用大模型理解环境语义，让球形张拉整体机器人在未知场景中高效导航。
Don't Just Search, Understand: Semantic Path Planning Agent for Spherical Tensegrity Robots in Unknown Environments
- 引入大语言模型驱动的语义代理，根据环境动态调整感知范围。
- 1000次仿真中成功率100%，搜索空间比A*少37.2%且路径接近最优。
- 适合需要自主探索与复杂环境适应的机器人系统研究者。
具备固有动力学特性赋予其卓越抗扰性与适应性的球形张拉整体机器人是混合软硬结构的典型代表，也是优秀的移动平台。然而，在未知环境中为其规划路径面临巨大挑战，需在高效探索与鲁棒规划间取得平衡。传统路径规划器将环境视为几何网格，常导致冗余搜索，且在复杂场景中易失效，因其缺乏语义理解。为此，我们将未知环境中的路径规划重构为语义推理任务。提出基于大语言模型（LLM）的张拉整体机器人语义代理（SATPlanner），利用高层环境认知生成高效可靠的规划策略。SATPlanner核心为自适应观测窗口机制，借鉴LLM的‘快’与‘慢’思考范式，动态调节感知范围：在开阔区域缩小以快速穿越，在复杂障碍区扩大以推理布局。该机制使环境语义信念得以构建，搜索空间仅随路径长度线性增长（O(L)），同时保障路径质量。我们在1000次仿真中全面评估了SATPlanner，成功率达100%，显著优于其他实时规划算法。关键的是，其搜索空间较A*减少37.2%，且路径长度接近最优。最后，通过物理球形张拉整体机器人原型验证了SATPlanner的实际可行性。
原文摘要 · Abstract (English)
Endowed with inherent dynamical properties that grant them remarkable ruggedness and adaptability, spherical tensegrity robots stand as prototypical examples of hybrid softrigid designs and excellent mobile platforms. However, path planning for these robots in unknown environments presents a significant challenge, requiring a delicate balance between efficient exploration and robust planning. Traditional path planners, which treat the environment as a geometric grid, often suffer from redundant searches and are prone to failure in complex scenarios due to their lack of semantic understanding. To overcome these limitations, we reframe path planning in unknown environments as a semantic reasoning task. We introduce a Semantic Agent for Tensegrity robots (SATPlanner) driven by a Large Language Model (LLM). SATPlanner leverages high-level environmental comprehension to generate efficient and reliable planning strategies.At the core of SATPlanner is an Adaptive Observation Window mechanism, inspired by the "fast" and "slow" thinking paradigms of LLMs. This mechanism dynamically adjusts the perceptual field of the agent: it narrows for rapid traversal of open spaces and expands to reason about complex obstacle configurations. This allows the agent to construct a semantic belief of the environment, enabling the search space to grow only linearly with the path length (O(L)) while maintaining path quality. We extensively evaluate SATPlanner in 1,000 simulation trials, where it achieves a 100% success rate, outperforming other real-time planning algorithms. Critically, SATPlanner reduces the search space by 37.2% compared to the A* algorithm while achieving comparable, near-optimal path lengths. Finally, the practical feasibility of SATPlanner is validated on a physical spherical tensegrity robot prototype.
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