This article presents a model predictive controller (MPC) developed for the autonomous steering of concentric tube robots (CTRs). State-of-the-art CTR control relies on differential kinematics developed by local linearization of the CTRs mechanics model and cannot explicitly handle constraints on robot's joint limits or unstable configurations commonly known as snapping points. The proposed nonlinear MPC explicitly considers constraints on the robot configuration space (i.e., joint limits) and the robot's workspace (i.e., mixed boundary conditions on robot curvature). Additionally, the MPC calculates control decisions by optimizing the model-based predictions of future robot configurations. This way, it avoids configurations it cannot recover from, i.e., joint limits, singular configurations, and snapping. The proposed controller is evaluated via simulations and experimental studies with a variety of trajectories of increasing complexity. Simulation results demonstrate the capability of MPC to avoid singularities while satisfying robot mechanical constraints. Experimental results demonstrate that our solution enables following of trajectories unattainable by state-of-the-art controllers with mean error corresponding to 1% of robot arclength.

Concentric tube robots (CTR) can traverse tightly curved paths and offer dexterity in constrained environments, making them advantageous for minimally invasive surgical scenarios that experience strict anatomical and surgical constraints. Their shape is controlled via rotation and translation of several concentrically arranged super-elastic precurved tubes that form the robot backbone. As the elastic energy accumulated in the backbone due to bending and twist of the tubes increases, robots can exhibit sudden snapping motions, which can damage the surrounding tissues. In this paper, we proposed an approach for closed-loop steering of a redundant CTR that allows for snap-free motion and enhances its force/velocity manipulability, increasing the capacity of the robot to move and/or exercise forces along any direction. First, a controller stabilizes the CTR end-effector on a desired time-variant trajectory. Next, an online optimizer uses the robot's redundant Degrees of Freedom (DoF) to reshape its manipulability in real-time and steer it away from potentially snapping configurations or increase its capacity in delivering force payloads. Simulations and experiments demonstrate the performance of the proposed control strategy. The controller can steer a generally unstable CTR along trajectories while avoiding instabilities with a mean error of 850 μm, corresponding to 0.6% of arclength, and improves robot ability to exercise forces by 55%.

This paper establishes the feasibility of robotically 3D printing biomaterials such as alginate hydrogels onto moving human anatomy and a stationary plane. The alginate hydrogels used are in-vivo compatible and a proven biomaterial for tissue scaffolds. We developed a control scheme for precision material deposition via piezo microjetting while tracking in real-time to continuously sense anatomy location and deposits material in a predefined trajectory derived from two pre-selected target geometries. We show that multilayer 3D structures can be created on a moving human hand with 1.6 mm average error and 87.8 % overall accuracy.

Safe, intuitive human-robot interaction requires that robots intelligently interface with their environments, ideally sensing and localizing physical contact across their link surfaces. We introduce a stretchable smart skin sensor that provides this function. Stretchability allows it to conform to arbitrary robotic link surfaces. It senses contact over nearly the entire surface, localizes contact position of a typical finger touch continuously over its entire surface (RMSE = 7.02mm for a 14.7cm×14.7cm area), and provides an estimate of the contact force. Our approach exclusively employs stretchable, flexible materials resulting in skin strains of up to 150%. We exploit novel carbon nanotube elastomers to create a two-dimensional potentiometer surface. Finite element simulations validate a simplified polynomial surface model to enable real-time processing on a basic microcontroller with no supporting electronics. Using only five electrodes, the skin can be scaled up to arbitrary sizes without needing additional electrodes. We designed, implemented, calibrated, and tested a prototype smart skin as a tactile sensor on a custom medical robot for sensing unexpected physical interactions. We experimentally demonstrate its utility in collaborative robotic applications by showing its potential to enable safer, more intuitive human-robot interaction.