Allerton 2015 Paper Abstract


Paper ThD5.5

Ji, Tianxi (Carnegie Mellon University), Chen, Siheng (Carnegie Mellon University), Varma, Rohan (Carnegie Mellon University), Kovačević, Jelena (Carnegie Mellon University)

Efficient Route Planning of Autonomous Vehicles Based on Graph Signal Recovery

Scheduled for presentation during the Invited Session "Graph Signal Processing" (ThD5), Thursday, October 1, 2015, 16:50−17:10, Lower Level

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Source Coding and Compression, Sparse Data Analysis


We use graph signal sampling and recovery techniques to plan routes for autonomous aerial vehicles. We propose a novel method that plans an energy-efficient flight trajectory by considering the influence of wind. We model the weather stations as nodes on a graph and model wind velocity at each station as a graph signal. We observe that the wind velocities at two close stations are similar, that is, the graph signal of wind velocities is smooth. By taking advantages of the smoothness, we only query a small fraction of it and recover the rest by using a novel graph signal recovery algorithm, which solves an optimization problem. To validate the effectiveness of the proposed method, we first demonstrate the necessity to take wind into account when planning route for autonomous aerial vehicles, and then show that the proposed method produces a reliable and energy-efficient route.



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