[1]WANG Junhe,JIANG Yong.Dynamic assembly algorithm based on deep reinforcement learning[J].CAAI Transactions on Intelligent Systems,2023,18(1):2-11.[doi:10.11992/tis.202201006]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
18
Number of periods:
2023 1
Page number:
2-11
Column:
学术论文—机器学习
Public date:
2023-01-05
- Title:
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Dynamic assembly algorithm based on deep reinforcement learning
- Author(s):
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WANG Junhe1; 2; 3; JIANG Yong1; 2
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1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China
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- Keywords:
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flexible cable model; dynamic noise; dynamic assembly; deep reinforcement learning; long short-term memory; sequential discount factor; temporal difference(λ); pre-training
- CLC:
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TP242.6
- DOI:
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10.11992/tis.202201006
- Abstract:
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A dynamic assembly algorithm based on deep reinforcement learning is proposed for complex dynamic noise perturbations in the dynamic assembly environment. Taking the contact force in a period of time as a state, the motion features are extracted through the long short-term memory. Define the sequence discount factor, and obtain the reward value at a certain moment through weighting the sub-reward at the previous moment. The robot can be adjusted to the desired position using inverse kinematics, with the action of model output as the Cartesian space displacement. In the meanwhile, an improved neural network parameter update method is proposed based on the temporal difference (λ) algorithm to shorten the model training time. Experimentally, training was conducted in the real scene upon pre-training in the simple environment with the circular hole-axis. According to the experiments, the proposed algorithm can well adapt to the flexible and dynamic assembly environment in a dynamic assembly task.