Pipelines, vital for fluid transport, pose an important yet challenging inspection task, particularly in small, flexible biological systems, that robots have yet to master. In this study, we explored the development of an innovative robot inspired by the ovipositor of parasitic wasps to navigate and inspect pipelines. The robot features a flexible locomotion system that adapts to different tube sizes and shapes through a mechanical inflation technique. The flexible locomotion system employs a reciprocating motion, in which groups of three sliders extend and retract in a cyclic fashion. In a proof-of-principle experiment, the robot locomotion efficiency demonstrated positive linear correlation (r=0.6434) with the diameter ratio (ratio of robot diameter to tube diameter). The robot showcased a remarkable ability to traverse tubes of different sizes, shapes and payloads with an average of (70%) locomotion efficiency across all testing conditions, at varying diameter ratios (0.7-1.5). Furthermore, the mechanical inflation mechanism displayed substantial load-carrying capacity, producing considerable holding force of (13 N), equivalent to carrying a payload of approximately (5.8 Kg) inclusive the robot weight. This novel soft robotic system shows promise for inspection and navigation within tubular confined spaces, particularly in scenarios requiring adaptability to different tube shapes, sizes, and load-carrying capacities. This novel design serves as a foundation for a new class of pipeline inspection robots that exhibit versatility across various pipeline environments, potentially including biological systems.

翻译：管道作为流体输送的关键设施，其检测任务重要且具有挑战性，尤其是在小型柔性生物系统中，机器人技术尚未成熟。本研究探索了一种受寄生蜂产卵器启发的创新机器人，用于管道导航与检测。该机器人采用机械充气技术，实现了适应不同管径与形状的柔性 locomotion 系统。该系统通过三组滑块循环伸缩实现往复运动。概念验证实验表明，机器人 locomotion 效率与管径比（机器人直径与管道直径之比）呈正线性相关（r=0.6434）。在管径比0.7-1.5的范围内，机器人展现出穿越不同尺寸、形状及负载管道的卓越能力，平均 locomotion 效率达70%。此外，机械充气机构具有显著承载能力，可产生13 N的保持力，等效于携带约5.8 kg的负载（含机器人自重）。这种新型软体机器人系统在管道受限空间检测与导航领域展现出应用潜力，尤其适用于需要适应不同管道形状、尺寸及负载能力的场景。该创新设计为新一代能适应多样化管道环境（包括生物系统）的多功能管道检测机器人奠定了基础。