Detection of passageways in natural foliage using biomimetic sonar
| dc.contributor.author | Wang, Ruihao | en |
| dc.contributor.author | Liu, Yimeng | en |
| dc.contributor.author | Müller, Rolf | en |
| dc.date.accessioned | 2024-02-22T13:41:02Z | en |
| dc.date.available | 2024-02-22T13:41:02Z | en |
| dc.date.issued | 2022-08-10 | en |
| dc.description.abstract | The ability of certain bat species to navigate in dense vegetation based on trains of short biosonar echoes could provide for an alternative parsimonious approach to obtaining the sensory information that is needed to achieve autonomy in complex natural environments. Although bat biosonar has much lower data rates and spatial (angular) resolution than commonly used human-made sensing systems such as LiDAR or stereo cameras, bat species that live in dense habitats have the ability to reliably detect narrow passageways in foliage. To study the sensory information that the animals may have available to accomplish this, we have used a biomimetic sonar system that was combined with a camera to record echoes and synchronized images from 10 different field sites that featured narrow passageways in foliage. The synchronized camera and sonar data allowed us to create a large data set (130 000 samples) of labeled echoes using a teacher-student approach that used class labels derived from the images to provide training data for echo-based classifiers. The performance achieved in detecting passageways based on the field data closely matched previous results obtained for gaps in an artificial foliage setup in the laboratory. With a deep feature extraction neural network (VGG16) a foliage-versus-passageway classification accuracy of 96.64% was obtained. A transparent artificial intelligence approach (class-activation mapping) indicated that the classifier network relied heavily on the initial rising flank of the echoes. This finding could be exploited with a neuromorphic echo representation that consisted of times where the echo envelope crossed a certain amplitude threshold in a given frequency channel. Whereas a single amplitude threshold was sufficient for this in the previous laboratory study, multiple thresholds were needed to achieve an accuracy of 92.23%. These findings indicate that despite many sources of variability that shape clutter echoes from natural environments, these signals contain sufficient sensory information to enable the detection of passageways in foliage. | en |
| dc.description.version | Accepted version | en |
| dc.format.extent | 11 page(s) | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | ARTN 056009 (Article number) | en |
| dc.identifier.doi | https://doi.org/10.1088/1748-3190/ac7aff | en |
| dc.identifier.eissn | 1748-3190 | en |
| dc.identifier.issn | 1748-3182 | en |
| dc.identifier.issue | 5 | en |
| dc.identifier.orcid | Mueller, Rolf [0000-0001-8358-4053] | en |
| dc.identifier.pmid | 35728778 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/118108 | en |
| dc.identifier.volume | 17 | en |
| dc.language.iso | en | en |
| dc.publisher | IOP | en |
| dc.relation.uri | https://www.ncbi.nlm.nih.gov/pubmed/35728778 | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | biosonar | en |
| dc.subject | passageway detection in foliage | en |
| dc.subject | field robotics | en |
| dc.subject | deep learning | en |
| dc.subject | transfer learning | en |
| dc.subject | transparent AI | en |
| dc.subject.mesh | Animals | en |
| dc.subject.mesh | Chiroptera | en |
| dc.subject.mesh | Humans | en |
| dc.subject.mesh | Echolocation | en |
| dc.subject.mesh | Biomimetics | en |
| dc.subject.mesh | Sound | en |
| dc.subject.mesh | Artificial Intelligence | en |
| dc.title | Detection of passageways in natural foliage using biomimetic sonar | en |
| dc.title.serial | Bioinspiration & Biomimetics | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
| dc.type.other | Article | en |
| dc.type.other | Journal | en |
| dcterms.dateAccepted | 2022-06-21 | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/Engineering | en |
| pubs.organisational-group | /Virginia Tech/Engineering/Mechanical Engineering | en |
| pubs.organisational-group | /Virginia Tech/Faculty of Health Sciences | en |
| pubs.organisational-group | /Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Engineering/COE T&R Faculty | en |