  This paper addresses the challenge of collision-free motion planning in
automated navigation within complex environments. Utilizing advancements in
Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we
propose the TD3-DWA algorithm, an innovative fusion of the traditional Dynamic
Window Approach (DWA) with the Twin Delayed Deep Deterministic Policy Gradient
(TD3). This hybrid algorithm enhances the efficiency of robotic path planning
by optimizing the sampling interval parameters of DWA to effectively navigate
around both static and dynamic obstacles. The performance of the TD3-DWA
algorithm is validated through various simulation experiments, demonstrating
its potential to significantly improve the reliability and safety of autonomous
navigation systems.
