Robot path planning algorithm with improved DDPG algorithm

This study focuses on enhancing the autonomous path planning capabilities of intelligent mobile robots, which are complex mechatronic systems combining various functionalities such as autonomous planning, behavior control, and environment sensing. Path planning is crucial for robot mobility, enabling them to navigate autonomously. We propose an improvement to the deep deterministic policy gradient (DDPG) method by leveraging deep reinforcement learning algorithms. Through extensive experimentation, our method demonstrates superior performance compared to traditional DDPG, with notable reductions in training time and iterations required to reach targets. Additionally, it reduces dead zone encounters during travel and enhances convergence speed. Our findings contribute fresh insights and strategies for enhancing mobile robot path planning in unfamiliar environments. Future research will explore further advancements, particularly in addressing dynamic obstacles and optimizing real-world navigation efficiency.