Existing Active SLAM methodologies face issues such as slow exploration speed and suboptimal paths. To address these limitations, we propose a hybrid framework combining a Path-Uncertainty Co-Optimization Deep Reinforcement Learning framework and a Lightweight Stagnation Detection mechanism. The Path-Uncertainty Co-Optimization framework jointly optimizes travel distance and map uncertainty through a dual-objective reward function, balancing exploration and exploitation. The Lightweight Stagnation Detection reduces redundant exploration through Lidar Static Anomaly Detection and Map Update Stagnation Detection, terminating episodes on low expansion rates. Experimental results show that compared with the frontier-based method and RRT method, our approach shortens exploration time by up to 65% and reduces path distance by up to 42%, significantly improving exploration efficiency in complex environments while maintaining reliable map completeness. Ablation studies confirm that the collaborative mechanism accelerates training convergence. Empirical validation on a physical robotic platform demonstrates the algorithm's practical applicability and its successful transferability from simulation to real-world environments.

翻译：现有的主动SLAM方法面临探索速度慢、路径规划次优等问题。为克服这些局限，本文提出一种混合框架，结合了路径-不确定性协同优化深度强化学习框架与轻量化停滞检测机制。路径-不确定性协同优化框架通过双目标奖励函数联合优化行进距离与地图不确定性，实现探索与利用的平衡。轻量化停滞检测通过激光雷达静态异常检测与地图更新停滞检测减少冗余探索，并在扩展率较低时终止训练回合。实验结果表明，与前沿边界法及RRT方法相比，本方法在复杂环境中将探索时间缩短最高达65%，路径距离减少最高达42%，在保持可靠地图完整性的同时显著提升了探索效率。消融研究证实协同机制加速了训练收敛。在实体机器人平台上的实证验证表明，该算法具备实际应用可行性，并成功实现了从仿真环境到真实场景的迁移。