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Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot’s driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.