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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. 

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar to event-triggered sampling, but maintains the robustness of traditional fixed-periodic sampling forming a compelling alternative to traditional vehicle control design.