# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 1 Introduction
In their approach, the robotic cone learns to reach a target location through a reward-based decision process trained within a controlled virtual environment. Previous research [18; 19] has proposed a formation control architecture for robotic cones using leader-follower strategies and model predictive control (MPC) methods. Their system enables multiple robotic cones to move in coordinated formations while tracking a desired configuration. The work also includes the design of robust controllers that account for dynamic uncertainties using fuzzy logic and optimised tracking strategies. Hierarchical planning and control approaches based on MPC have been used for the autonomous deployment and recovery of cone formations [20]. A lead robot plans the overall path in this model while follower units maintain formation through constrained predictive control. The proposed framework considers physical limitations and actuator constraints and was evaluated in both simulation and physical testbeds. Robotic cone-based lane closure systems have been developed using computer vision and decentralized Integral-Derivative Controller (PID) control to enable autonomous positioning [21]. Each cone-shaped mobile robot employs image segmentation, Hough transforms, and inertial sensors to navigate a test environment by visually tracking road markings. Although various advances in the robotisation of traffic cones have been reported in the literature [16; 17; 18; 19; 20; 21], the current state of the art still presents significant limitations that hinder their practical implementation in real roadwork settings. Previous studies have predominantly focused on developing autonomous navigation and control algorithms validated in simulated or controlled environments without comprehensively addressing the structural, regulatory, and safety requirements to operate in dynamic construction sites. The proposed platforms do not comply with European road signalling standards, such as UNE-EN 13422:2019 [22] or UNECE R65 [23], or lack high-precision positioning systems or redundant safety mechanisms, which limits their ability to ensure safe autonomous operation in real-world scenarios exposed to active traffic. The Remotely Piloted Safety Cone (RPSC) presented in this study represents a significant step toward practical implementation. Unlike existing experimental platforms, the RPSC was designed from the outset as an autonomous traffic cone compliant with key European standards (UNE-EN 13422:2019 [22], UNECE R65 [23], UNE-EN 12899-3:2008 [24], and UNE-EN 12352:2006 [25]) ensuring adherence to legal requirements for signage, visibility, weight, stability, and durability. The RPSC features a high-precision navigation architecture that integrates GNSS with Real-Time Kinematic (RTK), odometry, and sensor fusion, all managed through a robust framework based on Robot Operating System 2 (ROS2) and Micro Air Vehicle Link (MAVLink). For safety, it incorporates emergency stop mechanisms, visual alarms, automatic failsafe systems for communication losses or sensor failures, and an operational lifecycle that ensures safe system shutdown in response to anomalies. The RPSC offers a real industry-oriented approach, positioning itself as a viable solution for practical deployment and providing adequate protection for workers, operators, and road users. This research is structured to guide the reader through the complete development of the RPSC. Initially, the methodology is described in detail, including analysing operational and regulatory requirements and the proposed system's conceptual and technical design. Next, the hardware and software implementation phases are presented, emphasizing the modular integration and communication between subsystems. Next, the results obtained from experimental tests conducted under real-world conditions are presented, focusing on critical aspects such as positioning accuracy, trajectory tracking fidelity, and effectiveness in obstacle detection.