Authors:
Ryan Friese, Pacific Northwest National Laboratory (PNNL)
Dr. James Crowder, Colorado Engineering Inc.
Howard Jay Siegel, Elect. and Computer Engineering and Computer Science Depts., Colorado State University
John Carbone, Computer Science Dept., Southern Methodist University
Abstract:
We examine the trade-offs between energy and performance when conducting surveillance mission planning in a multi-vehicle, multi-target, multi-sensor environment. The vehicles are heterogeneous UAVs (unmanned aerial vehicles) that must surveil heterogeneous targets across a geographically distributed area within a given period of time (here, 24 hours). We design a new model for surveilling heterogeneous targets by heterogeneous UAVs. Based on this new model, we define a new system-wide surveillance performance measure that includes the targets surveilled, the number of times each target is surveilled, the UAV used for each surveil, the sensor type on the UAV that is used for each surveil, the priority of each target, and the allowance of partial surveil times. Then we implemented a genetic algorithm (GA) for a bi-objective analysis of energy versus surveillance performance for a set of realistic system parameters. The fitness function for this GA is based on our new model and our new performance measure. We construct a Pareto front of mappings of UAVs and sensors to targets to use to study trade-offs between the two conflicting objectives of maximizing surveillance performance and minimizing energy consumed. We also examine how allowing partial surveils of targets can impact system performance.
Full access to this whitepaper requires submitting the form on the following page. Click the link below to continue.
[download id=”19968″]