In the field of robotics and automation, path planning holds significant potential for optimizing field operations. These operations must cover the work area comprehensively and efficiently with minimal movement. To achieve these goals, coverage path planning (CPP) utilizing the Boustrophedon method is essential. However, in an experimental environment, CPP often results in missed work areas due to cumulative sensor errors and structural inconsistencies. This paper aimed to improve CPP by employing the Douglas-Peucker algorithm to simplify the work path and minimizing missed areas. Additionally, Edge Zone Path method was used to generate edge paths, enhancing safety of the trajectory. For experimental purposes, data were acquired from an actual barn. The work area was divided using three segmentation algorithms. Among these, the Voronoi Segmentation, which demonstrated superior performance, was used to extract the data. Experimental results indicated that the proposed optimized CPP improved path safety by maintaining a safe distance from obstacles during frequent turns. Additionally, the Coverage Ratio increased the coverage area of the autonomous robot by an average of 17% compared to the original CPP. These findings suggest that the proposed method can generate more efficient and safe work paths.