In the swarm exploration group we are working on algorithms that enable autonomous robots to localize gas sources. By taking samples of the gas concentration in the environment the robots are able to infer the sources.
Our approach can be applied in technical accident or disaster response scenarios, where toxic or explosive material is leaking. In such cases localizing the sources is of high interest and relevant to safety. However, for civil protection agencies searching for toxic gas leaks in an already contaminated environment implies threats for human operators. Thus, employing robotic platforms in those scenarios might be beneficial with respect to safety aspects. Moreover, robots with a certain level of autonomy simplify the work of a human operator, as compared to just teleoperated platforms. An autonomous robot can instantaneously interpret the collected data and decide based on them on its own.
However, autonomous robots in general lack of expert knowledge that would be implied in human operated mission. To bridge this gab, in our work we assist the robotic system by domain knowledge that is a-priori available about the environment to be explored. For example, the physical phenomenon of gas dispersion is well known and can be modeled mathematically.
In our studies swarm systems aided by such mathematical model of the gas dispersion process are able to localize the gas sources faster with fewer measurements compared to a system without this knowledge.