ArticlePLoS computational biology2018
A fully autonomous terrestrial bat-like acoustic robot.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A thermophone-based FM sonar for airborne ultrasonic sensing under drone flight noise.Scientific reports · 2026Article
- Bat echolocation signals based on the time-varying autoregressive method.Frontiers in zoology · 2025Article
- Analysis of echolocation behavior of bats in "echo space" using acoustic simulation.BMC biology · 2022Article
- A bio-mimetic miniature drone for real-time audio based short-range tracking.PLoS computational biology · 2022Article
- Estimate the Unknown Environment with Biosonar Echoes-A Simulation Study.Sensors (Basel, Switzerland) · 2021Article
- Bioinspired sonar reflectors as guiding beacons for autonomous navigation.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Echo View Cells From Bio-Inspired Sonar.Frontiers in neurorobotics · 2020Article
- Article
- Avoidance of non-localizable obstacles in echolocating bats: A robotic model.PLoS computational biology · 2019Article
- Classification of Sonar Targets in Air: A Neural Network Approach.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Echolocating bats rely on active sound emission (echolocation) for mapping novel environments and navigating through them. Many theoretical frameworks have been suggested to explain how they do so, but few attempts have been made to build an actual robot that mimics their abilities. Here, we present the 'Robat'-a fully autonomous bat-like terrestrial robot that relies on echolocation to move through a novel environment while mapping it solely based on sound. Using the echoes reflected from the environment, the Robat delineates the borders of objects it encounters, and classifies them using an artificial neural-network, thus creating a rich map of its environment. Unlike most previous attempts to apply sonar in robotics, we focus on a biological bat-like approach, which relies on a single emitter and two ears, and we apply a biological plausible signal processing approach to extract information about objects' position and identity.
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