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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Zhengcheng Shen Yi Gao Linh Kästner Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

The advancement of computer vision and machine learning has made datasets crucial for further research and applications. However, the creation and development of indoor mobile robots with advanced recognition capabilities are hindered by the lack of appropriate datasets. Existing image or video processing datasets are unable to depict observations from a moving robot accurately, and they do not contain the kinematics information necessary for robotic tasks. Synthetic data, on the other hand, are cost-effective to create and offer greater flexibility for adapting to various applications. Hence, they are widely utilized in both research and industry. In this paper, we propose the dataset HabitatDyn, which contains synthetic RGB videos, semantic labels, depth information, as well as kinetics information. HabitatDyn was created from the perspective of a mobile robot with a moving camera and contains 30 scenes featuring six different types of moving objects with varying velocities. To demonstrate the usability of our dataset, two existing algorithms are used for evaluation and an approach to estimate the distance between the object and camera is implemented based on these segmentation methods and evaluated through the dataset. With the availability of this dataset, we aspire to foster further advancements in the field of mobile robotics, leading to more capable and intelligent robots that can navigate and interact with their environments more effectively. The code is publicly available at https://***/ignc-research/HabitatDyn.

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Imagination-Augmented Cost Map for Local Navigation

Imagination-Augmented Cost Map for Local Navigation

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International Conference on Ubiquitous Robots (UR)

作者： Zhengcheng Shen Linh Kästner Magdalena Yordanova Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Autonomous navigation of mobile robots is an essential task for various industries. Sensor data is crucial to ensure safe and reliable navigation. However, sensor observations are often limited by different factors like field of view, sensor height or occlusion. Spatial imagination can assist to enhance the view and aid navigation in complex or unknown situations where only limited sensor observation is available. In this paper, we propose an imagination-enhanced navigation based on 2D laser scan data and semantic information to show the benefits of the imagination on navigation. The system contains an imagination module, which can predict the entire restricted area of the object, either occupied area or restricted area for common sense, based on laser scanner data with semantic label. The imagination module is trained in a supervised manner using a collected training dataset from a 2D simulator. Four different imagination models are trained, and the imagination results are evaluated. Subsequently, the imagination results are integrated into the local and global cost map to benefit the navigation procedure. The approach is validated on three different test maps, with seven different paths for each map. The quality and numeric results showed that the agent with the imagination module could generate more reliable paths without passing beneath the object or access the predefined restricted area.

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Predicting Navigational Performance of Dynamic Obstacle Avoidance Approaches Using Deep Neural Networks

Predicting Navigational Performance of Dynamic Obstacle Avoi...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Linh Kästner Alexander Christian Ricardo Sosa Mello Bo Li Bassel Fatloun Jens Lambrecht Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Over the past decades, countless autonomous navigation and dynamic obstacle avoidance approaches have been proposed by various research works. However, to bridge the gap between research and industries, these approaches are required to be extensively evaluated and benchmarked within various different setting, scenarios, and maps. However, conducting these test runs is tedious and time-consuming. Furthermore, simulation runs and test on real robots can not always cover all potentially occurring scenarios or are inaccurate in certain settings and circumstances especially when a high number of pedestrians or other dynamic entities are involved. In this paper, we propose an approach to predict the navigational performance of navigation approaches for new and unknown maps, scenarios, and robots without the necessity to conduct the actual test runs. Therefore, we acquire a large dataset consisting of thousands of evaluation runs within crowded environments from both simulation and real-world runs, which were conducted using the arena-bench platform of our previous works [1] and trained several neural network architectures to predict relevant navigational performance metrics such as collision rates or path efficiency. We demonstrate the feasibility of our neural networks by predicting the most relevant metrics with up to 95 percent accuracy compared to the groundtruth data acquired by an actual simulation run. Using this approach could prove beneficial for a number of applications and save valuable time and costs in that the performance of new navigation algorithms for crowded environments can be estimated and predicted on new maps, scenarios, and on new robots. We made the code publicly available at https://***/ignc-research/navprediction.

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Arena-Web - A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches

arXiv

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arXiv 2023年

作者： Kästner, Linh Carstens, Reyk Nahrwold, Lena Liebig, Christopher Shcherbyna, Volodymyr Lee, Subhin Lambrecht, Jens The Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to non-experts and complex to develop. In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience. In this paper, we present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners for various robotic platforms and scenarios. The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience. With Arena-Web and its interface, training and developing Deep Reinforcement Learning agents is simplified and made easy without a single line of code. The web-app is free to use and openly available under the link stated in the supplementary materials. © 2023, CC BY.

