通过方向感知的凸自由区域生成实现未知和杂乱环境中的安全导航
Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation
AI总结：
本文提出一种结合候选运动方向与机器人几何的凸自由区域生成框架，通过连续安全轨迹生成实现持续无碰撞运动，实验表明该方法在杂乱2D导航场景中生成更符合后续导航的自由区域，并在3D和真实世界中验证了其扩展性和实用性。
摘要：
凸自由区域为机器人在未知和杂乱环境中提供结构化且易于优化的碰撞免费空间表示。然而，现有方法通常仅根据周围障碍物几何扩大局部碰撞免费区域。在杂乱环境中，此类策略可能无法生成同时容纳机器人几何并保持候选运动方向上可通行扩展的区域，从而限制后续通行，尤其是在狭窄通道中。即使此类区域可用，安全运动生成仍具挑战性，因为离散化轨迹样本的安全检查不能保证当机器人几何被显式建模时连续碰撞免费运动。为了解决这些问题，我们提出了一种导航框架，将候选运动方向和机器人几何联合纳入凸自由区域生成，并通过连续安全轨迹生成实现连续碰撞免费运动。在每个区域内，该框架执行几何感知的目标姿态选择和轨迹生成，同时结合Lipschitz基的连续安全认证和局部细化。生成的自由区域和候选运动在基于区域的图中维护，以支持增量规划。在杂乱2D导航场景中的定量结果表明，所提方法生成的自由区域更符合后续通行，并实现了可靠的无碰撞导航，而额外的3D和真实世界实验在四足机器人和无人机上验证了该框架的扩展性和实用性。开源项目可在https://github.com/ZhichengSong6/FRGraph找到。
英文摘要：
Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered environments. However, existing methods typically enlarge local collision-free regions mainly according to surrounding obstacle geometry. In cluttered environments, such strategies may fail to generate regions that both accommodate robot geometry and preserve traversable extension along candidate motion directions, thereby limiting downstream traversal, especially in narrow passages. Even when such a region is available, safe motion generation remains challenging, because safety checking at discretized trajectory samples does not guarantee continuously collision-free motion when robot geometry is modeled explicitly. To address these issues, we propose a navigation framework that jointly incorporates candidate motion directions and robot geometry into convex free-region generation, and achieves continuously collision-free motion through continuous-safe trajectory generation. Within each region, the framework performs geometry-aware target pose selection and trajectory generation, together with Lipschitz-based continuous safety certification and local refinement. The resulting free regions and candidate motions are maintained in a region-based graph to support incremental planning. Quantitative results in cluttered 2D navigation scenarios show that the proposed method generates free regions better aligned with downstream traversal and enables reliable collision-free navigation, while additional 3D and real-world experiments on a quadrupedal robot and a UAV demonstrate the extensibility and practical applicability of the framework. The open-source project can be found at https://github.com/ZhichengSong6/FRGraph.
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。
