VikPath：用于自监督路径规划中有效避障的视觉Kansformer框架

中文摘要 AI 辅助

路径规划是人工智能与自主系统中的基础问题。传统基于启发式的算法（如A*）依赖预定义的启发式函数引导搜索过程，虽在结构化环境中有效，但在复杂、障碍物密集的场景中搜索效率会大幅下降，此时手工设计的启发式函数能提供的指导十分有限。近期研究探索了基于学习的方法以提升路径规划效率，但多数现有方法依赖监督学习，需要由传统规划器生成的标签或手动标注的标签，因此其性能固有地受底层监督质量的影响，且当标注启发式函数无法捕捉复杂环境结构时性能会下降。此外，现有方法主要针对路径长度优化，对障碍物间距与轨迹平滑性关注有限，这会导致生成的路径在真实环境中难以执行或存在安全隐患。为解决这些局限，我们提出VikPath，这一自监督路径规划框架同时考虑障碍物邻近度与路径平滑性。其核心是我们提出的新型视觉Kansformer模块，无需依赖带标签的轨迹即可学习障碍物分布的表示，使模型能更好地适应复杂环境。我们进一步引入急转惩罚项，以鼓励生成更平滑、更具实际可执行性的路径。大量实验表明，与最先进（SOTA）方法相比，VikPath在保持生成路径平滑性的同时，平均障碍物间距提升3.28%，推理延迟降低87.07%。

英文摘要

Pathfinding is a fundamental problem in artificial intelligence and autonomous systems. Traditional heuristic-based algorithms, such as A*, rely on predefined heuristic functions to guide the search process. Although effective in structured environments, their search efficiency can degrade substantially in complex, obstacle-rich scenarios, where handcrafted heuristics may provide limited guidance. Recent studies have explored learning-based approaches to improve pathfinding efficiency; however, most existing methods rely on supervised learning and require labels generated by conventional planners or obtained through manual annotation. As a result, their performance is inherently influenced by the quality of the underlying supervision and may degrade when the labeling heuristics fail to capture complex environmental structures. Moreover, existing methods primarily optimize for path length while paying limited attention to obstacle clearance and trajectory smoothness, which can lead to paths that are difficult or unsafe to execute in real-world environments. To address these limitations, we propose $\Design$, a self-supervised pathfinding framework that jointly considers obstacle proximity and path smoothness. At its core, our novel \textit{Vision Kansformer} module learns representations of obstacle distributions without relying on labeled trajectories, enabling the model to better adapt to complex environments. We further introduce a sharp-turn penalty to encourage smoother and more practically executable paths. Extensive experiments demonstrate that, compared with state-of-the-art (SOTA) approaches, $\Design$ achieves an average of 3.28\% greater obstacle clearance and 87.07\% lower inference latency while maintaining smooth path generation.

发表机构

• University of California, Irvine（加利福尼亚大学欧文分校）

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