PUTN: A Plane-fitting based Uneven Terrain Navigation Framework
PUTN: A Plane-fitting based Uneven Terrain Navigation Framework
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DOI：
10.1109/iros47612.2022.9981038
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Zhu Jian;Zih-Rong Lu;Xiao Zhou;Bin Lan;Anxing Xiao;Xueqian Wang;Bin Liang
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文献类型：
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作者：
Zhu Jian;Zih-Rong Lu;Xiao Zhou;Bin Lan;Anxing Xiao;Xueqian Wang;Bin Liang
地面机器人的自主导航已被广泛用于室内结构化的2D环境，但在户外3D的非结构化环境中，尤其是在粗糙的，不均匀的地形中，提出了一个基于平面的不稳定的地形导航框架（PUTN），以解决这一问题。提出了基于样本的算法，称为平面拟合RRT*（PF-RRT*）以获得稀疏的轨迹，每个采样点对应于自定义的遍历性索引，并在点云上连接到串联的较高轨迹。作为训练集。最后，使用非线性模型预测控制（NMPC）进行本地计划，通过将遍历索引和成本函数的不确定性添加到实时点云中产生的障碍，安全的运动计划算法具有平稳的速度和强大的范围，以验证有效性。 https://github.com/jianzhuozhuthu/putn ..
Autonomous navigation of ground robots has been widely used in indoor structured 2D environments, but there are still many challenges in outdoor 3D unstructured environments, especially in rough, uneven terrains. This paper proposed a plane-fitting based uneven terrain navigation framework (PUTN) to solve this problem. The implementation of PUTN is divided into three steps. First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based algorithm called Plane Fitting RRT*(PF- RRT*) is proposed to obtain a sparse trajectory. Each sampling point corresponds to a custom traversability index and a fitted plane on the point cloud. These planes are connected in series to form a traversable “strip”. Second, Gaussian Process Regression is used to generate traversability of the dense trajectory interpolated from the sparse trajectory, and the sampling tree is used as the training set. Finally, local planning is performed using nonlinear model predictive control (NMPC). By adding the traversability index and uncertainty to the cost function, and adding obstacles generated by the real-time point cloud to the constraint function, a safe motion planning algorithm with smooth speed and strong robustness is available. Experiments in real scenarios are conducted to verify the effectiveness of the method. The source code is released for the reference of the community11Source code: https://github.com/jianzhuozhuTHU/putn..