DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft SystemsUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.info:eu-repo/semantics/publishedVersio

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Evaluating Distributed Computation Offloading Scalability for Multiple Robots

Perception in robotics using modern deep learning approaches is often computationally expensive. Cloud robotics and edge robotics provide possible solutions to this. In particular, computation offloading permits compute-constrained robots to offload compute tasks from the robot itself to more capable servers (the remote brain). However, the scalability of computation offloading for robots sharing a WiFi network has typically not been considered. In this work, we investigated the scalability of offloading to the cloud and edge for deployments of multiple robots in real-world settings and network conditions. We interpret typical network performance metrics such as latency, throughput, and packet loss in terms of their effect on robot computations to help to understand this question. To characterize the problem further, we introduce three different static offloading profiles based on the sensing capabilities of currently available robot platforms and examined these in a number of experiments on both simulated and physical wireless networks. Our results show that WiFi network capacity is much less than advertised when subjected to offloading data traffic and indicates limitations on the number of robots that can concurrently use computation offloading in a given space and the amount of data that they can transfer. This has significant implications for the dense deployment of robots that depend on computation offloading to meet their required service level

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed

The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection

Deep learning technology has been widely used in edge computing. However, pandemics like covid-19 require deep learning capabilities at mobile devices (detect respiratory rate using mobile robotics or conduct CT scan using a mobile scanner), which are severely constrained by the limited storage and computation resources at the device level. To solve this problem, we propose a three-tier architecture, including robot layers, edge layers, and cloud layers. We adopt this architecture to design a non-contact respiratory monitoring system to break down respiratory rate calculation tasks. Experimental results of respiratory rate monitoring show that the proposed approach in this paper significantly outperforms other approaches. It is supported by computation time costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution operation, similarity calculation, processing one-minute length respiratory signals, respectively. And the computation time costs of our three-tier architecture are less than that of edge+cloud architecture and cloud architecture. Moreover, we use our three-tire architecture for CT image diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19 proves that our three-tire architecture is useful for resolving tasks on deep learning networks by edge equipment. There are broad application scenarios in smart hospitals in the future

