Browsing by Author "Jahromi Shirazi, Masoud"

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    Dynamics of Multi-Agent Systems with Bio-Inspired Active and Passive Sensing
    Jahromi Shirazi, Masoud (Virginia Tech, 2020-10-22)
    Active sensors, such as radar, lidar and sonar, emit a signal into the environment and gather information from its reflection. In contrast, passive sensors such as cameras and microphones rely on the signals emitted from the environment. In the current application of active sensors in multi-agent autonomous systems, agents only rely on their own active sensing and filter out any information available passively. However, fusing passive and active sensing information may improve the accuracy of the agents. Also, there is evidence that bats who use biosonar eavesdrop on a conspecific's echolocation sound, which shows a successful example of implementing active and passive sonar sensor fusion in nature. We studied the effect of such information fusion in the framework of two problems: the collective behavior in a multi-agent system using the Vicsek model and the canonical robotics problem of Simultaneous Localization And Mapping (SLAM). Collective behavior refers to emergence of a complex behavior in a group of individuals through local interaction. The Vicsek model is a well-established flocking model based on alignment of individuals with their neighbors in the presence of noise. We studied the aligned motion in a group in which the agents employ both active and passive sensing. Our study shows that the group behavior is less sensitive to measurement accuracy compared to modeling precision. Therefore, using measurement values of the noisier passive sonar can be beneficial. In addition, the group alignment is improved when the passive measurements are not dramatically noisier than active measurements. In the SLAM problem, a robot scans an unknown environment building a map and simultaneously localizing itself within that map. We studied a landmark-based SLAM problem in which the robot uses active and passive sensing strategies. The information provided passively can improve the accuracy of the active sensing measurements and compensate for its blind spot. We developed an estimation algorithm using Extended Kalman Filter and employed Monte Carlo simulation to find a parameter region in which fusing passive and active sonar information improves the performance of the robot. Our analysis shows this region is aligned within the common range of active sonar parameters.
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    Eavesdropping like a bat: Towards fusing active and passive sonar for a case study in simultaneous localization and mapping
    Jahromi Shirazi, Masoud; Abaid, Nicole (2021-05-22)
    Among so-called active sensors that use self-generated signals, sonar sensors are more challenging to implement than lidar and radar due in part to their limited angular field of sensing. A common solution to this challenge is scanning sensors that sweep an angular range with successive measurements. However, scanning sensors are particularly problematic for sonar because of the relatively slow sound speed and the inertia of the sonar head. Studies of bat behaviour suggest that bats may eavesdrop on their conspecifics during group flight. In other words, they fuse information gathered by their own active sonar with information they receive by passively listening to peers. Because bats are extremely skilled in using sonar, this behaviour inspired an investigation into whether fusing active and passive sonar can be a solution to the challenges of implementing sonar sensors. A model of fused sensing is defined, and a numerical simulation is used to answer this question on the test bed problem of simultaneous localization and mapping (SLAM). The simulation results show that when the angular range of active sonar and associated noise is relatively small, the robot's performance in solving SLAM is improved.