Improved PPO Optimization for Robotic Arm Grasping Trajectory Planning and Real-Robot Migration.

ABSTRACT: Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the accurate, collision-free grasping of randomly appearing objects in dynamic obstacles through three key innovations: a probabilistically enhanced simulation environment with a 20% obstacle generation rate; an optimized state-action space featuring 12-dimensional environment coding and 6-DoF joint control; and an SA-PPO algorithm that dynamically adjusts the learning rate to balance exploration and convergence. Experimental results show a 6.52%

SUBMITTER: Li C 

PROVIDER: S-EPMC12431029 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature