The use of unmanned vehicles in Reconnaissance, Surveillance, and Target Acquisition (RSTA) applications has
received considerable attention recently. Cooperating land and air vehicles can support multiple sensor modalities
providing pervasive and ubiquitous broad area sensor coverage. However coordination of multiple air and land vehicles
serving different mission objectives in a dynamic and complex environment is a challenging problem. Swarm
intelligence algorithms, inspired by the mechanisms used in natural systems to coordinate the activities of many entities
provide a promising alternative to traditional command and control approaches. This paper describes recent advances in
a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of
swarming unmanned systems. The results of a recent demonstration at NASA's Wallops Island of multiple Aerosonde
Unmanned Air Vehicles (UAVs) and Pioneer Unmanned Ground Vehicles (UGVs) cooperating in a coordinated RSTA
application are discussed. The vehicles were autonomously controlled by the onboard digital pheromone responding to
the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm
self-organized to perform total area surveillance, automatic target detection, sensor cueing, and automatic target
recognition with no central processing or control and minimal operator input. Complete autonomy adds several safety
and fault tolerance requirements which were integrated into the basic pheromone framework. The adaptive algorithms
demonstrated the ability to handle some unplanned hardware failures during the demonstration without any human
intervention. The paper describes lessons learned and the next steps for this promising technology.
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