Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms
(2013)
Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion, the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposes an integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitive micro-drone. We compare the performance of the proposed system in simulations and real-world experiments against two commonly used tracking algorithms adapted for aerial exploration missions.
In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach for GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.
The calibration of the integrated sensors in a multisensor system has gained in interest over the last years. In this paper we introduce an enhanced calibration process, which is based on the preceding study described in. The enhancement consists of the integration of a gyroscope. So far only the accelerometer and the magnetic field sensor were taken into account for the calibration process. Due to this improvement we reach a better approximation of the accelerometer and the magnetic field sensor. Additionally, we minimize the standard
deviation of the single sensors and improve the accuracy of the positioning of a moving person.