Safe Bipedal Path Planning via Control Barrier Functions for Polynomial Shape Obstacles Estimated Using Logistic Regression
Safe Bipedal Path Planning via Control Barrier Functions for Polynomial Shape Obstacles Estimated Using Logistic Regression
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DOI：
10.1109/icra48891.2023.10160671
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Chengyang Peng;Octavian A. Donca;Guillermo A. Castillo;Ayonga Hereid
中科院分区：
文献类型：
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作者：
Chengyang Peng;Octavian A. Donca;Guillermo A. Castillo;Ayonga Hereid
安全路径规划对于双足机器人在安全关键环境中运行至关重要。常见的路径规划算法（例如 RRT 或 RRT*）通常使用几何或运动学碰撞检查算法来确保通往目标位置的无碰撞路径。然而，此类方法可能会生成不符合步行机器人动力学约束的非平滑路径。事实证明，控制屏障函数 (CBF) 可以与 RRT/RRT* 集成，以合成动态可行的无碰撞路径。然而，由于构建适当的障碍函数来表示不规则形状的障碍物具有挑战性，现有的工作仅限于简单的圆形或椭圆形障碍物。在本文中，我们提出了一种基于 CBF 的 RRT* 算法，用于双足机器人，以生成穿过具有多个多项式形状障碍物的空间的无碰撞路径。特别是，我们使用逻辑回归从环境网格图构建多项式障碍函数来表示不规则形状的障碍物。此外，我们还开发了多步CBF转向控制器，以确保自由空间探索的效率。所提出的方法首先在差分驱动模型的仿真中得到验证，然后在具有随机放置障碍物的实验室环境中使用 3D 人形机器人 Digit 进行实验评估。
Safe path planning is critical for bipedal robots to operate in safety-critical environments. Common path planning algorithms, such as RRT or RRT*, typically use geometric or kinematic collision check algorithms to ensure collision-free paths toward the target position. However, such approaches may generate non-smooth paths that do not comply with the dynamics constraints of walking robots. It has been shown that the control barrier function (CBF) can be integrated with RRT/RRT*to synthesize dynamically feasible collision-free paths. Yet, existing work has been limited to simple circular or elliptical shape obstacles due to the challenging nature of constructing appropriate barrier functions to represent irregularly shaped obstacles. In this paper, we present a CBF-based RRT* algorithm for bipedal robots to generate a collision-free path through space with multiple polynomial-shaped obstacles. In particular, we used logistic regression to construct polynomial barrier functions from a grid map of the environment to represent irregularly shaped obstacles. Moreover, we developed a multi-step CBF steering controller to ensure the efficiency of free space exploration. The proposed approach was first validated in simulation for a differential drive model, and then experimentally evaluated with a 3D humanoid robot, Digit, in a lab setting with randomly placed obstacles.