<p>Riveting is widely used as a primary connection method in the assembly of large aircraft structural components. During the drilling and riveting process, robotic systems typically rely on preassembled-hole positioning to improve hole-making accuracy. However, this process often requires compensation for deviations between the nominal 3D model and the actual workpiece, resulting in increased positioning time and reduced accuracy in subsequent operations. To address these challenges, this paper proposes a hybrid visual-ranging servo control method based on deep reinforcement learning (DRL-HVRS) for robotic motion planning in preassembled hole alignment tasks. The DRL-HVRS controller takes the observed image features and laser-ranging features as inputs and employs the TD3 algorithm to dynamically optimize the key parameters of the HVRS, thereby generating an effective motion trajectory for the robot. Finally, a robotic simulation environment was developed in CoppeliaSim for training and evaluation. Compared with existing PBVS and IBVS methods, this work is the first to integrate TD3 deep reinforcement learning with a hybrid visual-ranging servo model, enabling adaptive optimization of weighting and velocity factors, thereby improving the positioning success rate, convergence speed, and generalization performance. Experimental results on rivet hole insertion tasks demonstrate the effectiveness and generalizability of the proposed approach. In 50 random trials on a 15-mm-thick workpiece, the proposed method achieved a positioning success rate of 82.0%, compared with 54.0% for position-based visual servoing (PBVS) and 62.0% for image-based visual servoing (IBVS). The method exhibits satisfactory adaptability under the tested conditions, indicating its potential for efficient and high-precision assembly of small-curvature aircraft structural components.</p>