B-PaDY: robot co-worker in a bumper assembly line

Abstract

In an automobile assembly line, many processes require control and human intervention. A human's dexterity and ability to react to unpredictable changes in production volume and product specifications are necessary for these processes. Thus, it remains difficult to rely on robots or other automated systems. Conversely, some tasks, such as moving to a tool rack and delivering parts, do not require human skill. In the bumper assembly process of automobile assembly, a worker must move to pick up the bumper twice in one procedure. This task may strain the worker and increase work time for each bumper. To address this situation, we developed a new co-worker robot, named B-PaDY, to work in cooperation with human workers on the automobile bumper assembly line. This co-worker robot is not directly involved in the assembly processes, but performs tasks to support human workers. The robot delivers the bumper to the worker on the production line at the right time. We propose hardware concepts and designs to ensure worker's safety and allow for handling of all bumpers types. This robot uses a suspended rail system and handles the bumper, attaching to its reverse side. Additionally, we propose a method to determine the proper delivery timing based on a logistic regression model and confirm its effectiveness by performing experiments. We statistically model the worker's state upon completion of the assembly task and stochastically determine the proper timing. By recognizing the worker's behavior and supporting the worker with appropriate timing, our system effectively supplies the bumper to the worker and improves work efficiency.

Human-machine collaboration in managerial decision making

“…Several empirical reports have illustrated that humans and machines can indeed augment each other, thereby producing better results than either one could do alone (e.g., Bader and Kaiser, 2019;Grover et al, 2020;Kinugawa et al, 2016;Wang et al, 2016). In line with this "augmentation" philosophy, our findings more broadly emphasize that-rather than asking where humans fit in the loop of machines-it seems much more important for organizations to find out where machines fit into existing teams and departments.…”

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

“…The productivity of a manufacturing system is related to several measures such as the throughput (that is the number of units produced per period), the ressources consumtion for a given output, the number of defective items produced, etc. Researchers confirm the hypothesis that power, velocity, predictability, repeatability, and precision of robots in combination with human intelligence, creativeness and skills increase the productivity of assembly lines (Michalos et al, 2014;Ore et al, 2015;Akella et al, 1999;Sadik & Urban, 2017b;Kinugawa et al, 2016). As cobots handle some tasks, adding a cobot to a fully manual line reduces the takt time, and thus increases the throughput.…”

Human-Robot Trust Assessment From Physical Apprehension Signals

“…Shared work space 28 [13, 24, 34, 36-38, 42, 49, 59, 60, 67-70, 72-74, 76-78, 88, 91, 96, 99, 105, 112, 115, 122] Handover 22[16, 20, 21, 26, 32,40,41,46,53,54,63,65,71,80,89,94,95,98,111,118,119,123] …”