  Autonomous collision avoidance requires accurate environmental perception;
however, flight systems often possess limited sensing capabilities with
field-of-view (FOV) restrictions. To navigate this challenge, we present a
safety-aware approach for online determination of the optimal sensor-pointing
direction $\psi_\text{d}$ which utilizes control barrier functions (CBFs).
First, we generate a spatial density function $\Phi$ which leverages CBF
constraints to map the collision risk of all local coordinates. Then, we
convolve $\Phi$ with an attitude-dependent sensor FOV quality function to
produce the objective function $\Gamma$ which quantifies the total observed
risk for a given pointing direction. Finally, by finding the global optimizer
for $\Gamma$, we identify the value of $\psi_\text{d}$ which maximizes the
perception of risk within the FOV. We incorporate $\psi_\text{d}$ into a
safety-critical flight architecture and conduct a numerical analysis using
multiple simulated mission profiles. Our algorithm achieves a success rate of
$88-96\%$, constituting a $16-29\%$ improvement compared to the best heuristic
methods. We demonstrate the functionality of our approach via a flight
demonstration using the Crazyflie 2.1 micro-quadrotor. Without a priori
obstacle knowledge, the quadrotor follows a dynamic flight path while
simultaneously calculating and tracking $\psi_\text{d}$ to perceive and avoid
two static obstacles with an average computation time of 371 $\mu$s.
