论文标题
在机器人计划和控制中，用于可扩展代理碰撞检查的配置空间分解
Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control
论文作者
论文摘要
在复杂的高维环境中的实时机器人运动计划仍然是一个空旷的问题。运动计划算法及其基本碰撞检查器对任何机器人控制堆栈至关重要。碰撞检查占据了机器人运动计划中计算时间的很大一部分。现有的碰撞调查器在速度和准确性之间进行权衡，并缩放到高维，复杂的环境。我们提出了一种新型的空间分解方法，该方法使用k均值聚类在正向运动学空间中进行了聚类，以加速代理碰撞检查。我们使用这些分解的子空间Fastron（一种内核感知器算法）训练单个配置空间模型，从而产生了紧凑而高度准确的模型，可以快速查询并更好地扩展到更复杂的环境。我们证明了这种新方法，称为分解的快速感知器（D-fastron），在7-DOF Baxter机器人上，与最新的几何碰撞检查器相比，平均生产29倍的碰撞检查速度快29倍，最高9.8倍的运动计划。
Real-time robot motion planning in complex high-dimensional environments remains an open problem. Motion planning algorithms, and their underlying collision checkers, are crucial to any robot control stack. Collision checking takes up a large portion of the computational time in robot motion planning. Existing collision checkers make trade-offs between speed and accuracy and scale poorly to high-dimensional, complex environments. We present a novel space decomposition method using K-Means clustering in the Forward Kinematics space to accelerate proxy collision checking. We train individual configuration space models using Fastron, a kernel perceptron algorithm, on these decomposed subspaces, yielding compact yet highly accurate models that can be queried rapidly and scale better to more complex environments. We demonstrate this new method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter robot producing on average 29x faster collision checks and up to 9.8x faster motion planning compared to state-of-the-art geometric collision checkers.
