Safety-Aware Perception for Autonomous Collision Avoidance in Dynamic Environments
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909144523472896 |
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| author | Bena, Ryan M. Zhao, Chongbo Nguyen, Quan |
| author_facet | Bena, Ryan M. Zhao, Chongbo Nguyen, Quan |
| contents | 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 $ψ_\text{d}$ 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 $ψ_\text{d}$ which maximizes the perception of risk within the FOV. We incorporate $ψ_\text{d}$ 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 $ψ_\text{d}$ to perceive and avoid two static obstacles with an average computation time of 371 $μ$s. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13929 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safety-Aware Perception for Autonomous Collision Avoidance in Dynamic Environments Bena, Ryan M. Zhao, Chongbo Nguyen, Quan Robotics Systems and Control 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 $ψ_\text{d}$ 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 $ψ_\text{d}$ which maximizes the perception of risk within the FOV. We incorporate $ψ_\text{d}$ 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 $ψ_\text{d}$ to perceive and avoid two static obstacles with an average computation time of 371 $μ$s. |
| title | Safety-Aware Perception for Autonomous Collision Avoidance in Dynamic Environments |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2403.13929 |