Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings

Sunny Katyara, Suchita Sharma, Praveen Damacharla, Carlos Garcia Santiago, Francis O'Farrell, Philip Long
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Abstract

Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concerning environment perception and robot control. In this research, we introduce a novel approach for collaborative environment monitoring and robot motion regulation to address this multifaceted problem. Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent quadrants, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover, we also leverage 3D visual information to track dynamic human articulations and extended intrusions. Drawing upon the fused sensory data from 2D and 3D perceptual spaces, our proposed hierarchical controller stably regulates robot velocity, validated using Lasalle in-variance principle. Empirical evaluations demonstrate that our approach significantly reduces task execution time and system response delay, resulting in improved efficiency and resilience within collaborative settings.
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合作共赢:在灵活的工业环境下优化系统效率和复原力
设计一种高效、有弹性的人机协作策略,不仅要维护共享工作空间的安全性和人体工程学,还要提高协作设置的性能和灵活性,这对环境感知和机器人控制提出了重大挑战。我们的研究根据 ISO 13855 和 TS 15066 标准,利用二维激光信息,提出了新颖的安全监控区域计算和划分方法。此外,我们还利用三维视觉信息来跟踪人类的动态动作和扩展入侵。利用来自二维和三维感知空间的融合感知数据,我们提出的分层控制器可以稳定地调节机器人的速度,并通过拉萨尔内方差原理进行了验证。经验评估表明,我们的方法大大缩短了任务执行时间和系统响应延迟,从而提高了协作环境下的效率和适应能力。
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