用张量压缩配置空间，实现高效几何感知的机器人路径规划
Coupling Tensor Trains with Graph of Convex Sets: Effective Compression, Exploration, and Planning in the C-Space
- 用张量列车压缩高维配置空间，降低计算复杂度
- 在仿真和真实机器人上实现更高质量的轨迹生成
- 适合需要高效路径规划的复杂场景机器人系统
我们提出TANGO（Tensor ANd Graph Optimization），一种融合张量压缩与结构化图优化的新式运动规划框架，可实现高效且可扩展的轨迹生成。传统基于优化的规划器如图结构凸集（GCS）虽能生成平滑最优轨迹，但通常依赖于高维配置空间的预定义凸表征，这对一般机器人任务而言往往不可行。TANGO进一步采用张量列车分解，以压缩形式近似可行配置空间，实现对任务相关区域的快速发现与估计。这些区域被嵌入类GCS结构中，支持兼顾系统约束与环境复杂性的几何感知规划。通过耦合张量压缩与结构化图推理，TANGO实现了高效、几何感知的运动规划，并为未来机器人系统提供更具表现力与可扩展性的配置空间表示。平面及真实机器人的严格仿真验证了其有效压缩能力与更优轨迹质量。
原文摘要 · Abstract (English)
We present TANGO (Tensor ANd Graph Optimization), a novel motion planning framework that integrates tensor-based compression with structured graph optimization to enable efficient and scalable trajectory generation. While optimization-based planners such as the Graph of Convex Sets (GCS) offer powerful tools for generating smooth, optimal trajectories, they typically rely on a predefined convex characterization of the high-dimensional configuration space-a requirement that is often intractable for general robotic tasks. TANGO builds further by using Tensor Train decomposition to approximate the feasible configuration space in a compressed form, enabling rapid discovery and estimation of task-relevant regions. These regions are then embedded into a GCS-like structure, allowing for geometry-aware motion planning that respects both system constraints and environmental complexity. By coupling tensor-based compression with structured graph reasoning, TANGO enables efficient, geometry-aware motion planning and lays the groundwork for more expressive and scalable representations of configuration space in future robotic systems. Rigorous simulation studies on planar and real robots reinforce our claims of effective compression and higher quality trajectories.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。