# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 2 Research Background  
Several studies have been conducted to automate earthwork task planning. Initially, research was conducted to apply automation theories and methods developed in robotics to construction projects [17]. This study developed a prototype for automated earthwork planning following the principles of classical planning models in computer science. Two key technologies were used to implement this prototype: the Library for Efficient Modeling and Optimization in Networks (LEMON) graph algorithm library, and Planning Domain Definition Language (PDDL). The LEMON library represents the construction site as a directed graph to optimize earthwork tasks. PDDL is used to define the planning domain and problems, enabling the system to generate sequentially and update transport tasks, thereby planning an optimized material flow while avoiding temporal and spatial conflicts. Unlike other studies that configure simulations in controlled environments, this prototype generates the flow of materials and work sequence in real time. Studies have also reviewed technologies for equipment tracking and asset management, safety management, equipment motion estimation and machine control, remote control, and autonomous driving to automate earthwork equipment [18]. This research highlighted the importance of key technologies such as GPS (global positioning system), RFID (radio frequency identification), UWB (ultra-wideband), and computer vision for productivity, safety, and operational efficiency. Additionally, the study emphasized the need for further development of remote control and autonomous systems for major equipment to achieve higher levels of automation. In South Korea, there has been research aimed at developing an optimal path algorithm for dump trucks in earthworks [19]. This study developed an optimal path algorithm by reflecting on actual equipment operations through surveys conducted with construction managers and equipment operators. To determine the optimal path, the algorithm considered essential and necessary factors, such as distance, road conditions, slope, fuel consumption, and the number of steering maneuvers. These factors were assigned weights based on their importance determined through surveys and were used to calculate the optimal path within a grid-based terrain model. The developed system provides optimal paths for construction-equipment operators, thereby enhancing productivity and safety. Another study focused on the path generation for dozens at earthwork sites [20]. This research presented a compact coverage path planning (CCPP) approach for autonomous bulldozers, considering standardized construction techniques and movement rules. For field validation, this study used a BINN (bio-inspired neural network) hybrid A* algorithm integrated with a sensor system. The work area for the dozer was modeled using a grid map, where each grid point had an associated neuron activity value. The algorithm generates a path that covers the entire work area while avoiding obstacles. Another study developed a dozer path-planning algorithm aimed at minimizing the operational time and enabling immediate application to equipment [21]. Unlike other studies, this research did not represent a worksite with a grid-based map. Instead, it modeled the scenario in 3D to calculate the overall volume of a single-material pile. Sensors attached to the dozer were used to map the terrain and calculate the volume-handling points along the edges of the material. The material is then pushed and handled with the generated path using the A* algorithm. This approach minimizes the total cost function, which includes the dozer movement and material handling efficiency. The terrain was continuously updated for each task via sensors, allowing path replanning as necessary. Various path generation algorithms have been researched for automating earthworks in road construction.