DFKI-LT - TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response

Ivana Kruijff-Korbayová, Francis Colas, Mario Gianni, Fiora Pirri, Joachim de Greeff, Koen Hindriks, Mark Neerincx, Petter Ögren, TomᨠSvoboda, Rainer Worst
TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response
1 KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. volume 29 number 2, Pages 193-201, Springer, 2/2015
 
This paper describes the project TRADR: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response. Experience shows that any incident serious enough to require robot involvement will most likely involve a sequence of sorties over several hours, days and even months. TRADR focuses on the challenges that thus arise for the persistence of environment models, multi-robot action models, and human-robot teaming, in order to allow incremental capability improvement over the duration of a mission. TRADR applies a user centric design approach to disaster response robotics, with use cases involving the response to a medium to large scale industrial accident by teams consisting of human rescuers and several robots (both ground and airborne). This paper describes the fundamentals of the project: the motivation, objectives and approach in contrast to related work.
 
Files: BibTeX, s13218-015-0352-5