347 research outputs found

    FPGA-Based Hardware Accelerators for Deep Learning in Mobile Robotics

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    The increasing demand for real-time low-power hardware processing systems, endowed with the capacity to perform compute-intensive applications, accentuated the inadequacy of the conventional architecture of multicore general-purpose processors. In an effort to meet this demand, edge computing hardware accelerators have come to the forefront, notably with regard to deep learning and robotic systems. This thesis explores preeminent hardware accelerators and examines the performance, accuracy, and power consumption of a GPU and an FPGA-based platform, both specifically designed for edge computing applications. The experiments were conducted using three deep neural network models, namely AlexNet, GoogLeNet, and ResNet-18, trained to perform binary image classification in a known environment. Our results demonstrate that the FPGA-based platform, particularly a Kria KV260 Vision AI starter kit, exhibited an inference speed of up to nine and a half times faster than that of the GPU-based Jetson Nano developer kit. Additionally, the empirical findings of this work reported as much as a quintuple efficiency over the Jetson Nano in terms of inference speed per watt with a mere 5.4\% drop in accuracy caused by the quantization process required by the FPGA. However, the Jetson Nano showed a 1.6 times faster inference rate with the AlexNet model over the KV260 and its deployment process proved to be less challenging

    Tool And Task Design Challenges For Left Handers: A Brief Review

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    Left-handed users are often overlooked by equipment and hand tool designers. Designers may assume that left handers will eventually learn to adapt themselves in a right-handed environment. However in reality left handers face a lot of difficulties when trying to perform tasks meant for right-handed users. Medical personnel are most affected by this issue, followed by workers in non-medical field such as services and manufacturing. This review aims to highlight the issues faced left handers by analysing common household product examples as well as hand tools in occupational settings. Discussions are centred on the differences between the right-handed tools versus left-handed tools, as well as task/procedural challenges faced by left handers. In summary, the notion of designing an ambidextrous tool may not be a realistic recommendation for tool manufacturers and designers

    ROS wrapper for real-time multi-person pose estimation with a single camera

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    For robots to be deployable in human occupied environments, the robots must have human-awareness and generate human-aware behaviors and policies. OpenPose is a library for real-time multi-person keypoint detection. We have considered the implementation of a ROS package that would allow the estimation of 2d pose from simple RGB images, for which we have introduced a ROS wrapper that automatically recovers the pose of several people from a single camera using OpenPose. Additionally, a ROS node to obtain 3d pose estimation from the initial 2d pose estimation when a depth image is synchronized with the RGB image (RGB-D image, such as with a Kinect camera) has been developed. This aim is attained projecting the 2d pose estimation onto the point-cloud of the depth image.Peer ReviewedPreprin

    A Survey of Knowledge Representation in Service Robotics

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    Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action Representations for Autonomous Robots - 22 Page

    Multi Agent Systems

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    Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

    Toxic gases detection and tolerance level classification using machine learning algorithms

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    Abstract— with rapid population increases, people are facing the challenge to maintain healthy conditions. One of the challenges is air pollution. Due to industrial development and vehicle usage air pollution is becoming a high threat to human life. This air pollution forms through various toxic contaminants. This toxic contamination levels increase and cause severe damage to the living things in the environment. To identify the toxic level present in the polluted air various methods were proposed by the authors, But failed to detect the tolerance level of toxic gases. This article discusses the methods to detect toxic gasses and classify the tolerance level of gasses present in polluted air. Various sensors and different algorithms are used for classifying the tolerance level. For this purpose “Artificial Sensing Methodology” (ASM), commonly known as e-nose, is a technique for detecting harmful gases. SO2-D4, NO2-D4, MQ-135, MQ136, MQ-7, and other sensors are used in artificial sensing methods (e-nose). “Carbon monoxide, Sulfur dioxide, nitrogen dioxide, and carbon dioxide” are all detected by these sensors. The data collected by sensors is sent to the data register from there it is sent to the Machine learning Training module (ML) and the comparison is done with real-time data and trained data. If the values increase beyond the tolerance level the system will give the alarm and release the oxygen

    2022 roadmap on neuromorphic computing and engineering

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    Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018^{18} calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community
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