Avoidance of non-localizable obstacles in echolocating bats: A robotic model.

Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. There...

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Main Authors: Carl Bou Mansour, Elijah Koreman, Jan Steckel, Herbert Peremans, Dieter Vanderelst
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-12-01
Series:PLoS Computational Biology
Online Access:https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007550&type=printable
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author Carl Bou Mansour
Elijah Koreman
Jan Steckel
Herbert Peremans
Dieter Vanderelst
author_facet Carl Bou Mansour
Elijah Koreman
Jan Steckel
Herbert Peremans
Dieter Vanderelst
author_sort Carl Bou Mansour
collection DOAJ
description Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot's obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence.
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spelling doaj-art-8b88d2dd6fd04652af47725092ed0b6e2025-08-20T02:55:13ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-12-011512e100755010.1371/journal.pcbi.1007550Avoidance of non-localizable obstacles in echolocating bats: A robotic model.Carl Bou MansourElijah KoremanJan SteckelHerbert PeremansDieter VanderelstMost objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot's obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007550&type=printable
spellingShingle Carl Bou Mansour
Elijah Koreman
Jan Steckel
Herbert Peremans
Dieter Vanderelst
Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
PLoS Computational Biology
title Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
title_full Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
title_fullStr Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
title_full_unstemmed Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
title_short Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
title_sort avoidance of non localizable obstacles in echolocating bats a robotic model
url https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007550&type=printable
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