Future NASA lander missions to icy moons will require completely automated, accurate, and data efficient calibration methods for the robot manipulator arms that sample icy terrains in the lander's vicinity. To support this need, this paper presents a Gaussian Process (GP) approach to the classical manipulator kinematic calibration process. Instead of identifying a corrected set of Denavit-Hartenberg kinematic parameters, a set of GPs models the residual kinematic error of the arm over the workspace. More importantly, this modeling framework allows a Gaussian Process Upper Confident Bound (GP-UCB) algorithm to efficiently and adaptively select the calibration's measurement points so as to minimize the number of experiments, and therefore minimize the time needed for recalibration. The method is demonstrated in simulation on a simple 2-DOF arm, a 6 DOF arm whose geometry is a candidate for a future NASA mission, and a 7 DOF Barrett WAM arm.

翻译：未来美国国家航空航天局（NASA）对冰卫星的着陆任务将要求对采样附近冰原地形的机器人机械臂实现全自动化、高精度且数据高效的标定方法。为满足这一需求，本文提出了一种基于高斯过程（GP）的经典机械臂运动学标定方法。该方法并非辨识修正后的迪纳维特-哈滕贝格运动学参数，而是利用一组高斯过程对机械臂在工作空间内的残余运动学误差进行建模。更重要的是，该建模框架允许采用高斯过程上置信界算法高效且自适应地选择标定测量点，从而最小化实验次数，进而缩短重新标定所需的时间。该方法通过仿真在简单的2自由度机械臂、作为NASA未来任务候选几何构型的6自由度机械臂以及7自由度巴雷特WAM机械臂上进行了验证。