Safety-Aware Perception for Autonomous Collision Avoidance in Dynamic Environments

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
$\text{88}-\text{96}\%$
, constituting a
$\text{16}-\text{29}\%$
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.