embedded routers and can achieve up to 79.27% reduction in unnecessary PE occupancy for an aggressively unrolled stencil kernel on a 18 x 16 CGRA at a (estimated) hardware resource overhead cost of 6.3x. This reduction in PE occupancy can be used, for example, to exploit instruction-level parallelism (ILP) through even more aggressive unrolling.

关键词： CGRA Routing architecture Design space exploration HPC RTL simulation

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG Signal with Correlation-Based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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2024 ieee international conference on computing, applications and systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 ieee.

关键词： Deep neural networks

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Optimized Monocular Depth Estimation With Reparameterization on embedded Devices 23

Optimized Monocular Depth Estimation With Reparameterization...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Siping Li, Renfa Tu, Xiaohan Xie, Guoqi Xu, Cheng College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Railway Police College Department of Image and Network Investigation Zhengzhou450053 China

ISBN: (纸本)9781665494571

Monocular depth estimation (MDE) has been a basic task in many fields. Currently, researchers usually improve the accuracy of MDE by designing deep neural networks, including complex branches or other components. Although complex networks achieve increased precision, they bring slow speed and high memory access cost (MAC) in embedded systems. To balance the speed, accuracy, and MAC for embedded systems, we propose a directly connected model (DCM), which relies on existing networks, for fast inference in embedded systems. DCM balances the speed, precision, and MAC with structural reparameterization that decouples its training and inference networks. To further reduce the MAC and latency of DCM for embedded systems, we construct a network optimization frame-work based on recent template-based optimization methods. In the framework, we respectively adopt the machine learning LightGBM and contextual simulated annealing algorithm as the cost model and optimizer to complete rapid optimization in embedded systems. Our comprehensive experiments confirm that our methods achieve a better trade-off among precision, latency, and MAC. The accuracy of our original DCM inference networks reaches at least 6.13% better than the state of the arts. The optimized DCM inference networks decrease latency, main MAC, and power consumption by 46.49%, 90.97%, and 65.31% on Jetson TX2 GPU, without unchanged accuracy. © 2021 ieee.

关键词： Training embedded systems Costs Power demand Smart cities Graphics processing units Estimation

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Decoding Driving Neural Dynamics: An Exploratory Survey of EEG applications in Unraveling Cognitive Processes in Virtual reality Environments

Decoding Driving Neural Dynamics: An Exploratory Survey of E...

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ieee SoutheastCon conference on Engineering the Future

作者： Sargolzaei, Saman Hatchett, Seth Okashita, Matthew Sargolzaei, Arman Univ Tennessee Dept Engn Martin TN 38238 USA Univ S Florida Dept Mech Engn Tampa FL USA

ISBN: (纸本)9798350317107;9798350317114

The exploratory survey embarks on the crossroads of neuroscience, virtual reality, and transportation research, exploring studies, located upon a systematic review, of the brain's cognitive processes, assessed by electroencephalography (EEG), within virtual reality (VR) based driving environments. In driving research, combining virtual reality with bio-physiological monitoring technology has recently opened up a revolutionary new field that promise never-before-seen insights into driving behaviors and cognitive processes and the creation of innovative technology that could significantly enhance road safety and driving experiences. The exploration found four major domains where the crucial link between human cognition and driving is studied. While most studies focused on driver fatigue detection, monitoring, and forecasting, studying the cognitive responses and brain dynamics in navigation, obstacle avoidance, and other distracting external stimuli are among the trends. Developing real-time advanced embedded monitoring systems technologies to guide future assisted and autonomous transportation technology was also noted. This survey aims to deepen our understanding of EEG applications in VR-based driving studies, unlock the potential for innovative interventions, and provide recommendations for prospective studies contributing to the evolution of intelligent transportation systems.

关键词： Brain analysis Cognitive Processes Driving behavior Electroencephalography (EEG) Intelligent Transportation systems Neuroscience Virtual reality (VR)

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