A fuzzy logic positioning system for an articulated robot arm

Martinez, J.; Bowles, J.B.; Mills, Patrick M.

doi:10.1109/fuzzy.1996.551750

Cited by 9 publications

(4 citation statements)

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“…Here, the δx, δy, δz, d , d and d generalized error parameters. Usually generalized parameters are introduced on the basis of different approximations, such as assumed modes, finite elements, or Ritz-Kantorovich expansions, with different implications on the model complexity and accuracy [9].…”

Section: Fig 1 Frame Displacement Due To Errorsmentioning

confidence: 99%

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Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network

Al-Khafaji¹,

Jweeg

2017

alkej

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The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinematic equation. To feed the neural network, experimental data were taken from an elastic robot arm for training the network, these data presented by joint angles, deformation variables and end-effector positions. The results of network training showed a good fit between the output results of the neural network and the targets data. In addition, this method for finding the inverse of kinematic equation proved its effectiveness and validation when applying the results of neural network practically in the robot’s operating software for controlling the real light robot’s position.

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Section: Fig 1 Frame Displacement Due To Errorsmentioning

confidence: 99%

“…• The homogenous transformation matrix between the O I and O 1 is presented by the matrix A 1 in equation (9).…”

Section: Fig 1 Frame Displacement Due To Errorsmentioning

confidence: 99%

Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network

Al-Khafaji¹,

Jweeg

2017

alkej

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“…Fuzzy logic [21] has only considered membership functions that have been fixed and somewhat arbitrarily chosen. In addition, we have to apply fuzzy inference to modeling systems whose rule structure is essentially predetermined by the user's interpretation of the characteristics of the variables in the model.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], a fuzzy reasoning system was applied for the positioning system of a non-redundant manipulator based on heuristic techniques. However, in this work, the path planning and trajectory planning were not considered and any performance criteria were not considered.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning of redundant manipulator using fuzzy clustering method

Agarwal

2011

Int J Adv Manuf Technol

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The solution of inverse kinematics and trajectory planning with performance criteria for a redundant manipulator is proposed with modification in fuzzy c-means. A new fuzzy clustering model based on a new generalized validity index based on weighted within-scatter metrics and betweencluster scatter metrics for the manipulator is proposed. In order to understand the proposed algorithm and to show its performance, two simulation studies of trajectory planning with manipulability criteria for a redundant manipulator are modeled to solve the problems of finding association rules in the data and of setting up an appropriate classification procedures. The problem of redundant manipulator (which is a multi-input, multi-output nonlinear system) is new in terms of solution by clustering method. The proposed algorithm for the trajectory planning of the manipulator is simulated using Matlab®. All practical steps, from data acquisition to model validation, are illustrated using a 4 degree of freedom robot manipulator. The simulated results are compared with the numerical methods of the trajectory planning. The results are presented graphically. The proposed method has the advantage of simplicity, flexibility, and good tracking performance.

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