1. 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

2. A. Kleiner, N. Behrens and H. Kenn2 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

3. A. Kleiner, N. Behrens and H. Kenn3 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 search 3G Phone PC GPS

4. A. Kleiner, N. Behrens and H. Kenn4 Data Integration Example Integration from data collected by the wearable computer To RoboCup Rescue To Google earth (GPX)

5. A. Kleiner, N. Behrens and H. Kenn5 Open research problem Improving 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

6. A. Kleiner, N. Behrens and H. Kenn6 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/proje cts/leica/ Red: GPS Data (Tuesday, clear sky) Green: GPS + PDR fusion GPS lost GPS Jump

7. A. Kleiner, N. Behrens and H. Kenn7 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 agents RFID Wristband

8. A. Kleiner, N. Behrens and H. Kenn8 MAS support for USAR Example1: Dijkstra based travel time estimation Legend Red (bright to dark)  estimated travel time White  unreachable area

9. A. Kleiner, N. Behrens and H. Kenn9 MAS support for USAR Example2: Informed coordination of victim search Legend Yellow  Targets assigned by the station Green  Found victims White  Explored buildings

10. A. Kleiner, N. Behrens and H. Kenn10 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