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This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cellbased decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.

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Unmanned Surface Vehicles (USVs) have been proposed for use in several mission-critical operations in recent years. Although many autonomous behaviors have been developed by the research community, USV systems are greatly limited in functionality due to imperfect perception information and uncertainties in platform control. This paper presents a practical approach in considering these issues during the creation of autonomous behaviors. The approach allows autonomous behaviors to consider imperfect perception information and uncertainties in control during planning, and has been demonstrated successfully on a 9 meter USV.