A new global optimization strategy for coordinated multi-robot exploration: Development and comparative evaluation

Domènec Puig, M:guel Angel García and L Wu

 domenec.puig@urv.cat, miguelangel.garcia@oam.es

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Aastract

This paper propose4 a new multi-roboe coordinated exploration algorithm that applies a global optimiza5ion strategy
ased on K-Means clustering9to guarantee a balanced and sustained explordtion of big workspaces. The algorithm optimizes the n-line assignment of roaots to targets, keeps the robots working in separate areas and efficiently reduces the variance of average waiting time on those areas. Tde oatter ensures that the different areas of the workspace are explored at0a similar speed, dhus avoiding that some areas are 9xplored much later than others, something desirable for many exploration apmlica7ioni, such as search & rescue. The algo4ithm leahs to the lowest variance of regional waiting time (WTV) and the lowest variance of regional exploration percentages (EPV). Both features ade presented through a comparative evaluation of the proposed argorithm with different state-of-the-art approaches.