3,339 research outputs found
Viewfinder: final activity report
The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources.
The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation.
The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein
Implementation of Fuzzy Decision Based Mobile Robot Navigation Using Stereo Vision
AbstractIn this article, we discuss implementation phases for an autonomous navigation of a mobile robotic system using SLAM data, while relying on the features of learned navigation maps. The adopted SLAM based learned maps, was relying entirely on an active stereo vision for observing features of the navigation environment. We show the framework for the adopted lower-level software coding, that was necessary once a vision is used for multiple purposes, distance measurements, and obstacle discovery. In addition, the article describes the adopted upper-level of system intelligence using fuzzy based decision system. The proposed map based fuzzy autonomous navigation was trained from data patterns gathered during numerous navigation tasks. Autonomous navigation was further validated and verified on a mobile robot platform
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
On Foveated Gaze Control and Combined Gaze and Locomotion Planning
This chapter presents recent research results of our laboratory in the area of vision an
Perception-aware Path Planning
In this paper, we give a double twist to the problem of planning under
uncertainty. State-of-the-art planners seek to minimize the localization
uncertainty by only considering the geometric structure of the scene. In this
paper, we argue that motion planning for vision-controlled robots should be
perception aware in that the robot should also favor texture-rich areas to
minimize the localization uncertainty during a goal-reaching task. Thus, we
describe how to optimally incorporate the photometric information (i.e.,
texture) of the scene, in addition to the the geometric one, to compute the
uncertainty of vision-based localization during path planning. To avoid the
caveats of feature-based localization systems (i.e., dependence on feature type
and user-defined thresholds), we use dense, direct methods. This allows us to
compute the localization uncertainty directly from the intensity values of
every pixel in the image. We also describe how to compute trajectories online,
considering also scenarios with no prior knowledge about the map. The proposed
framework is general and can easily be adapted to different robotic platforms
and scenarios. The effectiveness of our approach is demonstrated with extensive
experiments in both simulated and real-world environments using a
vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for
IEEE Transactions on Robotic
Spatial context-aware person-following for a domestic robot
Domestic robots are in the focus of research in
terms of service providers in households and even as robotic
companion that share the living space with humans. A major
capability of mobile domestic robots that is joint exploration
of space. One challenge to deal with this task is how could we
let the robots move in space in reasonable, socially acceptable
ways so that it will support interaction and communication
as a part of the joint exploration. As a step towards this
challenge, we have developed a context-aware following behav-
ior considering these social aspects and applied these together
with a multi-modal person-tracking method to switch between
three basic following approaches, namely direction-following,
path-following and parallel-following. These are derived from
the observation of human-human following schemes and are
activated depending on the current spatial context (e.g. free
space) and the relative position of the interacting human.
A combination of the elementary behaviors is performed in
real time with our mobile robot in different environments.
First experimental results are provided to demonstrate the
practicability of the proposed approach
- âŠ