Collective Bayesian Decision-Making in a Swarm of Miniaturized Robots for Surface Inspection
🤖 AI Summary
This work addresses the binary classification task of distinguishing vibrating versus non-vibrating regions over a 1 m × 1 m tiled surface. We propose a distributed Bayesian decision-making framework using a swarm of miniature wheeled robots, each equipped with an IMU for vibration sensing and infrared sensors for obstacle avoidance. A novel distributed information-sharing strategy enables efficient collaborative inference, significantly accelerating population-level Bayesian consensus while achieving superior trade-offs between speed and accuracy. Webots simulations and physical experiments demonstrate that our approach reduces average decision time by 20.52% compared to baseline methods, with only a 0.78% accuracy degradation. Validated across 100 simulation runs and 10 real-world trials, the method exhibits strong robustness and practicality. The core contribution is a lightweight, scalable distributed consensus mechanism, establishing a new paradigm for efficient collective perception in resource-constrained multi-agent systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Develop Bayesian decision-making for robot swarm surface inspection
Optimize vibration-based binary classification using miniaturized robots
Compare information sharing strategies for faster accurate decisions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian decision-making in miniaturized robot swarms
Vibration sensing with onboard IMUs for inspection
Particle Swarm Optimization for parameter tuning
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