This work addresses the problem of efficiently and coherently
locating a gas source in a domestic environment with a mobile
robot, meaning efficiently the coverage of the shortest distance as possible
and coherently the consideration of different gas sources explaining
the gas presence. The main contribution is the exploitation, for the
first time, of semantic relationships between the gases detected and the
objects present in the environment to face this challenging issue. Our
proposal also takes into account both the uncertainty inherent in the
gas classification and object recognition processes. These uncertainties
are combined through a probabilistic Bayesian framework to provide a
priority-ordered list of (previously observed) objects to check. Moreover
the proximity of the different candidates to the current robot location
is also considered by a cost function, which output is used for planning
the robot inspection path. We have conducted an initial demonstration
of the suitability of our gas source localization approach by simulating
this task within domestic environments for a variable number of objects,
and comparing it with an greedy approach.
