用机载深度相机实现无人机在复杂环境中的快速安全导航。
Rapid Quadrotor Navigation in Diverse Environments using an Onboard Depth Camera
- 基于历史深度观测的前向弧运动规划，实时避障。
- 相比现有方法，复杂环境中成功率达24%提升。
- 适合搜救等需高机动与高安全性的场景。
搜索与救援环境具有复杂的三维结构（如狭小空间、瓦砾和倒塌物），要求飞行机器人具备敏捷性和机动性。由于系统受尺寸、重量和功耗（SWaP）限制，快速导航对最大化覆盖范围至关重要。机载自主系统必须具备鲁棒性以避免碰撞，否则可能危及救援人员与受困者。以往研究虽提出了高速导航方案，但较少关注搜救应用中的安全性，且未在多样化环境中验证。本文通过使用前向弧运动基元的反应式规划器，利用历史RGB-D观测，在靠近障碍物时实现安全机动。每轮规划中均预设安全停止动作，若下一轮无法生成可行路径则执行该动作。方法在数千次仿真中评估，并部署于洞穴、森林等多样环境。结果表明，相比最先进方法，成功率提升24%。
原文摘要 · Abstract (English)
Search and rescue environments exhibit challenging 3D geometry (e.g., confined spaces, rubble, and breakdown), which necessitates agile and maneuverable aerial robotic systems. Because these systems are size, weight, and power (SWaP) constrained, rapid navigation is essential for maximizing environment coverage. Onboard autonomy must be robust to prevent collisions, which may endanger rescuers and victims. Prior works have developed high-speed navigation solutions for autonomous aerial systems, but few have considered safety for search and rescue applications. These works have also not demonstrated their approaches in diverse environments. We bridge this gap in the state of the art by developing a reactive planner using forward-arc motion primitives, which leverages a history of RGB-D observations to safely maneuver in close proximity to obstacles. At every planning round, a safe stopping action is scheduled, which is executed if no feasible motion plan is found at the next planning round. The approach is evaluated in thousands of simulations and deployed in diverse environments, including caves and forests. The results demonstrate a 24% increase in success rate compared to state-of-the-art approaches.
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