Journal Article

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

Priya, B. Meenakshi, Denesh Babu, M., Maheswari, C.

2025

Overview

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.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Algorithms / Artificial intelligence / Decision making / Electric vehicles / Electronics in navigation / Energy management / global path planning / goal-seeking motion / Markov processes / Neural networks / Planning / reward function / robot navigation steering control / Robotics / Robots / Sensors / Threshold distance / vector angle