# Design and Mechanical Behavior Research of Highway Guardrail Patrol Robot
## 1 Introduction  
With the rapid development of road traffic systems, traffic accidents have become an inevitable concern. Manual inspections, while commonly used, are often time-consuming, labor-intensive, and inefficient, and pose significant safety risks. Although fixed road testing methods reduce some of these risks, their coverage is limited and often insufficient for comprehensive assessments. In recent years, numerous studies have explored the application of robotics in the field of road traffic. For instance, in the area of bridge detection [1, 2, 3, 4, 5, 6, 7], robots equipped with cameras have been developed to scan and inspect entire bridge structures. [PERSON] et al. pioneered the application of automatic crack detection in robotic bridge scanning, proposing a method for the automatic identification of concrete bridge cracks. This approach enables robots to analyze data and quickly assess bridge safety after completing the scans [8]. [PERSON] et al. addressed the issue of limited measuring points in bridge safety monitoring by introducing a global displacement detection method based on natural texture features. This method fundamentally resolves the problem of damage identification caused by sparse monitoring data [9]. Additionally, [PERSON] et al. proposed a novel approach using Deep Reinforcement Learning (DRL) to control unmanned aerial vehicles (UAVs) for bridge crack detection. By integrating CannyEdge detection and Convolutional Neural Networks (CNNs), their method demonstrates superior detection quality compared to traditional techniques [10]. Robots have also been widely applied in the field of tunnel detection [11; 12; 13; 14; 15]. [PERSON] et al. developed a robotic system called ROBO-SPECT for highway tunnel inspections. This system enables the accurate, rapid, and reliable inspection and evaluation of tunnel linings under safer working conditions and during continuous traffic operations [16]. Similarly, [PERSON] et al. designed a suspension robot equipped with multiple sensors for autonomous tunnel detection and monitoring. This robot is capable of collaborating with firefighters and rescue personnel to perform timely rescue missions in emergency situations [17]. Robots are also being utilized in road surface detection [18; 19; 20; 21; 22]. [PERSON] et al. proposed a method based on layered visual sensors to achieve robust detection in complex road environments [23]. [PERSON] et al. introduced a flood-filled road detection method capable of adapting to various road types. This method allows robots to select the appropriate controllers based on the road surface conditions or safe routes [24]. [PERSON] et al. reviewed the advancements in railway inspection robots and conducted a feasibility analysis by testing a typical track inspection robot prototype on a specific railway line [25]. Additionally, [PERSON] et al. developed a curb detection method for mobile robots, enabling them to distinguish between roads and sidewalks in urban residential areas using stereo vision technology [26]. Although extensive research has been conducted on the application of robots in the field of road traffic, there remains a significant gap in the development of robots specifically designed for highway patrol, inspection, and surveillance. Currently, the most widely deployable robotic technology for such tasks is the unmanned aerial vehicle (UAV). However, UAVs face critical limitations, including short endurance times and restricted payload capacities [27; 28]. To effectively serve the road traffic sector, it is essential to fundamentally improve the endurance and load-bearing capabilities of UAVs. Ground-based robots generally offer significantly longer endurance compared to aerial systems, and can be equipped with charging stations for autonomous recharging. This capability allows them to operate stably even under harsh weather conditions, such as rain, snow, and sandstorms.