An open extended reality platform supporting dynamic robot paths for studying human-robot collaboration in manufacturing
参考中译:支持动态机器人路径的开放延展实境平台，用于研究制造业中的人机协作


          

刊名：The International Journal of Advanced Manufacturing Technology
作者：Angelidis, Antonios（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
Plevritakis, Emmanuel（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
Vosniakos, George-Christopher（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
Matsas, EliasAngelidis, Antonios（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, GreeceNatl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
Plevritakis, Emmanuel（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
Vosniakos, George-Christopher（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
Matsas, Elias（Natl Tech Univ Athens, Sch Mech Engn, Mfg Technol Lab, Heroon Polytehniou 9, Athens 15780, Greece）
刊号：737E0004
ISSN：0268-3768
出版年：2025
年卷期：2025, vol.138, no.1
页码：3-15
总页数：13
分类号：TH166
关键词：Industrial cobot；；Human robot collaboration；；Extended Reality；；Dynamic collision avoidance；；Safety
参考中译：工业协作机器人;;人类机器人协作;;延展实境;;动态碰撞避免;;安全
语种：eng
文摘：Human-robot collaboration (HRC) in manufacturing allows advantageous distribution of tasks, e.g. exploiting robot accuracy and human dexterity, safety being of paramount importance. Safety is mostly linked to avoiding collisions between the human and the robot but the pertinent measures adopted should prolong task duration as little as possible. In order to test such measures in HRC pertinent algorithms need to be applied, which is made possible without jeopardising human safety only in an Extended Reality environment. In order to implement path planning algorithms and human-robot interaction rules freely the environment must be open. In this work, the development of such an environment is presented and demonstrated by example of laying up carbon fibre fabric sheets in a mould. An existing open platform was substantially extended by embedding robot control functionality concerning motion, path and trajectory planning emphasizing static and dynamic obstacle detection, interactive input and manipulation and real-time path planning, whereas trajectory planning focused on ensuring acceptability of joint motion solutions using inverse kinematics. Two different real-time path planning methods are embedded in the environment as representative examples. The first one is the established 'Rapidly exploring Random Tree' (RRT) algorithm followed by path optimization. The second one is 'Machine-Learned Path Planning' (MLPP) a prototype machine learning model trained using linear regression with Gaussian noise based on safe path planning data generated by users. The evaluation criteria of these methods were the number and severity of collisions as well as the total completion time of the manufacturing task. In the particular case examined, the machine learning technique proved much faster than RRT but not as safe, despite its potential. However, the openness of the XR platform enables testing of any other strategy supporting HRC in manufacturing before it is actually transcribed to the real robot controller.
参考中译：制造业中的人机协作（HRC）允许任务的有利分配，例如利用机器人的准确性和人类的灵活性，安全性至关重要。安全性主要与避免人类和机器人之间的碰撞有关，但采取的相关措施应尽可能少地延长任务持续时间。为了在HRC中测试此类措施，需要应用相关算法，只有在延展实境环境中才能在不危及人类安全的情况下实现这一目标。为了自由地实施路径规划算法和人机交互规则，环境必须是开放的。在这项工作中，这种环境的发展，并展示了铺设碳纤维织物片在模具中的例子。现有的开放式平台通过嵌入机器人控制功能进行了实质性扩展，这些功能涉及运动、路径和轨迹规划，强调静态和动态障碍物检测、交互式输入和操纵以及实时路径规划，而轨迹规划则侧重于确保使用逆运动学的关节运动解决方案的可接受性。两种不同的实时路径规划方法嵌入在环境中作为代表性的例子。第一个是建立“快速探索随机树”（RRT）算法，然后进行路径优化。第二个是“机器学习路径规划”（MLPP），这是一种基于用户生成的安全路径规划数据，使用带有高斯噪声的线性回归训练的原型机器学习模型。这些方法的评价标准是碰撞的数量和严重程度以及制造任务的总完成时间。在研究的特定案例中，机器学习技术被证明比RRT快得多，但尽管有潜力，却不那么安全。然而，XR平台的开放性使得能够在实际转录到真正的机器人控制器之前测试制造中支持HRC的任何其他策略。



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