Existing Q-Learning-based path planning methods face significant bottlenecks in large-scale collaboration, dynamic interference adaptation, and regional value differentiation, failing to meet the practical needs of mountain search and rescue. This study proposes HGR-QL, an optimized Q-Learning method for large-scale multi-UAV operations. Referencing remote sensing datasets, a 50 × 50 dynamic grid environment is constructed by integrating 20% fixed obstacles and 10 moving interference sources, highly simulating real mountain features. Integrating the individual Q-tables and the regional shared Q-tables, the hierarchical independent Q-table architecture is designed, balancing local autonomy and global collaboration. To guide UAVs focusing on remote sensing-identified high-value areas, an innovative multi-level gradient collision avoidance reward function is constructed, avoiding task deviation. Comparative experiments across three scenarios with four baselines and ablation tests validate the core modules. Results show HGR-QL outperforms peers in key metrics: in the dynamic interference scenario, it achieves a 74.47% task completion rate, 25.44 collisions, and a stable 100.00 ms communication delay. HGR-QL provides a lightweight, scalable solution, effectively enhancing the efficiency, safety, and stability of mountain search and rescue and supporting the “golden 72 h” rescue window.

In multi-UAV mountain search and rescue scenarios, the perception system of multi-UAV suffers from low utilization of noise resources, poor collaboration of multi-modal data, and a persistent imbalance between speed and detection accuracy. The paper proposes a federated multi-modal perception method based on terrain-adaptive variational positive-incentive noise (FedVPN). The framework transforms complex mountain interference into task-related beneficial noise, constructs a privacy-preserving federated multi-modal collaborative architecture for distributed feature fusion, and adopts a two-stage training pipeline. Under three typical scenarios, FedVPN outperforms all five baseline methods. In the basic scenario, it achieves an F1-score of 89.23% with a noise gain rate of 7.86%. Under dynamic interference conditions and large-scale heterogeneous environments, the performance decay is only 3.59% and the rescue response time is reduced to 48.60 s. The method significantly improves the accuracy, robustness, and efficiency of the perception module for autonomous rescue systems.