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 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 lightweight 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.
翻译:复杂高维环境中的实时机器人运动规划仍然是一个尚未解决的问题。 运动规划算法及其基本碰撞校验器对于任何机器人控制堆积都至关重要。 碰撞检查占用了机器人运动规划中大部分计算时间。 现有的碰撞检查器在速度和精确度与规模之间作出权衡,对高维、复杂环境来说是差的。 我们展示了一种新型的空间分解方法,在前视空间使用K-Means集群加速代理碰撞检查。 我们在这些分解的子空间上用Fastron(内核过敏算法)来培训个人配置空间模型, 使用这些分解的子空间模型, 产生轻量但高度精确的模型, 能够快速被查询, 并更大规模地扩大到更复杂的环境。 我们展示了这种新方法,叫做解剖快速 Perpheptron(D-Fatron), 使用7DOFBest机器人, 平均产生29x更快的碰撞检查, 和9.8x速度的动作规划, 与最先进的几何碰撞检查器相比, 我们展示了这种新方法。