Rendezvous of Glowworm-Inspired Robot Swarms at Multiple Source Locations: A Sound Source Based Real-Robot Implementation

This paper presents a novel glowworm metaphor based distributed algorithm that enables a minimalist mobile robot swarm to effectively split into subgroups, exhibit simultaneous taxis towards, and rendezvous at multiple source locations. The locations of interest could represent radiation sources such as nuclear and hazardous aerosol spills spread within an unknown environment. The glowworm algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. A key feature of the GSO algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal function. We conduct sound source localization experiments, using a set of four wheeled robots (christened Glowworms), to validate the glowworm approach to the problem of multiple source localization. We also examine the behavior of the glowworm algorithm in the presence of uncertainty due to perceptional noise. A comparison with a gradient based approach reveals the superiority of the glowworm algorithm in coping with uncertainty.