An Evaluation of Plume Tracking as a Strategy for Gas Source Localization in Turbulent Wind Flows

Abstract

Gas source localization is likely the most direct application of a mobile robot endowed with gas sensing capabilities. Multiple algorithms have been proposed to locate the gas source within a known environment, ranging from bio-inspired to probabilistic ones. However, their application to real-world conditions still remains a major issue due to the great difficulties those scenarios bring, among others, the common presence of obstacles which hamper the movement of the robot and notably increase the complexity of the gas dispersion. In this work, we consider a plume tracking algorithm based on the well-known silkworm moth strategy and analyze its performance when facing different realistic environments characterized by the presence of obstacles and turbulent wind flows. We rely on computational fluid dynamics and the open source gas dispersion simulator GADEN to generate realistic gas distributions in scenarios where the presence of obstacles breaks down the ideal downwind plume. We first propose some modifications to the original silkworm moth algorithm in order to deal with the presence of obstacles in the environment (avoiding collisions) and then analyze its performance within four challenging environments.

Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

“…It refers to the use of mobile robots in combination with a sensing technology to perceive gas release in the environment [30]. Recent advancements in the design and development of gas sensors and drone technology as well as the adaptation of Artificial Intelligence and Machine Learning techniques for processing signals from sensors and robotics control have revealed RO as interesting research field in many practical applications [31,32].…”

“…A general and powerful extension in this research field suggested in different studies as a potential future development, is the adaptation of methodologies for the third spatial dimension and for augmenting the solution to a multirobot collaboration scheme. These improvements in many cases may help to reduce the time needed to cover a large area and improve at the same time the quality of the sampled data when a low-cost multisensory system is used [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”