The flexibility and dexterity of cable-driven continuum robots (CDCRs) make them well suited for intricate tasks such as minimally invasive surgery. However, the complexity of accurately modeling their dynamics has limited their broader adoption and effective control. Current models either oversimplify the dynamics by assuming quasi-static conditions or over complicate them, making real-time application challenging. Additionally, many existing models neglect the critical coupling between the robot's body and actuator dynamics, a factor essential for accurate control. In this paper, we propose a new minimal dynamics model for CDCRs that strikes a balance between simplicity and accuracy. Our model captures the essential dynamics of both the robot and its actuators, providing a practical tool for control design. We also establish connections between our model and those used for other robotic systems, enabling the transfer of well-established control strategies to CDCRs. The model is validated through hardware experiments, demonstrating its ability to effectively address complex control challenges in CDCR applications.

In this work, we address the problem of robust segmentation of a continuum robot from images without the need for training data or markers. We present a method that leverages information about the kinematics of these robots to produce an estimate of the robot shape, which is refined through optimization over global image statistics. Our approach can be straightforwardly applied to any continuum robot design and is able to handle partial occlusions of the robot body, as well as challenging background conditions. We validate our method experimentally for a concentric tube robot in a simulated surgical environment and show that our method significantly outperforms a naive projection of the robot shape and color thresholding, which is commonly used in current vision-based estimation algorithms for these robots. Overall, this work has the potential to improve the viability of vision-based state estimation for continuum robots in real-world settings.

Soft, growing robots have the ability to conform to their environment and traverse highly curved paths that would typically prove challenging for other robot designs. As they navigate through these constrained and cluttered environments, there is often significant interaction between the robot and its surroundings. In this work, we propose a method to enable tactile perception for growing robots, which utilizes commercially available, flexible sensors that measure the curvature of the robot shape at multiple locations. Our method consists of both a pouch design to enable seamless integration of the sensors with the material of the growing robot, as well as an algorithm for determining the location of point contacts along the robot body. We validate our proposed approach experimentally using a 3.5 cm robot that can grow to be 53 cm long. We show that we can localize a force applied to various locations along its length with an average error of$3.444\pm1.38$cm when the robot is unactuated and$4.62\pm 0.95$cm when the robot is actuated. Additionally, we characterize the minimum distance required for our tactile sensing approach to discriminate between two separate contact points along the robot body to be 23.5 cm. Finally, we apply our method to a growing robot exploring an unknown environment and show that we are able to effectively determine when and where the growing robot collides with an unknown obstacle.