动态安全距离增强强化学习，让机器人在不确定环境中更安全地规划路径。
SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning
- 基于动态安全距离的强化学习框架，实时调整轨迹以应对未知环境。
- 在模拟与真实机器人上均实现更高成功率和更快计算效率。
- 适合对安全性要求高的移动机器人、自动驾驶等场景使用。
本研究提出一种基于动态安全距离的强化学习框架，用于动态不确定环境中的局部运动规划。该规划器结合实时轨迹优化与自适应间隙分析，可在部分可观测条件下有效评估可行性。为应对未知场景下的安全关键计算，引入增强型在线学习机制，通过构建动态安全距离实时修正空间轨迹，同时保持控制不变性。大量实验评估（包括消融研究与主流算法对比）表明，该框架在成功率和计算效率方面均表现优异。其有效性在仿真与物理机器人平台上均得到验证。
原文摘要 · Abstract (English)
This study presents a dynamic safety margin-based reinforcement learning framework for local motion planning in dynamic and uncertain environments. The proposed planner integrates real-time trajectory optimization with adaptive gap analysis, enabling effective feasibility assessment under partial observability constraints. To address safety-critical computations in unknown scenarios, an enhanced online learning mechanism is introduced, which dynamically corrects spatial trajectories by forming dynamic safety margins while maintaining control invariance. Extensive evaluations, including ablation studies and comparisons with state-of-the-art algorithms, demonstrate superior success rates and computational efficiency. The framework's effectiveness is further validated on both simulated and physical robotic platforms.
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