One of the primary challenges facing the modern small-unit tactical team is the ability of the unit to safely and
effectively search, explore, clear and hold urbanized terrain that includes buildings, streets, and subterranean
dwellings. Buildings provide cover and concealment to an enemy and restrict the movement of forces while
diminishing their ability to engage the adversary. The use of robots has significant potential to reduce the risk to
tactical teams and dramatically force multiply the small unit's footprint. Despite advances in robotic mobility, sensing
capabilities, and human-robot interaction, the use of robots in room clearing operations remains nascent.
CHAMP is a software system in development that integrates with a team of robotic platforms to enable them to
coordinate with a human operator performing a search and pursuit task. In this way, the human operator can either give
control to the robots to search autonomously, or can retain control and direct the robots where needed. CHAMP's
autonomy is built upon a combination of adversarial pursuit algorithms and dynamic function allocation strategies that
maximize the team's resources. Multi-modal interaction with CHAMP is achieved using novel gesture-recognition
based capabilities to reduce the need for heads-down tele-operation. The Champ Coordination Algorithm addresses
dynamic and limited team sizes, generates a novel map of the area, and takes into account mission goals, user
preferences and team roles. In this paper we show results from preliminary simulated experiments and find that the
CHAMP system performs faster than traditional search and pursuit algorithms.