Browsing by Author "Nayak, Anshul"

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    Cooperative Perception of Connected Vehicles for Safety
    Eskandarian, Azim; Ghorai, Prasenjit; Nayak, Anshul (Safe-D National UTC, 2023-04)
    In cooperative perception, reliably detecting surrounding objects and communicating the information between vehicles is necessary for safety. However, vehicle-to-vehicle transmission of huge datasets or images can be computationally expensive and often not feasible in real time. A robust approach to ensure cooperation involves relative pose estimation between two vehicles sharing a common field of view. Detecting the object and transferring its location information in real time is necessary when the object is not in the ego vehicle’s field of view. In such scenarios, reliable and robust pose recovery of the object at each instant ensures the ego vehicle accurately estimates its trajectory. Once pose recovery is established, the object’s location information can be obtained for future trajectory prediction. Deterministic predictions provide only point estimates of future states which is not trustworthy under dynamic traffic scenarios. Estimating the uncertainty associated with the predicted states with a certain level of confidence can lead to robust path planning. This study proposed quantifying this uncertainty during forecasting using stochastic approximation, which deterministic approaches fail to capture. The current method is simple and applies Bayesian approximation during inference to standard neural network architectures for estimating uncertainty. The predictions between the probabilistic neural network models were compared with the standard deterministic models. The results indicate that the mean predicted path of probabilistic models was closer to the ground truth when compared with the deterministic prediction. The study has been extended to multiple datasets, providing a comprehensive comparison for each model.
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    Effect of Kinematics and Caudal Fin Properties on Performance of a Freely-Swimming Fin
    Nayak, Anshul (Virginia Tech, 2020-12-23)
    Traditionally, underwater vehicles have been using propellers for locomotion but they are not only inefficient but generate large acoustic signature. Researchers have taken inspiration from efficient swimmers like fish to address the issue with alternate propulsion mechanism. Mostly, research on fish locomotion involved studying a foil tethered to a fixed point inside uniform flow. A major drawback of such study is that neither it resembles a freely swimming fish nor it takes into consideration the dynamics of moving fish on propulsive forces. Hence, in our current study, we focus on comparing the performance of a free swimming fin over tethered fin both experimentally and numerically. Experimentally, we focus on the oscillatory form of locomotion where the caudal fin pitches to generate necessary thrust as seen in boxfish. We intend to investigate the Caudal fin kinematics and its physical properties on locomotion performance. To better understand, we build an automated robo-physical model that swims in a circular path so as to carry extensive experiments. We focus on understanding the effect of flexibility, shape and thickness of caudal fin on performance. Currently, we have studied three different flexibility and for each flexibility, we studied three different shape. We found there must be an optimal flexibility for minimising the Cost of Transport (COT). We also propose that the steady forward speed linearly varies with tail tip velocity. Furthermore, we investigated the effect of thickness of fin and considered uniform and tapered fin with equal area moment of inertia. Numerically, we investigated the effect of phase offset between heave and pitch motion on the performance of a freely swimming fin and compared that to a tethered fin. A freely-swimming fin self propels and moves with steady speed while a tethered fin remains stationary and actuates under uniform flow. We model the fin as a rigid body undergoing prescribed motion in an inviscid fluid and solved for coupled interaction using panel method. We show the effect of phase offset for optimum performance and found a significant difference between tethered and freely swimming fin.