Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are non-identifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call Lumped Error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the Lumped Error and show the efficiency of this parameter reduction.
翻译:机器人操纵器在任何时间都通过视觉反馈来控制,机器人和相机框架之间的转换必须为人所知。 但是,如果相机只能捕捉机器人操纵器的一部分,以便更好地了解正在互动的环境,则对基到相机转换的校准错误有更大的敏感性。 机器人控制过程中的第二个不确定性来源是联合角度测量中的不准确性,这种不准确性可能是定位偏差和复合传输效应(如反斜和电缆拉伸)造成的。 在这项工作中,当感官链在相机视图中部分可见时,我们将这两组未知参数汇集到一个统一的问题配方中。 我们证明这些参数是无法辨别的,意味着对它们进行明确的估计是不可行的。 为了克服这一点,我们得出了一套我们称之为Lumped错误的更小的参数,因为它将校准和联合角度测量的错误拼凑在一起。 粒子过滤法在模拟中提出并测试了两个真正的世界机器人来估计Lumped错误并显示该参数降低的效率。