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.

翻译：自主避碰需要精准的环境感知，然而飞行系统常因视场角限制而具备有限的感知能力。为应对这一挑战，我们提出一种基于安全感知的在线最优传感器指向角$\psi_\text{d}$确定方法，该方法利用控制障碍函数。首先，我们构建空间密度函数$\Phi$，通过CBF约束映射所有局部坐标的碰撞风险；随后，将$\Phi$与姿态相关的传感器FOV质量函数进行卷积，生成目标函数$\Gamma$以量化给定指向角下的总观测风险；最终，通过寻找$\Gamma$的全局最优解，确定使FOV内风险感知最大化的$\psi_\text{d}$值。我们将$\psi_\text{d}$集成至安全关键飞行架构中，并基于多种仿真任务剖面进行数值分析。所提算法成功率可达88%-96%，较最优启发式方法提升16%-29%。我们采用Crazyflie 2.1微型四旋翼飞行器进行实物飞行验证，在无先验障碍物信息条件下，四旋翼沿动态飞行路径同步计算与跟踪$\psi_\text{d}$，以371$\mu$s的平均计算时间成功感知并规避两个静态障碍物。