Robot elastostatic calibration facilitates high-accuracy positioning with high payload. Stiffness identification is an important step in this. Poses and forces/moments chosen for stiffness identification determine compensation quality because they influence the propagation of errors impacting stiffness identification to compensation errors. For predefined applications, poses and forces/moments for stiffness identification that maximize positioning accuracy must be selected. Also, two error sources influence stiffness identification, namely, deflection measurement uncertainty and errors in forces/moments applied. Both these error sources’ impact on compensation quality must be minimized. This paper introduces a framework to choose poses and forces/moments for stiffness identification which minimizes above mentioned error sources’ impact on compensation quality. It also maximizes accuracy after compensation at any pose(s), along any axe(s) and with any load(s) that the specified application demands. This framework is applicable for non-over-constrained robots in which considering compliance only along active joints is sufficient, like for most serial-robots and hexapods. Its efficacy was validated using simulated and experimental elastostatic calibrations of a bipod and a high-precision positioning hexapod, respectively. Using this framework to optimize robot geometric calibration is also discussed.

机器人的弹性定标有助于实现高载荷下的高精度定位。刚度识别是其中的重要一步。选择用于刚度识别的姿势和力/力矩决定补偿质量，因为它们会影响误差的传播，从而影响刚度识别到补偿误差。对于预定义的应用，必须选择姿势和力/力矩来识别刚度，以最大程度地提高定位精度。同样，有两个误差源会影响刚度识别，即挠度测量不确定度和施加的力/力矩误差。这两个误差源对补偿质量的影响必须最小化。本文介绍了一种选择姿势和力/力矩进行刚度识别的框架，该框架可以最大程度地减少上述误差源对补偿质量的影响。它还可以在补偿后在任何姿势，任何轴上以及指定应用所需的任何负载下使精度最大化。此框架适用于非过度约束的机器人，其中仅考虑沿活动关节的顺应性就足够了，就像大多数串行机器人和六足动物一样。分别使用两脚架和高精度定位六脚架的模拟和实验弹力校准验证了其功效。还讨论了使用该框架优化机器人几何校准。以及指定应用程序需要的任何轴和负载。此框架适用于非过度约束的机器人，其中仅考虑沿活动关节的顺应性就足够了，就像大多数串行机器人和六足动物一样。分别使用两脚架和高精度定位六脚架的模拟和实验弹力校准验证了其功效。还讨论了使用该框架优化机器人几何校准。以及指定应用程序需要的任何轴和负载。此框架适用于非过度约束的机器人，其中仅考虑沿活动关节的顺应性就足够了，就像大多数串行机器人和六足动物一样。分别使用两脚架和高精度定位六脚架的模拟和实验弹力校准验证了其功效。还讨论了使用该框架优化机器人几何校准。