An Evaluation of Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

Small uncrewed aerial systems (sUAS) provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework, Controller Instability-preventing Configuration-Aware Drone Adaptation (CICADA). CICADA dynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype of CICADA using a popular open source sUAS flight control software and experimented with a large number of different configurations in simulation. We then performed a case study with physical drones to determine if our framework will work in practice. Experimental results show that CICADA ’s adaptations reduce controller instability and enable the sUAS to recover from up to 33.8% of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes. These safety-focused adaptations can mitigate unsafe behavior in 52.9% to 64.7% of dangerous configurations. We further show that rule-based approaches can be leveraged to automatically select an appropriate adaptation strategy based on the severity of instability encountered, with up to a 14.2% improvement over direct adaptation. Finally, we introduce a variation of our primary adaptation strategy designed to allow more cautious adaptation with limited configuration information, which gets within 6.7% of our primary adaptation strategy despite not requiring an optimal knowledge base.