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

This paper proposes a new multi-robot coordinated exploration algorithm that applies a global optimization strategy based on K-Means clustering to guarantee a balanced and sustained exploration of big workspaces. The algorithm optimizes the on-line assignment of robots to targets, keeps the robots working in separate areas and efficiently reduces the variance of average waiting time on those areas. The latter ensures that the different areas of the workspace are explored at a similar speed, thus avoiding that some areas are explored much later than others, something desirable for many exploration applications, such as search & rescue. The algorithm leads to the lowest variance of regional waiting time (WTV) and the lowest variance of regional exploration percentages (EPV). Both features are presented through a comparative evaluation of the proposed algorithm with different state-of-the-art approaches.