Mapping Unknown Environments through Passive Deformation of Soft, Growing Robots

When faced with an unstructured environment filled with an unknown number and size of obstacles on a chaotic terrain, it can be a challenge to determine the best method for navigating and mapping the space. This problem, known as Simultaneous Localization and Mapping (SLAM), has typically been approached using vision-based solutions, but these solutions require clear visual conditions in order to function optimally. A different approach to sensing environments has been explored in soft robotic systems, specifically by sensing changes in the environment through sensing changes in the robot's configuration. Building on this idea, we introduce a method of mapping based on colliding with and deforming around obstacles using a soft, growing robot. Instead of avoiding obstacles, as is typically done to protect robots, we take advantage of the soft, growing robot's compliance in order to navigate through, and collect information about, the environment. Through the construction and testing of a geometry-based simulation, we analyzed the behavior and effectiveness of this approach for mapping by generating random launch positions and collecting information from contacted obstacles and traversed regions. Through a myriad of randomly generated environments, we determine that: 1) the density of obstacles in an environment has minimal impact on mapping abilities and 2) at least 70% of each environment tested can be mapped by deploying 20 or fewer soft, growing robots.