Paper Title
Designing a Neural Network to Train a Bot to Traverse through an Arbitrary Course

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.