Autonomous unmanned systems: traversing the bibliometric terrain of genetic algorithm-based path planning

Abstract

This paper presents a comprehensive bibliometric analysis of the field of Autonomous Unmanned Systems (AUS), focusing specifically on genetic algorithm-based path planning, from 1997 to 2023. The study aims to map the evolution, trends, and future directions in this dynamic domain, highlighting the growing significance of genetic algorithms in the advancement of AUS. Our methodology integrates advanced bibliometric and text mining techniques, utilizing data from Scopus to provide both quantitative and qualitative insights. The analysis covers a corpus of 504 documents from 326 sources, revealing an increasing trajectory in research publications, particularly from 2010 onwards. This trend reflects the expanding academic and industrial interest in more sophisticated and efficient path planning methods for AUS. The paper identifies key thematic clusters, including optimization algorithms, energy and path efficiency, and communication technologies, emphasizing the necessity of interdisciplinary approaches in the field. Despite significant progress, challenges remain in safety, regulatory compliance, and enhancing the robustness and energy efficiency of path planning algorithms. The findings indicate a shift from foundational research to more applied and specialized areas, with potential new directions focusing on refining algorithms for specific applications and exploring integration with emerging technologies. Our study provides a comprehensive overview of the development of genetic algorithm-based path planning in AUS, offering valuable insights for future research directions. It underscores the importance of this field in various sectors and its potential for significant advancements in operational efficiency and effectiveness of autonomous unmanned systems.