NRI: FND: Safe and Efficient Robot Collaboration System (SERoCS) for Next Generation Intelligent Industrial Co-Robots

在目前的自动化工厂中，人类和机器人通常是分开工作的，部分原因是安全原因，部分原因是完全的机器人自动化一直是一个目标。然而，近年来，人们已经认识到，当机器人走出笼子，被允许在一个共享的工作空间与人类工人合作时，会有巨大的机会。这种协作既利用了人类的智慧、适应性和灵活性，也利用了机器人的耐力、强度和可靠性。在人与机器人之间的任何协作（即协作机器人）中，重要的是要考虑并确保人的安全以及机器人的最佳性能。本项目旨在建立一套安全高效的机器人协作系统（SERoCS）的设计原则。在工厂之外，SERoCS还可以应用于其他环境，例如由机器人辅助人类移动，以及人类驾驶车辆和自动驾驶车辆共用同一条道路的自动驾驶情况。SERoCS由三部分组成：(1)环境监测的鲁棒认知算法，(2)安全人机协作的最优任务规划算法，以及(3)安全人机交互（HRI）的安全运动规划和控制算法。认知环境监测算法的研究涉及认知模型库的构建和人类行为在线预测与自适应算法的实现。此外，安全人机协作的任务规划算法需要构建一个从人类演示中学习到的运动技能库，并将其与使用所学技能进行在线任务规划和目标生成的算法相关联。安全运动规划和控制算法将采用两层结构，包括面向效率的长期规划层和面向安全HRIs的短期安全控制层。SERoCS将显著扩展协作机器人的技能集，并在操作过程中防止和最小化人机碰撞和机器人-机器人碰撞的发生。

In current automated factories, humans and robots typically work separately, partly for safety reasons and partly because full robotic automation has been a goal. In recent years, however, it has been recognized that there are tremendous opportunities when robots are brought out of their cages and allowed to collaborate with human workers in a shared workspace. Such collaboration takes advantage of both the intelligence, adaptability and flexibility of humans and the endurance, strength and reliability of robots. In any collaboration between humans and robots, i.e. co-robots, it is important to consider and ensure both the safety of the humans and the best performance of the robots. This project aims to establish a set of design principles for a safe and efficient robot collaboration system (SERoCS). Outside of factories, SERoCS may be applied in other settings, such as with mobility assistance of humans by robots and automated driving situations where human-driven vehicles and autonomous vehicles share the same road.SERoCS consists of three parts: (1) robust cognition algorithms for environment monitoring, (2) optimal task planning algorithms for safe human-robot collaboration, and (3) safe motion planning and control algorithms for safe human-robot interactions (HRI). Research on cognition environment monitoring algorithms involves the construction of a cognition model library and the implementation of an algorithm for online prediction and adaptation of human behavior. In addition, task planning algorithms for safe human-robot collaboration require the construction of a motion skill library learned from human demonstrations and its association with an algorithm for online task planning and objective generation using learned skills. The two-layer structure that will be employed for safe motion planning and control algorithms comprises a long-term, efficiency-oriented planning layer and a short-term, safety-oriented control layer for safe HRIs. The SERoCS will signicantly expand the skill sets of the co-robots and prevent and minimize occurrences of human-robot collision and robot-robot collision during operation.