A novel intelligent mobile robot navigation technique for avoiding obstacles using RBF neural network

Panigrahi, Pratap Kumar; Ghosh, Sandip; Parhi, Dayal R.

doi:10.1109/ciec.2014.6959038

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…To achieve intelligent mobile robot control in unknown environment, Panigrahi et al [168] proposed a redial basis function (RBF) NN approach for mobile robot path planning. Farooq et al [169] also contributed by designing an intelligent autonomous vehicle capable of navigating noisy and unknown environment without collision using ANN.…”

Section: Artificial Neural Network Path Planning Methodmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

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Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

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Section: Artificial Neural Network Path Planning Methodmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…7(a) and Fig. 7(b) show the comparison of simulation result of proposed WNN algorithm with RBFN [14]. It is obvious that the speed of WNN algorithm is faster than RBFN with same environment.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The algorithm was developed to minimize the use of outer perimeter of obstacle by considering a few important points on the obstacle's perimeter to generate a complete path from starting point to a target. Panigrahi et al [14] have implemented a Radial Basis Function (RBF) neural network based path planning controller for navigation of mobile robot in unknown environment. The aim of the algorithm is to make collision free path when a mobile robot is allowed to move from starting point to a target point.…”

Section: Previous Workmentioning

confidence: 99%

Navigation of autonomous mobile robot using different activation functions of wavelet neural network

Panigrahi¹,

Ghosh²,

Parhi³

2015

Archives of Control Sciences

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An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

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“…The paper 79 has discussed about path planning using adaptive neuro-fuzzy inference system for mobile robot. Wavelet neural network for navigation of mobile robot has been discussed by Panigrahi et al [80][81][82] have focused on adaptive neuro fuzzy technique for control of mobile robot. Pham et al 83 have used neural network technique for path planning of mobile robots.…”

Section: Analysis Of Various Ai Techniques Used For Navigationmentioning

confidence: 99%

Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi¹

2018

IRATJ

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In this paper, a review analysis has been carried out on navigational methodologies of robots with the help of various artificial intelligence techniques such as Fuzzy Logic, Neural Network, Genetic Algorithm, Particle Swarm Optimisation (PSO) and other Artificial Intelligence techniques. During the survey analysis a systematic review has been done and shown how various artificial intelligence techniques can be successfully used for control and navigation of various types of robots subjected to various environmental conditions.

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A novel intelligent mobile robot navigation technique for avoiding obstacles using RBF neural network

Cited by 14 publications

References 10 publications

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Navigation of autonomous mobile robot using different activation functions of wavelet neural network

Advancement in navigational path planning of robots using various artificial and computing techniques

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