一句话：将 Surrealist 仿真测试生成框架迁移至 ANYmal 四足机器人，并完成六个月工业评估。
动态环境下机器人导航的鲁棒性验证十分困难，传统测试难以覆盖完整的运行需求。本文介绍 Surrealist 这一原本面向 UAV 的基于仿真的测试生成框架在工业中的落地：利用基于搜索的算法自动生成具有挑战性的避障场景，并应用于 ANYmal 四足工业巡检机器人。试点阶段，生成的测试套件揭示了某实验算法的关键弱点（成功率 40.3%），同时为另一算法提供了优越鲁棒性的基准（成功率 71.2%）。随后该框架被集成进 ANYbotics 工作流并进行六个月工业评估，用于测试五个专有算法。正式调研确认其价值：改进开发流程、发现关键故障、提供客观基准并增强整体验证流水线。
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering failures often missed by manual testing. In a pilot phase, generated test suites revealed critical weaknesses in one experimental algorithm (40.3% success rate) and served as an effective benchmark to prove the superior robustness of another (71.2% success rate). The framework was then integrated into the ANYbotics workflow for a six-month industrial evaluation, where it was used to test five proprietary algorithms. A formal survey confirmed its value, showing it enhances the development process, uncovers critical failures, provides objective benchmarks, and strengthens the overall verification pipeline.
DOI 原文 ·
@inproceedings{paperbot9271,
title = {Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems},
author = {Sajad Khatiri and Francisco Eli Viña Barrientos and Maximilian Wulf and Paolo Tonella and Sebastiano Panichella},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00277}
}