Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots

TL;DR

This paper presents a model and simulation framework for soft growing robots to improve environmental mapping and exploration by leveraging their passive deformation during collisions.

Contribution

It introduces a collision behavior model and a geometry-based simulator for soft growing robots, enabling efficient environment exploration and mapping.

Findings

The model accurately predicts collision behavior during discrete turns.

The simulator effectively maps unknown environments using Monte Carlo sampling.

The approach rapidly approaches optimal exploration actions in various environments.

Abstract

Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active degrees of freedom. Soft growing robots in particular have shown promise in navigation of unstructured environments due to their passive deformation. If their collisions and subsequent deformations can be better understood, soft robots could be used to understand the structure of the environment from direct tactile measurements. In this work, we propose the use of soft growing robots as mapping and exploration tools. We do this by first characterizing collision behavior during discrete turns, then leveraging this model to develop a geometry-based simulator that models robot trajectories in 2D environments. Finally, we demonstrate the model and simulator validity by mapping unknown environments using Monte Carlo sampling to estimate the optimal next deployment given current knowledge. Over both uniform and non-uniform environments, this selection method rapidly approaches ideal actions, showing the potential for soft growing robots in unstructured environment exploration and mapping.