UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU

Author:

Walter Bryan1,Sannier Adrian2,Reiners Dirk3,Oliver James3

Affiliation:

1. Virtual Reality Applications Center, Iowa State University, Ames, IA 50011

2. Virtual Reality Applications, Center Iowa State University, Ames, IA 50011

3. Virtual Reality Applications, Center Iowa State, University Ames, IA 50011

Abstract

Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention, and common swarm control methods require expensive computational power. This paper begins with a discussion on how to improve upon current user interfaces and then reviews a swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. Next, a more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the graphics processing unit (GPU) in today's graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU's pipeline [1]. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this paper presents a vision of how to combine these new interface ideas and performance enhancements into an effective swarm control interface and training simulator.

Publisher

SAGE Publications

Subject

Engineering (miscellaneous),Modelling and Simulation

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1. Autonomous Recognition of Collective Motion Behaviours in Robotic Swarms from Video using a Deep Neural Network;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

2. Swarm Metaverse for Multi-Level Autonomy Using Digital Twins;Sensors;2023-05-19

3. On Countermeasures against Cooperative Fly of UAV Swarms;Drones;2023-03-02

4. From animal collective behaviors to swarm robotic cooperation;National Science Review;2023-02-16

5. Stigmergic, Diegetic Guidance of Swarm Construction;2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C);2021-09

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