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类模仿植物生长与运动方式、旨在适应环境的机器人，具有在狭窄空间、危险地形和难以到达区域中导航的潜在应用价值。本研究探索了深度强化Q学习算法在杂乱环境中辅助软体生长机器人导航的应用。所提出的算法利用软体机器人的柔顺性，将机器人与环境之间的交互作用纳入决策过程。仿真结果表明，该算法能有效提升软体机器人在受限空间中的导航效率与效能。本研究为应对生长机器人（特别是软体机器人）在真实场景中规划障碍感知路径所面临的挑战，提供了一种具有前景的解决途径。