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.

翻译：机器人集群可有效服务于多种传感与检测应用。某些检测任务需要二元分类决策。本研究针对基于振动传感的表面检测任务构建实验平台，并研究了一种贝叶斯两结果决策算法在微型轮式机器人集群中的应用。机器人需分别检测由振动与非振动瓷砖组成的1米×1米拼贴表面，并根据瓷砖主要类型进行集体分类。机器人通过板载惯性测量单元（IMU）感知振动，并利用红外传感器阵列实现避障。我们基于Webots机器人仿真器和粒子群优化（PSO）方法开发了仿真与优化框架。通过分析两种现有信息共享策略，我们提出了一种新策略，使集群能快速达成准确的分类决策。首先在仿真中寻找实现高效采样的最优参数，随后通过100次随机仿真和10次真实实验，将所提策略与两种现有策略进行对比。结果表明，所提方法能促使集群以更快的速度做出决策，平均决策时间提升最高达20.52%，而准确率仅下降0.78%。