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