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(RA-L 23)动态环境中自主避碰的安全感知
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https://www.youtube.com/watch?v=UwwFhpAp13M&t=1s 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_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_d which maximizes the perception of risk within the FOV. We incorporate psi_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_d to perceive and avoid two static obstacles with an average computation time of 371 us. https://ieeexplore.ieee.org/abstract/document/10272635
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