关键词： Websites

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Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems

arXiv

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arXiv 2023年

作者： Kästner, Linh Meusel, Marvin Bhuiyan, Teham Lambrecht, Jens The Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of ground vehicles and has been utilized in various areas of navigation such as cruise control, lane changing, or obstacle avoidance. However, most research works either focus on providing an end-to-end solution training the whole system using Deep Reinforcement Learning or focus on one specific aspect such as local motion planning. This however, comes along with a number of problems such as catastrophic forgetfulness, inefficient navigation behavior, and non-optimal synchronization between different entities of the navigation stack. In this paper, we propose a holistic Deep Reinforcement Learning training approach in which the training procedure is involving all entities of the navigation stack. This should enhance the synchronization between- and understanding of all entities of the navigation stack and as a result, improve navigational performance. We trained several agents with a number of different observation spaces to study the impact of different input on the navigation behavior of the agent. In profound evaluations against multiple learning-based and classic model-based navigation approaches, our proposed agent could outperform the baselines in terms of efficiency and safety attaining shorter path lengths, less roundabout paths, and less collisions. © 2023, CC BY.

关键词： Reinforcement learning

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Design and Implementation of a QoL Improvement System for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger 8

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Pramono, Andi Budiman, Satrio Arif Farchan, Muchammad Mappaturi, Andi Baso School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Brawijaya University Information System Computer Science Faculty Malang65145 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia Islamic State University of Maulana Malik Ibrahim Faculty of Science and Technology Architecture Department Malang Indonesia

ISBN: (纸本)9798350326093

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development. © 2023 IEEE.

关键词： Industrial emissions

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Design and Implementation of an EMG-based System for Monitoring and Help Request

Design and Implementation of an EMG-based System for Monitor...

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2022 International Conference on Smart Systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

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Using Eye-Tracking to Evaluate Novel Augmented Reality Applications for Maritime Operations 68th

Using Eye-Tracking to Evaluate Novel Augmented Reality Appli...

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68th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2024

作者： Mallam, Steven C. Attarzadeh, Amin Samanta, Akash Bhattacharjee, Ankan Nordby, Kjetil Fisheries and Marine Institute Memorial University of Newfoundland St. John’sNLA1C 5R3 Canada Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’s Canada Institute of Design The Oslo School of Architecture and Design Akershus Norway

This paper presents the developments across a multi-year collaborative industry-academia R&D project designing and testing novel Augmented Reality (AR) solutions for differing maritime operations and work tasks. We describe the step-wise approach taken in our Human Factors testing program exploring the effects of AR on maritime operators. This paper focuses specifically on our empirical simulator laboratory experiments using differing data collection designs, tools and operational scenarios to investigate operator Situation Awareness, cognitive workload, performance and usability. Moving from comparatively rudimentary measures using static scenarios, desktop data collections and post hoc video analysis to implementing eye-tracking and automated object identification in dynamic scenarios and full-mission simulated environments, we present an overview of how our testing program has evolved and lessons learned. Further, we discuss ongoing research and future plans to better understand the effects of AR on maritime end-users to implement human-centred solutions more effectively in maritime settings. © 2024 Human Factors and Ergonomics Society.

关键词： Head-up displays

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Classification of Arrhythmia ECG Signals using Convolutional Neural Network

Classification of Arrhythmia ECG Signals using Convolutional...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Marija Habijan Irena Galić Aleksandra Pizurica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia TELIN-GAIM Faculty of Engineering and Architecture Ghent University Ghent Belgium

The electrocardiogram (ECG) has been established as a reliable tool for monitoring cardiovascular health. Vast amount of ECG recordings can pose a challenge for its processing and analysis and seeking out experts to analyze such a large amount of ECG data can deplete valuable medical resources. Recently, there has been a growing interest in automatic methods for accurate heartbeat categorization. Our motivation behind this work is to improve efficiency and accuracy for arrhythmia classification based on convolutional neural networks (CNNs). We first pre-process the input signals by normalizing them using a z-score. Following the data segmentation, the under-represented classes are oversampled using random duplication. By randomly duplicating the samples of underrepresented categories and eliminating instances from the overrepresented categories, this approach minimizes the imbalance in the training data. We propose an efficient 12-layer 1D-CNN to classify cardiac arrhythmia into five classes. We demonstrate the oversampling technique’s impact on the proposed CNN’s performance by comparing its performance before and after its application.

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