A Localization Framework for Boundary Constrained Soft Robots

Abstract

Soft robots possess unique capabilities for adapting to the environment and interacting with it safely. However, their deformable nature also poses challenges for controlling their movement. In particular, the large deformations of a soft robot make it difficult to localize its individual body parts, which in turn impedes effective control. This paper introduces a novel localization framework designed for soft robots that are constrained by boundaries and benefit from unique hardware architecture. To this end, we propose a method that exploits the flexible boundaries of the robot to create an onboard sensor capable of measuring the relative distances between its sub-robots. This measurement data is incorporated into a linear Kalman filter for accurate localization. We evaluate the framework's performance in benchmark and dynamic cases and demonstrate its effectiveness in improving localization accuracy compared to an IMU-based approach. The results also show that the proposed method achieves sufficient localization accuracy for contact-based mapping, enabling the robot to sense the location of obstacles in the environment. Finally, we validate the proposed framework using a physical prototype of a boundary-constrained soft robot and demonstrate its ability to accurately estimate the robot's shape. This framework has the potential to enable soft robots to autonomously navigate and map unknown environments, which could be beneficial for a variety of exploration tasks.