The right direction to smell: Efficient sensor planning strategies for robot assisted gas tomography

Abstract

Abstract-Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

Aerial-based gas tomography – from single beams to complex gas distributions

“…However, robots equipped with remote gas sensors need to estimate the optical path, which heavily depends on its localization and orientation accuracy. Furthermore, it is also important to select optimal measuring poses, as the reconstruction quality depends heavily on the chosen sensing geometry (Arain, Schaffernicht, Hernandez Bennetts, & Lilienthal, 2016;Arain, Trincavelli, Cirillo, Schaffernicht, & Lilienthal, 2015). The work presented in (Trincavelli et al, 2012) and (Bennetts et al, 2013) is the first attempt towards Robot-Assisted Gas Tomography (RAGT) with a ground-based robot, whereas this work constitutes the first attempt towards RAGT with an aerial robot.…”

Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot

“…The weight parameter g = ½0, 1 combines the two types of sensing coverages: a value close to 1 will prefer configurations with strong coverage of overall high concentration areas, and a value close to 0 prefer configurations that keep h c in the middle of the circular sectors (f, r) of the configurations. In our previous work (Arain et al, 2016), only a count of cells covered by a candidate configuration, which contain gas concentration above a set threshold, was considered for the sensing coverage index. Consequently, entirely different gas distributions covered by two configurations could still be assigned the same coverage indices.…”

“…Our sensor planning algorithm for gas mapping finds informative sampling poses of overlapping sensing coverage with different viewpoints to accurately reconstruct the gas distribution. The initial version of this algorithm is presented in Arain et al (2016) and fundamentally improved in this article: a new procedure is developed for the estimation of the areas of interest (hotspots); improved multi-criteria weights for candidate measurement configurations; and the optimization problem is formulated to select a minimal set of measurement configurations for the desired reconstruction quality.…”

“…A straightforward approach for gas inspection, which we used in our previous work (Arain et al, 2016), is to carry out two subsequent robotic tours, one for the initial gas detection and the second for gas distribution mapping (two-tour mission strategy, 2 t - ARMEx ). In particular, for inspection in large environments, gas detection and gas distribution mapping should not be performed in separate phases to be efficient.…”

“…We introduced the evaluation of autonomous gas-sensing systems against human experts for the first time in Arain et al (2016). However, in those experiments, only one human expert participated who had to decide all the sensing configurations for a single robotic tour of gas distribution mapping in advance, i.e., without having access to updated gas maps during the inspection.…”