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Research Article

Year : 2015 | Volume: 1 | Issue: 1 | Pages: 46-56

Navigational Analysis for under water Mobile Robot based on Multiple ANFIS Approach

Shubhasri Kundu1*, Dayal R.Parhi2

doi:10.18831/james.in/2015011005

Corresponding author

Shubhasri Kundu*

Department of Mechanical Engineering, NIT Rourkela, Rourkela, India

  • 1. Department of Mechanical Engineering, NIT Rourkela, Rourkela, India

Received on: 06/03/2015

Revised on: 07/04/2015

Accepted on: 08/08/2015

Published on: 08/11/2015

  • Navigational Analysis for under water Mobile Robot based on Multiple ANFIS Approach, Shubhasri Kundu, Dayal R.Parhi., 08/11/2015, Journal of Advances in Mechanical Engineering and Science, 1(1), 46-56, http://dx.doi.org/10.18831/james.in/12015011005.

    Published on: 08/11/2015

Abstract

Multiple neuro-fuzzy inference systems using hybrid learning algorithm as an adaptation mechanism have been focused here for navigation of autonomous underwater vehicle (AUV).The underwatervehiclecanbeexhibitedassix-dimensionalnonlinearandcoupled equations of motion associated with variations of hydro dynamic coefficients which are difficult to model in a realistic manner. Without earlier acquaintance, the feed-forward neuro-fuzzy controller can be directed to obtain the unknown parameters of the model which may aid motion planning strategy of underwaterrobotbyoverlookingthenonlineareffectsoftheAUVdynamics.By amending fuzzy membership function of neural networks, the benefits of fuzzy logic and neural network can be mingled, such as capability of FIS to deal with uncertainty, employing human perception and comprehensive approximation as well as adapting competence of neural networks. ANFIS has been trained with the hybrid-learning mechanism which employs back-propagation-based gradient descent approach and least squares estimate (LSE)to estimate parameters of the model. This approach instigates faster decision- making, obstacle avoidance and also tracking targets. The simulated analysis may authenticate that the heuristic navigational approach is able to negotiate with chaotic environment during navigation of under-water robot.

Keywords

ANFIS, Hybrid learning, Optimal path, Obstacle avoidance, Steering angle, Target seeking behavior.