6,993 research outputs found
Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments
This paper presents our approach to develop a method for an unmanned ground
vehicle (UGV) to perform inspection tasks in nuclear environments using rich
information maps. To reduce inspectors' exposure to elevated radiation levels,
an autonomous navigation framework for the UGV has been developed to perform
routine inspections such as counting containers, recording their ID tags and
performing gamma measurements on some of them. In order to achieve autonomy, a
rich information map is generated which includes not only the 2D global cost
map consisting of obstacle locations for path planning, but also the location
and orientation information for the objects of interest from the inspector's
perspective. The UGV's autonomy framework utilizes this information to
prioritize locations to navigate to perform the inspections. In this paper, we
present our method of generating this rich information map, originally
developed to meet the requirements of the International Atomic Energy Agency
(IAEA) Robotics Challenge. We demonstrate the performance of our method in a
simulated testbed environment containing uranium hexafluoride (UF6) storage
container mock ups
Tracing commodities in indoor environments for service robotics
Daily life assistance for elderly people is one of the most promising scenarios for service robots in the the near future. In particular, the go-and-fetch task will be one of the most demanding tasks in these cases. In this paper, we present an informationally structured room that supports a service robot in the task of daily object fetching. Our environment contains different distributed sensors including a floor sensing system and several intelligent cabinets. Sensor information is send to a centralized management system which process the data and make it available to a service robot which is assisting people in the room. We additionally present the first steps of an intelligent framework used to maintain information about locations of commodities in our informationally structured room. This information will be used by the service robot to find objects under people requests. One of the main goal of our intelligent environment is to maintain a small number of sensors to avoid interfering with the daily activity of people, and to reduce as much as possible invasion of their privacy. In order to compensate this limited available sensor information, our framework aims to exploit knowledge about people's activity and interaction with objects, to infer reliable information about the location of commodities. This paper presents simulated results that demonstrate the suitability of this framework to be applied to a service robotic environment equipped with limited sensors. In addition we discuss some preliminary experiments using our real environment and robot
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
The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
Service robots are appearing more and more in our daily life. The development
of service robots combines multiple fields of research, from object perception
to object manipulation. The state-of-the-art continues to improve to make a
proper coupling between object perception and manipulation. This coupling is
necessary for service robots not only to perform various tasks in a reasonable
amount of time but also to continually adapt to new environments and safely
interact with non-expert human users. Nowadays, robots are able to recognize
various objects, and quickly plan a collision-free trajectory to grasp a target
object in predefined settings. Besides, in most of the cases, there is a
reliance on large amounts of training data. Therefore, the knowledge of such
robots is fixed after the training phase, and any changes in the environment
require complicated, time-consuming, and expensive robot re-programming by
human experts. Therefore, these approaches are still too rigid for real-life
applications in unstructured environments, where a significant portion of the
environment is unknown and cannot be directly sensed or controlled. In such
environments, no matter how extensive the training data used for batch
learning, a robot will always face new objects. Therefore, apart from batch
learning, the robot should be able to continually learn about new object
categories and grasp affordances from very few training examples on-site.
Moreover, apart from robot self-learning, non-expert users could interactively
guide the process of experience acquisition by teaching new concepts, or by
correcting insufficient or erroneous concepts. In this way, the robot will
constantly learn how to help humans in everyday tasks by gaining more and more
experiences without the need for re-programming
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