Neuro-Fuzzy Navigation of a Mobile Robot in an Unknown Environment

Bouchiba, Fayçal; Nouibat, Wahid

doi:10.15866/ireaco.v8i3.6102

Cited by 1 publication

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“…The path planning of the end-effector can be defined as a sequence of translation and rotation from the starting position to the desired one, while avoiding static and dynamic obstacles in the workspace of the manipulator [1]- [2].…”

Section: Introductionmentioning

confidence: 99%

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Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques

Benchikh¹,

Ahmed‐Foitih²,

Bouzgou³

2020

IREACO

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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 (E c) generated by each obstacle shape; the second one is the trajectory length penalty (E l). 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.

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Section: Introductionmentioning

confidence: 99%

“…E is the transformation matrix from the tool frame to the end-effector frame, thus f E T can be written as follows: computing and identifying the terms of the two matrices of the equation(1)and(2), the following expression can be defined…”

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confidence: 99%