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The rapid advancement of Deep Reinforcement Learning (DRL) has opened new opportunities for improving the autonomy and energy efficiency of Unmanned Aerial Vehicles (UAVs) in collaborative missions. In this paper, we propose E-MAPPO-CTDE (Energy-aware Multi-Agent Proximal Policy Optimization with Centralized Training and Decentralized Execution), a novel trajectory optimization framework designed for UAV swarms operating in constrained environments. Unlike existing approaches, E-MAPPO-CTDE integrates a realistic energy consumption model based on displacement, acceleration, and flight time into a centralized learning process that yields decentralized, real-time decision policies. Each UAV learns to generate collision-free, communication-aware, and energy efficient trajectories using a multi objective reward function. Through extensive simulations involving static obstacles and multiple agents, E-MAPPO-CTDE demonstrates significant energy savings and improved path coordination. These results confirm the potential of our framework for scalable deployment in real-world scenarios such as surveillance, search and rescue, and logistics.

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Intelligent Path Planning for UAV Swarms via Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Hafedh Jouini,
  • Hamza Gharsellaoui,
  • Mohamed Khalgui

摘要

The rapid advancement of Deep Reinforcement Learning (DRL) has opened new opportunities for improving the autonomy and energy efficiency of Unmanned Aerial Vehicles (UAVs) in collaborative missions. In this paper, we propose E-MAPPO-CTDE (Energy-aware Multi-Agent Proximal Policy Optimization with Centralized Training and Decentralized Execution), a novel trajectory optimization framework designed for UAV swarms operating in constrained environments. Unlike existing approaches, E-MAPPO-CTDE integrates a realistic energy consumption model based on displacement, acceleration, and flight time into a centralized learning process that yields decentralized, real-time decision policies. Each UAV learns to generate collision-free, communication-aware, and energy efficient trajectories using a multi objective reward function. Through extensive simulations involving static obstacles and multiple agents, E-MAPPO-CTDE demonstrates significant energy savings and improved path coordination. These results confirm the potential of our framework for scalable deployment in real-world scenarios such as surveillance, search and rescue, and logistics.