Publish In
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN
Journal Home
Volume Issue
Volume-7, Issue-7  ( Jul, 2019 )
Paper Title
Designing a Neural Network to Train a Bot to Traverse through an Arbitrary Course
Author Name
Chitrang Agarwal, Somesh Bhandarkar, Chirag Bharambe, Naimisha Churi, Sunil P. Khachane
Student, MCT’s Rajiv Gandhi Institute of Technology, Versova Andheri(West) Mumbai-400053, India Asst.Professor, MCT’s Rajiv Gandhi Institute of Technology,Versova Andheri(West) Mumbai-400053, India
In path planning operations, one of the fundamental issues is obstacle avoidance. If the machine can avert all static or dynamic obstacles effectively, path planning will become easier and more accurate. Genetic and neuroevolutionary algorithms have demonstrated to be effective for solving this optimization dilemma. These models mimic the concept of natural evolution and have the aptitude to search progressive spaces and make choices in the most optimal way. One direct application of these techniques is the development of automotive vehicles. In this paper we describe a neuro evolutionary approach that proposes the evolution of chromosome attitudes that helps us generate a neural network which in turn efficiently controls a simulated bot to avoid obstacles. The proposed project is a game that covers the interaction between entities namely bots, terrain, static obstacles and other such conflicts. In order to maximize efficiency, we have used multiple test cases enabling us to evaluate their results in a real‐ time scenario. Keywords - Neural Network, Genetic Algorithm, Neuro Evolution, Obstacle Avoidance.
  View Paper