Neural Navigation Approach of an Autonomous Mobile Robot in a Partially Structured Environment

Chohra, Amine; Sif, F.; Talaoubrid, S.

doi:10.1016/b978-0-08-042366-1.50040-3

Cited by 11 publications

(28 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to this hierarchical structure, complexity of system has been reduced resulting in faster response time. Our work is similar to that reported in [6]. However, instead of using the third neural network, we have used simple decision logic to generate the final motion commands for the robot.…”

Section: Introductionsupporting

confidence: 61%

“…The performance of local model network is compared with both multilayer perceptron and radial basis function networks with time taken by the robot to reach the destination as performance index and is found to outperform both these networks. In [6], design of a navigation controller composed of three neural sub-networks is presented. The first two controllers are responsible for most important behaviors of intelligent vehicle namely target localization and obstacle avoidance.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments

Farooq¹,

Amar²,

Asad³

et al. 2014

IJCEE

View full text Add to dashboard Cite

Abstract-In this paper, design of an intelligent autonomous vehicle is presented that can navigate in noisy and unknown environments without hitting the obstacles in its way. The vehicle is made intelligent with the help of two multilayer feed forward neural network controllers namely 'Hurdle Avoidance Controller' and 'Goal Reaching Controller' with back error propagation as training algorithm. Hurdle avoidance controller ensures collision free motion of mobile robot while goal reaching controller helps the mobile robot in reaching the destination. Both these controllers are trained offline with the data obtained during experimental run of the robot and implemented with low cost AT89C52 microcontrollers. The computational burden on microcontrollers is reduced by using piecewise linearly approximated version of tangent-sigmoid activation function of neurons. The vehicle with the proposed controllers is tested in outdoor complex environments and is found to reach the set targets successfully. I. INTRODUCTIONNavigation is the ability of a mobile robot to reach the set targets by avoiding obstacles in its way. Thus essential behaviors for robot navigation are obstacle avoidance and goal reaching [1], [2]. Conventional control techniques can be used to build controllers for these behaviors; however, the environment uncertainty imposes a serious problem in developing the complete mathematical model of the system resulting in limited usability of these controllers. Thus some kind of intelligent controllers are required that can cope with the changing environment conditions. Amongst the various artificial intelligence techniques available in literature, neural networks offer promising solution to robot navigation problem because of their ability to learn complex non linear relationships between input sensor values and output control variables. This ability of neural networks has attracted many researchers across the globe in developing neural network based controllers for reactive navigation of mobile robots in indoor as well as outdoor environments. In [3], a collision free path between source and destination is constructed based on neural networks for mobile robot navigation in partially structured environments. The proposed scheme uses two neural networks to accomplish the task. First neural network is a principal component analysis (PCA) network with generalized Hebbin rule and is used to find a free space using ultrasonic range finder data. The second neural network is a multilayer perceptron (MLP) network with back-propagation training algorithm and is used to find a safe direction for robot movement while avoiding the nearest obstacles. The proposed scheme is implemented in real time on Intel Pentium 350 MHz processor and robot is found to avoid all the obstacles in reaching the destination from start point. In [4], kohonen and region-feature neural networks have been used to address global self localization problem of mobile robot which is an essential behavior to determine the current position of the robot duri...

show abstract

Section: Introductionsupporting

confidence: 61%

mentioning

confidence: 99%

Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments

Farooq¹,

Amar²,

Asad³

et al. 2014

IJCEE

View full text Add to dashboard Cite

show abstract

“…They include the artificial potential field methods, graph search techniques, road maps and neural network models. [15][16][17][18][19] While the algorithms generated from graph search techniques and road maps are elegant, they are computationally intensive and at times suffer from the problem of too close. 20 The neural network models, although robust are predominantly based on learning and estimations.…”

Section: Background On Collision Avoidance Schemesmentioning

confidence: 99%

Motion planning and posture control of multiple n-link doubly nonholonomic manipulators

2015

View full text Add to dashboard Cite

SUMMARYThe paper considers the problem of motion planning and posture control of multiple n-link doubly nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves), artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic mobile manipulators subjected to such constraints becomes therefore a challenging navigational and steering problem that few papers have considered in the past. Our approach to developing the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a Lyapunov control scheme that is not only intuitively understandable but also allows simple but rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that their final orientation approximated the desired orientation. Computer simulations illustrate these results.

show abstract

“…Mahmud et al [66] have presented the vision (camera) sensor based Kohonen-type artificial neural network for intelligent navigation of mobile robot. Chohra et al [67] have designed intelligent autonomous navigation structure for a vehicle using multi-layered neural networks (NN). Brahmi et al [68] have solved the path planning and localization problem of mobile robot using recurrent neural network (RNN).…”

Section: Hybridization Of Neural Network and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

176

View full text Add to dashboard Cite

Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

show abstract

Neural Navigation Approach of an Autonomous Mobile Robot in a Partially Structured Environment

Cited by 11 publications

References 10 publications

Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments

Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments

Motion planning and posture control of multiple n-link doubly nonholonomic manipulators

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Contact Info

Product

Resources

About