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Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities.
A permanent-magnet synchronous motor (PMSM) servo drive for lightweight robotic platforms that have a high torque demand at low rotational speeds has been developed. First, a current-independent torque controller is derived and cascaded with a speed and position controller, while merely an encoder is used as a sensor device in combination with a speed estimator. Finally, the speed estimator output is compared to gyroscope measurements and the overall functioning is verified on a real system.
In this paper, we introduce a nano aerial robot swarm for indoor air quality monitoring applications such as occupational health and safety of (industrial) workplaces. The concept combines a robotic swarm composing of nano Unmanned Aerial Vehicles (nano UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight metal oxide gas sensors for measuring the Total Volatile Organic Compound (TVOC) in ppb and estimating the eCO2 (equivalent calculated carbon-dioxide) concentration in ppm. TVOC is a measure for the indoor air quality. An indoor localization and positioning system will be used to estimate the absolute 3D position of the swarm like GPS. Based on this novel indoor air quality monitoring concept, the development and validation of new algorithms in the field of Mobile Robot Olfaction (MRO) are planned, namely gas source localization and gas distribution mapping. A test scenario will be built up to validate and optimize the gas-sensitive nano aerial robot swarm for the intended applications.
Small unmanned aircraft are used increasingly as flying sensor platforms to perform tasks such as gas distribution mapping or environmental monitoring. Sometimes, these applications require knowledge of the ambient wind field.
In general, it would be interesting to avoid any additional hardware, both on ground and on the aircraft. Here, we present an approach to estimate the wind vector just by exploiting the state variables of the aircraft.
Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring.
A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used.
Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms.
For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing.
In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account.
Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model.
The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. In this paper, we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios.
The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. Here we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios.