Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques
| Autor: | Kamel Bouzgou, Loubna Bouhalassa, Zoubir Ahmed-Foitih, Laredj Benchikh |
|---|---|
| Přispěvatelé: | Laboratory of Power Systems, Solar Energy and Automation L.E.P.E.S.A, Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB), Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Davesne, Frédéric |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Soft computing
Artificial neural network Computer science Path Planning Manipulator arm Neural Network Workspace [SPI.AUTO]Engineering Sciences [physics]/Automatic Computer Science::Robotics Kinematic Modeling [SPI.AUTO] Engineering Sciences [physics]/Automatic Geometric Modeling Control and Systems Engineering Control theory Artificial Intelligence FANUC Robot Robot Minification Motion planning Geometric modeling Robotic arm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | International Review of Automatic Control International Review of Automatic Control, 2020, 13 (4), pp.171--181 International Review of Automatic Control, Praise worthy prize, 2020, 13 (4), pp.171--181 |
| ISSN: | 1974-6059 2533-2260 |
| Popis: | International audience; In this paper, direct and inverse geometric models for a 6 degrees of freedom manipulator robot arm are developed, and a set of homogeneous matrices are generated by Denavit-Hartenberg formalism. Moreover, a path planning method based on soft computing techniques is presented, which consists of using the neural network to model the end-effector workspace, and then determining the optimal trajectory to reach a desired position. The optimization of the trajectory depends on the minimization of the cost function, defined by the sum of two energies. The first one is the collision penalty (Ec) generated by each obstacle shape; the second one is the trajectory length penalty (El). Four steps are considered; at first, the configuration of the robot workspace where any assumption is taken into account. The robot is assimilated to a particle that moves inside a small two-dimensional space, and the obstacle is a polygon. Second, the Multilayer Perceptron Neural Network (MLP neural network) is used for the robot workspace modeling. Model block inputs are robot’s previous position values and the outputs are robot’s following position values. Then, the proposed approach is applied to optimize the cost function and to determine the end-effector trajectory in case of obstacles. The obtained result is a smooth trajectory, which represents robot motion. Finally, the presented approach is validated on a FANUC robot arm in a virtual environment, where the simulation results show this approach efficiency. |
| Databáze: | OpenAIRE |
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