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A networked robot system for wireless network emulation 07

A networked robot system for wireless network emulation

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Proceedings of the 1st international conference on robot communication and coordination

作者： Tzi-cker Chiueh Rupa Krishnan Pradipta De Jui-Hao Chiang Stony Brook University

ISBN: (纸本)9789639799080

A major barrier to advancing modern wireless networking research is the lack of an effective wireless network simulation platform that simultaneously offers high fidelity, scalability, reproducibility and ease of use. MiNT [8], [7] is an innovative wireless network emulation platform that is specifically designed to satisfy all these desirable properties. To support reconfigurable network topology and wireless node mobility, MiNT is built on a networked robot system that carries wireless networking equipments and is designed to be completely tetherless, capable of supporting 24X7 operation, and low-cost. This paper describes the design, implementation and evaluation of this networked robot system. Each robot node in MiNT is an irobot's Roomba, which is modified to house an embedded PC equipped with multiple wireless networking interfaces and to re-charge the embedded PC through Roomba's built-in self-charging mechanism. For robot navigation and movement, MiNT's networked robot system supports a computer vision-based robot positioning mechanism and a collision avoidance-driven trajectory planning component. Finally, MiNT provides an interactive control interface and visualization interface to give users real-time visibility into and full control over the MiNT testbed.

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Multiple Hypothesis for Object Class Disambiguation from Multiple Observations

Multiple Hypothesis for Object Class Disambiguation from Mul...

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International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Susana Brandão Manuela Veloso João P. Costeira DEEC - 1ST Universidade de Lisboa Portugal Computer Science Dep. Carnegie Mellon University USA

The current paper addresses the problem of object identification from multiple3D partial views, collected from different view angles with the objective of disambiguating between similar objects. We assume a mobile robot equipped with a depth sensor that autonomously collects observations from an object from different positions, with no previous known pattern. The challenge is to efficiently combine the set of observations into a single classification. We approach the problem with a multiple hypothesis filter that allows to combine information from a sequence of observations given the robot movement. We further innovate by off-line learning neighborhoods between possible hypothesis based on the similarity of observations. Such neighborhoods translate directly the ambiguity between objects, and allow to transfer the knowledge of one object to the other. In this paper we introduce our algorithm, Multiple Hypothesis for Object Class Disambiguation from Multiple Observations, and evaluate its accuracy and efficiency.

关键词： Three-dimensional displays Shape Object recognition Heating Mobile robots Temperature measurement

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robot teaching assistant and physical programming class for programming education of young children 20

Robot teaching assistant and physical programming class for ...

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Proceedings of the 2020 1st International conference on Control, robotics and Intelligent System

作者： Xianyu Qi Wei Wang Ziwei Liao Xiaoyu Zhang Lin Xue Xu Zhang Jing Li Tong Fang Ran Wei Beihang University China Beijing Evolver Robotics Technology Co. Ltd China The High School Affiliated to Renmin University of China China Fengtai School of the High School Affiliated to Renmin University of China China

ISBN: (纸本)9781450388054

This paper proposes a programming class for young children. In order to reduce the burden on teachers and attract students' interest, a robot teaching assistant is used to explain programming knowledge, verify and run programs. Different from writing programs on a computer, the physical board programming is employed to develop logical thinking of young children and prevent them from the vision harm of facing computer screens for a long time and the lack of reality from immersion in the virtual world. Based on the knowledge points of programming, we have designed a course with 16 lessons. The course has been successfully applied in many kindergartens and elementary schools. We use the questionnaires for students and teachers to evaluate the course and the experimental results show that it is effective.

关键词： Programming education robot teaching assistant Physical programming

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A Wearable robotic Object Manipulation Aid for the Visually Impaired

A Wearable Robotic Object Manipulation Aid for the Visually ...

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IEEE International conference on Micro/Nano Sensors for AI, Healthcare, and robotics

作者： Xiaoping Liu He Zhang Lingqiu Jin Cang Ye Dept. of Systems Engineering at the University of Arkansas at Little Rock Dept. of Computer Science Virginia Commonwealth University

ISBN: (纸本)9781538694091;9781538694084

Due to loss of vision, it becomes a challenging task for a visually impaired individual to locate and grasp a target object. This paper presents a hand-worn assistive device that may assist a visually impaired person in detecting a target object and maintaining alignment with the object while approaching it. The device consists of a sensing module and a guiding module. The sensing module uses an RGB-D camera to detect and track the target object. The guiding module computes hand-object misalignment, determines the desired hand movement (DHM) for hand-object alignment, and uses a cable-driven exoskeleton mechanism to guide the user's hand to align with the target object. The guiding module helps to maintain hand-object alignment while the hand is approaching the object. A prototype of the device was developed and its usability was validated by experiments with human subjects.

