Bioinspired solution to finding passageways in foliage with sonar.

Finding narrow gaps in foliage is an important component skill for autonomous navigation in densely vegetated environments. Traditional approaches are based on collecting large amounts of data with high spatial resolution. However, the biosonar systems of bats that live in dense habitats demonstrate that finding gaps is possible based on sensors with angular resolutions that are poor compared to technologies such as man-made sonar and lidar. To investigate these capabilities, we have used a biomimetic sonar head to ensonify artificial hedges in the laboratory. We found that a conventional approach based on echo energy performed poorly on detecting gaps with the area under the receiver operating characteristic (ROC) curve ranging from 0.69 to 0.75 depending on the distance to the hedge and gap width. A deep-learning approach based on a convolutional neural network (CNN) operating on the echo spectrograms achieved area under the ROC curve (AUC) values between 0.94 and 0.97. Class activation mapping indicated that the rising flank of the echoes was critical for detecting the gaps. As a consequence, a simple code consisting of first threshold-crossing times was able to almost reproduce the performance of the CNN classifier (AUC 0.9 to 0.95). This demonstrates that the echo waveforms contained patterns that were indicative of a gap in the foliage but did not suffer from the comparatively large beamwidth used.