This paper is focused on assembly tasks executed by an industrial robotic manipulator in the presence of uncertainties. The goal is to achieve higher levels of autonomy and flexibility of robotic systems in the execution of such tasks. In particular, as a well-established paradigm of assembly tasks, a Peg-in-Hole task has been considered, where the pose of the target object with respect to the robot is known with uncertainties far larger than the task tolerance, e.g., due to manual positioning of the object in the workcell. The proposed approach is based on the reconstruction of the object surface by means of a number of point clouds provided by a depth sensor. The reconstruction is then compared with a known CAD model of the surface, in order to localize the position and tilt of the holes. Finally, the peg insertion is performed in two steps: a search phase, in which the peg tip gently slides on the surface following a trajectory described by Lissajous functions, and a mechanical coupling phase, in which a compliant behavior is imposed to the peg. Experiments on a collaborative manipulator confirm that the proposed approach allows to achieve a better degree of autonomy and flexibility for a class of robotic tasks in partially structured environments.

本文的重点是在存在不确定性的情况下由工业机器人操纵器执行的装配任务。目标是在执行此类任务时实现机器人系统更高水平的自主性和灵活性。特别是，作为装配任务的成熟范例，Peg-in-Hole 已经考虑了任务，其中目标物体相对于机器人的姿态是已知的，其不确定性远大于任务容差，例如，由于物体在工作单元中的手动定位。所提出的方法基于通过深度传感器提供的大量点云重建物体表面。然后将重建与已知的表面 CAD 模型进行比较，以便定位孔的位置和倾斜度。最后，钉子插入分两个步骤执行：搜索阶段，其中钉子尖端按照李萨如函数描述的轨迹在表面上轻轻滑动，以及机械耦合阶段，其中对钉子施加顺从行为。