Military planners envision a future of unmanned vehicle swarms that can self-organize to provide superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying the effectiveness of the manned forces. Deploying swarms requires new tactics to take advantage of this capability. Working with military experts we developed a suite of swarm tactics for unmanned air and ground vehicles supporting a full Company with a mission to secure an objective in an urban area. The air vehicles create and maintain perimeter security around the objective. They map the area and maintain tracks on all vehicles and people in the perimeter. They also provide persistent, stealthy communication relay support to all the ground forces. The ground vehicles surveil the key intersections, provide scout and rear security to the squads and scout out alternative routes through the city. The unmanned vehicles execute decoy operations and their behaviors are designed to mask the actual task they are performing through seemingly random movement and regular swapping of tasks. These tactics were implemented in software and evaluated in a 3D model of an urban area taken from a city in the Midwest and implemented in Unity. This paper describes the tactics, algorithms, and experimental setup and reports the results.
Advances in unmanned systems enable smaller, less expensive platforms that can be deployed in high numbers or swarms providing superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying their effectiveness. But swarming system remain a laboratory curiosity with only a few live demonstrations of limited scope. Swarms are complex and their behavior is difficult to predict requiring skilled engineers and hand-tuning for each mission. Based on 20 years of experience designing swarms for military missions, the Design of Self-Organizing Adaptive Robotic Swarms (DSOARS) is an engineering environment which addresses the three core challenges of swarm design: (1) decomposing mission tasks into the behaviors of the swarm entities, (2) configuring the size of the swarm to a specific mission, and (3) verifying that the resulting swarm behavior consistently achieves the mission goals with a high level of confidence. DSOARS addresses these challenges through two primary innovations: (1) a means to create verified swarm design patterns that decompose high level mission tasks into individual behaviors and (2) a constructive test environment that simultaneously optimizes and characterizes the swarm performance against a range of possible mission conditions. Users with no swarm expertise can specify the requirements and constraints of their mission and DSOARS will configure a swarm that can meet those objectives with performance guarantees. This paper describes the approach and reports experimental results building and configuring a suite of swarm tactics for an urban mission.
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.