用自动化测试发现工业机器人导航的隐藏缺陷，提升可靠性。
Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
- 用搜索算法自动生成高难度避障场景。
- 测试暴露某算法仅40.3%成功率，另一算法达71.2%。
- 已在工业流程中落地，适合机器人开发与验证团队。
在动态环境中保障机器人导航鲁棒性是关键挑战，传统测试方法难以覆盖全部运行需求。本文将原本用于无人机的模拟测试生成框架Surrealist应用于ANYmal四足机器人进行工业巡检。该方法通过搜索算法自动构建具有挑战性的避障场景，揭示了人工测试常遗漏的故障。试点阶段，生成的测试集暴露出某实验算法仅40.3%的成功率，并作为客观基准验证了另一算法71.2%的更高鲁棒性。该框架随后被集成至ANYbotics工作流，进行了为期六个月的工业评估，用于测试五款专有算法。正式调查确认其价值：显著提升开发效率，发现关键缺陷，提供客观评测标准，强化整体验证流程。
原文摘要 · Abstract (English)
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.
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