发表机构
TU Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DynoFluxBench基准框架，用于比较动态环境中的运动动力学规划器，并开发三个融合时空方法的规划器，其中ST-Db-RRT求解速度最高提升32倍。
AI 中文摘要
离开结构化、静态环境的机器人必须在移动障碍物中规划出运动动力学可行且安全的运动。然而，目前没有同时结合这两个方面的专门基准框架。为克服这一不足，我们提出了DynoFluxBench，一个用于在已知、动态且到达时间无界的环境中比较运动动力学规划器的框架。为展示其实用性并建立强基线，我们开发了三个专用规划器，分别命名为ST-Db-RRT、ST-GBRRT和KIST，它们融合了运动动力学和时空方法，覆盖了不同的运动动力学搜索范式：ST-Db-RRT使用轨迹优化，以随机选择的间断有界运动原语进行扩展，而KIST和ST-GBRRT则以不同的启发式引导维护一棵运动动力学可行的树。我们分析了这些新规划器在动态环境中的概率完备性保证。最后，我们使用DynoFluxBench评估了ST-Db-RRT、ST-GBRRT和KIST，结果表明ST-Db-RRT找到首个解的速度最高提升32倍，而KIST和ST-GBRRT在轨迹优化脆弱时仍具价值。视频和进一步分析可在该https URL找到。
英文摘要
Robots that leave structured, static environments must plan motions that are kinodynamically feasible and safe among moving obstacles. However, there are no dedicated benchmark frameworks that combine both aspects. To overcome this, we present DynoFluxBench, a framework to compare kinodynamic planners in known, dynamic environments with unbounded arrival time. To demonstrate its utility and establish strong baselines, we develop three dedicated planners, named ST-Db-RRT, ST-GBRRT, and KIST, that fuse kinodynamic and space-time methods, covering different kinodynamic search paradigms: ST-Db-RRT expands with randomly selected discontinuity-bounded motion primitives using trajectory optimization, whereas KIST and ST-GBRRT maintain a kinodynamically feasible tree with different heuristic guidance. We analyze the probabilistic completeness guarantees of those new planners in dynamic environments. Finally, we evaluate ST-Db-RRT, ST-GBRRT, and KIST using DynoFluxBench, showing that ST-Db-RRT reaches a first solution up to 32 times faster, while KIST and ST-GBRRT remain valuable where trajectory optimization is fragile. Videos and further analysis can be found at https://dynofluxbench.github.io/dynofluxbench/.
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