Abstract
Coexisting-cooperative-cognitive (Tri-Co) robots are advanced systems designed for interaction with environ-ments, humans, and other robots. Collaborative robots (cobots), a subset of Tri-Co robots, specifically work safely alongside humans. When it comes to improving cobot performance, understanding the relationship between their technical parameters and performance indices becomes crucial. This paper proposes a method that combines the idea of experimental science and statistics, using Pearson correlation analysis to find this relationship. We define local Pearson correlation coefficients and influential indicators to measure each technical parameter's impact on performance indices. A case study on Rethink Sawyer (Sawyer) cobot validates our theoretical framework and underscores the practical applicability of our method.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024 |
| Pages | 862-867 |
| Number of pages | 6 |
| Edition | 2024 |
| ISBN (Electronic) | 9781665481090 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024 - Bangkok, Thailand Duration: Dec 10 2024 → Dec 14 2024 |
Conference
| Conference | 2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024 |
|---|---|
| Country/Territory | Thailand |
| City | Bangkok |
| Period | 12/10/24 → 12/14/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
ASJC Scopus subject areas
- Information Systems
- Mechanical Engineering
- Control and Optimization