This paper studies a generally applicable, sensitive, and intuitive error
metric for the assessment of robotic swarm density controller performance.
Inspired by vortex blob numerical methods, it overcomes the shortcomings of a
common strategy based on discretization, and unifies other continuous notions
of coverage. We present two benchmarks against which to compare the error
metric value of a given swarm configuration: non-trivial bounds on the error
metric, and the probability density function of the error metric when robot
positions are sampled at random from the target swarm distribution. We give
rigorous results that this probability density function of the error metric
obeys a central limit theorem, allowing for more efficient numerical
approximation. For both of these benchmarks, we present supporting theory,
computation methodology, examples, and MATLAB implementation code.