Kavli Affiliate: Radhika Nagpal
| First 5 Authors: Darren Chiu, Radhika Nagpal, Bahar Haghighat, ,
| Summary:
Robot swarms can be tasked with a variety of automated sensing and inspection
applications in aerial, aquatic, and surface environments. In this paper, we
study a simplified two-outcome surface inspection task. We task a group of
robots to inspect and collectively classify a 2D surface section based on a
binary pattern projected on the surface. We use a decentralized Bayesian
decision-making algorithm and deploy a swarm of miniature 3-cm sized wheeled
robots to inspect randomized black and white tiles of $1mtimes 1m$. We first
describe the model parameters that characterize our simulated environment, the
robot swarm, and the inspection algorithm. We then employ a noise-resistant
heuristic optimization scheme based on the Particle Swarm Optimization (PSO)
using a fitness evaluation that combines decision accuracy and decision time.
We use our fitness measure definition to asses the optimized parameters through
100 randomized simulations that vary surface pattern and initial robot poses.
The optimized algorithm parameters show up to a 55% improvement in median of
fitness evaluations against an empirically chosen parameter set.
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