Dynamic assembly sequence selection using reinforcement learning

2004 ◽  
Author(s):  
G. Lowe ◽  
B. Shirinzadeh
Keyword(s):  
Reinforcement Learning ◽  
Assembly Sequence ◽  
Sequence Selection ◽  
Dynamic Assembly

A Dynamic Assembly Modeling Method for Satellite Final Assembly Sequence Planning

2010 ◽  
Vol 156-157 ◽  
pp. 332-338
Author(s):  
Yuan Zhang ◽  
Kai Fu Zhang ◽  
Jian Feng Yu ◽  
Lei Zhao
Keyword(s):  
Dynamic Model ◽  
Static Model ◽  
Modeling Method ◽  
Assembly Sequence Planning ◽  
Assembly Model ◽  
Assembly Sequence ◽  
Final Assembly ◽  
Model Tree ◽  
Assembly Sequences ◽  
Dynamic Assembly

To study the effect of assembly process information combining disassemble and assemble on satellite assembly sequence, this paper presents an object-oriented and assembly information integrated model, which is composed of static model and dynamic model. The feasibility determination based on Cut-set theory is presented and the construction algorithm of dynamic model is established by static model, the dynamic assembly model tree is obtained by analyzing in layers and verifying possible states using this algorithm, where the assembly model tree includes all the geometric feasible assembly sequences of satellite. Finally, this modeling method is verified by a satellite product.

scholarly journals Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot

Robotics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 104
Author(s):  
Joris De Winter ◽  
Albert De Beir ◽  
Ilias El Makrini ◽  
Greet Van de Perre ◽  
Ann Nowé ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Solution Space ◽  
Assembly Sequence ◽  
Assembly Task ◽  
Human Interactions ◽  
Simulated Environment ◽  
Learning Speed ◽  
Reward Shaping ◽  
Feedback Strategies ◽  
Changing Knowledge

The assembly industry is shifting more towards customizable products, or requiring assembly of small batches. This requires a lot of reprogramming, which is expensive because a specialized engineer is required. It would be an improvement if untrained workers could help a cobot to learn an assembly sequence by giving advice. Learning an assembly sequence is a hard task for a cobot, because the solution space increases drastically when the complexity of the task increases. This work introduces a novel method where human knowledge is used to reduce this solution space, and as a result increases the learning speed. The method proposed is the IRL-PBRS method, which uses Interactive Reinforcement Learning (IRL) to learn from human advice in an interactive way, and uses Potential Based Reward Shaping (PBRS), in a simulated environment, to focus learning on a smaller part of the solution space. The method was compared in simulation to two other feedback strategies. The results show that IRL-PBRS converges more quickly to a valid assembly sequence policy and does this with the fewest human interactions. Finally, a use case is presented where participants were asked to program an assembly task. Here, the results show that IRL-PBRS learns quickly enough to keep up with advice given by a user, and is able to adapt online to a changing knowledge base.

ASPW-DRL: assembly sequence planning for workpieces via a deep reinforcement learning approach

2019 ◽  
Vol 40 (1) ◽  
pp. 65-75
Author(s):  
Minghui Zhao ◽  
Xian Guo ◽  
Xuebo Zhang ◽  
Yongchun Fang ◽  
Yongsheng Ou
Keyword(s):  
Reinforcement Learning ◽  
Assembly Sequence Planning ◽  
Planning System ◽  
Assembly Sequence ◽  
Training Environment ◽  
Good Decision ◽  
Content Type ◽  
Sequence Planning ◽  
Simulation Engine ◽  
Physics Simulation

Purpose This paper aims to automatically plan sequence for complex assembly products and improve assembly efficiency. Design/methodology/approach An assembly sequence planning system for workpieces (ASPW) based on deep reinforcement learning is proposed in this paper. However, there exist enormous challenges for using DRL to this problem due to the sparse reward and the lack of training environment. In this paper, a novel ASPW-DQN algorithm is proposed and a training platform is built to overcome these challenges. Findings The system can get a good decision-making result and a generalized model suitable for other assembly problems. The experiments conducted in Gazebo show good results and great potential of this approach. Originality/value The proposed ASPW-DQN unites the curriculum learning and parameter transfer, which can avoid the explosive growth of assembly relations and improve system efficiency. It is combined with realistic physics simulation engine Gazebo to provide required training environment. Additionally with the effect of deep neural networks, the result can be easily applied to other similar tasks.

Research on Orbit Assembly Strategy of Large-scale Space Truss Structure

2021 ◽  
Vol 16 ◽  
Author(s):  
Ye Dai ◽  
Chao-Fang Xiang ◽  
Yu-Dong Bao ◽  
Yun-Shan Qi ◽  
Wen-Yin Qu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Scale Space ◽  
Assembly Sequence ◽  
Q Learning ◽  
Assembly Strategy ◽  
Periodic Module ◽  
Space Trusses ◽  
Optimal Assembly

Background: With the rapid development of spatial technology and mankind's continuous exploration of the space domain, expandable space trusses play an important role in the construction of space station piggyback platforms. Therefore, the study of the in-orbit assembly strategy for space trusses has become increasingly important in recent years. The spatial truss assembly strategy proposed in this paper is fast and effective, and it is applied for the construction of future large-scale space facilities effectively. Objective: The four-prismatic truss periodic module is taken as the research object, and the assembly process of the truss and the assembly behaviors of the spatial cellular robot serving for on-orbit assembly are expressed. Methods: The article uses a reinforcement learning algorithm to study the coupling of truss assembly sequence and robot action sequence, then uses a q-learning algorithm to plan the strategy of the truss cycle module. Results: The robot is trained through the greedy strategy and avoids the failure problem caused by assembly uncertainty. The simulation experiment proves that the Q-learning algorithm of reinforcement learning used for planning the on-orbit assembly sequence of the truss periodic module structures is feasible, and the optimal assembly sequence with the least number of assembly steps obtained by this strategy. Conclusion: In order to address the on-orbit assembly issues of large spatial truss structures in the space environment, we trained the robots through greedy strategy to prevent failure due to the uncertainty conditions both in the strategy analysis and in the simulation study.Finally, the Q-learning algorithm in reinforcement learning is used to plan the on-orbit assembly sequence in the truss cycle module, which can obtain the optimal assembly sequence in the minimum number of assembly steps.

A dynamic assembly model for assembly sequence planning of complex product based on polychromatic sets theory

2012 ◽  
Vol 32 (2) ◽  
pp. 152-162 ◽  
Author(s):  
Zhijia Xu ◽  
Yuan Li ◽  
Jie Zhang ◽  
Hui Cheng ◽  
Shoushan Jiang ◽  
...  
Keyword(s):  
Assembly Sequence Planning ◽  
Assembly Model ◽  
Assembly Sequence ◽  
Complex Product ◽  
Sequence Planning ◽  
Dynamic Assembly ◽  
Sets Theory ◽  
Polychromatic Sets Theory

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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