Studies from Tokyo Institute of Technology in the Area of Robotics Described (Se quential updating of minimum set of dynamics parameters by stochastic identifica tion)
Studies from Tokyo Institute of Technology in the Area of Robotics Described (Se quential updating of minimum set of dynamics parameters by stochastic identifica tion)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s. According to news reporting out of the Tokyo Institute of Technology by NewsR x editors, research stated, “A model-based controller is effective for highly ac curate and fast control of a robot.” Our news journalists obtained a quote from the research from Tokyo Institute of Technology: “The dynamics model is derived from its equations of motion, and the minimum set of the dynamics parameters are experimentally identified. However, the experimental data includes the influence of both noise and un-modeled dynami cs, the obtained model will be one of approximated solutions. From these conside rations, the approximated model has to well represent the robot dynamics around the reference motion, and for high accuracy, new data needs to be added every ti me an experiment is conducted, which causes a lot of computation. The authors ha ve proposed stochastic identification method to obtain suitable parameters for c ontrol system design. However, in this method, because of computational complexi ty of weighted least square mean, it is difficult to add new motion data. In thi s paper, we propose a sequentially updating parameter identification method base d on statistical properties of the conventional method.”
Key words
Tokyo Institute of Technology/Emerging Technologies/Machine Learning/Robot/Robotics
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