Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost. This is especially crucial for real-world applications where robots need to navigate diverse terrains in outdoor environments with limited opportunities for recharging or refueling. Despite its practical importance, cost-efficient path planning across heterogeneous terrains has received relatively limited attention in prior work. In this paper, we propose LLM-Advisor, a prompt-based, planner-agnostic framework that leverages large language models (LLMs) as non-decisive post-processing advisors for cost refinement, without modifying the underlying planner. While we observe that LLMs may occasionally produce implausible suggestions, we introduce two effective hallucination-mitigation strategies. We further introduce two datasets, MultiTerraPath and RUGD_v2, for systematic evaluation of cost-efficient path planning. Extensive experiments reveal that state-of-the-art LLMs, including GPT-4o, GPT-4-turbo, Gemini-2.5-Flash, and Claude-Opus-4, perform poorly in zero-shot terrain-aware path planning, highlighting their limited spatial reasoning capability. In contrast, the proposed LLM-Advisor (with GPT-4o) improves cost efficiency for 72.37% of A*-planned paths, 69.47% of RRT*-planned paths, and 78.70% of LLM-A*-planned paths. On the MultiTerraPath dataset, LLM-Advisor demonstrates stronger performance on the hard subset, further validating its applicability to real-world scenarios.

翻译：跨多地形的成本高效路径规划是机器人导航中的关键任务，其目标是在起点与终点之间找到一条既能避开障碍物又能最小化总体行进成本的路径。这对于现实世界应用尤为重要，因为机器人需要在户外环境中穿越多样化地形，且充电或补充燃料的机会有限。尽管具有重要的实际意义，跨异构地形的成本高效路径规划在以往工作中受到的关注相对有限。本文提出LLM-Advisor，一个基于提示、与规划器无关的框架，该框架利用大语言模型作为非决策性后处理顾问进行成本优化，而无需修改底层规划器。虽然我们观察到LLM偶尔可能产生不合理的建议，但我们引入了两种有效的幻觉缓解策略。我们进一步引入了两个数据集MultiTerraPath和RUGD_v2，用于系统评估成本高效路径规划。大量实验表明，包括GPT-4o、GPT-4-turbo、Gemini-2.5-Flash和Claude-Opus-4在内的先进大语言模型在零样本地形感知路径规划中表现不佳，凸显了其有限的空间推理能力。相比之下，所提出的LLM-Advisor（使用GPT-4o）将A*规划路径的成本效率提升了72.37%，将RRT*规划路径提升了69.47%，将LLM-A*规划路径提升了78.70%。在MultiTerraPath数据集上，LLM-Advisor在困难子集上表现出更强的性能，进一步验证了其在现实场景中的适用性。