A DDDAS test-bed was developed utilizing web-services middleware to communicate between a “real-world” UAV swarms and agent-based simulations. Six Parrot AR.Drones 2.0 quadrocopters were demo’ed as a “real world” UAV swarm communicating over the test-bed to a ground-based command & control application. Experiments were run testing the effects of intra-swarm communication protocols on target discovery.
A second investigation examined dynamic mission scheduling for swarms by incorporating DDDAS into a global-local hybrid-planning scheme. A global agent utilized simulation to determine optimal task assignment, while agents locally determine the execution order for assigned tasks.
A third project explored DDDAS in the swarm engineering context. Building upon previous work in swarm robotics, a new agent behavior for swarm search was added to introduce optimal swarm formation assignment. An agent-based simulation was developed for incorporation into a DDDAS framework for dynamic formation assignment.
A fourth investigation applied the DDDAS paradigm to two swarm command and control scenarios. In both scenarios, UAV swarms were augmented with mission-specific sensors, providing real-time measurements to a ground operator. Based on real-time measurements, the operator could adjust a single, global swarm parameter to achieve mission objectives. Together, these four projects further the DDDAS paradigm by incorporating simulations into real-time command and control of UAV swarms.
A fifth ongoing investigation is quantifying swarm performance with agent-based modeling. The modeling is demonstrating the utility of an explanatory model in the DDDAS framework, and provided an interesting juxtaposition of a bottom-up model analyzed by a top-down clustering algorithm, where both calculated results based on the distance of agent neighbors.
A sixth ongoing investigation is extending the test-bed to incorporate enhanced UAV and remote control technology to test more realistic UAV swarms. This activity is funded in part by an AFSOR DURIP.