自动化大规模机器人场景验证，提升测试可复现性与效率
RoboVAST: Automated Scenario-Based Validation of Robots at Scale
- 用组合式建模和声明式配置定义测试场景
- 在5个室内地图上完成超10万次运行，累计模拟1800小时
- 适合需要大规模可靠验证的机器人研发团队
机器人系统验证高度依赖测试环境。传统场景选择与调整多为人工、经验驱动，难以扩展，影响结果可复现性与结论可信度。本文提出一种基于场景的方法论，通过组合式建模与形式化描述，明确场景维度的变异、实例化、执行与解读方式。在此基础上，构建RoboVAST框架，支持声明式验证计划、插件式场景生成及容器化可扩展执行，并集成结果分析。实验基于导航数据集，涵盖5480种场景配置，在5个室内地图上进行超过10万次运行，覆盖不同路径、传感器噪声、软件参数与障碍物设置，总计模拟运行超过1800小时，行驶距离达1873公里。每个配置重复20次，有效区分系统性故障与随机异常。
原文摘要 · Abstract (English)
Validation of robotic systems critically depends on the operating conditions under which they are assessed. Scenario selection and variation are often manual, experience-driven, and difficult to scale, which harms reproducibility and weakens validation conclusions. We propose a scenario-based methodology that models scenarios compositionally and formalizes how these dimensions are varied, instantiated, executed, and interpreted. Building on this, we introduce RoboVAST, a framework that realizes declarative campaign specifications, plugin-based scenario generation, and scalable containerized execution with integrated result analysis. We demonstrate the approach with a navigation dataset comprising 5480 scenario configurations and over 100000 execution runs across five indoor maps with varied paths, sensor noise, software parameters, and obstacle settings, totaling more than 1800 hours of simulated operation and 1873 km traveled. Twenty repetitions per configuration allow us to distinguish systematic failures from stochastic anomalies.
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