局部最小值逃逸器：未知环境中鲁棒导航的程序化子目标生成

中文摘要 AI 辅助

在未知和部分可观测环境中的无地图导航对移动机器人而言仍然具有挑战性，尤其是当局部最小值阻止机器人向目标前进时。现有的局部导航方法通常缺乏显式的逃逸机制，而深度强化学习（DRL）方法通常通过奖励设计和策略优化隐式地学习恢复行为。在本工作中，我们提出了LME（局部最小值逃逸器），一个程序化的分层框架，显式地生成和推理子目标以引导机器人脱离局部最小值区域。LME仅依靠局部观测，并使用可解释的启发式标准来选择候选子目标，这些标准同时考虑周围障碍物几何形状和候选位置的安全性。然后，局部规划器生成朝向所选子目标的低级运动指令。该设计使LME能够在统一框架内处理有和没有局部最小值的环境，同时保持对底层局部规划器的独立性，并且无需额外训练。在模拟和真实环境中的大量实验表明，LME提供了鲁棒的导航性能，并能泛化到具有挑战性的未见场景。此外，生成的子目标可用于引导不同的局部规划器，显著提高其逃逸局部最小值的能力。在差速驱动和四足机器人上的成功部署进一步证明了所提出框架的实际适用性和通用性。

英文摘要

Mapless navigation in unknown and partially observable environments remains challenging for mobile robots, particularly when local minima prevent the robot from making progress toward its goal. Existing local navigation methods often lack an explicit mechanism for escaping such situations, while deep reinforcement learning (DRL) approaches typically learn recovery behaviors implicitly through reward design and policy optimization. In this work, we propose \textbf{LME} (Local-Minimum Escaper), a programmatic hierarchical framework that explicitly generates and reasons subgoals to guide robots out of local-minimum regions. LME operates solely on local observations and selects candidate subgoals using interpretable heuristic criteria that account for both surrounding obstacle geometry and candidate-location safety. A local planner then generates low-level motion commands toward the selected subgoal. This design enables LME to handle environments both with and without local minima within a unified framework, while remaining independent of the underlying local planner and requiring no additional training. Extensive experiments in simulated and real-world environments demonstrate that LME provides robust navigation performance and generalizes to challenging unseen scenarios. Furthermore, the generated subgoals can be used to guide different local planners, substantially improving their ability to escape local minima. Successful deployments on both differential-drive and quadruped robots further demonstrate the practical applicability and generality of the proposed framework.

发表机构

• Eastern Institute of Technology（东方理工）
• Tsinghua University（清华大学）
• Polytechnic University（香港理工大学）

机构由 AI 辅助整理，请以论文原文为准。