Integration of Bayesian Inference and Anemotaxis for Robotics Gas Source Localization in a Large Cluttered Outdoor Environment

Abstract

Finding a gas source in a cluttered outdoor environment using autonomous robot is a complex challenge. The gas movement is difficult to predict as it is significantly affected by the wind and the shape of objects in the environment. In this paper, we propose a new probabilistic model and an integration of Bayesian inference and anemotaxis methods used for a robot to find a gas source in a large cluttered outdoor environment. An autonomous robot installed with a gas sensor is expected to find the location of the gas source after the gas leak occurs for a particular time. The advantage of the Bayesian inference technique has been presented previously so that a robot can find the gas source in an isolated indoor building without any significant wind flow. The large environment is divided into some particular regions. A set of probability density function was collected previously from a large amount of gas dispersion simulation to estimate the maximum likelihood of where the gas source is. The challenge gets more extensive if the Bayesian inference method is applied in an outdoor and cluttered environment. Instead of only measuring the gas concentration, the wind angle is also used as the wind profile significantly affects the gas dispersion. Therefore, the probability model is modified to allow the wind direction as a new variable. Moreover, an anemotaxis method is incorporated as the decision-making support as it may be more efficient to direct the robot explicitly to the upwind direction. Evaluations of the proposed method were carried out and its advantage was shown through simulation in a number of different scenarios.

Multi-Robot Gas Sources Localization and Mapping Using Adaptive Voronoi-PSO and Bayesian Inference

“…Moreover, the wind profile obtained from the CFD simulations can be leveraged to generate gas dispersion patterns. This integration allows for a comprehensive understanding of how gas particles disperse within the environment under the influence of complex wind dynamics [11].…”

“…The joint metric score (M ) is calculated using (11). The normalized area covered and C NLPD are multiplied together and then divided by the normalized time to find the gas sources.…”

“…The GADEN simulator [9] is a suitable tool for generating gas distribution, assuming the gas behaves as filaments. Building upon the work of [10], [11] successfully employed a set of gas distribution patterns generated by the GADEN simulator. Bayesian inference methods were then used to localize a single gas source.…”