https://hal.archives-ouvertes.fr/hal-02943285Bouhalassa, LoubnaLoubnaBouhalassaLaboratory of Power Systems, Solar Energy and Automation L.E.P.E.S.A - USTO MB - Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran]Benchikh, LaredjLaredjBenchikhIBISC - Informatique, BioInformatique, Systèmes Complexes - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-SaclayAhmed-Foitih, ZoubirZoubirAhmed-FoitihLaboratory of Power Systems, Solar Energy and Automation L.E.P.E.S.A - USTO MB - Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran]Bouzgou, KamelKamelBouzgouIBISC - Informatique, BioInformatique, Systèmes Complexes - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-SaclayPath Planning of the Manipulator Arm FANUC Based on Soft Computing TechniquesHAL CCSD2020Artificial IntelligenceNeural NetworkPath PlanningManipulator armGeometric ModelingKinematic ModelingFANUC Robot[SPI.AUTO] Engineering Sciences [physics]/Automatic[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingDavesne, Frédéric2020-09-18 21:38:302022-05-12 14:24:012020-09-21 17:04:35enJournal articlesapplication/pdf1In 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.