通过优化路径与地图不确定性，提升机器人探索效率
PUL-SLAM: Path-Uncertainty Co-Optimization with Lightweight Stagnation Detection for Efficient Robotic Exploration
- 双目标奖励机制联合优化路径长度与地图不确定性
- 探索时间缩短65%，路径距离减少42%，复杂环境表现优
- 轻量级停滞检测避免重复探索，适合真实机器人部署
现有主动SLAM方法存在探索速度慢、路径不优的问题。为此，提出一种结合路径-不确定性协同优化的深度强化学习框架与轻量级停滞检测机制的混合框架。路径-不确定性协同优化框架通过双重目标奖励函数，同时优化旅行距离与地图不确定性，平衡探索与利用。轻量级停滞检测通过激光雷达静态异常检测和地图更新停滞检测，在扩张率低时终止探索回合，减少冗余探索。实验结果表明，相比基于前缘的方法和RRT方法，本方法在复杂环境中将探索时间缩短最多65%，路径距离减少最多42%，显著提升探索效率并保持地图完整性。消融实验证实协同机制加速训练收敛。在物理机器人平台上的实证验证表明该算法具备实际应用价值，并成功实现从仿真到真实环境的迁移。
原文摘要 · Abstract (English)
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.
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