Grow-to-Shape Control of Variable Length Continuum Robots via Adaptive Visual Servoing

In this paper, we propose an adaptive eye-to-hand vision-based control methodology, which enables a closed-loop grow-to-shape capability for variable length continuum manipulators in 2D. Our method utilizes shape features of the continuum robot, i.e. module curvature and length, which are obtained from the image. Our adaptive control algorithm servos the robot to converge and track the desired values of these features in the image space without the need of a robot model. As a result the robot starts from a minimum length configuration and grows into a given desired shape, always staying on the course of the desired shape. We believe that this approach unlocks capabilities for variable length continuum robots by leveraging their actuation redundancy and avoiding obstacles while carrying out object manipulation or inspection tasks in cluttered and constrained environments. We perform experiments in simulations and on a real robot to assess the performance of our visual servoing algorithm. Our experimental results demonstrate the controllers ability to accurately converge the current features to their references, for a variety of desired shapes in the image, while ensuring a smooth tracking response. We also present some proof of concept results demonstrating the effectiveness of this technique for controlling the robot in constrained environments. Markedly, this is the first successful demonstration for automatic grow-to-shape control using visual feedback for variable length continuum manipulators.