Abstract. Jinping Underground lab for Nuclear Astrophysics (JUNA) will take the advantage of the ultralow background in Jinping underground lab, high current accelerator based on an ECR source and highly sensitive detector to study directly a number of crucial reactions to the hydrostatic stellar evolution for the first time at their relevant stellar energies. In its first phase, JUNA aims at the direct measurements of 25 Mg(p,γ) 26 Al, 19 F(p,α) 16 O, 13 C(α,n) 16 O and 12 C(α,γ) 16 O. The experimental setup, which include the accelerator system with high stability and high intensity, the detector system, and the shielding material with low background, will be established during the above research. The current progress of JUNA will be given. Article available at
Abstract. Jinping Underground lab for Nuclear Astrophysics (JUNA) will take the advantage of the ultralow background in Jinping underground lab, high current accelerator based on an ECR source and highly sensitive detector to study directly a number of crucial reactions to the hydrostatic stellar evolution for the first time at their relevant stellar energies. In its first phase, JUNA aims at the direct measurements of 25 Mg(p,γ) 26 Al, 19 F(p,α) 16 O, 13 C(α,n) 16 O and 12 C(α,γ) 16 O. The experimental setup, which include the accelerator system with high stability and high intensity, the detector system, and the shielding material with low background, will be established during the above research. The current progress of JUNA will be given. Article available at
In this paper, we propose a novel strategy for human-robot impedance mapping to realize an effective execution of human-robot collaboration. The endpoint stiffness of the human arm impedance is estimated according to the configurations of the human arm and the muscle activation levels of the upper arm. Inspired by the human adaptability in collaboration, a smooth stiffness mapping between the human arm endpoint and the robot arm joint is developed to inherit the human arm characteristics. The estimation of stiffness term is generalized to full impedance by additionally considering the damping and mass terms. Once the human arm impedance estimation is completed, a Linear Quadratic Regulator is employed for the calculation of the corresponding robot arm admittance model to match the estimated impedance parameters of the human arm. Under the variable admittance control, robot arm is governed to be complaint to the human arm impedance and the interaction force exerted by the human arm endpoint, thus the relatively optimal collaboration can be achieved. The radial basis function neural network is employed to compensate for the unknown dynamics to guarantee the performance of the controller. Comparative experiments have been conducted to verify the validity of the proposed technique.INDEX TERMS Impedance estimated model, variable admittance control, physical human-robot collaboration, neural networks.
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