Neural Network Based Inverse Kinematic Solution of a 5 DOF Manipulator for Industrial Application

Aravinddhakshan, S.; Apte, Sudhanv; Saxena, Akash

doi:10.1088/1742-6596/1969/1/012010

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…From forward kinematics equations the training data for ANN were obtained, where the model of the manipulator is visualized by MATLAB Simulink to evaluate the path errors [8]. S. Aravinddhakshan et al [9] experienced two neural networks for a 5DOFs industrial manipulator, where the networks differed in the input training data, one for only end effector position and the other for the complete pose information. The results showed the first network errors are very minimal compared to other networks.…”

Section: Introductionmentioning

confidence: 99%

Arduino-based implementation of kinematics for a 4 DOF robot manipulator using artificial neural network

Rashid,

Sarhan,

Hassan

2024

Diagnostyka

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Real-time motion control is basically dependent on robot kinematics analysis where there is no unique solution of the inverse kinematics of serial manipulators. The predictive artificial neural network is a powerful one for finding appropriate results based on training data. Therefore, an artificial neural network is proposed to implement on Arduino microcontroller for a 4-DOF robot manipulator where the inverse kinematics problem was partitioned to suit the low specification of the board CPU. With MATALB toolbox, multiple neural network configuration based were trained and experienced for the best fit of the dimensionless feedforward data that is obtained from the forward kinematics of reachable workspace with average MSE of 6.5e-7. The results showed the superior of the proposed networks reducing the joints error by 90 % at least with comparing to traditional one. An Arduino on-board program was developed to control the robot independly based on pre validated parameters of the network. The experimental results approved the proposed method to implement the robot in real time with maximum error of (± 0.15 mm) in end effector position. The presented approach can be applied to achieve a suitable solution of n-DOF self-depend robot implementation using micro stepping actuators.

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Section: Introductionmentioning

confidence: 99%

Arduino-based implementation of kinematics for a 4 DOF robot manipulator using artificial neural network

Rashid,

Sarhan,

Hassan

2024

Diagnostyka

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“…Gao et al [16] proposed an excitation function-based improved BP neural network for robot inverse solution. Aravinddhakshan et al [17] completed the inverse solution of an industrial grade 5 degree of freedom manipulator with path planning through a neural network. Aydogmus et al [18] completed the inverse solution of a humanoid robotic arm by using Bayesian-optimized deep neural network structure.…”

Section: Introductionmentioning

confidence: 99%

Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network

Lin,

Xu,

et al. 2024

Applied Sciences

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In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm’s end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements.

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Inverse Kinematic Solution for 6-R Industrial Robot Manipulator Using Convolution Neural Network

Kumhar¹,

Kukshal²

2022

Lecture Notes in Mechanical Engineering

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Neural Network Based Inverse Kinematic Solution of a 5 DOF Manipulator for Industrial Application

Cited by 4 publications

References 6 publications

Arduino-based implementation of kinematics for a 4 DOF robot manipulator using artificial neural network

Arduino-based implementation of kinematics for a 4 DOF robot manipulator using artificial neural network

Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network

Inverse Kinematic Solution for 6-R Industrial Robot Manipulator Using Convolution Neural Network

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