A large area multi-finger configuration power SiGe HBT device （with an emitter area of about 880μm^2） was fabricated with 2μm double-mesa technology. The maximum DC current gain β is 214. The BVCEO is up to 10V,a...A large area multi-finger configuration power SiGe HBT device （with an emitter area of about 880μm^2） was fabricated with 2μm double-mesa technology. The maximum DC current gain β is 214. The BVCEO is up to 10V,and the BVCBO is up to 16V with a collector doping concentration of 1 × 10^17cm^-3 and collector thickness of 400nm. The device exhibits a maximum oscillation frequency fmax of 19. 3GHz and a cut-off frequency fT of 18.0GHz at a DC bias point of Ic = 30mA and VCE = 3V.MSG （maximum stable gain） is 24.5dB,and U （Mason unilateral gain） is 26.6dB at 1GHz. Due to the novel distribution layout, no notable current gain fall-off or thermal effects are observed in the I-V characteristics at high collector current.展开更多
The self-heating effect severely limits device performance and reliability.Although some studies have revealed the heat distribution ofβ-Ga_(2)O_(3) MOSFETs under biases,those devices all have small areas and have di...The self-heating effect severely limits device performance and reliability.Although some studies have revealed the heat distribution ofβ-Ga_(2)O_(3) MOSFETs under biases,those devices all have small areas and have difficulty reflecting practical con-ditions.This work demonstrated a multi-fingerβ-Ga_(2)O_(3) MOSFET with a maximum drain current of 0.5 A.Electrical characteris-tics were measured,and the heat dissipation of the device was investigated through infrared images.The relationship between device temperature and time/bias is analyzed.展开更多
The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that...The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that in the grasping process and,different control algorithm should be taken in the two process. A position-force hybrid control algorithm is proposed which is applied to the control system of the University of Science and Technology Beijing double-thumb robot hand successfully.展开更多
The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm,and the existing methods can’t guarantee that the initial values fully satisfy the friction co...The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm,and the existing methods can’t guarantee that the initial values fully satisfy the friction cone constraints of contact point in the optimization process of gradient flow algorithm.In order to improve safety margin and prevent the finger from slipping at contact point,we present an iterative method of safe initial values with safety margin detection and develop a gradient flow optimization algorithm based on the safe initial values.Firstly,the safety margin is defined which more intuitively reflects the margin of the grasping forces at contact point.The resulting safe initial values can be achieved by the detection of desired safety margin at each iteration.Secondly,the safe initial values are usually not optimal,even with the valid initial values,and it can’t always ensure that the finger contact force always satisfies the friction cone constraints during the optimization.It is an effective way to eliminate the unreliable initial values in the optimization and obtain a safer initial values by increasing the safety margin.By transforming the safe initial values into an initial point of the gradient flow algorithm,the final optimized values of grasping forces can be generated efficiently by gradient flow iteration.Grasp examples of the soft multi-fingered hand indicate the effectiveness of the general solution of the force optimization algorithm based on safety margin detection.The method eliminates the shortcomings of the gradient flow optimization process caused by the initial value problem and provides a more accurate and reliable force optimization result for multi-fingered dexterous manipulation.展开更多
We present a mathematical method for acceleration workspace analysis of cooperating multi-finger robot systems using a model of point-contact with friction. A new unified formulation from dynamic equations of cooperat...We present a mathematical method for acceleration workspace analysis of cooperating multi-finger robot systems using a model of point-contact with friction. A new unified formulation from dynamic equations of cooperating multi-finger robots is derived considering the force and acceleration relationships between the fingers and the object to be handled. From the dynamic equation, maximum translational and rotational acceleration bounds of an object are calculated under given constraints of contact conditions, configurations of fingers, and bounds on the torques of joint actuators for each finger. Here, the rotational acceleration bounds can be applied as an important manipulability index when the multi-finger robot grasps an object. To verify the proposed method, we used a set of case studies with a simple multi-finger mechanism system. The achievable acceleration boundary in task space can be obtained successfully with the proposed method and the acceleration boundary depends on the configurations of fingers.展开更多
文摘A large area multi-finger configuration power SiGe HBT device （with an emitter area of about 880μm^2） was fabricated with 2μm double-mesa technology. The maximum DC current gain β is 214. The BVCEO is up to 10V,and the BVCBO is up to 16V with a collector doping concentration of 1 × 10^17cm^-3 and collector thickness of 400nm. The device exhibits a maximum oscillation frequency fmax of 19. 3GHz and a cut-off frequency fT of 18.0GHz at a DC bias point of Ic = 30mA and VCE = 3V.MSG （maximum stable gain） is 24.5dB,and U （Mason unilateral gain） is 26.6dB at 1GHz. Due to the novel distribution layout, no notable current gain fall-off or thermal effects are observed in the I-V characteristics at high collector current.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant Nos.61925110,62004184 and 62234007the Key-Area Research and Development Program of Guangdong Province under Grant No.2020B010174002.
