Robotic path planning using evolutionary neural network

Abstract

Path planning is a well-known problem in mobile robot. The robot needs to move from the starting point to the destination point and avoid obstacles. To create an agent that able to reach some destinations in the given environment, such agent need some abilities to processing a collection of information obtained by its sensors and roam freely in the environment. In this paper, we design mobile agents to solve local path planning problems in 3D environment by using Evolutionary Neural Network (ENN) algorithm. ENN combines Evolutionary Algorithm (EA) and Neural Network algorithm. We chose Genetic Algorithm (GA) for the EA part and designing a simple feed forward neural network for the neural networks part. We evaluate what kind of ENN configuration values that works best in a local path planning problem. Experiment results show that the lowest iterations rate is 1.8 with one hidden layer and 50 hidden nodes when the population size is 50.