Neural Networks in Mobile Robot Motion

TL;DR

This paper presents a neural network-based approach for autonomous mobile robot path planning and obstacle avoidance in partially structured environments, utilizing ultrasound data and two neural networks for safe navigation.

Contribution

It introduces a novel method combining two neural networks to determine free space and safe directions, enhancing autonomous robot motion planning in complex environments.

Findings

Successful simulation of collision-free paths

Effective obstacle avoidance with neural networks

Potential for real-time autonomous navigation

Abstract

This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the "free" space using ultrasound range finder data. The second neural network "finds" a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.