Core Viewpoint - The article discusses a new paradigm for drone navigation called "high-low drone collaboration," where a high-altitude drone acts as a "panoramic commander" for global perception and reasoning, while a low-altitude drone serves as a "ground scout" for precise navigation and target search, enabling quick target identification in complex environments [1]. Group 1: Navigation and Target Identification - The high-low drone collaboration allows for efficient target finding, as a single drone may either fly too high to see ground details or too low to recognize larger landmarks [1]. - The system can also locate small targets, such as a dog, through coordinated efforts [3]. - If the target has specific letters or descriptions, the drones can match these precisely [5]. - The drones can achieve accurate identification based on environmental details surrounding the target [7]. Group 2: Data Set and Framework Development - The research team constructed the HaL-13k dataset to support their tasks, enhancing the original UAV-Need-Help dataset with high-altitude drone trajectory and perception data [9][11]. - A collaborative framework named AeroDuo was designed and evaluated in the Openuav simulation environment, demonstrating effective balance among environmental coverage, navigation accuracy, and autonomy [9]. Group 3: Enhancements in Perception and Decision-Making - To improve the perception and decision-making capabilities of the high-low drone system, a multi-modal unified framework called Pilot-LLM was developed, utilizing large language models for multi-modal reasoning [13]. - A global map construction module was proposed to integrate historical information from high-altitude drones, enhancing environmental understanding and target localization [13]. - A lightweight decoder is used to generate target probability distribution maps, balancing exploration capabilities and spatial modeling effectiveness [14]. Group 4: Low-Altitude Drone Navigation Strategy - The low-altitude drone employs a three-stage navigation search strategy, starting with selecting high-confidence areas based on the high-altitude drone's predicted probability map [16]. - A reinforcement learning-based obstacle avoidance strategy is utilized for safe and flexible path execution [16]. - The collaborative model can be easily expanded to multiple drone cooperation, allowing the high-altitude drone to predict multiple potential target locations and assign tasks to various low-altitude drones using optimization algorithms [16]. Group 5: Real-World Application and Future Prospects - Optimizing action control ensures safe obstacle avoidance, and supplementing real-world data for model training aids in transitioning the high-low drone system from simulation to real-world scenarios [17]. - The research findings are set to be published in ACM MM 2025, indicating ongoing advancements in drone technology [10].