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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. 

Cyber-physical systems interact with the world through software controlling physical effectors. Carefully designed controllers, implemented as safety-critical control software, also interact with other parts of the software suite, and may be difficult to separate, verify, or maintain. Moreover, some software changes, not intended to impact control system performance, do change controller response through a variety of means including interaction with external libraries or unmodeled changes only existing in the cyber system (e.g., exception handling). As a result, identifying safety-critical control software, its boundaries with other embedded software in the system, and the way in which control software evolves could help developers isolate, test, and verify control implementation, and improve control software development. In this work we present an automated technique, based on a novel application of machine learning, to detect commits related to control software, its changes, and how the control software evolves. We leverage messages from developers (e.g., commit comments), and code changes themselves to understand how control software is refined, extended, and adapted over time. We examine three distinct, popular, real-world, safety-critical autopilots—ArduPilot, Paparazzi UAV, and LibrePilot to test our method demonstrating an effective detection rate of 0.95 for control-related code changes.