# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 2 Materials and Methods
### Design
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Function** \\ \hline GNSS system with RTK capabilities & Determines position outdoors with precision. \\ \hline IMU & Provides linear and angular acceleration data to complement GNSS readings \\ \hline Magnetometer & Determines orientation relative to Earth’s magnetic field, aiding \\  & heading accuracy \\ \hline Encoders & Tracks wheel or motor displacement to correct positional estimation \\ \hline Sensors for the ‘Sense and Avoid’ system & Detects and avoids obstacles using LiDAR, ultrasonic sensors, stereo cameras, \\  & or infrared sensors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Required sensors for autonomous navigation. Figure 2: Movements of a rover with a skid-steer type configuration: (a) straight forward motion; (b) curve displacement; (c) spin on its axis. Table 3 summarises the OPU's capabilities, highlighting its pivotal role in integrating the RPSC's subsystems for improved safety and efficiency. The OPU's architecture supports a decentralized operational approach, significantly reducing latency compared to cloud-based alternatives and enabling rapid response time, an essential requirement in dynamic roadwork environments. This decentralized framework is integrated with a communication system that utilizes Wi-Fi technology. These specifications ensure continuous and reliable data exchange with control stations and other robots. The OPU implements a dedicated safety subsystem to enhance system reliability and integrate control and safety mechanisms. In case of a failure or loss of communication, the OPU autonomously halts the RPSC and activates high-intensity visual alarms driven through General Purpose Input/Output (GPIO). These independent safety functions, complemented by an emergency kill switch that interrupts the power supply to the entire system, provide the necessary robustness to operate in high-risk environments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **Key Function 1** & **Key Function 2** & **Key Function 3** \\ \hline Sensor Fusion and State Estimation & Implementation of EKF and similar techniques for state estimation & Configuration of the primary position estimation source & Adjustment of sensor weighting \\ \hline Propulsion Management and Motor Control & Generation of PWM signals for speed and direction control & Implementation of PID for trajectory regulation & Definition of acceleration and deceleration ramps \\ \hline Sensor Calibration and Configuration & Calibration of IMU, magnetometer, and encoders & Configuration of filters to minimise measurement errors & Adjustment of additional sensor positions \\ \hline Navigation and Trajectory Control & Support for predefined trajectories and waypoint navigation & Real-time trajectory adaptation based on sensor data & Speed and acceleration limit configuration \\ \hline Operation Modes: Management and Remote Control & Ability to operate in autonomous, semi-autonomous, or manual mode & Capability to receive remote commands and react to emergencies & Management of communication interfaces for data exchange \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Functional modules of the autopilot software.