Robot Navigation in Human Environments

For the near future, we envision service robots that will help us with everyday chores in home, office, and urban environments. These robots need to work in environments that were designed for humans and they have to collaborate with humans to fulfill their tasks. In this thesis, we propose new methods for communicating, transferring knowledge, and collaborating between humans and robots in four different navigation tasks. In the first application, we investigate how automated services for giving wayfinding directions can be improved to better address the needs of the human recipients. We propose a novel method based on inverse reinforcement learning that learns from a corpus of human-written route descriptions what amount and type of information a route description should contain. By imitating the human teachers' description style, our algorithm produces new route descriptions that sound similarly natural and convey similar information content, as we show in a user study. In the second application, we investigate how robots can leverage background information provided by humans for exploring an unknown environment more efficiently. We propose an algorithm for exploiting user-provided information such as sketches or floor plans by combining a global exploration strategy based on the solution of a traveling salesman problem with a local nearest-frontier-first exploration scheme. Our experiments show that the exploration tours are significantly shorter and that our system allows the user to effectively select the areas that the robot should explore. In the second part of this thesis, we focus on humanoid robots in home and office environments. The human-like body plan allows humanoid robots to navigate in environments and operate tools that were designed for humans, making humanoid robots suitable for a wide range of applications. As localization and mapping are prerequisites for all navigation tasks, we first introduce a novel feature descriptor for RGB-D sensor data and integrate this building block into an appearance-based simultaneous localization and mapping system that we adapt and optimize for the usage on humanoid robots. Our optimized system is able to track a real Nao humanoid robot more accurately and more robustly than existing approaches. As the third application, we investigate how humanoid robots can cover known environments efficiently with their camera, for example for inspection or search tasks. We extend an existing next-best-view approach by integrating inverse reachability maps, allowing us to efficiently sample and check collision-free full-body poses. Our approach enables the robot to inspect as much of the environment as possible. In our fourth application, we extend the coverage scenario to environments that also include articulated objects that the robot has to actively manipulate to uncover obstructed regions. We introduce algorithms for navigation subtasks that run highly parallelized on graphics processing units for embedded devices. Together with a novel heuristic for estimating utility maps, our system allows to find high-utility camera poses for efficiently covering environments with articulated objects. All techniques presented in this thesis were implemented in software and thoroughly evaluated in user studies, simulations, and experiments in both artificial and real-world environments. Our approaches advance the state of the art towards universally usable robots in everyday environments.Roboternavigation in menschlichen Umgebungen In naher Zukunft erwarten wir Serviceroboter, die uns im Haushalt, im Büro und in der Stadt alltägliche Arbeiten abnehmen. Diese Roboter müssen in für Menschen gebauten Umgebungen zurechtkommen und sie müssen mit Menschen zusammenarbeiten um ihre Aufgaben zu erledigen. In dieser Arbeit schlagen wir neue Methoden für die Kommunikation, Wissenstransfer und Zusammenarbeit zwischen Menschen und Robotern bei Navigationsaufgaben in vier Anwendungen vor. In der ersten Anwendung untersuchen wir, wie automatisierte Dienste zur Generierung von Wegbeschreibungen verbessert werden können, um die Beschreibungen besser an die Bedürfnisse der Empfänger anzupassen. Wir schlagen eine neue Methode vor, die inverses bestärkendes Lernen nutzt, um aus einem Korpus von von Menschen geschriebenen Wegbeschreibungen zu lernen, wie viel und welche Art von Information eine Wegbeschreibung enthalten sollte. Indem unser Algorithmus den Stil der Wegbeschreibungen der menschlichen Lehrer imitiert, kann der Algorithmus neue Wegbeschreibungen erzeugen, die sich ähnlich natürlich anhören und einen ähnlichen Informationsgehalt vermitteln, was wir in einer Benutzerstudie zeigen. In der zweiten Anwendung untersuchen wir, wie Roboter von Menschen bereitgestellte Hintergrundinformationen nutzen können, um eine bisher unbekannte Umgebung schneller zu erkunden. Wir schlagen einen Algorithmus vor, der Hintergrundinformationen wie Gebäudegrundrisse oder Skizzen nutzt, indem er eine globale Explorationsstrategie basierend auf der Lösung eines Problems des Handlungsreisenden kombiniert mit einer lokalen Explorationsstrategie. Unsere Experimente zeigen, dass die Erkundungstouren signifikant kürzer werden und dass der Benutzer mit unserem System effektiv die zu erkundenden Regionen spezifizieren kann. Der zweite Teil dieser Arbeit konzentriert sich auf humanoide Roboter in Umgebungen zu Hause und im Büro. Der menschenähnliche Körperbau ermöglicht es humanoiden Robotern, in Umgebungen zu navigieren und Werkzeuge zu benutzen, die für Menschen gebaut wurden, wodurch humanoide Roboter für vielfältige Aufgaben einsetzbar sind. Da Lokalisierung und Kartierung Grundvoraussetzungen für alle Navigationsaufgaben sind, führen wir zunächst einen neuen Merkmalsdeskriptor für RGB-D-Sensordaten ein und integrieren diesen Baustein in ein erscheinungsbasiertes simultanes Lokalisierungs- und Kartierungsverfahren, das wir an die Besonderheiten von humanoiden Robotern anpassen und optimieren. Unser System kann die Position eines realen humanoiden Roboters genauer und robuster verfolgen, als es mit existierenden Ansätzen möglich ist. Als dritte Anwendung untersuchen wir, wie humanoide Roboter bekannte Umgebungen effizient mit ihrer Kamera abdecken können, beispielsweise zu Inspektionszwecken oder zum Suchen eines Gegenstands. Wir erweitern ein bestehendes Verfahren, das die nächstbeste Beobachtungsposition berechnet, durch inverse Erreichbarkeitskarten, wodurch wir kollisionsfreie Ganzkörperposen effizient generieren und prüfen können. Unser Ansatz ermöglicht es dem Roboter, so viel wie möglich von der Umgebung zu untersuchen. In unserer vierten Anwendung erweitern wir dieses Szenario um Umgebungen, die auch bewegbare Gegenstände enthalten, die der Roboter aktiv bewegen muss um verdeckte Regionen zu sehen. Wir führen Algorithmen für Teilprobleme ein, die hoch parallelisiert auf Grafikkarten von eingebetteten Systemen ausgeführt werden. Zusammen mit einer neuen Heuristik zur Schätzung von Nutzenkarten ermöglicht dies unserem System Beobachtungspunkte mit hohem Nutzen zu finden, um Umgebungen mit bewegbaren Objekten effizient zu inspizieren. Alle vorgestellten Techniken wurden in Software implementiert und sorgfältig evaluiert in Benutzerstudien, Simulationen und Experimenten in künstlichen und realen Umgebungen. Unsere Verfahren bringen den Stand der Forschung voran in Richtung universell einsetzbarer Roboter in alltäglichen Umgebungen