关键词： Thumb Exoskeletons Cameras Feature extraction DC motors robot sensing systems Object detection

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RGB-D Videos Human Action Recognition: A Taxonomy-Based Survey to Learn the Discriminative Features

RGB-D Videos Human Action Recognition: A Taxonomy-Based Surv...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International conference on

作者： Girish Padhan Electrical & Electronics Engineering Vikash Institute of Technology (Biju patnaik University of Technology) Bargarh Odisha India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

In computer vision human action recognition plays an important role in the modern era. Here a sensor can acquired accurately the human action and their interactions from a previously unseen data sequence. Human activity identification in video sequences is a hotspot for computer vision research owing to its practical implications. It encompasses security, surveillance, healthcare, robotics, animation, sports analysis, smart home automation and behavioral analysis. The goal of AI society is to create a system that can observe and understand human behavior and actions completely independently. For example, a robot assistant might aid a patient undergoing home monitoring by analyzing the most effective method of exercise and so avoiding additional injuries, thereby increasing the robot's usefulness to society. This kind of smart technology will be immensely useful to us since it eliminates the need for unnecessary doctor visits, lowers healthcare costs, and allows for constant remote monitoring of the patient. Many feature-based methods, both manually designed and automatically taught, have emerged in the past two decades for identifying human actions in video footage. Traditional methods of human activity identification rely on meticulously designed characteristics that drill down to the most fundamental of motions. Over the last several years, deep learning algorithms have made great progress in a number of different domains, including human location prediction, object recognition, segmentation, audio analysis, object tracking, and super-resolution. In visual recognition tasks, the deep learning model is also crucial. Instead of manually extracting the characteristics, deep learning-based methods provide a more efficient and time-saving alternative. Handcrafted features solutions were shown to be effective, however they over-relied on feature descriptors when attempting action categorization. This type of problem needed additional man hours and specialized knowledge

关键词： Deep learning computer vision Visualization Medical services robot sensing systems Feature extraction Behavioral sciences

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Human Detection System

Human Detection System

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International conference on

作者： S. Muthuselvan S Harinisree B Mohanapriya R Sahana Information Technology department KCG College of Technology Tamil Nadu India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The goal of this project is to develop a human detection robot that may be used as a backup mechanism to save lives in case of an emergency. Developing dependable surveillance systems is essential for both safety and security. The human detection model is a fundamental part of any surveillance system. Recent developments in embedded systems and hardware make it feasible to create a low-cost real-time person detecting system. This study investigates the architecture of human detection and tracking techniques over non-overlapping field of views. A search technique is presented to make up for each of these shortcomings. In an experimental context, the rate and accuracy of human detection using the suggested technique was evaluated.

关键词： Wireless communication computer vision Wireless sensor networks Accuracy Surveillance Real-time systems Sensors Safety Security Testing

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Design and Evaluation of a Self-Driving Prototype Using Raspberry Pi and EzBlock

Design and Evaluation of a Self-Driving Prototype Using Rasp...

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Emerging Technologies for Dependable Internet of Things (ICETI), International conference on

作者： Mahmoud Nabeel Younus Ahmed Khazal Younis Technical Engineering College Northern Technical University Mosul Iraq Technical Engineering College for Computer and AI/Mosul Northern Technical University Mosul Iraq

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Self-driving cars are an innovative technology in transportation that seeks to eliminate human mistakes, save lives, and increase the efficiency of traffic flow. In this work, a self-driving prototype with a Raspberry Pi 4 Model B, several sensor components, and their assessment were proposed as ultrasonic and grayscale sensors, a Raspberry Pi camera, and a SunFounder robot hat, which are all managed via a mobile application from Ezblock studio. To ensure that control and decision-making are in real-time, algorithms were incorporated into the EzBlock system. The prototype was tested in various scenarios, employing multiple levels of lighting and path types and straight and curved paths with sharp and curved corners. The system's high performance was demonstrated in good lighting, whereby it could detect lanes, signs, and obstacles and avoid them. However, some difficulties were observed during tests, such as sharp corners, wide paths, and low light conditions, causing problems with camera-based detection. This work demonstrates the viability of low-cost, Raspberry Pi-based self-driving prototypes for applications such as autonomous vehicles.

关键词： Performance evaluation Face recognition robot vision systems Prototypes Lighting Transportation Gray-scale Cameras Acoustics Thermal stability

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Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness

Clarifying Myths About the Relationship Between Shape Bias, ...

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canadian conference on computer and robot vision, CRV

作者： Zahra Golpayegani Patrick st-Amant Nizar Bouguila Gina Cody School of Engineering Concordia University Montreal Canada Zetane Systems Inc.

Deep learning models can perform well when evaluated on images from the same distribution as the training set. However, applying small perturbations in the forms of noise, artifacts, occlusions, blurring, etc. to a model's input image and feeding the model with out-of-distribution (OOD) data can significantly drop the model's accuracy, making it not applicable to real-world scenarios. Data augmentation is one of the well-practiced methods to improve model robustness against OOD data; however, examining which augmentation type to choose and how it affects the OOD robustness remains understudied. There is a growing belief that augmenting datasets using data augmentations that improve a model's bias to shape-based features rather than texture-based features results in increased OOD robustness for Convolutional Neural Networks trained on the ImageNet-1K dataset. This is usually stated as “an increase in the model's shape bias results in an increase in its OOD robustness”. Based on this hypothesis, some works in the literature aim to find augmentations with higher effects on model shape bias and use those for data augmentation. By evaluating 39 types of data augmentations on a widely used OOD dataset, we demonstrate the impact of each data augmentation on the model's robustness to OOD data and further show that the mentioned hypothesis is not true; an increase in shape bias does not necessarily result in higher OOD robustness. By analyzing the results, we also find some biases in the ImageNet-1K dataset that can easily be reduced using proper data augmentation. Our evaluation results further show that there is not necessarily a trade-off between in-domain accuracy and OOD robustness, and choosing the proper augmentations can help increase both in-domain accuracy and OOD robustness simultaneously.

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