Robotic Eavesdropping: Effects of Bioinspired Acoustic Sensing on Tracking and Estimation

Abstract

Active sensors, such as radar, lidar, and sonar, emit signals into the environment and analyze the reflections to gather information such as distance, bearing, and, with more complex processing, shape and material. Conversely, passive sensors such as microphones and cameras, rely on signals produced by objects in the environment to collect data. This deprives the sensors of the ability to directly detect distance unless used in arrays, but affords them the benefits of being concealed and saving energy. In modern applications, we see active sensors filtering out any signals not originating from their transducers as if they were noise. However, contemporary research has shown that echolocating bats have the capability of taking advantage of both active and passive echolocation. By fusing the information a bat can gather from a conspecific's echoes with their own, it is suggested that more data may become available to the eavesdropping bat. Taking bioinspiration from these suggested abilities, we seek to explore the question of how fusing active and passive ultrasonic sensing may effect the information available to a robotic vehicle. Our first investigation was an experimental verification of the capabilities of a stereo sensor for passively tracking an ultrasonic sound source using limited a priori information about the target being tracked. Our results pos- itively supported a previous simulation study and showed that the Bayesian estimator was further able to recover from divergences due to hardware and software limitations. Break- ing from the limited assumptions of the previous work, we began a full investigation of the fusion of active and passive sensing with a numerical investigation of the effects of these sensing techniques on a robotic vehicle performing simultaneous localization and mapping (SLAM). The SLAM problem consists of robot that is placed in an unknown environment, which it proceeds to map and localize itself within. By ensonifying the environment with a stationary beacon, we compared the performance of the vehicle when using active, passive, and fused sensing strategies. Building upon previous numerical simulations, we found supporting evidence that, when information available through active sensing is limited, incorporating passive measurements improves the information available to the vehicle and may also improve the accuracy of its map and localization. Finally, we took the first step to fully realizing our initial goal by numerically investigating robotic eavesdropping on two dynamics vehicles. This work showed promising results for the continued investigation of fused sensing strategies and also highlighted the importance of formation control and landmark initialization.