Einfluss der Bewegungsbahn in der Mensch-Roboter-Kollaboration
In the course of the digital transformation, flexibility is one of the biggest challenges in the industry. Traditional automation leads to fast and precise execution of assembly tasks, but often at the expense of flexibility. Human-robot collaboration can combine the strengths of the employee and the robot, placing the human at the center of the system design to minimize stress and increase the sense of safety. Characteristics such as speed, appearance, form of movement, and position of the robot during human-robot collaboration influence humans. Anthropomorphic characteristics such as human joint angles, speeds and predictable movements of the robot lead to more trust and an increased sense of safety. Additionally, anthropomorphic human-robot collaboration can increase efficiency and perceived teamwork. The concept of this study addresses these aspects. An articulated arm robot was developed and manufactured for an empirical study to provide support during assembly in a laboratory test (n = 37). The system allows the test subjects to program the robot's path independently. It uses motion data from a motion-capturing suit to calculate the joint angles of the right arm in real time and transfer them to the robot. This allowed the test person to independently program the supporting activity of the robot during the test series. In two assembly runs, the robot provided support by means of a point-to-point and a self-generated individual path. The results of the study show that a jerky change in distance relative to the robot only occurs in the point-to-point path. The subjective questionnaire data supports the motion capture data. Trust in the robot was rated significantly higher for the individual path than for the point-to-point path. Furthermore, the number of fixations on the robot during the assembly activity was lower for the individual path. These findings can be incorporated into future research and development into the design of human-robot collaboration.
Publication Details
- Journal
- Universitätsbibliothek Kassel
- Published
- 2025-01-01
- DOI
- https://doi.org/10.17170/kobra-2025060411198
- Citations
- 1
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 1.21