Graph-Propagation-Based Kinematic Algorithm for In-Pipe Truss Structure Robots

Robots designed for in-pipe navigation, inspection, and repair require flexibility for intricate pipeline traversal and the strength to carry payloads. However, conventional wheeled in-pipe robots face challenges in simultaneously achieving both substantial flexibility and payload-carrying capacity. A superior approach involves utilizing truss robots with redundant joints and linkages for pipe shape adaptation and actuation force dis- tribution, providing significant advantages for complex pipeline navigation and heavy payload delivery. However, the kinematics of truss robots is computationally expensive for conventional Jacobian-based algorithms due to their complicated structural constraints. To address this limitation, we propose a novel algorithm for efficient truss-robot-kinematics computation using Graph Propagation (GP) method. Our method computes both forward kinematics and Jacobian in a propagative manner. It also guarantees geometric constraints with the Sigmoid function as the boundary. In simulation experiments, our algorithm accelerates pipe shape adaptation computation by 5.2∼16.4 times compared to finite difference methods. The practical feasibility of our method is assessed through physical in-pipe crawling experiments using a truss robot prototype. Additionally, the prototype’s ability to carry heavy payloads is demonstrated through payload-carrying experiments, which results in 2∼4 times heavier payload capacity compared to two-wheeled robot approaches. We also showcase the versatility of proposed method in addressing manipulation tasks, indicating its generalizability across diverse applications. We believe this work could provide a unique algorithmic framework for truss robot kinematics formulation and computation, which will enable next generation of in-pipe robots to be more adaptive to complex environments and formidable toward real-world applications.