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In recent years, dronesDrone have emerged as a prominent platform for implementing control systems in path planningPath planning. Among these systems, reinforcement learningReinforcement Learning (RL) has garnered significant attention for its potential applications. ThisReinforcement LearningDrone chapter presents a bibliometric analysis of research articles that apply reinforcement learningReinforcement Learning techniques for droneDrone path planningPath planning, as published and indexed in Scopus. Ninety-eight research articles published between 2018 and August 2024 were analyzed. The bibliometric maps were generated using the VOS Viewer software tool and Litmaps. The findings reveal incremental advancements in the field, including trends in annual publications, leading authors, funding sponsors, areas of study, types of publications, and contributions from leading countries, along with correlations to key aspects of reinforcement learningReinforcement Learning methodologies. Notable observations include a surge in publications during 2023 and 2024, conference papers predominance, and computer science’s significant influence. Reinforcement learningReinforcement Learning shows great potential for enhancing droneDrone autonomy, efficiency, and adaptability. These insights aim to guide future research efforts in optimizing RL techniques for UAV operations.

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Reinforcement Learning for Drone Path Planning: A Bibliometric Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Susana Borrego-Dominguez,
  • Laura Jiménez-Beristain,
  • Enrique Efrén García-Guerrero,
  • José Jaime Esqueda-Elizondo,
  • César Ortega-Corral,
  • Ulises Jesús Tamayo-Pérez,
  • Diego Armando Trujillo-Toledo,
  • Oscar Adrian Aguirre-Castro,
  • César Alberto López-Mercado,
  • Everardo Inzunza-Gonzaález

摘要

In recent years, dronesDrone have emerged as a prominent platform for implementing control systems in path planningPath planning. Among these systems, reinforcement learningReinforcement Learning (RL) has garnered significant attention for its potential applications. ThisReinforcement LearningDrone chapter presents a bibliometric analysis of research articles that apply reinforcement learningReinforcement Learning techniques for droneDrone path planningPath planning, as published and indexed in Scopus. Ninety-eight research articles published between 2018 and August 2024 were analyzed. The bibliometric maps were generated using the VOS Viewer software tool and Litmaps. The findings reveal incremental advancements in the field, including trends in annual publications, leading authors, funding sponsors, areas of study, types of publications, and contributions from leading countries, along with correlations to key aspects of reinforcement learningReinforcement Learning methodologies. Notable observations include a surge in publications during 2023 and 2024, conference papers predominance, and computer science’s significant influence. Reinforcement learningReinforcement Learning shows great potential for enhancing droneDrone autonomy, efficiency, and adaptability. These insights aim to guide future research efforts in optimizing RL techniques for UAV operations.