Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Job scheduling methods for reducing waiting time variance
Computers and Operations Research
Coordinated multi-robot exploration
IEEE Transactions on Robotics
A two-level approach for multi-robot coordinated exploration of unstructured environments
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Quantitative and qualitative coordination for multi-robot systems
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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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.