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 ψd which utilizes control barrier functions (CBFs). First, we generate a spatial density function Φ which leverages CBF constraints to map the collision risk of all local coordinates. Then, we convolve Φ with an attitude-dependent sensor FOV quality function to produce the objective function Γ which quantifies the total observed risk for a given pointing direction. Finally, by finding the global optimizer for Γ, we identify the value of ψd which maximizes the perception of risk within the FOV. We incorporate ψd 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 quadro- tor follows a dynamic flight path while simultaneously calculating and tracking ψd to perceive and avoid two static obstacles with an average computation time of 371 μs.