Abstract
In this paper, we present a flow field partition method that extracts the key features, which are the spatial and temporal variation of the flow field. The partition method is developed based on K-means algorithm. In the case where the temporal pattern of the flow field contains only a periodic tidal component, we propose an algorithm that partitions the flow field into static clusters of piece-wise constant flow, by performing K-means clustering over the time-averaged flow field. Then the method is extended to partitioning the flow field into clusters of uniform time-varying flow, by fitting the spatially averaged flow in each static partitioned region to a parametric flow model. Simulation results of partitioning both a simulated jet flow field, as well as the ocean surface flow data into time-invariant and time-varying uniform flow are presented to demonstrate that the proposed method can represent the true flow field with significantly less amount of data. Result of using Method of Evolving Junctions to plan the time-optimal path in the partitioned flow field is also presented to demonstrate that the proposed flow partitioning method can be applied to facilitate path planning, and can reduce the path planning computational cost.
Mengxue Hou
Assistant Professor, Electrical Engineering
My research interests include robotic autonomy, mobile sensor networks, and human robot interaction. I aim to devise practical, computationally-efficient, and provably-correct algorithms that prepare robotic systems to be cognizant, taskable, and adaptive, and can collaborate with human operators to co-exist in a complex, ever-changing and unknown environment.