Local-Minimum Escaper: Programmatic Subgoal Generation for Robust Navigation in Unknown Environments

2026-09-30

AI Summary

This study addresses the problem of navigation stagnation caused by local minima for mobile robots in unknown environments by proposing the LME hierarchical framework. Relying solely on local observations, this framework generates interpretable sub-goals through explicit procedural mechanisms and heuristic geometric reasoning to guide robots out of dead ends. Its core innovation lies in being training-free and agnostic to the underlying planner, handling both local-minimum and non-local-minimum scenarios within a unified paradigm while offering high interpretability and strong generalizability. Simulation and real-world experiments demonstrate that the proposed method significantly enhances the escape capabilities of various planners and has been successfully deployed on both wheeled and quadrupedal robot platforms.

Abstract

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.

Problem

• mapless navigation
• local minima
• unknown environments
• mobile robots
• subgoal generation

Innovation

• Local-Minimum Escape
• Programmatic Subgoal Generation
• Mapless Navigation
• Hierarchical Framework
• Planner-Agnostic