Contributed talk in Perception 1, July 29, 2019, 3:30 p.m. in room USB.4.005

Robotic models of obstacle avoidance in bats: assessing the benefit of acoustic gaze scanning in complex environments

Carl Bou Mansour, Elijah Koreman, Dennis Laurijssen, Jan Steckel, Herbert Peremans, Dieter Vanderelst

watch Publication Echolocating bats can avoid obstacles in complete darkness relying on their sonar system. Under experimental conditions, these animals can infer the 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 obstacle avoidance in bats. We implement two obstacle avoidance strategies based on interaural level differences: a Gaze Scanning Strategy and a Fixed Head Strategy. This allows us to test whether the acoustic gaze scanning observed in hunting bats might also be beneficial to bats trying to avoid obstacles. Both strategies were successful at avoiding obstacles in cluttered environments. However, the Fixed Head Strategy performed better. Acoustic gaze scanning might then 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.