Optimization of Collective Bayesian Decision-Making in a Swarm of Miniaturized Vibration-Sensing Robots

TL;DR

This paper presents a Bayesian decision-making algorithm for a swarm of miniaturized vibration-sensing robots performing infrastructure inspection, introducing a novel information sharing strategy and optimized parameters that improve decision speed and robustness.

Contribution

The work introduces a new information sharing strategy and an optimization framework for Bayesian decision algorithms in robotic swarms, validated through simulations and real-world experiments.

Findings

Proposed strategy outperforms existing sharing strategies in decision time.

Optimized parameters improve robustness across complex environments.

Non-optimized parameters perform well only in simpler scenarios.

Abstract

Inspection of infrastructure using static sensor nodes has become a well established approach in recent decades. In this work, we present an experimental setup to address a binary inspection task using mobile sensor nodes. The objective is to identify the predominant tile type in a 1mx1m tiled surface composed of vibrating and non-vibrating tiles. A swarm of miniaturized robots, equipped with onboard IMUs for sensing and IR sensors for collision avoidance, performs the inspection. The decision-making approach leverages a Bayesian algorithm, updating robots' belief using inference. The original algorithm uses one of two information sharing strategies. We introduce a novel information sharing strategy, aiming to accelerate the decision-making. To optimize the algorithm parameters, we develop a simulation framework calibrated to our real-world setup in the high-fidelity Webots robotic simulator. We evaluate the three information sharing strategies through simulations and real-world experiments. Moreover, we test the effectiveness of our optimization by placing swarms with optimized and non-optimized parameters in increasingly complex environments with varied spatial correlation and fill ratios. Results show that our proposed information sharing strategy consistently outperforms previously established information-sharing strategies in decision time. Additionally, optimized parameters yield robust performance across different environments. Conversely, non-optimized parameters perform well in simpler scenarios but show reduced accuracy in complex settings.