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

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Název: Improved PPO Optimization for Robotic Arm Grasping Trajectory Planning and Real-Robot Migration.
Autoři: Li, Chunlei, Liu, Zhe, Li, Liang, Ji, Zeyu, Li, Chenbo, Liang, Jiaxing, Li, Yafeng
Zdroj: Sensors (14248220); Sep2025, Vol. 25 Issue 17, p5253, 25p
Témata: TRAJECTORY optimization, SIMULATED annealing, INDUSTRIAL robots, REINFORCEMENT learning, LEARNING
Abstrakt: 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% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt: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% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. [ABSTRACT FROM AUTHOR]
ISSN:14248220
DOI:10.3390/s25175253