Autonomous Robot Control via Autonomy Levels

In the future, unmanned systems will gain decision-making intelligence that enables them to autonomously operate in clusters to perform collaborative tasks. For successful field deployment of unmanned systems, operators will need confidence that autonomous decision making leads to optimal behaviors in uncertain environments. Adjustable autonomy technologies, concepts, and simulation environments evaluating teaming behaviors will enable researchers to develop these systems. Network and sensing advances have created the opportunity for increased mission performance, but at the expense of greater complexity in sensor coordination and analysis. Current unmanned systems are typically teleoperated and are labor intensive, relying on human operators and their decision-making capabilities to perform mission tasks. Today, mission and sensing complexity that are managed through increased automation of lower-level functions (e.g., mechanical-system controls) help operators focus on higher-level decisions. The lower-order decisionmaking algorithms under development include those for waypoint following and collision detection and avoidance. Some of these algorithms have been incorporated in operational platforms.