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.

翻译：软体生长机器人是一种仿照植物生长与运动方式设计、能够适应环境的机器人，在穿越狭窄空间、危险地形及难以到达区域等场景具有潜在应用价值。本研究探索将深度强化Q学习算法应用于软体生长机器人在杂乱环境中的导航。该算法利用软体机器人的柔性特性，将机器人-环境交互作用融入决策过程。仿真结果表明，该算法显著提升了软体机器人在受限空间内高效、有效的导航能力。本研究为生长机器人（特别是软体机器人）在现实场景中规划障碍感知路径提供了一种有前景的解决方案。