Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of robot systems, safety flaws often stay hidden, especially at early design stages, when a physical implementation is not yet available for testing. Simulation-based testing is a possible way to identify hazards in an early stage. However, creating simulation conditions in which hazards become observable can be difficult. Brute-force or Monte-Carlo-approaches are often not viable for hazard identification, due to large search spaces. This work addresses this problem by using a human model and an optimization algorithm to generate high-risk human behavior in simulation, thereby exposing potential hazards. A proof of concept is shown in an application example where the method is used to find hazards in an industrial robot cell.

翻译：在安全方面,必须彻底验证工业人类机器人合作系统。工人暴露的潜在危险越早,费用就越低。由于机器人系统的复杂性,安全缺陷往往隐藏在隐蔽的早期设计阶段,特别是在尚不能进行实际操作测试的早期阶段。模拟测试是早期识别危险的一种可能方式。然而,由于搜索空间大,创造危险难以观测的模拟条件,布鲁特力或蒙特-卡洛-方略往往不易识别危险。这项工作通过使用人造模型和优化算法来解决这一问题,在模拟中产生高风险人类行为,从而暴露潜在危险。在应用实例中展示了概念的证明,该方法被用于在工业机器人细胞中发现危险。