Stable Nonlinear Position Control Law for Mobile Robot Using Genetic Algorithm and Neural Network

In this paper, adaptive tracking control is applied to improve performances of an underactuated quadrotor helicopter with respect to attitude and position control. Firstly, the dynamic model is presented. Then a new trajectory tracking algorithm is designed by using the sigma-pi neural network and backstepping. The paper designs the sigma-pi neural network compensation control law and gives the Lyapunov-type stability analysis. Then the corresponding numerical simulations are performed by using MATLAB. Simulation results are shown to demonstrate the effectiveness of the proposed control strategy, which could reduce tracking error, decrease tracking time, and improve the anti-interference ability of the system.

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Object learning and detection using evolutionary deformable models for mobile robot navigation

Robotica ◽

10.1017/s0263574707003633 ◽

2008 ◽

Vol 26 (1) ◽

pp. 99-107 ◽

Cited By ~ 10

Author(s):

M. Mata ◽

J. M. Armingol ◽

J. Fernández ◽

A. de la Escalera

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Mobile Robot ◽

Robot Navigation ◽

Vision System ◽

Deformable Models ◽

Mobile Robot Navigation ◽

Detailed Model ◽

Symbolic Information ◽

Object Learning

SUMMARYDeformable models have been studied in image analysis over the last decade and used for recognition of flexible or rigid templates under diverse viewing conditions. This article addresses the question of how to define a deformable model for a real-time color vision system for mobile robot navigation. Instead of receiving the detailed model definition from the user, the algorithm extracts and learns the information from each object automatically. How well a model represents the template that exists in the image is measured by an energy function. Its minimum corresponds to the model that best fits with the image and it is found by a genetic algorithm that handles the model deformation. At a later stage, if there is symbolic information inside the object, it is extracted and interpreted using a neural network. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented in this article, showing the effectiveness and capacity of the system.

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Neuro-Evolution of Mobile Robot Controller

MENDEL ◽

10.13164/mendel.2019.1.039 ◽

2019 ◽

Vol 25 (1) ◽

pp. 39-42

Author(s):

Ivan Sekaj ◽

Ladislav Cíferský ◽

Milan Hvozdík

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Mobile Robot ◽

Simulation Environment ◽

Robot Controller ◽

2D Simulation ◽

Neural Network Controller ◽

The Neural Network ◽

Network Controller ◽

Design For Control

We present a neuro-evolution design for control of a mobile robot in 2D simulation environment. The mobile robot is moving in unknown environment with obstacles from the start position to the goal position. The trajectory of the robot is controlled by a neural network – based controller which inputs are information from several laser beam sensors. The learning of the neural network controller is based on an evolutionary approach, which is provided by genetic algorithm.

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