首页|Reports from Carnegie Mellon University Add New Data to Findings in Robotics (Gr aph-propagation-based Kinematic Algorithm for Inpipe Truss Structure Robots)
Reports from Carnegie Mellon University Add New Data to Findings in Robotics (Gr aph-propagation-based Kinematic Algorithm for Inpipe Truss Structure Robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Robotics. Acc ording to news originating from Pitts, Georgia,by NewsRx correspondents, resear ch stated, “Robots designed for in-pipe navigation, inspection, and repair requi re flexibility for intricate pipeline traversal and the strength to carry payloa ds. However, conventional wheeled in-pipe robots face challenges in simultaneous ly achieving both substantial flexibility and payload-carrying capacity.” Financial support for this research came from Advanced Research Projects Agency - Energy. Our news journalists obtained a quote from the research from Carnegie Mellon Uni versity, “A superior approach involves utilizing truss robots with redundant joi nts and linkages for pipe shape adaptation and actuation force distribution, pro viding significant advantages for complex pipeline navigation and heavy payload delivery. However, the kinematics of truss robots is computationally expensive f or conventional Jacobian-based algorithms due to their complicated structural co nstraints. To address this limitation, we propose a novel algorithm for efficien t truss-robot-kinematics computation using Graph Propagation (GP) method. Our me thod computes both forward kinematics and Jacobian in a propagative manner. It a lso guarantees geometric constraints with the Sigmoid function as the boundary. In simulation experiments, our algorithm accelerates pipe shape adaptation compu tation by 5.2 similar to 16.4 times compared to finite difference methods. The p ractical feasibility of our method is assessed through physical in-pipe crawling experiments using a truss robot prototype. Additionally, the prototype’s abilit y to carry heavy payloads is demonstrated through payload-carrying experiments, which results in 2 similar to 4 times heavier payload capacity compared to two-w heeled robot approaches. We also showcase the versatility of proposed method in addressing manipulation tasks, indicating its generalizability across diverse ap plications.”
PittsGeorgiaUnited StatesNorth and Central AmericaAlgorithmsEmerging TechnologiesMachine LearningNano-robo tRobotRoboticsCarnegie Mellon University
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