发表机构
Centre national de la recherche scientifique（法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出LiDARFlow算法，基于面板公式和机载LiDAR在线构建障碍物表示，生成避障场与引导场集成，实现未知环境中微型飞行器的实时无碰撞引导，经室内飞行实验验证其轻量高效。
AI 中文摘要
本文提出了一种仅使用机载传感器在未知、杂乱环境中运行的微型飞行器引导算法。该方法基于从空气动力学势流理论中推导出的面板公式，并根据局部感知到的障碍物生成平滑、无碰撞的引导向量。该方法通过从机载LiDAR测量中在线构建和更新障碍物表示，扩展到了未知环境。由此产生的避障场与名义引导向量场集成，以产生最终的控制输入。该系统在室内飞行测试中通过两种场景进行了实验验证：航路点导航和方向引导。在两种情况下，飞行器均成功完成任务，同时仅使用机载感知实时避开所有障碍物。结果表明，该方法计算量轻，适合机载实现，其中点云处理被确定为主要实际限制。这些结果支持在未知环境中进行轻量级机载引导的可行性。
英文摘要
This paper presents a guidance algorithm for micro aerial vehicles operating in unknown, cluttered environments using only onboard sensing. The method is based on a panel formulation originally derived from aerodynamic potential-flow theory and generates smooth, collision-free guidance vectors from locally perceived obstacles. The approach is extended to unknown environments by constructing and updating the obstacle representation online from onboard LiDAR measurements. The resulting obstacle-avoidance field is integrated with a nominal guiding vector field to produce the final control input. The system is experimentally validated in indoor flight tests under two scenarios: waypoint navigation and directional guidance. In both cases, the vehicle successfully completes its task while avoiding all obstacles in real time using only onboard perception. The results demonstrate that the method is computationally lightweight and suitable for onboard implementation, with pointcloud processing identified as the main practical limitation. These results support the feasibility of lightweight onboard guidance in unknown environments.
Journal refIMAV - International Micro Air Vehicle Conference and Competition, Sep 2026, Strasbourg, France
