JACIII Vol.26 No.1 pp. 51-57
doi: 10.20965/jaciii.2022.p0051


Moving Object Grasping Method of Mechanical Arm Based on Deep Deterministic Policy Gradient and Hindsight Experience Replay

Jian Peng*,**,***,† and Yi Yuan*,**,***

*School of Automation, China University of Geosciences
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

***Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Corresponding author

July 29, 2021
November 2, 2021
January 20, 2022
mechanical arm, moving object grabbing, DDPG-HER

The mechanical arm is an important component in many types of robots; however, in certain production lines, the conventional grasp strategy cannot satisfy the demands of modern production because of several interference factors such as vibration, noise, and light pollution. This paper proposes a new grasping method for manipulators in stamping automatic production lines. Considering the factors that affect grasping in the production environment, the deep deterministic policy gradient (DDPG) method is selected in this study as the basic reinforcement-learning algorithm, and this algorithm is used to grasp moving objects in stamping automatic production lines. Owing to the low success rate of the conventional DDPG algorithm, the hindsight experience replay (HER) is used to improve the sample utilization efficiency of the agent and learn more effective tracking strategies. Simulation results show an 82% mean success rate of the optimized DDPG-HER algorithm, which is 31% better than that of the conventional DDPG algorithm. This method provides ideas for the research and design of the sorting system used in stamping automation production lines.

The initial positions P1 and P2 of two different mechanical arms are selected

The initial positions P1 and P2 of two different mechanical arms are selected

Cite this article as:
J. Peng and Y. Yuan, “Moving Object Grasping Method of Mechanical Arm Based on Deep Deterministic Policy Gradient and Hindsight Experience Replay,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.1, pp. 51-57, 2022.
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Last updated on Apr. 22, 2024