# Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
## 1 Introduction  
The rapid advancement of modern society has significantly increased the importance of security and surveillance in various sectors. As the demand for enhanced security measures grows, the number of areas and environments requiring constant monitoring has risen sharply. This has created a substantial reliance on human resources to maintain safety and security. To address these challenges, numerous studies have explored the potential of intelligent mobile robots to enhance surveillance efficiency and reduce the dependence on manual labor [1, 2, 3, 4, 5]. Intelligent mobile robots can perform continuous monitoring, detect anomalies, and respond to security threats with minimal human intervention [6, 7, 8]. These robots are equipped with advanced sensors and decision-making algorithms, enabling them to navigate complex environments and provide real-time feedback. Recent advancements in artificial intelligence and robotics have further improved their capabilities, including better path planning, object recognition, and obstacle avoidance [9, 10, 11]. Furthermore, the integration of IoT technology allows these robots to collaborate with existing security infrastructure, offering a more comprehensive approach to modern surveillance. This development not only reduces operational costs but also enhances the reliability and consistency of security systems [12, 13]. To effectively utilize intelligent mobile robots for security and patrol tasks, navigation challenges must be addressed [14, 15, 16]. For mobile robots to solve these challenges, several key issues need to be resolved. First, mobile robots require a map that accurately representstheir surrounding environment and a path planning algorithm to perform various service tasks [17; 18]. The robot must independently navigate to its destination using the environment map. Second, mobile robots must be equipped with an obstacle avoidance algorithm. When encountering obstacles while following a path generated from the environment map, the robot must utilize its sensors to analyze the surroundings and safely avoid the obstacle [19; 20]. Additionally, the robot must be capable of dynamically updating its environment map in real time to adapt to changes in its surroundings [21; 22; 23; 24]. This capability ensures reliable navigation in dynamic or unknown environments. Furthermore, the integration of robust decision-making algorithms enables mobile robots to prioritize tasks efficiently, ensuring optimal performance in complex security and patrol operations. These features are essential for the deployment of intelligent robots in real-world applications, where unpredictability and rapid decision-making are common. Numerous studies have been conducted to develop algorithms that generate safe paths for robots [25; 26; 27; 28]. Path generation algorithms can generally be categorized into two types: those that utilize occupancy grid-based maps and those that rely on topology maps. One widely used approach in grid-based methods is the distance transform algorithm, which creates paths by calculating distance values from the destination to the robot's starting position. In this algorithm, the robot follows the path by moving toward locations with progressively lower distance values until it reaches its destination. While grid-based path search algorithms are capable of generating precise paths, they present significant limitations. One major drawback is their inability to adapt to new obstacles that may appear during the robot's movement. Additionally, the computational time required for path calculation increases significantly as the size of the map grows. These challenges highlight the need for more efficient and adaptive approaches to path planning, particularly in dynamic and large-scale environments. Advanced algorithms that combine the precision of grid-based methods with the adaptability of topological approaches could offer a promising solution to these issues. A commonly used path generation algorithm that utilizes a topological map is Depth First Search (DFS).