Achieving autonomous operation in complex natural environment remains an unsolved challenge. Conventional engineering approaches to this problem have focused on collecting large amounts of sensory data that are used to create detailed digital models of the environment. However, this only postpones solving the challenge of identifying the relevant sensory information and linking it to action control to the domain of the digital world model. Furthermore, it imposes high demands in terms of computing power and introduces large processing latencies that hamper autonomous real-time performance. Certain species of bats that are able to navigate and hunt their prey in dense vegetation could be a biological model system for an alternative approach to addressing the fundamental issues associated with autonomy in complex natural environments. Bats navigating in dense vegetation rely on clutter echoes, i.e. signals that consist of unresolved contributions from many scatters. Yet, the animals are able to extract the relevant information from these input signals with brains that are often less than 1 g in mass. Pilot results indicate that information relevant to location identification and passageway finding can be directly obtained from clutter echoes, opening up the possibility that the bats' skill can be replicated in man-made autonomous systems.
Keywords: autonomy; bats; biosonar; deep learning; sensing.
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