Neural Network based Inverse Kinematics Solution for Trajectory Tracking of a Robotic Arm

Duka, Adrian-Vasile

doi:10.1016/j.protcy.2013.12.451

Cited by 159 publications

(85 citation statements)

References 14 publications

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“…According to the ANN based solving inverse kinematics results in the literature [2–6, 10, 11], the proposed approach in this study has a minimized error in the inverse kinematics solution. Table 3 shows system performance comparison between this study and other studies from literature.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…A traditional design for ANN is used in many studies [6, 10, 11]. In order to utilize the advantages of this proposed method, traditional ANN is designed in this study to solve the inverse kinematics.…”

Section: Traditional Design Of Artificial Neural Networkmentioning

confidence: 99%

“…A feedforward neural network was used for planar of three-link manipulators. The result showed the best performance at epoch 9 with mean squared error (MSE) of 0.0054387 [11]. The kinematics of three DOF was introduced for the lower limb of the humanoid robot.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Almusawi

lger

Kapucu

2016

Computational Intelligence and Neuroscience

123

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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Traditional Design Of Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Almusawi

lger

Kapucu

2016

Computational Intelligence and Neuroscience

123

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“…Kinematics deals with the geometric motion of a robotic manipulator and the inverse kinematics is considered as the most popular and effective method of controlling robot arm [7]. This method proposes the neural network which is trained by the data given by the direct kinematics .This means the end effecter's position and orientation are given as inputs and the neural network identifies which joint configuration corresponds to the given values of the end effector [4].…”

Section: Nowadaysmentioning

confidence: 99%

Inverse kinematics solution of a five joint robot using Feed forward and Elman network

Raj

Raglend

Anand

2015

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]

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Robotics is one of the emerging fields in today's industries and the motion of a manipulator can be expressed based on the kinematical approach. Generally the process of computing the inverse kinematics solution is a tough assignment. By this method the joint angles of the end effector can be retrieved corresponding to the position and orientation which is provided as input. Here the Neural Network approach is adopted and the algorithms employed are Feed forward and Elman Back propagation Neural Network.Since these algorithms are put to use, the inverse kinematics of a five DOF robot can solved and the fetched result will be more precise and less complex, thus finally it can analyzed and compared.

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“…The computation of the inverse kinematics of robots is well studied in the literature [7][8][9][10][11][12][13], to name a few. There are multiple approaches to calculating the inverse kinematics.…”

Section: Introductionmentioning

confidence: 99%

A Closed Loop Inverse Kinematics Solver Intended for Offline Calculation Optimized with GA

Bjørlykhaug

2018

Robotics

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This paper presents a simple approach to building a robotic control system. Instead of a conventional control system which solves the inverse kinematics in real-time as the robot moves, an alternative approach where the inverse kinematics is calculated ahead of time is presented. This approach reduces the complexity and code necessary for the control system. Robot control systems are usually implemented in low level programming language. This new approach enables the use of high level programming for the complex inverse kinematics problem. For our approach, we implement a program to solve the inverse kinematics, called the Inverse Kinematics Solver (IKS), in Java, with a simple graphical user interface (GUI) to load a file with desired end effector poses and edit the configuration of the robot using the Denavit-Hartenberg (DH) convention. The program uses the closed-loop inverse kinematics (CLIK) algorithm to solve the inverse kinematics problem. As an example, the IKS was set up to solve the kinematics for a custom built serial link robot. The kinematics for the custom robot is presented, and an example of input and output files is also presented. Additionally, the gain of the loop in the IKS is optimized using a GA, resulting in almost a 50% decrease in computational time.

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Neural Network based Inverse Kinematics Solution for Trajectory Tracking of a Robotic Arm

Cited by 159 publications

References 14 publications

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Inverse kinematics solution of a five joint robot using Feed forward and Elman network

A Closed Loop Inverse Kinematics Solver Intended for Offline Calculation Optimized with GA

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