Safety-Aware Perception for Autonomous Collision Avoidance in Dynamic Environments

Abstract

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.

References in corpus (4)

• Control Barrier Function Based Quadratic Programs for Safety Critical Systems
• PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments
• An Active Sense and Avoid System for Flying Robots in Dynamic Environments
• Flatness-based Quadcopter Trajectory Planning and Tracking with Continuous-time Safety Guarantees

Cited by in corpus (2)

• Visibility-Aware RRT* for Safety-Critical Navigation of Perception-Limited Robots in Unknown Environments
• MPS: A New Method for Selecting the Stable Closed-Loop Equilibrium Attitude-Error Quaternion of a UAV During Flight