GPF-BG: A Hierarchical Vision-Based Planning Framework for Safe Quadrupedal Navigation
GPF-BG: A Hierarchical Vision-Based Planning Framework for Safe Quadrupedal Navigation
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DOI：
10.1109/icra48891.2023.10160804
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Shiyu Feng;Ziyi Zhou;Justin S. Smith;M. Asselmeier;Ye Zhao;P. Vela
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文献类型：
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作者：
Shiyu Feng;Ziyi Zhou;Justin S. Smith;M. Asselmeier;Ye Zhao;P. Vela
通过未知环境的安全四足导航是一个具有挑战性的问题。本文提出了一个层次化的基于视觉的规划框架（GPF-BG）集成我们以前的全球路径跟踪（GPF）导航系统和基于间隙的局部规划器使用贝塞尔曲线，所谓的$B$ézier间隙（BG）。这种基于BG的轨迹合成可以生成光滑的轨迹，并保证点质量机器人的安全性。通过基于非点、矩形几何形状的间隙分析扩展，可以保证理想化四足运动模型的安全性，并显着提高实际四足机器人模型的安全性。稳定的感知空间提高了在影响感知的振荡内部身体运动下的性能。通过不同基准配置下的仿真和真实的实验，测试安全航行性能。GPF-BG在所有实验中具有最佳的安全性结果。
Safe quadrupedal navigation through unknown environments is a challenging problem. This paper proposes a hierarchical vision-based planning framework (GPF-BG) integrating our previous Global Path Follower (GPF) navigation system and a gap-based local planner using Bézier curves, so called $B$ézier Gap (BG). This BG-based trajectory synthesis can generate smooth trajectories and guarantee safety for point-mass robots. With a gap analysis extension based on non-point, rectangular geometry, safety is guaranteed for an idealized quadrupedal motion model and significantly improved for an actual quadrupedal robot model. Stabilized perception space improves performance under oscillatory internal body motions that impact sensing. Simulation-based and real experiments under different benchmarking configurations test safe navigation performance. GPF-BG has the best safety outcomes across all experiments.