Reducing swarming theory to practice for UAV control

摘要

The introduction of increasingly sophisticated unmanned aerial vehicles (UAVs) into the U.S. force structure has shown that the cognitive operator burden increases with the technical complexity of the robot. It takes more humans to control advanced UAVs than simple ones, and this burden is bound to increase when there are more UAVs to control. One promising approach for reducing the ratio of operators to UAVs can be found in a robotic theory that mimics emerging systems found in nature - swarming. An examination of the state-of-the art in swarm intelligence applications in the academic, commercial, and military research community indicates that this approach to robotic control may be achieving the critical mass necessary to exit the laboratory, but, despite its promise, swarm robotic control software is technically far behind its target hardware. This paper explores opportunities for reducing robotic swarming theory to practice for UAVs by identifying candidate swarm algorithms, examples of their utility to military robotics, and the challenges associated with swarm programming. We conclude with a systematic, theory-driven approach to metrics for gaining an accurate picture of man/machine team performance.