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首页> 外文期刊>Journal of manufacturing science and engineering: Transactions of the ASME >Human-Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior Intelligence
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Human-Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior Intelligence

机译:智能制造中的人力机器人协作:机器人反应行为智能

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摘要

To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot's decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human-robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI's underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human-robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application.
机译:为了在智能制造中实现安全有效的人-机器人协作(HRC),传感、认知和预测的无缝集成到机器人控制器中对于异构环境(机器人、人和设备)中的实时感知、响应和通信至关重要。具体的研究目标是在其控制体系结构中提供机器人主动自适应协作智能(PACI)和切换逻辑,以便在给定先验知识和指定任务的预定义执行计划的情况下,赋予机器人优化和动态适应其运动的能力。挑战在于增强机器人的决策过程,使其具有更强的态势感知能力,并在受到不同程度的人机交互时产生智能机器人行为/反应,同时保持安全和生产效率。机器人的反应行为是通过基于成本函数的切换逻辑激活最合适的高级控制器实现的。PACI的底层分割和切换逻辑框架被证明具有高度的模块化和灵活性。在模拟环境中验证了所开发的控制结构在不同人机交互水平下的性能。向物理e.DO机器人发送开环命令,以演示所提出的框架在实际应用中的行为。

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