让软体机器人蛇在复杂3D地形中自主导航
Learning, locomotion, and navigation of soft synthetic snakes in three-dimensional, heterogeneous environments
- 用生物启发的控制模型降低高自由度机器人的控制难度
- 先学基础运动模式，再组合成复杂地形适应策略
- 在真实三维场景中验证了导航可靠性，适合机器人控制研究
无肢陆生动物展现出卓越的运动灵活性与控制能力，远超当前工程仿制品。本文提出一种计算框架，使软体合成蛇能够在非结构化、异质的三维地形中自主导航。方法基于生物启发的驱动与感知模型，降低高自由度连续体结构的控制复杂性，并融入强化学习架构以生成环境穿越策略。训练首先在简化、均质地形中学习基本运动模式，再将其组合为复杂地貌的自适应策略。通过在基于真实影像重建的高保真3D环境中部署，验证了系统的鲁棒性与导航能力。该工作构建了物理真实的仿真平台，为连续体系统在自然地形中的控制提供了实用洞见。
原文摘要 · Abstract (English)
Limbless terrestrial animals exhibit exceptional locomotor versatility and control, currently unmatched by engineered counterparts. Here, we introduce a computational framework that enables soft synthetic snakes to navigate unstructured, heterogeneous 3D terrains. Our approach is grounded in bio-inspired actuation and sensing models that reduce the control complexity inherent to high-degree-of-freedom, continuum bodies. These models are integrated into a reinforcement learning architecture to derive environment-traversing policies. Training first occurs in simplified, homogeneous terrains to learn locomotion primitives. These are then composed into adaptive strategies for complex landscapes. We demonstrate robustness by deploying a snake in high-fidelity 3D environments reconstructed from real-world imaging, achieving reliable navigation. Overall, this work provides a physically-realistic simulation platform and practical insights for the control of continuum systems in natural terrains.
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