首页|Reports from Johannes Kepler University Highlight Recent Findings in Robotics (Dedicated Dynamic Parameter Identification for Deltalike Robots)

Reports from Johannes Kepler University Highlight Recent Findings in Robotics (Dedicated Dynamic Parameter Identification for Deltalike Robots)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on Ro botics. According to news reporting out ofLinz, Austria, by NewsRx editors, res earch stated, “Dynamics simulation of parallel kinematic manipulators(PKM) and non-linear control methods require a precisely identified dynamics model and exp licitgeneralized mass matrix. Standard methods, which identify so-called dynami c base-parameters, are notsufficient to this end.”Financial support for this research came from LCM K2 Center for Symbiotic Mechat ronics.Our news journalists obtained a quote from the research from Johannes Kepler Uni versity, “Algorithmsfor identifying the complete set of dynamic parameters were proposed for serial manipulators. A dedicatedidentification method for PKM doe s not exist, however. Such a method is introduced here for the largeclass of De lta-like PKM exploiting the parallel structure and making use of model simplific ations specificto this class. The proposed method guarantees physical consisten cy of the identified parameters, and inparticular a positive definite generaliz ed mass matrix. The method is applied to a simulated model withexactly known pa rameters, which allows for verification of the obtained dynamic parameters. The resultsshow that the generalized mass matrix, the acceleration, and the Corioli s, gravitation and friction termsin the equations of motion (EOM) are well appr oximated. The second example is a real 4-DOF industrialDelta robot ABB IRB 360- 6/1600. For this robot, a physically consistent set of inertia and friction parameters is identified from measurements.”

LinzAustriaEuropeEmerging Technolo giesMachine LearningNano-robotRobotRoboticsJohannes Kepler University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.6)