  Robot swarms can effectively serve a variety of sensing and inspection
applications. Certain inspection tasks require a binary classification
decision. This work presents an experimental setup for a surface inspection
task based on vibration sensing and studies a Bayesian two-outcome
decision-making algorithm in a swarm of miniaturized wheeled robots. The robots
are tasked with individually inspecting and collectively classifying a 1mx1m
tiled surface consisting of vibrating and non-vibrating tiles based on the
majority type of tiles. The robots sense vibrations using onboard IMUs and
perform collision avoidance using a set of IR sensors. We develop a simulation
and optimization framework leveraging the Webots robotic simulator and a
Particle Swarm Optimization (PSO) method. We consider two existing information
sharing strategies and propose a new one that allows the swarm to rapidly reach
accurate classification decisions. We first find optimal parameters that allow
efficient sampling in simulation and then evaluate our proposed strategy
against the two existing ones using 100 randomized simulation and 10 real
experiments. We find that our proposed method compels the swarm to make
decisions at an accelerated rate, with an improvement of up to 20.52% in mean
decision time at only 0.78% loss in accuracy.
