Neural Network Algorithm for Complete Coverage Path Planning in Industrial Robotic Platforms: A Simulation-Based Study

Abstract

This thesis discusses complete coverage path planning (CPP) algorithms used for robotic systems in dynamic and changing environments. The focus is on the Neural Network algorithm [9] and its adaptation for practical use on an industrial-ready robotic platform. Various approaches to CPP are described, including offline and online algorithms, and a structured approach using grid mapping-based methods. The thesis also mentions the physical implementation of the algorithm on a multi-robotic system and discusses the limitations of current methods for industrial applications. The objective of the research is to develop a system for complete coverage path planning with higher coverage completeness, lower path repetition rate, and less path execution time. The research is limited to real-world simulations using Gazebo World and Robot Operating System (ROS).