文摘The self-heating effect severely limits device performance and reliability.Although some studies have revealed the heat distribution ofβ-Ga_(2)O_(3) MOSFETs under biases,those devices all have small areas and have difficulty reflecting practical con-ditions.This work demonstrated a multi-fingerβ-Ga_(2)O_(3) MOSFET with a maximum drain current of 0.5 A.Electrical characteris-tics were measured,and the heat dissipation of the device was investigated through infrared images.The relationship between device temperature and time/bias is analyzed.
文摘The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that in the grasping process and,different control algorithm should be taken in the two process. A position-force hybrid control algorithm is proposed which is applied to the control system of the University of Science and Technology Beijing double-thumb robot hand successfully.
基金National Natural Science Foundation of China(51305180)International Science&Technology Cooperation Program of China(2014DFR10620)Shandong Provincial Natural Science Foundation(ZR2013FM026,ZR2014YL009)
文摘The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm,and the existing methods can’t guarantee that the initial values fully satisfy the friction cone constraints of contact point in the optimization process of gradient flow algorithm.In order to improve safety margin and prevent the finger from slipping at contact point,we present an iterative method of safe initial values with safety margin detection and develop a gradient flow optimization algorithm based on the safe initial values.Firstly,the safety margin is defined which more intuitively reflects the margin of the grasping forces at contact point.The resulting safe initial values can be achieved by the detection of desired safety margin at each iteration.Secondly,the safe initial values are usually not optimal,even with the valid initial values,and it can’t always ensure that the finger contact force always satisfies the friction cone constraints during the optimization.It is an effective way to eliminate the unreliable initial values in the optimization and obtain a safer initial values by increasing the safety margin.By transforming the safe initial values into an initial point of the gradient flow algorithm,the final optimized values of grasping forces can be generated efficiently by gradient flow iteration.Grasp examples of the soft multi-fingered hand indicate the effectiveness of the general solution of the force optimization algorithm based on safety margin detection.The method eliminates the shortcomings of the gradient flow optimization process caused by the initial value problem and provides a more accurate and reliable force optimization result for multi-fingered dexterous manipulation.
文摘We present a mathematical method for acceleration workspace analysis of cooperating multi-finger robot systems using a model of point-contact with friction. A new unified formulation from dynamic equations of cooperating multi-finger robots is derived considering the force and acceleration relationships between the fingers and the object to be handled. From the dynamic equation, maximum translational and rotational acceleration bounds of an object are calculated under given constraints of contact conditions, configurations of fingers, and bounds on the torques of joint actuators for each finger. Here, the rotational acceleration bounds can be applied as an important manipulability index when the multi-finger robot grasps an object. To verify the proposed method, we used a set of case studies with a simple multi-finger mechanism system. The achievable acceleration boundary in task space can be obtained successfully with the proposed method and the acceleration boundary depends on the configurations of fingers.
