Analogical representations of naive physics
Artificial Intelligence
A Bayesian multiple-hypothesis approach to edge grouping and contour segmentation
International Journal of Computer Vision
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Communications of the ACM - Robots: intelligence, versatility, adaptivity
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Uncertainty Management in Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
Towards Learning Naive Physics by Visual Observation: Qualitative Spatial Representations
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Robot Odor Localization: A Taxonomy and Survey
International Journal of Robotics Research
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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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 naive physics for practical robotics applications.