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.

翻译：机器人群体可承担空中、水域及地面环境中的多种自动化感知与检测任务。本文研究了一种简化的二值结果地面检测任务：我们指派一组机器人对二维表面区域进行检测，并基于投影在该表面的二值模式进行集体分类。采用去中心化贝叶斯决策算法，部署由3厘米微型轮式机器人组成的群体，检测尺寸为1米×1米的随机黑白瓷砖。首先描述了表征仿真环境、机器人群体及检测算法的模型参数，然后采用基于粒子群优化（PSO）的噪声鲁棒性启发式优化方案，其适应度评估综合了决策准确率与决策时间。通过100次改变表面图案和机器人初始位姿的随机仿真实验，基于所定义的适应度指标对优化参数进行评估。与经验参数集相比，优化后的算法参数在适应度中位数上提升了高达55%。