Dynamic Compressed Sensing of Unsteady Flows with a Mobile Robot

10/16/2021
by   Sachin Shriwastav, et al.
0

Large-scale environmental sensing with a finite number of mobile sensor is a challenging task that requires a lot of resources and time. This is especially true when features in the environment are spatiotemporally changing with unknown or partially known dynamics. However, these dynamic features often evolve in a low-dimensional space, making it possible to capture their dynamics sufficiently well with only one or several properly planned mobile sensors. This paper investigates the problem of dynamic compressed sensing (DCS) of an unsteady flow field, which takes advantage of the inherently low dimensionality of the underlying flow dynamics to reduce number of waypoints for a mobile sensing robot. The optimal sensing waypoints are identified by an iterative compressed sensing algorithm that optimizes the flow reconstruction based on the proper orthogonal decomposition (POD) modes. An optimized robot trajectory is then found to traverse these waypoints while minimizing the energy consumption, time, and flow reconstruction error. Simulation results in an unsteady double-gyre flow field is presented to demonstrate the efficacy of the proposed algorithms. Experimental results with an indoor quadcopter are presented to show the feasibility of the resulting trajectory.

READ FULL TEXT

page 2

page 7

research
03/18/2021

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Intelligent mobile sensors, such as uninhabited aerial or underwater veh...
research
11/15/2013

Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities

As a lossy compression framework, compressed sensing has drawn much atte...
research
08/23/2021

Dynamic Orthogonal Matching Pursuit for Signal Reconstruction

Orthogonal matching pursuit (OMP) is one of the mainstream algorithms fo...
research
05/04/2020

Dynamic Compressed Sensing for Real-Time Tomographic Reconstruction

Electron tomography has achieved higher resolution and quality at reduce...
research
02/20/2018

Bias Compensation in Iterative Soft-Feedback Algorithms with Application to (Discrete) Compressed Sensing

In all applications in digital communications, it is crucial for an esti...
research
03/07/2019

Stronger L2/L2 Compressed Sensing; Without Iterating

We consider the extensively studied problem of ℓ_2/ℓ_2 compressed sensin...
research
06/23/2017

Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

We adopt data structure in the form of cover trees and iteratively apply...

Please sign up or login with your details

Forgot password? Click here to reset