Research on obstacle avoidance path planning of robotic manipulator based on deep reinforcement learning

In recent years, there have been significant advancements in path planning and obstacle avoidance for robotic manipulators. This paper introduces a novel approach to path planning for robotic manipulators by employing the Deep Deterministic Policy Gradient (DDPG) algorithm. The manipulator model is developed using SolidWorks software and integrated into the Simulink environment, incorporating two spherical obstacles. The experimental results demonstrate the efficacy of the DDPG algorithm in navigating complex environments and avoiding obstacles. The proposed method is validated through extensive simulations, demonstrating enhanced path efficiency and collision avoidance compared to traditional approaches.