Safe and efficient robot manipulation in uncertain clustered environments has been recognized to be a key element of future intelligent industrial robots. Unlike traditional robots that work in structured and deterministic environments, intelligent industrial robots need to operate in dynamically changing and stochastic environments with limited computation resources. This paper proposed a hierarchical long short term safety system (HLSTS), where the upper layer contains a long term planner for global reference trajectory generation and the lower layer contains a short term planner for real-time emergent safety maneuvers. Additionally, a hierarchical coordinator is proposed to enable smooth coordination of the two layers by compensating the communication delay through trajectory modification. The theoretical results verify that the long term planner can always find a feasible trajectory (feasibility guarantee); and the short term planner can guarantee safety in the probabilistic sense. The proposed architecture is validated in industrial settings in both simulations and real robot experiments, where the robot is interacting with randomly moving obstacles while performing a goal reaching task. Experimental results demonstrate that the proposed HLSTS framework not only guarantees safety but also improves task efficiency.

在不确定的集群环境中安全高效地操纵机器人已被认为是未来智能工业机器人的关键要素。与在结构化和确定性环境中工作的传统机器人不同，智能工业机器人需要在计算资源有限的动态变化和随机环境中运行。本文提出了一种分层长期短期安全系统（HLSTS），其中上层包含一个用于生成全局参考轨迹的长期规划器，下层包含一个用于实时紧急安全机动的短期规划器。此外，提出了一种分层协调器，通过轨迹修改补偿通信延迟，从而实现两层的平滑协调。理论结果验证了长期规划者总能找到一条可行的轨迹（可行性保证）；短期规划者可以保证概率意义上的安全。所提出的架构在模拟和真实机器人实验的工业环境中得到验证，其中机器人在执行目标到达任务时与随机移动的障碍物交互。实验结果表明，所提出的 HLSTS 框架不仅保证了安全性，而且提高了任务效率。机器人在执行目标达成任务时与随机移动的障碍物交互。实验结果表明，所提出的 HLSTS 框架不仅保证了安全性，而且提高了任务效率。机器人在执行目标达成任务时与随机移动的障碍物交互。实验结果表明，所提出的 HLSTS 框架不仅保证了安全性，而且提高了任务效率。