MARKET-BASED COMPLEX TASK ALLOCATION FOR MULTIROBOT TEAMS

In order for a team of autonomous robots to perform a complex mission effectively, an efficient assignment of tasks to robots must be determined. Existing multirobot task allocation algorithms treat tasks as simple, indivisible entities. However, when dealing with complex tasks, the structure and semantics of the tasks can be exploited to produce more efficient team plans by giving individual robots the ability to come up with new ways to perform a task, or by allowing multiple robots to cooperate by sharing the subcomponents of a task, or both. In this paper we detail a method for efficiently allocating a set of complex tasks to a robot team. The advantages of explicitly modeling complex tasks during the allocation process is demonstrated by a comparison of our approach with existing task allocation allocation algorithms in an area reconnaissance scenario. An implementation on a team of outdoor robots further validates our approach.