Publikation
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
In: KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. (KI), Vol. 29, No. 2, Pages 193-201, Springer, 2/2015.
Zusammenfassung
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.