Dynamic Compressed Sensing of Unsteady Flows with a Mobile Robot
This work addresses resource-intensive environmental sensing for applications like fluid dynamics monitoring, but it is incremental as it builds on compressed sensing and proper orthogonal decomposition methods.
The paper tackles the problem of efficiently sensing spatiotemporally changing unsteady flow fields using mobile robots by leveraging low-dimensional dynamics to reduce waypoints, resulting in optimized trajectories that minimize energy, time, and reconstruction error as demonstrated in simulations and indoor quadcopter experiments.
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.