wearable computing meets mas: a real-world interface for the robocuprescue simulation platform
DESCRIPTION
Wearable Computing meets MAS: A real-world interface for the RoboCupRescue simulation platform. Motivation Wearable computing Data integration MAS solutions for USAR. * University of Freiburg ** Center of Computing Technology (TZI) Bremen. - PowerPoint PPT PresentationTRANSCRIPT
A. Kleiner*, N. Behrens** and H. Kenn**Wearable Computing meets MAS: A real-world interface for the RoboCupRescue simulation platform
Motivation Wearable computing Data integration MAS solutions for USAR
* University of Freiburg
** Center of Computing Technology (TZI) Bremen
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Why to integrate sensor data during search and rescue?
Situation awareness: Where am I: problem of self-localization Where to go: Connectivity between
places has changed What to communicate: Destroyed places
are difficult to describe Getting simulation and MAS closer
to reality: Exchange of real data for analysis and
training Development and improvement of
disaster simulators Close-to-reality development of multi-
agent software
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The current test system GPS-based localization and data collection
with a wearable device No additional cognitive load, e.g. system collects data
in the background Trajectories are collected and send to a server via
GPRS/UMTS Data integration on the server-side
Generation of connectivity network annotated with observations
Data exchange with the RoboCup Rescue kernel via the GPX protocol
Coordination of exploration and victim search3G
PhonePC
GPS
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Data Integration ExampleIntegration from data collected by the wearable computer
To RoboCup Rescue
To Google earth (GPX)
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Open research problemImproving GPS accuracy in urban areas
GPS routing on a road network is solved?!
Urban Search And Rescue: Road network destroyed Multiple signal path problem if
close to buildings Weak signal within buildings
Solution: Multi-agent SLAM* by agents attached to humans
*Simultaneous Localization And Mapping (SLAM)
GPS Track on a cloudy day
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Pedestrian Dead Reckoning Based on the work of Q.
Ladetto at EPFL Idea: Estimate length and
direction of step based on motion sensor data
Fusion of GPS and PDR position estimates
Implementation: Michael Dippold (Master Student at TZI) http://auriga.wearlab.de/projects/leica/
Red: GPS Data (Tuesday, clear sky) Green: GPS + PDR fusion
GPS lost
GPS Jump
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Solution for the future:Application of a SLAM technique, borrowed from robotics
MA SLAM implies a data association and estimation problem
Pose estimation: Dead reckoning from accelerometers,
gyroscopes and step counters Data association:
Partially GPS localization with high accuracy, e.g. if close to stationary posts outside the buildings
Detection of RFID tags within buildings Central integration of data from
multiple agentsRFID
Wristband
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MAS support for USARExample1: Dijkstra based travel time estimation
Legend
Red (bright to dark) estimated travel timeWhite unreachable area
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MAS support for USARExample2: Informed coordination of victim search
Legend
Yellow Targets assigned by the stationGreen Found victimsWhite Explored buildings
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Future visions
Distributed SLAM by “wearable” agents, attached to human task forces
RoboCup Rescue as a unified MAS benchmark based on real data
RoboCup Rescue as an unified platform for responders to train and evaluate real rescue missions