Obstacle-Aware Navigation of Soft Growing Robots via Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning approach for obstacle-aware navigation of soft growing robots, enabling effective movement through cluttered and confined environments by leveraging the robot's flexibility.

Contribution

It introduces a novel deep Q-learning algorithm that incorporates environmental interactions for soft robot navigation in complex spaces.

Findings

Improved navigation efficiency in simulations

Enhanced obstacle avoidance capabilities

Demonstrated adaptability to cluttered environments

Abstract

Soft growing robots, are a type of robots that are designed to move and adapt to their environment in a similar way to how plants grow and move with potential applications where they could be used to navigate through tight spaces, dangerous terrain, and hard-to-reach areas. This research explores the application of deep reinforcement Q-learning algorithm for facilitating the navigation of the soft growing robots in cluttered environments. The proposed algorithm utilizes the flexibility of the soft robot to adapt and incorporate the interaction between the robot and the environment into the decision-making process. Results from simulations show that the proposed algorithm improves the soft robot's ability to navigate effectively and efficiently in confined spaces. This study presents a promising approach to addressing the challenges faced by growing robots in particular and soft robots general in planning obstacle-aware paths in real-world scenarios.