Robot Control and Sensor Integration: Remote Robotics for Education
Puustinen, Sami (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202603033652
https://urn.fi/URN:NBN:fi:amk-202603033652
Tiivistelmä
This thesis investigates the implementation and practicality of remote robotics control systems for educational purposes within university settings. The study focuses on developing a solution for controlling a robot remotely while extracting essential sensor data for study purposes. The goal is to provide a remote learning platform for students, enabling an interactive robotics education regardless of physical location.
A methodical approach is employed to establish network connectivity between the robot (operating on a Jetson Orin Nano Dev Kit) and a personal laptop. By establishing bidirectional communication through UDP or TCP socket programming in Python, the study achieves a successful transition from local Wi-Fi connection dependency to VPN network integration.
Despite hardware defects, camera malfunctions, and poor technical support, the project successfully achieved remote transmission and visualization of LIDAR, IMU, velocity, and battery data. Additionally, the study expands the original control capabilities to include remote operation of the mounted mechanical arm via standard keyboard inputs across separate network environments through VPN implementation.
The results demonstrate both the potential and practical challenges of implementing such systems for educational applications. This study provides valuable details for educational institutions considering similar technological integration for remote learning in robotics.
A methodical approach is employed to establish network connectivity between the robot (operating on a Jetson Orin Nano Dev Kit) and a personal laptop. By establishing bidirectional communication through UDP or TCP socket programming in Python, the study achieves a successful transition from local Wi-Fi connection dependency to VPN network integration.
Despite hardware defects, camera malfunctions, and poor technical support, the project successfully achieved remote transmission and visualization of LIDAR, IMU, velocity, and battery data. Additionally, the study expands the original control capabilities to include remote operation of the mounted mechanical arm via standard keyboard inputs across separate network environments through VPN implementation.
The results demonstrate both the potential and practical challenges of implementing such systems for educational applications. This study provides valuable details for educational institutions considering similar technological integration for remote learning in robotics.