| Missions that involve tasks such as search-and-rescue or reconnaissance have traditionally involved humans, perhaps with the assistance of one or more robots. The robots, or unmanned systems, are typically teleoperated---operated by remote control to inspect a suspicious object, for example. With the advent of newer and less expensive forms of autonomy and improved human-robot communication, the robots are becoming more capable of acting as peers to their human counterparts, rather than just tools. As the diversity of mixed human-robot teams is increased, so is the complexity of trying to answer questions regarding configuration: what robots should be used, how many, and how many humans should be employed in the teaming process?;This paper presents the research in search of that hybrid approach. Our solution is a decision support approach that employs a hybrid of simulation and artificial intelligence techniques, using a progressive-refinement queuing model to quickly bypass the least desirable configurations. This approach, realized in a software tool, considers the mission requirements and a priori data to determine the optimal team to perform that mission, given a pool of human and robotic resources. |
