Online learning of virtual impedance parameters in non-contact impedance control using neural networks.
Impedance control is one of the most effective methods for controlling the interaction between a manipulator and a task environment. In conventional impedance control methods, however, the manipulator cannot be controlled until the end-effector contacts task environments. A noncontact impedance control method has been proposed to resolve such a problem. This method on only can regulate the end-point impedance, but also the virtual impedance that works between the manipulator and the environment by using visual information. This paper proposes a learning method using neural networks to regulate the virtual impedance parameters according to a given task. The validity of the proposed method was verified through computer simulations and experiments with a multijoint robotic manipulator.
Tsuji
Toshio
T
Terauchi
Mutsuhiro
M
Tanaka
Yoshiyuki
Y
D000465:Algorithms; D001185:Artificial Intelligence; D004548:Elasticity; D017097:Electric Impedance; D005246:Feedback; D009038:Motion; D016571:Neural Networks, Computer; D009862:Online Systems; D010363:Pattern Recognition, Automated; D010456:Peptides, Cyclic; D010812:Physical Stimulation / Q000379:methods; D012371:Robotics / Q000379:methods*; D013314:Stress, Mechanical
D010456:Peptides, Cyclic; C062796:viscosin