Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation

2026-04-26

AI Summary

This work addresses the challenge of generating convex free regions that simultaneously account for robot geometry and feasible motion directions in unknown cluttered environments, where existing methods often fail to ensure collision-free trajectories over their entire duration due to discrete sampling. The paper proposes a novel navigation framework that integrates motion-direction awareness with geometric constraints to construct direction–geometry co-optimized convex free regions. By incorporating a Lipschitz continuity–based safety verification mechanism, the approach enables continuous and provably safe trajectory planning. Furthermore, it supports incremental construction of region maps and geometry-aware selection of goal poses. Evaluated in both 2D and 3D simulated environments as well as real-world scenarios, the method significantly enhances the compatibility of free regions with downstream path planning, enabling reliable collision-free navigation for legged robots and drones.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered environments. However, existing methods typically enlarge local collision-free regions mainly according to surrounding obstacle geometry. In cluttered environments, such strategies may fail to generate regions that both accommodate robot geometry and preserve traversable extension along candidate motion directions, thereby limiting downstream traversal, especially in narrow passages. Even when such a region is available, safe motion generation remains challenging, because safety checking at discretized trajectory samples does not guarantee continuously collision-free motion when robot geometry is modeled explicitly. To address these issues, we propose a navigation framework that jointly incorporates candidate motion directions and robot geometry into convex free-region generation, and achieves continuously collision-free motion through continuous-safe trajectory generation. Within each region, the framework performs geometry-aware target pose selection and trajectory generation, together with Lipschitz-based continuous safety certification and local refinement. The resulting free regions and candidate motions are maintained in a region-based graph to support incremental planning. Quantitative results in cluttered 2D navigation scenarios show that the proposed method generates free regions better aligned with downstream traversal and enables reliable collision-free navigation, while additional 3D and real-world experiments on a quadrupedal robot and a UAV demonstrate the extensibility and practical applicability of the framework. The open-source project can be found at https://github.com/ZhichengSong6/FRGraph.

Problem

• convex free regions
• robot navigation
• collision-free motion
• cluttered environments
• continuous safety

Innovation

• convex free-region generation
• direction-aware navigation
• continuous safety certification
• geometry-aware trajectory planning
• region-based graph