The robotic manipulation of deformable linear objects has shown great potential in a wide range of real-world applications. However, it presents many challenges due to the objects' complex nonlinearity and high-dimensional configuration. In this paper, we propose a new shape servoing framework to automatically manipulate elastic rods through visual feedback. Our new method uses parameterized regression features to compute a compact (low-dimensional) feature vector that quantifies the object's shape, thus, enabling to establish an explicit shape servo-loop. To automatically deform the rod into a desired shape, the proposed adaptive controller iteratively estimates the differential transformation between the robot's motion and the relative shape changes; This valuable capability allows to effectively manipulate objects with unknown mechanical models. An auto-tuning algorithm is introduced to adjust the robot's shaping motions in real-time based on optimal performance criteria. To validate the proposed framework, a detailed experimental study with vision-guided robotic manipulators is presented.

翻译：可变形线性物体的机器人操作在众多实际应用中展现出巨大潜力，然而，由于物体复杂的非线性特性及高维构型空间，该任务面临诸多挑战。本文提出一种新型形态伺服框架，通过视觉反馈实现弹性杆的自动操控。该方法采用参数化回归特征计算紧凑（低维）特征向量以量化物体形态，从而构建显式形态伺服闭环。为使弹性杆自动变形至目标形态，所提出的自适应控制器迭代估计机器人运动与相对形态变化之间的微分变换关系，此关键能力使得无需已知力学模型即可有效操控物体。进一步，本文引入一种自整定算法，基于最优性能准则实时调整机器人的成形运动。为验证该框架有效性，本文展示了基于视觉引导机器人操作器的详细实验研究。