Robot vision inspection has become a key technology in modern automobile manufacturing. However, existing robot motion planning methods often ignore the influence of surface curvature on automobiles, leading to poor robot motion performance and reduced inspection quality. To address this challenge, this paper proposes a robot vision inspection path-pose integrated planning method that considers curvature weights in the target inspection area. First, the curvature weighting influence is considered to divide the automotive body into regions to be inspected. Based on the curvature distribution, point cloud slicing technology is used to generate robot inspection path points. Additionally, through a series of experiments, the optimal angle range between the robot’s end-effector z-axis direction and the normal vector of the surface to be inspected is analyzed. A coupled curvature-weighted optimal angle function for the robotic arm’s posture is established to achieve posture planning during the robot inspection process. Finally, the effectiveness of the proposed method and its advantages in path-attitude integrated planning were verified through simulation. The results show that compared to traditional direct planning methods, the proposed method reduces the average displacement of each joint by 3.98%, enables smooth movement without impact in areas with sudden curvature changes on the automotive body, and achieves higher inspection efficiency.