Dynamic Compressed Sensing of Unsteady Flows with a Mobile Robot

TL;DR

This paper presents a method for efficiently sensing unsteady flow fields using a mobile robot by leveraging low-dimensional flow dynamics, optimized waypoint selection, and energy-efficient trajectories, validated through simulations and indoor experiments.

Contribution

It introduces an iterative compressed sensing algorithm for dynamic flow sensing and an optimal trajectory planning method for mobile robots, reducing resource use in environmental monitoring.

Findings

Effective flow reconstruction with fewer waypoints.

Validated approach through simulations and indoor quadcopter experiments.

Reduced energy and time consumption in flow sensing tasks.

Abstract

Large-scale environmental sensing with a finite number of mobile sensors 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. Fortunately, 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 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 modes. An optimal sampling 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.