Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controls
This work addresses the challenge of manipulating deformable linear objects for robotics applications, representing an incremental advance in shape servoing methods.
The paper tackles the problem of robotic manipulation of elastic rods by proposing a shape servoing framework that uses parameterized regression features and adaptive control to deform rods into desired shapes without prior mechanical models, validated through experiments with vision-guided manipulators.
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.