The usage of multi-robot systems to complete monotonous yet complex tasks has become increasingly popular. One such category is tasks that require the complete coverage of an area, such as the task of vacuuming. The undertaking of a complete coverage task by a singular mobile floor cleaning robot requires a minimum of path planning capabilities to prevent the recleaning of previously cleaned areas. When more than one robot is utilized to complete the same coverage task, there must be some form of global strategy implemented that can aid the multi-robot system in reducing the amount of coverage overlap, idle time, and overall time required to complete the vacuuming task. Such global strategies often utilize a method of decomposing the larger task into smaller subtasks which are then allocated among the number of robots within the system. However, many of these strategies are either static in their task allocation or are based on a singular robot system to accomplish the complete coverage task. The algorithm for global strategy proposed in this thesis presents a methodology for utilizing the techniques of triangular mesh decomposition, Traveling Salesman Problem optimization, and dynamic flip task allocation for multiple floor cleaning robots.
