This study aims to develop a remote-control system for unmanned vehicles that enables real-time operation of both an actual vehicle and an autonomous robotic platform. The proposed system employs a Logitech G29 steering wheel and pedal set to capture ...
This study aims to develop a remote-control system for unmanned vehicles that enables real-time operation of both an actual vehicle and an autonomous robotic platform. The proposed system employs a Logitech G29 steering wheel and pedal set to capture steering, acceleration, and braking inputs, transmitting them via TCP-based communication to both the vehicle and the robot. Experiments were conducted using a modified Hyundai Santa Fe DM and an AgileX Robotics SCOUT 2.0 platform, both sharing a unified communication structure and user interface (UI) framework for operation under the same control environment.
The system was implemented in C++ and ROS under Windows 11 and Ubuntu 20.04 environments. The vehicle-based module focuses on camera data and applies a Bird’s Eye View–based driving assistance algorithm that predicts and visualizes the steering trajectory in real time, enhancing driver stability and situational awareness without LiDAR input. In contrast, the SCOUT 2.0 platform was developed with an emphasis on UI design and implementation to improve remote operator intuitiveness and situational understanding. The UI integrates live video feeds, steering angles, velocity, and control response indicators, providing visual feedback for trajectory prediction and system responsiveness during remote operation.
Experimental results showed that the vehicle platform achieved an average input transmission delay of 1.34 ms and a video processing time of 22.81 ms, satisfying the performance target (≤ 40 ms). The robot platform’s UI module maintained an average frame rendering latency of approximately 28 ms, demonstrating real-time responsiveness suitable for continuous remote operation.
These results confirm that the proposed system achieves stable, real-time remote control through an intuitive UI framework for both vehicle and robot platforms, providing a practical foundation for future research on 5G-based teleoperation and hybrid autonomous–manual control systems.