Abstract
Echolocating bat species that inhabit dense vegetation are promising model systems for achieving autonomy in complex natural environments. To replicate the skills of the bats in extracting information about complex environment that can support autonomous navigation in a man-made system three aspects deserve particular attention: (i) encoding of relevant sensory information; (ii) information extraction; and (iii) integration of information encoding and extraction with each other and the respective context. To facilitate progress towards understanding these issues, a biomimetic sonar system is being developed that is aimed at recreating the flexibility and variability that bats exhibit in the configuration of the emission and reception baffles of their biosonar systems (i.e., noseleaves and pinnae). Analyzing the significance of these dynamic effects on sensory information encoding requires an understanding of the stimulus ensemble of biosonar tasks in natural environment. To this end, the biomimetic sonar system has been used to collect large data sets with echoes that pertain to fundamental navigation tasks such as localization and passageway finding. Deep-learning methods have been shown to be a good match for analyzing this echo data. Finally, ongoing research is directed at data acquisition and inference with the specifics of a given habitat and biosonar task.
Published Version
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