# Dynamic Robot Navigation in Confined Indoor Environment: Unleashing the Perceptron-Q Learning Fusion

## TL;DR

This paper introduces a new robot navigation model that combines perceptron learning and Q-learning to improve performance in dynamic indoor environments.

## Contribution

The novel perceptron-Q learning fusion (PQLF) model enhances robot navigation in dynamic, confined spaces.

## Key findings

- The PQLF model achieves a reduced moving cost of 1.1 and a detour percentage of 7.8%.
- The model outperforms existing methods in dynamic robot navigation scenarios.
- The proposed model uses sensor data to make real-time decisions in a Markov Decision Process framework.

## Abstract

Robot navigation in confined spaces has gained popularity in recent years, but offline planning assumes static obstacles, which limits its application to online path-planning. Several methods have been introduced to perform an efficient robot navigation process. However, various existing methods mainly depend on pre-defined maps and struggle in a dynamic environment. Also, diminishing the moving costs and detour percentages is important for real-world scenarios of robot navigation systems. Thus, this study proposes a novel perceptron-Q learning fusion (PQLF) model for Robot Navigation to address the aforementioned difficulties. The proposed model is a combination of perceptron learning and Q-learning for enhancing the robot navigation process. The robot uses the sensors to dynamically determine the distances of nearby, intermediate, and distant obstacles during local path-planning. These details are sent to the robot’s PQLF Model-based navigation controller, which acts as an agent in a Markov Decision Process (MDP) and makes effective decisions making. Thus, it is possible to express the Dynamic Robot Navigation in a Confined Indoor Environment as an MDP. The simulation results show that the proposed work outperforms other existing methods by attaining a reduced moving cost of 1.1 and a detour percentage of 7.8%. This demonstrates the superiority of the proposed model in robot navigation systems.