Recently, machine learning (ML) methods have been developed for increasing the accuracy of robot mechanisms. Complex mechanical issues such as non-linear friction, backlash, flexibility of structure transmission elements can cause these errors and they are hard to model. ML requires training data and the above mechanical phenomena are highly dependent on position of the robot in the workspace and also on its velocity, especially near zero velocity in both directions where non-linearities such as Streibek and Coulomb friction are most pronounced. It is well known that success of ML methods depends on amount of training data and it is expensive/time consuming to collect data from physical robot motion. We therefore address the problem of searching for trajectories in the 6D space of positions and velocities which collect the most information in the least amount of time. This reduces to a special case of the traveling-salesman problem in that the robot must be programmed to visit sampled points in the position-velocity phase space most efficiently. Two goals of this work are 1) Computationally study the difficulty of the TSP in this application by applying it to X, Y, Z motion in 3D space (6D phase space) and 2) assess the effectiveness of an extremely simple Nearest Neighbor search algorithm compared to random sampling of the search space. Results confirm that Nearest Neighbor heuristic searching produces significantly better trajectories than random sampling in this application.

翻译：近年来，机器学习方法已被开发用于提高机器人机构的精度。复杂的机械问题（如非线性摩擦、反向间隙、结构传动元件的柔韧性）可能导致这些误差，且难以建模。机器学习需要训练数据，而上述机械现象高度依赖于机器人在工作空间中的位置及其速度，尤其在接近双向零速度区域时，斯特里贝克摩擦和库仑摩擦等非线性效应最为显著。众所周知，机器学习方法的成功取决于训练数据量，而从物理机器人运动中收集数据成本高昂且耗时。因此，我们研究如何在位置与速度构成的六维空间中寻找能以最短时间获取最多信息的轨迹。这可归结为旅行商问题的一个特例：需要以最高效方式规划机器人访问位置-速度相空间中的采样点。本工作的两个目标是：1）通过将其应用于三维空间（六维相空间）中的X、Y、Z运动，计算研究该应用中旅行商问题的求解难度；2）评估极其简单的最近邻搜索算法相较于随机采样搜索空间的有效性。结果证实，在该应用中最近邻启发式搜索生成的轨迹显著优于随机采样方法。