Limbless robots have the potential to maneuver through cluttered environments that conventional robots cannot traverse. As illustrated in their biological counterparts such as snakes and nematodes, limbless locomotors can benefit from interactions with obstacles, yet such obstacle-aided locomotion (OAL) requires properly coordinated high-level self-deformation patterns (gait templates) as well as low-level body adaptation to environments. Most prior work on OAL utilized stereotyped traveling-wave gait templates and relied on local body deformations (e.g., passive body mechanics or decentralized controller parameter adaptation based on force feedback) for obstacle navigation, while gait template design for OAL remains less studied. In this paper, we explore novel gait templates for OAL based on tools derived from geometric mechanics (GM), which thus far has been limited to homogeneous environments. Here, we expand the scope of GM to obstacle-rich environments. Specifically, we establish a model that maps the presence of an obstacle to directional constraints in optimization. In doing so, we identify novel gait templates suitable for sparsely and densely distributed obstacle-rich environments respectively. Open-loop robophysical experiments verify the effectiveness of our identified OAL gaits in obstacle-rich environments. We posit that when such OAL gait templates are augmented with appropriate sensing and feedback controls, limbless locomotors will gain robust function in obstacle rich environments.

翻译：无肢机器人具备在传统机器人无法穿越的杂乱环境中灵活移动的潜力。如同蛇和线虫等生物原型所展示的，无肢运动体能够通过与障碍物的交互获得益处，然而这类障碍辅助运动需要高阶自变形模式（步态模板）与环境适应的低阶身体变形之间的协调配合。现有障碍辅助运动研究多采用刻板的行波步态模板，并通过局部身体变形（如被动体力学或基于力反馈的分散控制器参数自适应）实现避障，而面向障碍辅助运动的步态模板设计仍鲜有研究。本文基于几何力学工具探索适用于障碍辅助运动的新型步态模板——该工具此前仅应用于均匀环境。我们将几何力学的应用范围拓展至富含障碍物的环境：具体而言，建立了将障碍物存在映射为优化中方向约束的模型，由此识别出分别适用于稀疏和密集分布障碍环境的新颖步态模板。开环机器人物理实验验证了所识别的障碍辅助运动步态在富障碍环境中的有效性。我们提出，当此类障碍辅助运动步态模板配合适当的传感与反馈控制时，无肢运动体将在复杂障碍环境中获得稳健的运行能力。