Sniffbots to the Rescue – Fog Services for a Gas-Sniffing Immersive Robot Collective

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung


Gas accidents frequently turn industrial or civil structures into extremely dangerous environments. Disasters like the Ahrtal flood in summer 2021 destroy infrastructures such as the gas grid and the power grid, so that people loose control and suddenly find themselves confronted with explosions, suffocation, and death. This paper presents a case study of a robot collective identifying gas leaks with a gas-sniffing wireless sensor network, while providing immersive inspection and tele-operation in the dangerous areas. So-called Sniffbots work in a minimal communication infrastructure, construct world maps autonomously, use them to find gas leaks, remotely inspect, and attempt to close them. To this end, the fog of a Sniffbot should offer services, such as sniff-sensor data aggregation, calculation of points of interest in 2-D and 3-D, virtual reality immersion, remote gripping, as well as autonomous control of flying and driving. While this paper discusses a prototype system still under development, the experiments show the fantastic capabilities of modern gas-sniffing sensors in an immersive robotic fog. Sniffbots, though, at this moment in time, being very expensive robot collectives, will be a very valuable aid in the future to save the life of people in gas disasters.


TitelService-Oriented and Cloud Computing
Redakteure/-innenFabrizio Montesi, George Angelos Papadopoulos, Wolf Zimmermann
Herausgeber (Verlag)Springer, Cham
ISBN (elektronisch)978-3-031-04718-3
ISBN (Print)978-3-031-04717-6
PublikationsstatusVeröffentlicht - 1 Jan. 2022

Externe IDs

Scopus 85128953846
dblp conf/esocc/AssmannBCDWULPR22
unpaywall 10.1007/978-3-031-04718-3_1
Mendeley 3e28e23e-e0b1-381a-ab18-982e80497281
ORCID /0000-0001-7436-0103/work/142240355
ORCID /0000-0003-2571-8441/work/142240523
ORCID /0000-0001-5165-4459/work/142248237
ORCID /0000-0002-6311-3251/work/142248742
ORCID /0000-0002-9899-1409/work/142249198