On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

Supplementary information files for article: 'Information-based search for an atmospheric release using a mobile robot: algorithm and experiments'

Supplementary information files for article: 'Information-based search for an atmospheric release using a mobile robot: algorithm and experiments'.The video shows a robot is autonomously navigated to search for and estimate parameters of a simulated hazardous release. It moves to new positions based on what it thinks it will learn from the subsequent measurement. It is able to estimate the location of the release and important parameters such as its release rate. This can be used to forecast the spread of material or to assess emissions. The pink dots projected on the floor in the video represent random samples used in the Monte Carlo estimation algorithm. Each dot is a hypothesized release including its release rate and the meteorological parameters used to predict the spread from the source.Abstract:Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring; thus, it has been an active research area for several years known as source term estimation (STE). This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes' theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with the low-cost gas sensor responds to the hazardous source simulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for STE based on the mobile robot and the low-cost sensor.</div