Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information

Mobile in‐situ sensor platforms such as U nmanned A erial V ehicles can be used in environmental monitoring. In time‐critical monitoring scenarios as for example in emergency response, and in the exploration of highly dynamic phenomena, obtaining the relevant data with one or few mobile sensors is challenging. It requires an intelligent sampling strategy that integrates prior information and adapts to the dynamics of the observed phenomenon, based on the collected sensor data. Available information about the observed phenomenon may be incomplete or imprecise and therefore insufficient for quantitative modeling. We address this problem by reasoning about the plume movement and size on a qualitative level and present an algorithm for tracking a dynamic plume that integrates this qualitative information with the collected sensor data. We evaluate our algorithm using simulated data sets of three different moving and expanding gas plumes. By means of simulations we show that the qualitative methods can be used to infer new information about the properties of a moving plume and to adapt the sensor movement for tracking the plume. Both can be done with low computational effort, without absolute positioning capability of the sensor, and with less input information than required by quantitative approaches.