Spiking based Cellular Learning Automata (SCLA) algorithm for mobile robot motion formulation

TL;DR

This paper introduces SCLA, a novel approach combining cellular automata and spiking neural networks to efficiently guide a mobile robot to its target from any starting point, improving path reinforcement and reducing training time.

Contribution

The paper presents a new integrated method called Spiking based Cellular Learning Automata (SCLA) for robot navigation, combining cellular automata with spiking neural networks for enhanced learning.

Findings

Reinforces proper navigation paths effectively.

Reduces training time for robot path learning.

Improves decision-making in dynamic environments.

Abstract

In this paper a new method called SCLA which stands for Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks. The environment consists of multiple squares of the same size and the robot only observes the neighboring squares of its current square. It should be stated that the robot only moves either up and down or right and left. The environment returns feedback to the learning automata to optimize its decision making in the next steps resulting in cellular automata training. Simultaneously a spiking neural network is trained to implement long term improvements and reductions on the paths. The results show that the integration of both cellular automata and spiking neural network ends up in reinforcing the proper paths and training time reduction at the same time.