Localization of Growing Robot through Obstacle Collision

While traditional rigid robots are widely used in almost all applications today, their rigidity restricts the use of these robots in environments where interaction with the surroundings or humans is inevitable. This is where soft robots come into play. Due to their compliant and adaptable nature, these robots can safely interact with humans and traverse through unpredictable, cluttered environments. This research focuses on the navigation of a special class of soft growing robots called Vine robots. Vine robots can easily maneuver through tight spaces and rough terrain and have an added advantage of speed over general soft robots. In this work, we develop a model which localizes the Vine robot in an unknown surrounding by giving us the position of the tip of the robot at every instant. The model exploits the passive steering of growing robots using obstacle aided navigation. The robot is sensorized to record the orientation of the its tip and the total length it has grown to. This data along with the force generated on collision with the environment is used to localize the robot in space. The localization model is implemented using the sensor data. The accuracy of this model is then verified by comparing the tip position of the robot we have calculated with its predicted position and the actual position as measured by an overhead camera. It is concluded that the robot can be localized in an environment with a maximum error of 7.65 cm (10\%) when the total length the robot has grown to is 170 cm.