用大模型思维优化机器人路径规划，提升动态环境下的安全性和成功率。
Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach
- 引入类大模型启发的语义风险代价函数，指导A*搜索避开高危区域。
- 在15×15网格中成功率达62.0%，较基线提升9.7%以上。
- 适合需要高安全性的自主导航场景，如仓储、医疗配送。
将大语言模型推理思想融入经典机器人路径规划是新兴方向。本文提出一种语义风险感知启发式（SRAH）规划器，将类大模型的代价函数嵌入A*框架，惩罚几何杂乱或高风险区域，并在检测到动态障碍时启动闭环重规划。在15×15网格世界中，静态障碍密度20%，随机动态障碍条件下，200次随机试验显示，SRAH任务成功率62.0%，优于广度优先搜索（BFS，56.5%）9.7%相对提升，远超无重规划贪心算法（4.0%）。进一步分析显示，语义代价塑造在不同障碍密度下均提升导航性能，证明轻量级类大模型启发式能显著增强自主机器人导航的安全性与鲁棒性。
原文摘要 · Abstract (English)
The integration of Large Language Model (LLM) reasoning principles into classical robot path planning represents a rapidly emerging research direction. In this paper, we propose a Semantic Risk-Aware Heuristic (SRAH) planner that encodes LLM-inspired cost functions penalising geometrically cluttered or high-risk zones into an A$^*$ search framework, augmented with closed-loop replanning upon dynamic obstacle detection. We evaluate SRAH against two established baselines Breadth-First Search (BFS) with replanning and a Greedy heuristic without replanning across 200 randomised trials in a $15{\times}15$ grid-world with 20\% static obstacle density and stochastic dynamic obstacles. SRAH achieves a task success rate of 62.0\%, outperforming BFS (56.5\%) by 9.7\% relative improvement and Greedy (4.0\%) by a large margin. We further analyse the trade-off between planning overhead, path efficiency, and failure-recovery count, and demonstrate via an obstacle-density ablation that semantic cost shaping consistently improves navigation across environments of varying difficulty. Our results suggest that even lightweight, LLM-inspired heuristics provide measurable safety and robustness gains for autonomous robot navigation.
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