用于改进代理冲突检测的正运动学核
Forward Kinematics Kernel for Improved Proxy Collision Checking
摘要：
核函数可以在机器人技术中用于比较机器人的不同姿态，诸如在碰撞检查、逆运动学和运动规划中。这些比较通常基于联合空间测量提供距离度量，并且每秒执行数百或数千次，连续地用于变化的环境。很少有例子存在于创建新的内核，尽管它们在机器人控制和规划的计算性能和鲁棒性的显着影响。我们引入了一个新的核函数的基础上的正运动学（FK）比较机器人操作器的配置。我们将新的FK内核集成到我们的代理碰撞检查器Fastron中，Fastron以前在碰撞检查和运动规划方面表现出显着的速度改进。与以前使用的径向基函数内核相比，使用新的FK内核，我们实现了代理碰撞检查速度的两倍加速，内存减少了8倍，并将7自由度机器人手臂的分类准确率从75%提高到95%以上。与最先进的几何碰撞检查器相比，使用FK内核，碰撞检查现在快了9倍。为了展示该方法的广泛性，我们将Fastron FK应用于OMPL中的各种运动规划器，一致显示出更快的机器人规划。
英文摘要：
Kernel functions may be used in robotics for comparing different poses of a robot, such as in collision checking, inverse kinematics, and motion planning. These comparisons provide distance metrics often based on joint space measurements and are performed hundreds or thousands of times a second, continuously for changing environments. Few examples exist in creating new kernels, despite their significant effect on computational performance and robustness in robot control and planning. We introduce a new kernel function based on forward kinematics (FK) to compare robot manipulator configurations. We integrate our new FK kernel into our proxy collision checker, Fastron, that previously showed significant speed improvements to collision checking and motion planning. With the new FK kernel, we realize a two-fold speedup in proxy collision check speed, 8 times less memory, and a boost in classification accuracy from 75% to over 95% for a 7 degrees-of-freedom robot arm compared to the previously-used radial basis function kernel. Compared to state-of-the-art geometric collision checkers, with the FK kernel, collision checks are now 9 times faster. To show the broadness of the approach, we apply Fastron FK in OMPL across a wide variety of motion planners, showing unanimously faster robot planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
