Marcos Barreto

Ubiquitous robotics: Recent challenges and future trends

Ambient intelligence, ubiquitous and networked robots, and cloud robotics are new research hot topics that have started to gain popularity among the robotics community. They enable robots to acquire richer functionalities and open the way for the composition of a variety of robotic services with three functions: semantic perception, reasoning and actuation. Ubiquitous robots (ubirobots) overcome the limitations of stand-alone robots by integrating them with web services and ambient intelligence technologies. The overlap that exists now between ubirobots and ambient intelligence makes their integration worthwhile. It targets to create a hybrid physical-digital space rich with a myriad of proactive intelligent services that enhance the quality and the way of our living and working. Furthermore, the emergence of cloud computing initiates the massive use of a new generation of ubirobots that enrich their cognitive capabilities and share their knowledge by connecting themselves to cloud infrastructures. The future of ubirobots will certainly be open to an unlimited space of applications such as physical and virtual companions assisting people in their daily living, ubirobots that are able to co-work alongside people and cooperate with them in the same environment, and physical and virtual autonomic guards that are able to protect people, monitor their security and safety, and rescue them in indoor and outdoor spaces. This paper introduces the recent challenges and future trends on these topics.

Applied ontologies and standards for service robots

Service robotics is an emerging application area for human-centered technologies. The rise of household and personal assistance robots forecasts a human-robot collaborative society. One of the robotics communitys major task is to streamline development trends, work on the harmonization of taxonomies and ontologies, along with the standardization of terms, interfaces and technologies. It is important to keep the scientific progress and public understanding synchronous, through efficient outreach and education. These efforts support the collaboration among research groups, and lead to widely accepted standards, beneficial for both manufacturers and users. This article describes the necessity of developing robotics ontologies and standards focusing on the past and current research efforts. In addition, the paper proposes a roadmap for service robotics ontology development. The IEEE Robotics & Automation Society is sponsoring the working group Ontologies for Robotics and Automation. The efforts of the Working group are presented here, aiming to connect the cutting edge technology with the users of these services-the general public.

Defining positioning in a core ontology for robotics

Unambiguous definition of spatial position and orientation has crucial importance for robotics. In this paper we propose an ontology about positioning. It is part of a more extensive core ontology being developed by the IEEE RAS Working Group on ontologies for robotics and automation. The core ontology should provide a common ground for further ontology development in the field. We give a brief overview of concepts in the core ontology and then describe an integrated approach for representing quantitative and qualitative position information.

Auto-tuning methodology to represent landform attributes on multicore and multi-GPU systems

Auto-Tuning techniques have been used in the design of routines in recent years. The goal is to develop routines which automatically adapt to the conditions of the computational system, in such a way that efficient executions are obtained independently of the users experience. This paper aims to explore programming routines that can be automatically adapted to the computational system conditions, making possible to use Auto-Tuning methodology to represent landform attributes on multicores and multi-GPU systems.

Robot ontologies for sensor- and Image-guided surgery

Robots and robotics are becoming more complex and flexible, due to technological advancement, improved sensing capabilities and machine intelligence. Service robots target a wide range of applications, relying on advanced Human-Robot Interaction. Medical robotics is becoming a leading application area within, and the number of surgical, rehabilitation and hospital assistance robots is rising rapidly. However, the complexity of the medical environment has been a major barrier, preventing a wider use of robotic technology, thus mostly teleoperated, human-in-the-loop control solutions emerged so far. Providing smarter and better medical robots requires a systematic approach in describing and translating human processes for the robots. It is believed that ontologies can bridge human cognitive understanding and robotic reasoning (machine intelligence). Besides, ontologies serve as a tool and method to assess the added value robotic technology brings into the medical environment. The purpose of this paper is to identify relevant ontology research in medical robotic, and to review the state-of-the-art. It focuses on the surgical domain, fundamental terminology and interactions are described for two example applications in neurosurgery and orthopaedics.

