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Collective Bayesian Decision-Making in a Swarm of Miniaturized Robots for Surface Inspection

2024-04-12 10:48:59
Thiemen Siemensma, Darren Chiu, Sneha Ramshanker, Radhika Nagpal, Bahar Haghighat


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.

Abstract (translated)

机器人群可以有效地执行各种传感和检测应用。某些检测任务需要进行二分类决策。本文基于振动感测设置了一个表面检测任务,并研究了一个基于机器人中微型轮式无人机的贝叶斯双结局决策算法。这些机器人被要求分别检查和共同分类由振动和非振动瓷砖组成的一个1米x1米铺装表面。机器人使用车载IMU感知振动,并使用一系列红外传感器进行避障。我们开发了一个利用Webots机器人模拟器和Particle Swarm Optimization(PSO)方法的模拟和优化框架。我们考虑了两种现有信息共享策略,并提出了一个新的策略,允许群快速达到准确的分类决策。我们首先通过模拟找到最优参数,实现高效的采样,然后使用100个随机的模拟和10个真实实验对所提出的策略与两种现有策略进行了评估。我们发现,与两种现有策略相比,我们所提出的策略使群加速了决策,平均决策时间提高了20.52%。在精度损失仅占0.78%的情况下,平均决策时间提高了19.33%。



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