UC San Diego

Growing robots that achieve locomotion by extending from their tip, are inherently compliant and can safely navigate through constrained environments that prove challenging for traditional robots. However, the same compliance and tip-extension mechanism that enables this ability, also leads directly to challenges in their localization and control. In this thesis, we present a low-cost, wireless, permanent magnet-based method for localizing the tip of these robots in 5 degrees of freedom. A permanent magnet is placed at the robot tip, and an array of magneto-inductive sensors is used to measure the change in magnetic field as the robot moves through the workspace. We develop an approach to localization that combines analytical and machine learning techniques and show that it outperforms existing methods, particularly for localizing fast moving magnets. We also measure the position error over a 50 cm X 50 cm workspace with different magnet sizes to show that this approach can accommodate growing robots of different scales. Finally, we demonstrate our tracking in real time by growing a 12mm diameter robot through two different, constrained environments. On average for these experiments, our method of localization achieves a position error of 3.0+/-1.1mm and an orientation error of 6.5+/-5.4degrees