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Forward kinematic-like neural network for solving the 3D reaching inverse kinematics problems
(2017-11-03)
Srisuk, Pannawit
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Sento, Adna
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Kitjaidure, Yuttana
This paper presents the inverse kinematic solutions based on neural networks. General neural network approaches use data of the end-effector positions as an input and angle joints as an output to train the neural network for mapping the input to the output. However, the proposed method creates the custom networks from forward kinematic equations. This special structure makes the network like a position finder with ability to automatically adjust angle joints until the end-effector reaches the desired position by backpropagation with variable learning rate algorithm. Then, the solutions of angles can be found from the final weights and bias values. Moreover, the proposed network use less number of neurons and amount of the solution space is not depend on the training data. Finally, to evaluate the performance algorithm, the MATLAB Program is used to demonstrate a 4-DOF robotic arm movement in 3-dimensional. As a result, the proposed algorithm can help a robotic arm move to the desired position (3D reaching) quickly and correctly.
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Inverse kinematics solution using neural networks from forward kinematics equations
(2017-03-23)
Srisuk, Pannawit
;
Sento, Adna
;
Kitjaidure, Yuttana
This paper presents the inverse kinematics solution using the neural network for a robotic arm in 3-dimension. This paper creates neural networks to represent x, y and z position of the end-effector in the forward kinematics equations. The structure of the network has 4 layers; input layer, 2 hidden layers, and output layer. The input and output layers are defined as robotic arm angle and position of the end-effector, respectively. Then, the network updates the weights by the backpropagation with variable learning rate algorithm until reaching criteria that the output of the network is equal to the desired positions. Finally, the inverse kinematics solution is defined by the optimal weights of the network. To evaluate the performance algorithm, the MATLAB Program is used to demonstrate the robotic arm movement in 3-dimension. As a result, the proposed algorithm can help the robotic arm move to the desired position quickly and correctly.
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An intelligent system architecture for meal assistant robotic arm
(2017-03-23)
Sento, Adna
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Srisuk, Pannawit
;
Kitjaidure, Yuttana
A meal assistant robotic arm is necessary for disable people who cannot move by themselves such as paralysis patients, severely handicapped people, etc. Several research articles have been proposed. However, the system designs for the meal assistant robotic arm have been found for a small number of the improvements. Therefore, this paper presents a detailed study to innovate the meal assistant robotic arm. The proposed system design consists of four parts; 1) feature extraction algorithm using the Microsoft Kinect sensor to create the target position in 3-dimensional Cartesian coordinate, 2) inverse kinematic algorithm to convert the Cartesian coordinate into the joint angles, 3) controller algorithm. The proposed controller uses a new weight updating rule model of the neural network using multi-loop calculation based on the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, and 4) the 4-joint robotic arm. To evaluate the performances, the Matlab program is used to implement the overall system. The experimental results show that the meal assistant robotic arm system is able to track the human mouth in the 3-dimensional coordinate system.

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