Teleoperation system for multiple robots with intuitive hand recognition interface
Robotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human-machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human-machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human-machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw .
Authors
- Vivian Cremer Kalempa
- André Schneider de Oliveira (ORCID: https://orcid.org/0000-0002-8295-366X)
- Dieisson Martinelli
- Lucas Zick
Institutions
- Universidade Tecnológica Federal do Paraná (BR)
- Universidade do Estado de Santa Catarina (BR)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1038/s41598-024-80898-x
- Citations
- 6
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 1.97