Worldwide, there has been growing interest in the use of autonomous vehicles to execute cooperative missions of increasing complexity without constant supervision of human operators. In aerospace, for instance, unmanned systems have become ubiquitous in both military and civilian applications. Today, for example, unmanned air vehicles must execute military reconnaissance and strike operations, border patrol missions, forest fire detection, police surveillance, and recovery operations. Similarly, in the marine environment, autonomous marine vehicles are deployed to find mines in coastal waters, inspect underwater structures, help in the construction of marine habitat mappings in regions that are inaccessible to humans, and study the causes behind the disappearance of coral reefs, to name but a few.

In all these mission scenarios, the use of a cooperative group of vehicles supported by an inter-vehicle communications network (rather than a single heavily equipped vehicle) provides robustness to system failures, increases system overall reliability, and improves mission efficiency, especially when operating in large spatial domains. However, despite significant progress in the field of cooperative control, several challenges still need to be addressed to develop strategies capable of yielding robust performance of a fleet of vehicles in the presence of complex vehicle dynamics, communications constraints, and partial vehicle failures.

At the ACRL, in collaboration with the Naval Postgraduate School (Monterey, CA) and the Instituto Superior T├ęcnico (Lisbon, Portugal), we develop, implement, and test robust decentralized strategies for cooperative motion control of multiple vehicles that must meet stringent spatial and temporal constraints. In this work, tools from real-time optimization, Lyapunov-based stability analysis, robust control, graph theory, and logic-based communications are brought together for the development of decentralized robust cooperative control algorithms for time-critical cooperative path following, flocking, and swarming.