With the rapid development of affordable robots with embedded sensing and computation capabilities, we are quickly approaching a point at which real-life applications will involve the deployment of hundreds, if not thousands, of robots. Among these applications, significant research effort has been devoted to multi-agent search, where deploying numerous agents can greatly improve the time-efficiency and robustness of search. Motivated by such problems, this project considers the large-scale deployment of heterogeneous robots in time-critical scenarios, where search can be improved by combining the different motion and sensing capabilities of the agents.
To search a region as quickly as possible, a large number of heterogeneous robots could be deployed, e.g., aerial, ground, and amphibious robots. As is often the case, a central planner, such as a human strategist, might coordinate the search by grouping the robots into teams. By optimizing the number of teams and their capabilities, the strategist could efficiently cover a large area, while managing complexity to allow for rapid, online re-planning as more information is gathered. This work addresses the key question: How should the strategist form and coordinate teams of heterogeneous agents?