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Deploying decentralized control strategies for outdoor multi-agent Uncrewed Aircraft Systems (UASs) is challenging due to timing variations, packet loss, and computing resource limitations. In this work we address robustness to these conditions through a novel co-regulated control strategy that varies the periodicity of control inputs and communication with other agents. Co-regulation is applied to a decentralized hierarchical controller consisting of a global component governing inter-group coordination to multiple targets while a local component governs intra-group coordination of the agents as they progress to the target of interest. The control gains are “gain scheduled” according to current conditions while a cyber controller schedules the control and communication tasks for execution based on swarm performance. The control gains are found via reinforcement learning and the entire algorithm is deployed on a swarm consisting of 7 custom agents. Our results show the impact of rethinking swarming algorithms with computation and communication resource limitations in mind and indicate we can provide exceptional swarm control utilizing fewer resources while also improving the quality of service for an onboard, anytime collision avoidance algorithm. 

Uncrewed Aircraft Systems (UAS) are pivotal in numerous fields, requiring dependable software architectures that reinforce functionality and e!ciency. However, e"ective in-flight monitoring of these agents is often limited to verifying hardware performance and may lack monitors for more complex software systems. The problem is seen in small UAS multi-agent systems and swarms where bandwidth is minimal and computational resources are highly constrained. Here we introduce the development, processes, and evaluation of a Health Management and Control tool tailored for monitoring the health and operational status of essential UAS software architecture components. This tool facilitates system debugging and enhances operational e!ciency through diagnostics and recovery-focused health management. 

Unmanned Aircraft Vehicle (UAV) state estimation and navigation in GPS-denied environments has received a great deal of attention, with several researchers exploring a variety of compensating estimation methods. These methods vary in capability, and usually trade off estimation accuracy for simplicity and fewer resource requirements. More advanced estimation schemes, while capable of providing good state estimates for longer periods of time, may not be suitable for small, limited resource vehicles such as UAVs. Simpler and less-accurate estimation methods, while less capable, are useful for introducing the topic to students as well as helping researchers establish flight capabilities, and may be more suitable on limited hardware. The Autonomous Vehicle Laboratory’s (AVL) REEF Estimator was designed to expedite the development of a group’s GPS-denied flight capabilities through its simple and modular design. This work seeks to extend the application of the REEF Estimator by adapting it to fit the Ardupilot flight stack so that the estimator may be used on a readily available and NDAA-compliant flight controller, specifically, a Pixhawk Cube Blue. In addition, the REEF Estimator has been containerized to further facilitate its deployment between different vehicle architectures with minimal need for reconfiguration or setup.