SLAM and Shape Estimation for Soft Robots

Abstract

This paper describes Simultaneous Localization and Mapping (SLAM) techniques for mobile soft robots using on-board local sensors. The paper focuses on planar boundary-constrained swarms, which are comprised of identical modular sub-units, each flexibly connected to its neighbor. The sub-units themselves are not necessarily soft, but as the robot's size increases with respect to the size of the sub-units, the robot as a whole approaches a continuous system that exhibits the characteristics and behavior of a soft robot. Previous versions of this system have demonstrated grasping, shape formation, and tunneling; however, all prior embodiments have relied on external sensing for pose estimation. This paper is the first to demonstrate a fully self-sufficient boundary constrained swarm soft robot that does not rely on external pose estimation. The robot successfully navigates a maze-like environment while localizing and mapping the environment.