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The aim is to improve cooperative path planning for multiple UAVs using an enhanced reinforcement learning algorithm.

April 16, 2026ElectronicsOpen Access

Multi-UAV Cooperative Path Planning Method Based on an Improved MADDPG Algorithm

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The aim is to improve cooperative path planning for multiple UAVs using an enhanced reinforcement learning algorithm.
Developed a multi-agent deep deterministic policy gradient algorithm (PE-MADDPG) for path planning.
Constructed a simulation environment incorporating dynamic constraints and cooperation objectives.
Introduced prioritized experience replay to optimize sample usage during training.
Implemented an adaptive exploration-noise strategy to manage exploration capabilities.
Integrated a multi-head attention mechanism within the Critic network to better represent agent interactions.
PE-MADDPG achieved higher task completion rates compared to benchmark methods.
Demonstrated improved formation maintenance and flight efficiency during simulations.
Showed reduced energy consumption in urban inspection scenarios.

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This research demonstrates enhanced path planning efficiency for UAVs in urban inspections, indicating significant improvements in performance.

Zhang et al. (2026) studied this question.

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This research demonstrates enhanced path planning efficiency for UAVs in urban inspections, indicating significant improvements in performance.