A Tradeoff Analysis of a Cloud-Based Robot Navigation Assistant Using Stereo Image Processing

The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0

A short curriculum of the robotics and technology of computer lab

Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23 researchers that carry out their teaching and researching labor at the Escuela Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería Informática (Computer Engineering School). The main research fields are: a) Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes, c) Embedded and real-time systems, d) Parallel and massive processing computer architecture, d) Information Technologies for rehabilitation, handicapped and elder people, e) Web accessibility and usability In this paper, the Lab history is presented and its main publications and research projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo multidisciplinar de 23 investigadores que realizan su labor docente e investigadora en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las principales líneas de investigaciones son: a) Robótica industrial y móvil. b) Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo. e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las personas mayores. f) Usabilidad y accesibilidad Web. En este artículo se reseña la historia del grupo y se resumen las principales publicaciones y proyectos que ha conseguido en los últimos años

Estudio y evaluación de plataformas de distribución de cómputo intensivo sobre sistemas externos para sistemas empotrados.

Falta palabras claveNowadays, the capabilities of current embedded systems are constantly increasing, having a wide range of applications. However, there are a plethora of intensive computing tasks that, because of their hardware limitations, are unable to perform successfully. Moreover, there are innumerable tasks with strict deadlines to meet (e.g. Real Time Systems). Because of that, the use of external platforms with sufficient computing power is becoming widespread, especially thanks to the advent of Cloud Computing in recent years. Its use for knowledge sharing and information storage has been demonstrated innumerable times in the literature. However, its core properties, such as dynamic scalability, energy efficiency, infinite resources... amongst others, also make it the perfect candidate for computation off-loading. In this sense, this thesis demonstrates this fact in applying Cloud Computing in the area of Robotics (Cloud Robotics). This is done by building a 3D Point Cloud Extraction Platform, where robots can offload the complex stereo vision task of obtaining a 3D Point Cloud (3DPC) from Stereo Frames. In addition to this, the platform was applied to a typical robotics application: a Navigation Assistant. Using this case, the core challenges of computation offloading were thoroughly analyzed: the role of communication technologies (with special focus on 802.11ac), the role of offloading models, how to overcome the problem of communication delays by using predictive time corrections, until what extent offloading is a better choice compared to processing on board... etc. Furthermore, real navigation tests were performed, showing that better navigation results are obtained when using computation offloading. This experience was a starting point for the final research of this thesis: an extension of Amdahl’s Law for Cloud Computing. This will provide a better understanding of Computation Offloading’s inherent factors, especially focused on time and energy speedups. In addition to this, it helps to make some predictions regarding the future of Cloud Computing and computation offloading

Middleware platform for distributed applications incorporating robots, sensors and the cloud

Cyber-physical systems in the factory of the future will consist of cloud-hosted software governing an agile production process executed by autonomous mobile robots and controlled by analyzing the data from a vast number of sensors. CPSs thus operate on a distributed production floor infrastructure and the set-up continuously changes with each new manufacturing task. In this paper, we present our OSGibased middleware that abstracts the deployment of servicebased CPS software components on the underlying distributed platform comprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop components based on artificial neural networks, a technique that recently became very popular for sensor data analytics and robot actuation. We demonstrate a system where a robot takes actions based on the input from sensors in its vicinity

Intelligent vision-based navigation system for mobile robot: A technological review

Vision system is gradually becoming more important. As computing technology advances, it has been widely utilized in many industrial and service sectors. One of the critical applications for vision system is to navigate mobile robot safely. In order to do so, several technological elements are required. This article focuses on reviewing recent researches conducted on the intelligent vision-based navigation system for the mobile robot. These include the utilization of mobile robot in various sectors such as manufacturing, warehouse, agriculture, outdoor navigation and other service sectors. Multiple intelligent algorithms used in developing robot vision system were also reviewed

Offloading SLAM for Indoor Mobile Robots with Edge, Fog, Cloud Computing

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant per- centage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power con- sumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant per- centage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power con- sumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer