Improving the robustness of naïve physics airflow mapping, using Bayesian reasoning on a multiple hypothesis tree

Previous work on robotic odour localisation in enclosed environments, relying on an airflow model, has faced significant limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. We present a method for dealing with these uncertainties through the generation of multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. Experimental results show that this method is capable of improving the robustness of odour localisation in the presence of uncertainties, where previously it was incapable. The results further demonstrate the usefulness of naïve physics for practical robotics applications.