Waverider：利用分层多分辨率地图实现高效且反应式避障
Waverider: Leveraging Hierarchical, Multi-Resolution Maps for Efficient and Reactive Obstacle Avoidance
AI总结：
本文提出Waverider系统，结合wavemap分层地图与基于RMP的多分辨率避障算法，实现高效、低延迟（36ms）、大感知范围（30m）的避障，并在微型飞行器上验证，性能优于固定分辨率RMP和CHOMP。
摘要：
快速且可靠的避障是移动机器人的一项重要任务。在这项工作中，我们提出了一种高效的反应式系统，该系统在数百赫兹的频率下运行，以极少的资源消耗提供高质量的避障。我们的方法将wavemap（一种分层体积地图表示）与通过黎曼运动策略（RMP）制定的新颖的分层且可并行化的避障算法相结合。利用多分辨率避障策略，所提出的导航系统实现了精确、低延迟（36毫秒）且极其高效的避障，并具有非常大的感知半径（30米）。我们在室内和室外地图上进行了广泛的统计评估，验证了所提出的系统与固定分辨率RMP变体和CHOMP相比具有优势。最后，RMP公式允许将避障与额外目标（如目标寻求）无缝融合，以获得一个多功能且稳健的完整导航系统。我们在微型飞行器上部署了该系统，并展示了它如何穿越室内障碍赛道。我们的完整实现称为waverider，已作为开源提供。
英文摘要：
Fast and reliable obstacle avoidance is an important task for mobile robots. In this work, we propose an efficient reactive system that provides high-quality obstacle avoidance while running at hundreds of hertz with minimal resource usage. Our approach combines wavemap, a hierarchical volumetric map representation, with a novel hierarchical and parallelizable obstacle avoidance algorithm formulated through Riemannian Motion Policies (RMP). Leveraging multi-resolution obstacle avoidance policies, the proposed navigation system facilitates precise, low-latency (36ms), and extremely efficient obstacle avoidance with a very large perceptive radius (30m). We perform extensive statistical evaluations on indoor and outdoor maps, verifying that the proposed system compares favorably to fixed-resolution RMP variants and CHOMP. Finally, the RMP formulation allows the seamless fusion of obstacle avoidance with additional objectives, such as goal-seeking, to obtain a fully-fledged navigation system that is versatile and robust. We deploy the system on a Micro Aerial Vehicle and show how it navigates through an indoor obstacle course. Our complete implementation, called waverider, is made available as open source.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
