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  • Open Access

    ARTICLE

    Enhanced Coverage Path Planning Strategies for UAV Swarms Based on SADQN Algorithm

    Zhuoyan Xie1, Qi Wang1,*, Bin Kong2,*, Shang Gao1

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3013-3027, 2025, DOI:10.32604/cmc.2025.064147 - 03 July 2025

    Abstract In the current era of intelligent technologies, comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring, emergency rescue, and agricultural plant protection. Owing to their exceptional flexibility and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have emerged as the ideal platforms for accomplishing these tasks. This study proposes a swarm A*-guided Deep Q-Network (SADQN) algorithm to address the coverage path planning (CPP) problem for UAV swarms in complex environments. Firstly, to overcome the dependency of traditional modeling methods on regular terrain environments, this study proposes an improved cellular decomposition… More >

  • Open Access

    ARTICLE

    Energy-Efficient UAVs Coverage Path Planning Approach

    Gamil Ahmed1, Tarek Sheltami1,*, Ashraf Mahmoud1, Ansar Yasar2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3239-3263, 2023, DOI:10.32604/cmes.2023.022860 - 09 March 2023

    Abstract Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most… More >

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