## 1 Introduction
However, a precise and high-resolution geometric map must be built in advance, and the more complex the shape of the field, the more difficult it can be to generate a path. The method of using a geometric map is mainly used to generate autonomous paths for agricultural machinery used in upland fields or rice paddies. Typically, the shape of a field is a simple polygon, and there are usually no obstacles inside. In other words, the geometric map of the field can be simply expressed as the coordinates of the boundary points that make up the boundary of the field. Therefore, studies have been conducted to generate autonomous driving paths using the coordinates of field boundary points. [PERSON] et al. [11] inputted the coordinates of the field boundary points, converted them into a grid map, and researched generating a path based on the shortest distance using the A* algorithm for an autonomous tractor in a paddy field. This method has the disadvantage that the location accuracy of the generated waypoints may vary depending on the resolution of the grid map, and that the higher the resolution, the greater the computational amount. [PERSON] et al. [12] developed an infield route-planning program that can generate an X-type turning, pattern-based coverage path map that provides guidance lines for autonomous tillage tractors in the polygonal field based on inputted boundary points. [PERSON] et al. [13] generated an autonomous puddling and leveling tractor path by inputting the coordinates of field boundary points, generating interwork tracks, and applying a genetic algorithm to determine the work order of the interwork tracks that minimizes the headland turning distance. The above two studies are suitable for generating autonomous driving paths for wheeled agricultural machines such as tractors, but are inefficient for application to crawler-type agricultural machines capable of turning in place because they set unnecessary turning sections. In order to obtain the coordinates of the field boundary points, it is essential to obtain coordinates through surveying. However, since surveying requires manpower and costs, studies have also been conducted to generate an autonomous driving route by obtaining two reference points inside the field or obtaining boundary points from Google Maps. [PERSON] et al. [14] propose a method to generate the autonomous driving path of the harvester which defines two reference points inside the field to create a path, creates a straight work line by moving the straight line created as the reference point in parallel by the width of the work machine, and then applies the alpha turn method to create the autonomous driving path. In the above method, if the positions of the two reference points inside the field are incorrectly designated, the working straight lines generated through parallel movement may not include all of the inside of the field. [PERSON] and [PERSON] [15] presented a two-dimensional path planning platform using an edge-vertex path algorithm based on Laravel and Google Maps for the Autonomous Walk-Behind Hand Tractor. This algorithm automatically generates a route by considering the boundaries on the map, the distance between the routes, and the start and end points. However, due to the limitations of the map's location accuracy, the generated route is made up of location data with a large error, and thus has limitations in being applied to actual autonomous driving. In this paper, an autonomous driving path-generation algorithm for a crawler-type ridge-forming robot will be presented. Most current field farming already uses machines, so the shape of the fields is simple, and a crawler-type robot has the advantage of not having to consider rotation methods because they can rotate in place. In addition, since the shape of the field is simple, the geometric boundary of the field can be easily defined by obtaining the location data of the field boundary points through GNSS surveying.