Location Finding in Natural Environments with Biomimetic Sonar and Deep Learning

Bats are famous for their capability of navigating in dense forests for hundreds of kilometers within one night by using their sonar system. Airborne sonar hasn't been heavily used in the industrial world compared to other sensors such as lidar, radar, and cameras. In this study, we applied a biosonar robot to navigate in a dense forest with bat-like FM-CF ultrasonic signals with deep learning. The results presented show that airborne biosonar can classify different areas' plants, in addition to achieving a similar level of navigation granularity compared to GPS, which is about 6 meters of radius resolution. The time- frequency representations of echoes from the forest are used as input data to explore the biosonar navigation ability, and the state-of-the-art CNN deep network (Resnet 152) is used as the brain to do the echolocation in the dense forest. The navigation ability can be improved significantly by combining multiple 10 ms long echoes, however, the data size of the reflected waves is much smaller than the other popularly used sensors, as echo can be collected at a rate of 40 echoes per second. The results can prove that airborne sonar can be used to navigate in GPS-denied environments, and can be an important sensor used in a scenario when other sensors meet constraints, like in the sensor fusion applications.