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Browsing by Author "Zhang, Liujun"

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    Location Finding in Natural Environments with Biomimetic Sonar and Deep Learning
    Zhang, Liujun (Virginia Tech, 2022-10-24)
    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.
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    Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes
    Zhang, Liujun; Farabow, Andrew; Singhal, Pradyumann; Mueller, Rolf (IOP, 2023-01-20)
    Many bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that "clutter echoes" originating from vegetation can convey information on the environment they were recorded in -- despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The GPS location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.