The real-world localization and classification of multiple odours using a biologically based neurorobotics approach

Abstract

Autonomous robotic odour source classification and localization in real world environments is an essential step for applications such as humanitarian demining, environmental monitoring or search and rescue operations. However, at the moment this problem has only been solved by nature (e.g.: moths, bees, rats, dogs). Biological systems are capable and efficient at odour source localization in spite of the difficulties present in the real world such as turbulent environments, obstacles, predators or interfering odours. Here we aim at exploiting our understanding of the moth to solve this problem and we propose a biologically based model of moth behaviour. We implement our model on a robot that uses chemical sensors and we test its performance in a controlled environment. Further, we extend the behavioural model with a sensor front end that supports classification in order to deal with odour distractors. We show that our system is able to locate an odour source and map the chemical environment in the presence of distractors.