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Title
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Robotic models of obstacle avoidance in bats
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Author
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Abstract
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| Echolocating bats can avoid obstacles in complete darkness relying on their sonar system. Under experimental conditions, these animals can infer the 3D position of obstacles. However, in cluttered and complex environments their ability to locate obstacles is likely to be largely reduced, and they might need to rely on more robust cues that do not degrade as the complexity of the environment increases. Here, we present a robotic model of two hypothesized obstacle avoidance strategies in bats, both of which model observed behavior in bats: a Gaze Scanning Strategy and a Fixed Head Strategy. Critically, these strategies only employ interaural level differences and do not require locating obstacles. We found that both strategies were successful at avoiding obstacles in cluttered environments. However, the Fixed Head Strategy performed better. This indicates that acoustic gaze scanning, observed in hunting bats, might reduce obstacle avoidance performance. We conclude that strategies based on gaze scanning should be avoided when little or no spatial information is available to the bat, which corresponds to recent observations in bats. |
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Language
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English
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Source (journal)
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ALIFE 2019: THE 2019 CONFERENCE ON ARTIFICIAL LIFE
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Source (book)
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Conference on Artificial Life (ALIFE) - How Can Artificial Life Help, Solve Societal Challenges?, JUL 29-AUG 02, 2019, Newcastle upon Tyne, ENGLAND
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Publication
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Cambridge
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Mit press
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2019
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Volume/pages
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(2019)
, p. 463-464
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ISI
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000502629100081
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Full text (open access)
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