In this talk, we examine the problem of maintaining desirable safety characteristics in an operational environment under uncertain conditions. While operating in an uncertain environment, agents are faced with an uncertain model of themselves and of other agents. Enforcing operational safety parameters, such as minimum separation, desired speed, or spatial constraints, is difficult. We use an autonomous quadcopter operating in an unsafe and uncertain environment as a case study for developing solutions to this problem. We first derive an Adaptive Higher-Order Control Barrier Function (A-HOCBF) from quadrotor dynamics to enforce minimum separation under external disturbances, assuming no parametric uncertainty. This exercise highlights the impact of external disturbances on vehicle safety and establishes a baseline for performance comparisons. We then extend this derivation to adapt to parametric uncertainty resulting from (for example) faulty sensors or physical nonlinearities, using Adaptive Model-Free Control (AMFC) and other adaptive techniques. After establishing this robust-to-uncertainty control method, we explore ways to incorporate desirable airspace safety measures using Control Lyapunov Functions (CLFs). We examine how such safety measures can be made robust to uncertainty, to assure both safe flight (through A-HOCBF satisfaction) and coordinated flight (through CLF optimization). Finally, we discuss how approaches used in atmospheric flight uncertainty management and adaptation can be extended to in-space autonomy.