论文arXiv 2611.03254今天

TRAILS框架：自动生成满足运动学约束的室内导航仿真场景

针对机器人导航实验场景构建难题，提出TRAILS自动化框架。融合WFC布局生成与贝塞尔曲线轨迹规划，支持运动学约束下的参数化场景批量生成，代码已开源，显著提升遥操作与人机交互实验效率。

AI 整理

感知与导航
仿真与训练

本文由 AI 根据公开内容整理，可能有误，请以原文为准：TRAILS: Trajectory Generation for Automated Indoor Locomotion Studies（arXiv 2611.03254）

核心内容

针对虚拟现实与机器人导航研究中手动设计场景耗时且难以保证多样性的痛点，论文提出TRAILS自动化框架。该系统能一键生成包含环境布局、路径及时间参数化运动剖面的完整虚拟导航场景，有效解决受控实验中轨迹重复与几何/运动学约束冲突问题，相关代码已在GitHub开源，为具身智能研究提供标准化测试基准。

技术上采用受波函数坍缩（WFC）启发的约束式程序化环境生成器构建含走廊与障碍物的室内布局，结合优化的三次Bézier曲线生成轨迹，并施加运动学约束生成速度剖面。系统通过显式种子控制算法而非数据驱动模型，确保生成过程透明可复现；用户仅需少量直观参数即可批量产出时长一致但几何形态、贴墙距离及速度特征解耦的多样化场景，适配遥操作与人因实验需求。

关键看点

环境生成：基于波函数坍缩算法程序化构建室内布局，自动包含走廊、开放空间及障碍物结构
轨迹规划：采用优化三次Bézier曲线生成路径，结合运动学约束计算时间参数化速度剖面
变量解耦：生成等时长轨迹集，实现路径几何、墙壁接近度与速度特征的最小化系统依赖
工程特性：基于显式种子控制算法保障可复现性，仅需少量参数即可批量生成合规场景

工程与产业观察

该框架填补了具身智能研究中高保真、可配置仿真场景自动生成工具的空白。其显式算法路线规避了生成式AI的不确定性，特别适用于需要严格变量控制的遥操作与人因工程实验，为机器人导航算法评测提供了标准化、可复现的数据底座，降低了高质量仿真数据集的构建门槛。

英文摘要 (Abstract)

Virtual reality experiments involving navigation and simulated robot motion often require large, realistic environments and numerous distinct trajectories to avoid repetitive navigation while providing sufficient variation for controlled experiments. Designing such scenarios manually is labor-intensive, particularly when trajectories must satisfy predefined geometric and kinematic constraints while remaining diverse and comparable across experimental conditions. This paper presents TRAILS, an automated framework for generating complete virtual navigation scenarios consisting of an environment layout, a path, and a time-parameterized motion profile. The framework combines a constraint-based procedural environment generator inspired by Wave Function Collapse, trajectory generation using optimized cubic B'ezier curves, and speed-profile generation subject to kinematic constraints. The generated scenarios provide diverse indoor environments with corridors, open spaces, and obstacles, together with trajectories of identical duration that vary in path geometry, wall proximity, and velocity profiles while minimizing systematic dependencies between these characteristics. The framework is designed as an easy-to-use tool requiring only a small number of intuitive user-defined parameters to automatically generate large sets of distinct navigation scenarios satisfying prescribed geometric and kinematic constraints. By relying on explicit, seed-controlled algorithmic procedures rather than data-driven models, the generated scenarios remain transparent, reproducible, and configurable, making the framework well suited for telepresence and human-subject experiments. The code is available at https://github.com/JucciK/Trails .

作者：Juho Kalliokoski, Evan G. Center, Steven M. LaValle, Timo Ojala, Basak Sakcak