Title: A Next-Best-Smell Approach for Remote Gas Detection with a Mobile Robot,
Abstract: The problem of gas detection is relevant to many real-world applications,
such as leak detection in industrial settings and landfill monitoring. Using
mobile robots for gas detection has several advantages and can reduce danger
for humans. In our work, we address the problem of planning a path for a mobile
robotic platform equipped with a remote gas sensor, which minimizes the time to
detect all gas sources in a given environment. We cast this problem as a
coverage planning problem by defining a basic sensing operation -- a scan with
the remote gas sensor -- as the field of "view" of the sensor. Given the
computing effort required by previously proposed offline approaches, in this
paper we suggest a online coverage algorithm, called Next-Best-Smell, adapted
from the Next-Best-View class of exploration algorithms. Our algorithm
evaluates candidate locations with a global utility function, which combines
utility values for travel distance, information gain, and sensing time, using
Multi-Criteria Decision Making. In our experiments, conducted both in
simulation and with a real robot, we found the performance of the
Next-Best-Smell approach to be comparable with that of the state-of-the-art
offline algorithm, at much lower computational cost.