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 distribution, 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 $\sim$ 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 $\sim$ 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.