Multi-Robot Informative Path Planning in Unknown Environments Through Continuous Region Partitioning

Information collection is an important application of multi-robot systems especially in environments that are difficult to operate for humans. The objective of the robots is to maximize information collection from the environment while remaining in their path-length budgets. In this paper, we propose a novel multi-robot information collection algorithm that uses a continuous region partitioning approach to efficiently divide an unknown environment among the robots based on the discovered obstacles in the area, for better load-balancing. Our algorithm gracefully handles situations when some of the robots cannot communicate with other robots due to limited communication ranges.