Vine Robot Localization Via Collision

Localization of robots is a complex task that is often hindered by the sensors these systems use. Due to the majority of field robots being rigid, most of these sensing modalities have the same common faults, such as performance being hindered when their camera vision is obscured. In addition, rigid systems lack flexibility when traversing multiple environments: especially when traversing uneven and unpredictable ground. Soft robots, which can adaptably interact with the environment, could serve as a solution to both problems. One specific soft robot, the Vine Robot, has exhibited excellent performance while moving through constrained, unpredictable environments. This makes the Vine Robot an ideal candidate for a novel method of sensing and localizing in environments, obstacle collision localization. We use our understanding of the nature of Vine Robot motion to be able to predict the tip position of the robot at every instant based on sensor feedback. Through the single obstacle experiments, it was found that our algorithm can provide a precise picture of the tip position of the robot in differing environments. Further, in a multi obstacle demonstration, less than 5% max error relative to the full robot length was observed on the path prediction. Our study helps lay the foundation for a new method for Vine Robot localization using contact as a new sensing modality.