Neural network-based construction of inverse kinematics model for serial redundant manipulators

2019 ◽  
Vol 24 (4) ◽  
pp. 487-493
Author(s):  
Hideaki Takatani ◽  
Nozomu Araki ◽  
Takao Sato ◽  
Yasuo Konishi
2001 ◽  
Vol 38-40 ◽  
pp. 797-805 ◽  
Author(s):  
Eimei Oyama ◽  
Arvin Agah ◽  
Karl F. MacDorman ◽  
Taro Maeda ◽  
Susumu Tachi

Robotica ◽  
2004 ◽  
Vol 22 (6) ◽  
pp. 611-621 ◽  
Author(s):  
Yangmin Li ◽  
Sio Hong Leong

A method is proposed to solve the inverse kinematics and control problems of robot control systems using a cerebellar model articulation controller neural network combined with a genetic algorithm. Computer simulations and experiments with a 7-DOF redundant modular manipulator have demonstrated the effectiveness of the proposed method.


2013 ◽  
Vol 391 ◽  
pp. 114-117
Author(s):  
Run Sheng Hao

In this paper, the second order recurrent neural network is adopted to study the inverse kinematics problem of three degree-of-freedom planar redundant manipulators. The Simulation results show that the network can effectively solve the inverse kinematics problem of redundant manipulators, and it reaches to good precision of solution and solving speed.


2021 ◽  
Vol 11 (5) ◽  
pp. 2346
Author(s):  
Alessandro Tringali ◽  
Silvio Cocuzza

The minimization of energy consumption is of the utmost importance in space robotics. For redundant manipulators tracking a desired end-effector trajectory, most of the proposed solutions are based on locally optimal inverse kinematics methods. On the one hand, these methods are suitable for real-time implementation; nevertheless, on the other hand, they often provide solutions quite far from the globally optimal one and, moreover, are prone to singularities. In this paper, a novel inverse kinematics method for redundant manipulators is presented, which overcomes the above mentioned issues and is suitable for real-time implementation. The proposed method is based on the optimization of the kinetic energy integral on a limited subset of future end-effector path points, making the manipulator joints to move in the direction of minimum kinetic energy. The proposed method is tested by simulation of a three degrees of freedom (DOF) planar manipulator in a number of test cases, and its performance is compared to the classical pseudoinverse solution and to a global optimal method. The proposed method outperforms the pseudoinverse-based one and proves to be able to avoid singularities. Furthermore, it provides a solution very close to the global optimal one with a much lower computational time, which is compatible for real-time implementation.


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