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Designing Wire Mazes for Replicating Natural Echoes to Study Bat Biosonar Function
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DOI：10.1002/aisy.70499.png)
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Abstract
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Bats integrate biosonar sensing with agile flapping flight, making them a promising model for embodied artificial intelligence. Detailed study in complex natural habitats remains difficult, so laboratory paradigms such as mazes of thin wires are used to enable controlled observation. Whether such setups reproduce the acoustic characteristics of natural clutter, including distinct echo signatures associated with different vegetation, remains an open question. Systematic evaluation is computationally demanding because biosonar operates at high frequencies and involves many scatterers, where conventional numerical solvers become prohibitive. A validated framework is presented that combines efficient physical modeling with deep learning to guide wire maze design. An accelerated multiple scattering model for many cylinders provides broadband echo simulations at greatly reduced cost. These simulations train a convolutional classifier that tests acoustic distinguishability among different wire arrangements using echo spectrograms. The classifier reliably separates random wire arrays, indicating that the mazes generate repeatable, structured echo features rather than uninformative noise. The resulting tools enable the design of laboratory wire maze experiments that emulate natural biosonar sensing and maneuvering scenarios while reducing occlusions and supporting detailed recordings of flight and biosonar behavior.
Keywords:
acoustic scattering
deep learning
pattern recognition
time-frequency analysis
wire maze
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