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Unmanned vehicles have continued to become commonplace in modern society, with the recent adoption of small unmanned aerial systems (sUAS) in the commercial, entertainment and defense industries. Despite encouraging trends in sUAS and unmanned systems (UxS) development, these technologies deployed in the field are still, in large part, limited to teleoperation and/or semi-autonomous behaviors of a single agent. The United States Department of Defense (DoD) is interested in elevating this current state of technology, specifically of aerial swarms for intelligence, surveillance, and reconnaissance (ISR) missions. While methods exist for optimal control of multi-agent systems, there remain novel research gaps related to robust field performance. The Robotics Research Center (RRC) at the United States Military Academy (USMA) is working to develop a collaborative aerial swarming architecture (CASA) that enables decentralized command and control (C2) between unmanned aerial systems (UAS). The main factors that support CASA's decentralized capabilities are found in the dynamic allocation of tasks and the organization of data among sUAS platforms. This paper outlines CASA and its current capabilities. Task Allocation results are presented showing real-time task updates and allocation to a UAS swarm in a simulated environment.
Beason et al. (Fri,) studied this question.
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