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.

翻译：本文针对半静态环境中的过约束规划问题展开研究。规划目标是在避开所有硬约束区域的同时，最小化穿越最低风险区域的路径，以寻求最优可行解。传统方法通常依赖预定义的区域代价，限制了方法的泛化能力。此外，导航空间的连续性使得难以准确识别可通过区域而避免高估。为克服这些挑战，我们提出SuReNav——一种基于超像素图的约束松弛与导航方法，该方法模仿人类安全高效的导航行为。我们的框架包含三个组成部分：1）生成带区域约束的超像素图地图；2）利用基于人类演示数据训练的图神经网络进行区域约束松弛，以实现安全高效导航；3）通过松弛、规划与执行的交错迭代完成完整导航。我们在二维语义地图和OpenStreetMap三维地图上对所提方法与前沿基线进行了对比评估，结果表明该方法在保持效率与安全性平衡的同时，获得了最高的人类相似度评分。最后，我们通过四足机器人Spot在真实城市导航场景中验证了该方法具有良好的可扩展性与泛化性能。