scholarly journals A Behaviour-Based Architecture for Mapless Navigation Using Vision

10.5772/46200 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Mehmet Serdar Guzel ◽  
Robert Bicker
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Autonomous Robots ◽  
Monocular Vision ◽  
Challenging Problem ◽  
Simulation Environment ◽  
Dynamic Changes ◽  
Cluttered Environment ◽  
Navigation Strategy ◽  
Novel Method

Autonomous robots operating in an unknown and uncertain environment must be able to cope with dynamic changes to that environment. For a mobile robot in a cluttered environment to navigate successfully to a goal while avoiding obstacles is a challenging problem. This paper presents a new behaviour-based architecture design for mapless navigation. The architecture is composed of several modules and each module generates behaviours. A novel method, inspired from a visual homing strategy, is adapted to a monocular vision-based system to overcome goal-based navigation problems. A neural network-based obstacle avoidance strategy is designed using a 2-D scanning laser. To evaluate the performance of the proposed architecture, the system has been tested using Microsoft Robotics Studio (MRS), which is a very powerful 3D simulation environment. In addition, real experiments to guide a Pioneer 3-DX mobile robot, equipped with a pan-tilt-zoom camera in a cluttered environment are presented. The analysis of the results allows us to validate the proposed behaviour-based navigation strategy.

Neural network and fuzzy logic techniques based collision avoidance for a mobile robot

Robotica ◽  
1997 ◽  
Vol 15 (6) ◽  
pp. 627-632 ◽  
Author(s):  
Minglu Zhang ◽  
Shangxian Peng ◽  
Qinghao Meng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Perceptual Space ◽  
Space Partitioning ◽  
Reactive Navigation ◽  
3 Dimensional ◽  
Sensory Data ◽  
Cluttered Environment

This paper is concerned with a mobile robot reactive navigation in an unknown cluttered environment based on neural network and fuzzy logic. Reactive navigation is a mapping between sensory data and commands without planning. This article's task is to provide a steering command letting a mobile robot avoid a collision with obstacles. In this paper, the authors explain how to perform a currently perceptual space partitioning for a mobile robot by the use of an ART neural network, and then, how to build a 3-dimensional fuzzy controller for mobile robot reactive navigation. The results presented, whether experimented or simulation, show that our method is well adapted to this type of problem.

Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

Robotica ◽  
2019 ◽  
Vol 38 (9) ◽  
pp. 1627-1641
Author(s):  
Krishna Kant Pandey ◽  
Dayal R. Parhi
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Target Position ◽  
Reactive Control ◽  
Neural Network Approach ◽  
Cluttered Environment ◽  
Reactive Behavior ◽  
Behavior Based ◽  
And Performance ◽  
Point Detection

SUMMARYNavigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time.

New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

2016 ◽  
Vol 13 (2) ◽  
pp. 129-141 ◽  
Author(s):  
Anish Pandey ◽  
Dayal R. Parhi
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Network Architecture ◽  
Control Technique ◽  
Neural Network Architecture ◽  
Distance Information ◽  
Content Type ◽  
Cluttered Environment ◽  
Neural Network Controller ◽  
Network Controller

Purpose This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment. Design/methodology/approach Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment. Findings Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments. Originality/value The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.

scholarly journals Neuro-Evolution of Mobile Robot Controller

MENDEL ◽  
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.

scholarly journals DEVELOPMENT OF ARTIFICIAL NEURAL NETWORK STRUCTURES FOR PREDICTING NAVIGATION TASKS OF A MOBILE ROBOT

2020 ◽  
Vol 7 (3) ◽  
pp. 42-50
Author(s):  
Irem Sahmutoglu ◽  
Erhan AKDOGAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Robot ◽  
Trajectory Optimization ◽  
Mobile Robot Navigation ◽  
Network Structures ◽  
Multi Layer Perceptron ◽  
Simulation Environment ◽  
Network Output ◽  
Artificial Neural

Determining trajectories in mobile robot navigation tasks is a difficult process to apply with conventional methods. Therefore, intelligent techniques produce highly effective results in trajectory optimization and orientation prediction. In this study, two different ANN (Artificial Neural Network) structures have been developed for the navigation prediction of the SCITOS G5 mobile robot. For this aim, RBF (Radial Basis Function) and MLP (Multi-Layer Perceptron) structures were used. Information obtained from 24 sensors of the robot was used as network inputs and network output determines robot direction. Accordingly, structures that have 24 inputs and one output were created. The best performance network structures obtained were compared among them in simulation environment. Accordingly, RBF has been observed to produce more accurate results than MLP.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

scholarly journals A Novel Method for Credit Scoring based on Cost-sensitive Neural Network Ensemble

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wirot Yotsawat ◽  
Pakaket Wattuya ◽  
Anongnart Srivihok
Keyword(s):  
Neural Network ◽  
Credit Scoring ◽  
Neural Network Ensemble ◽  
Novel Method
Sign in / Sign up

Export Citation Format

Share Document