SuReNav: Superpixel Graph-based Constraint Relaxation for Navigation in Over-constrained Environments

2026-02-06

AI Summary

This work addresses the challenge of path planning in semi-static environments where hard constraints and risk zones coexist. The authors propose a constraint-relaxation navigation method based on superpixel graphs, which uniquely integrates superpixel representations with graph neural networks to learn intelligent relaxation of spatial constraints from human demonstrations. By employing an interleaved planning-and-execution framework, the approach ensures safety while enhancing navigation efficiency. Experimental results demonstrate that the method outperforms existing approaches in both 2D semantic maps and 3D urban environments derived from OpenStreetMap, achieving the highest human-likeness navigation score. Furthermore, its generalization capability and scalability are validated through real-world deployment on a Spot quadruped robot.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We address the over-constrained planning problem in semi-static environments. The planning objective is to find a best-effort solution that avoids all hard constraint regions while minimally traversing the least risky areas. Conventional methods often rely on pre-defined area costs, limiting generalizations. Further, the spatial continuity of navigation spaces makes it difficult to identify regions that are passable without overestimation. To overcome these challenges, we propose SuReNav, a superpixel graph-based constraint relaxation and navigation method that imitates human-like safe and efficient navigation. Our framework consists of three components: 1) superpixel graph map generation with regional constraints, 2) regional-constraint relaxation using graph neural network trained on human demonstrations for safe and efficient navigation, and 3) interleaving relaxation, planning, and execution for complete navigation. We evaluate our method against state-of-the-art baselines on 2D semantic maps and 3D maps from OpenStreetMap, achieving the highest human-likeness score of complete navigation while maintaining a balanced trade-off between efficiency and safety. We finally demonstrate its scalability and generalization performance in real-world urban navigation with a quadruped robot, Spot.

Problem

• over-constrained planning
• semi-static environments
• constraint relaxation
• navigation
• spatial continuity

Innovation

• superpixel graph
• constraint relaxation
• graph neural network
• human-like navigation
• over-constrained planning