Optimization and Evaluation of Multi Robot Surface Inspection Through Particle Swarm Optimization

TL;DR

This paper presents an optimization framework for multi-robot surface inspection tasks using Particle Swarm Optimization, demonstrating significant improvements in decision accuracy and efficiency in simulated environments.

Contribution

It introduces a PSO-based optimization method for tuning parameters of a decentralized inspection algorithm in multi-robot swarms, validated through extensive simulations.

Findings

Up to 55% improvement in median fitness evaluations.

Effective noise-resistant heuristic optimization scheme.

Validated with 100 randomized simulation scenarios.

Abstract

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 $1m\times 1m$. 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.