SLAM and Shape Estimation for Soft Robots
Mohammad Amin Karimi, David Cañones Bonham, Esteban Lopez, Ankit Srivastava, Matthew Spenko
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.
BibTeX
@inproceedings{iros2023_slamandshapeesti,
title = {SLAM and Shape Estimation for Soft Robots},
author = {Mohammad Amin Karimi and David Cañones Bonham and Esteban Lopez and Ankit Srivastava and Matthew Spenko},
booktitle = {IROS 2023},
year = {2023}
}