Dynamic heterogeneous team formation for robotic urban search and rescue.

Much work on coalition formation and maintenance exists from the standpoint of abstract agents. This has not yet translated well to robot teams, however: most multi-robot research has focused on pre-formed teams, with little attention to team formation and maintenance. This means solutions fail in challenging environments where equipment is easily lost, such as urban search and rescue. This paper describes a framework for coordinating a changing collection of heterogeneous robots in complex and dynamic environments such as disaster zones. The framework allows a team to reshape to compensate for lost or failed robots, including adding newly-encountered robots or additions from other teams, and also allows new teams to be formed dynamically. The framework also includes provisions for task discovery and assignment, under the conditions of changing team membership. We evaluate this framework through an implementation where robots perform exploration in order to locate victims in a simulated disaster environment.