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.
Liu et al. (2026) studied this question.