Automatic routine tuning to represent landform attributes on multicore and multi-GPU systems

Auto-tuning techniques have been used in the design of routines in recent years. The goal is to develop routines which automatically adapt to the conditions of the computational system in such a way that efficient executions are obtained independently of the end-user experience. This paper aims to explore programming routines that can be automatically adapted to the computational system conditions, making possible to use auto-tuning to represent landform attributes on multicores and multi-GPU systems using high- performance computing techniques for efficient solution of two-dimensional polynomial regression models that allow large problem instances to be addressed.

On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort

Data linkage refers to the process of identifying and linking records that refer to the same entity across multiple heterogeneous data sources. This method has been widely utilized across scientific domains, including public health where records from clinical, administrative, and other surveillance databases are aggregated and used for research, decision making, and assessment of public policies. When a common set of unique identifiers does not exist across sources, probabilistic linkage approaches are used to link records using a combination of attributes. These methods require a careful choice of comparison attributes as well as similarity metrics and cutoff values to decide if a given pair of records matches or not and for assessing the accuracy of the results. In large, complex datasets, linking and assessing accuracy can be challenging due to the volume and complexity of the data, the absence of a gold standard, and the challenges associated with manually reviewing a very large number of record matches. In this paper, we present AtyImo, a hybrid probabilistic linkage tool optimized for high accuracy and scalability in massive data sets. We describe the implementation details around anonymization, blocking, deterministic and probabilistic linkage, and accuracy assessment. We present results from linking a large population-based cohort of 114 million individuals in Brazil to public health and administrative databases for research. In controlled and real scenarios, we observed high accuracy of results: 93%–97% true matches. In terms of scalability, we present AtyImos ability to link the entire cohort in less than nine days using Spark and scaling up to 20 million records in less than 12s over heterogeneous (CPU+GPU) architectures.

Ontology for autonomous robotics

Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontologys strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.

Accelerating Docking Simulation Using Multicore and GPU Systems

Virtual screening methodologies have been used to help drug researchers to discover new medicine. The main goal of these methodologies is to help in the docking phase, reducing the vast chemical space (usually referred to have 1060 molecules) to a small number that can be more easily processed and tested. The docking phase tests which molecules better interact with a drug target, such as an enzyme or protein receptor. This process is very time consuming, as we need to test all possible combinations. So, hybrid parallel architectures comprised by multicore processors and multi-GPUs can be a suitable approach to this problem, as they reduce the execution time whereas allow for the exploitation of huge libraries of candidate molecules. In this paper, we present a methodology to increase docking performance through the parallelization of the AutoDock tool over multiprocessor and GPU hardware. The results show our multicore implementation achieves a maximum speedup of 8 times, while our GPU implementation reaches a speedup of 35 times and the hybrid implementation provides a maximum speedup of 80 times.

uStorage - A Storage Architecture to Provide Block-Level Storage Through Object-Based Storage

Block-level Storage is widely used to support heavy workloads. It can be directly accessed by the operating system, but it faces some durability issues, hardware limitations and performance degradation in geographically distributed systems. Object-based Storage Device (OSD) is a data storage concept widely used to support write-once-read-many (WORM) systems. Because OSD contains data, metadata and an unique identifier, it becomes very powerful and customizable. OSDs are ideal for solving the increasing problems of data growth and resilience requirements while mitigating costs. This paper describes a scalable storage architecture that uses OSD from a distributed P2P Cloud Storage system and delivers a Block-level Storage layer to the user. This architecture combines the advantages of the replication, reliability, and scalability of a OSD on commodity hardware with the simplicity of raw block for data-intensive workload. We retrieve data from the OSD in a set of blocks called buckets, allowing read-ahead operations to improve the performance of the raw block layer. Through this architecture we show the possibility of using OSD on the back end and deliver a storage layer based on raw blocks with better performance to the end user. We evaluated the proposed architecture based on the cache behavior to understand non-functional properties. Experiments were performed with different cache sizes. High throughput performance was measured for heavy workloads at the two storage layers.