Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
复制标题
利用多保真度模型实时规划未知环境中的敏捷飞行
DOI：
10.1109/icra.2019.8794248
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. How
中科院分区：
文献类型：
--
作者：
J. Tordesillas;B. Lopez;John Carter;J. Ware;J. How
通过未知环境的自主导航是一项具有挑战性的任务，需要实时定位，感知，规划和控制。具有这种能力的无人机已经开始出现在文献中，在轻型传感和计算方面取得了进展。虽然规划方法因平台而异，但许多算法采用分层规划架构，其中缓慢、低保真度的全局规划器指导快速、高保真度的局部规划器。然而，在未知的环境中，这种方法可能会导致不稳定或不稳定的行为，由于全球规划者之间的相互作用，其解决方案是不断变化的，和本地规划者;在全球计划中没有捕捉高阶动态的后果。这项工作提出了一个规划框架，其中使用多保真度模型，以减少本地和全球规划者之间的差异。我们的方法使用高，中，低保真度的模型组成的路径，捕捉高阶动态，同时保持计算易处理。此外，我们解决了一个快速的规划者和一个较慢的映射器之间的相互作用，通过考虑传感器数据尚未融合到地图在碰撞检查。这种用于敏捷飞行的新型映射和规划框架在模拟和硬件实验中得到了验证，显示在混乱的环境中重新规划时间为5-40 ms。
Autonomous navigation through unknown environments is a challenging task that entails real-time localization, perception, planning, and control. UAVs with this capability have begun to emerge in the literature with advances in lightweight sensing and computing. Although the planning methodologies vary from platform to platform, many algorithms adopt a hierarchical planning architecture where a slow, low-fidelity global planner guides a fast, high-fidelity local planner. However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan. This work proposes a planning framework in which multi-fidelity models are used to reduce the discrepancy between the local and global planner. Our approach uses high-, medium-, and low-fidelity models to compose a path that captures higher-order dynamics while remaining computationally tractable. In addition, we address the interaction between a fast planner and a slower mapper by considering the sensor data not yet fused into the map during the collision check. This novel mapping and planning framework for agile flights is validated in simulation and hardware experiments, showing replanning times of 5-40 ms in cluttered environments.