Vision-Based Autonomous Steering of a Miniature Eversion Growing Robot

This letter presents vision-based autonomous naviga- tion of a steerable soft growing robot. Our experimental platform is the previously presented MAMMOBOT, which is a small-diameter eversion growing robot with an embedded steerable catheter. The current manuscript first models the robot using kinematics (con- stant curvature) and mechanics (virtual work). Modelling consid- ers the potential misalignment between the everting sheath and the embedded catheter. Second, a switching control architecture is proposed, wherein a model-based controller is employed for rapid convergence to a target position, followed by a closed-loop proportional controller that minimises the system’s steady-state error. Feedback is visually provided from a calibrated stereo vision system. Target-positioning and trajectory-tracking experiments are conducted to evaluate the performance of the control archi- tecture. Experimental results demonstrate the superiority of the mechanics-based modelling and control approach, showing an av- erage accuracy of 0.67 mm (0.66% arclength) in target positioning experiments, and an accuracy of 0.72 mm (1.11% arclength) and 0.72 mm (1.01% arclength) for tracking a square trajectory and a circular trajectory, respectively. The autonomous steering frame- work is showcased within a 3D-printed mammary duct phantom. This work sets the stage for endoscope-based autonomous naviga- tion of MAMMOBOT and similar soft growing steerable robots.