Tip-growing eversion robots are renowned for their ability to access remote spaces through narrow passages. However, achieving reliable navigation remains a significant challenge. Existing solutions often rely on artificial muscles integrated into the robot body or active tip-steering mechanisms. While effective, these additions introduce structural complexity and compromise the defining advantages of eversion robots: their inherent softness and compliance. In this paper, we propose a passive approach to reduce bending stiffness by purposefully introducing buckling points along the robot's outer wall. We achieve this by integrating inextensible diameter-reducing circumferential bands at regular intervals along the robot body facilitating forward motion through tortuous, obstacle cluttered paths. Rather than relying on active steering, our approach leverages the robot's natural interaction with the environment, allowing for smooth, compliant navigation. We present a Cosserat rod-based mathematical model to quantify this behavior, capturing the local stiffness reductions caused by the constricting bands and their impact on global bending mechanics. Experimental results demonstrate that these bands reduce the robot's stiffness when bent at the tip by up to 91 percent, enabling consistent traversal of 180 degree bends with a bending radius of as low as 25 mm-notably lower than the 35 mm achievable by standard eversion robots under identical conditions. The feasibility of the proposed method is further demonstrated through a case study in a colon phantom. By significantly improving maneuverability without sacrificing softness or increasing mechanical complexity, this approach expands the applicability of eversion robots in highly curved pathways, whether in relation to pipe inspection or medical procedures such as colonoscopy.

翻译：尖端生长型翻转机器人以其通过狭窄通道进入远程空间的能力而著称。然而，实现可靠的导航仍然是一个重大挑战。现有解决方案通常依赖于集成到机器人本体的人工肌肉或主动尖端转向机制。这些方法虽然有效，但引入了结构复杂性，并损害了翻转机器人的固有优势：其内在的柔软性和顺应性。本文提出一种被动方法，通过有目的地在机器人外壁引入屈曲点来降低弯曲刚度。我们通过在机器人本体上以规则间隔集成不可延伸的减径周向带实现这一目标，从而促进机器人通过曲折、障碍物密布的路径向前运动。我们的方法不依赖主动转向，而是利用机器人与环境的自然交互，实现平滑、顺应的导航。我们提出了一个基于Cosserat杆的数学模型来量化这种行为，该模型捕捉了收缩带引起的局部刚度降低及其对全局弯曲力学的影响。实验结果表明，这些带在尖端弯曲时能将机器人的刚度降低高达91%，使其能够稳定通过弯曲半径低至25毫米的180度弯道——这显著低于标准翻转机器人在相同条件下可实现的35毫米弯曲半径。通过在一个结肠模型中的案例研究进一步证明了所提方法的可行性。该方法在不牺牲柔软性或增加机械复杂性的前提下显著提高了机动性，从而扩展了翻转机器人在高曲率路径中的适用性，无论是在管道检测还是结肠镜检查等医疗程序中。