Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization.

Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.