Multi‐station multi‐robot task assignment method based on deep reinforcement learning

This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks. The graph of welding spots distribution is encoded using the...

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Vydáno v:CAAI Transactions on Intelligence Technology Ročník 10; číslo 1; s. 134 - 146
Hlavní autoři: Zhang, Junnan, Wang, Ke, Mu, Chaoxu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Beijing John Wiley & Sons, Inc 01.02.2025
Wiley
Témata:
Algorithms
Artificial intelligence
Assignment problem
Attention
attention mechanism
Automation
Coding
Deep learning
deep reinforcement learning
graph neural network
Heuristic
industrial robot
Linear programming
Methods
Neural networks
Path planning
Process planning
Robots
Spot welding
task allocation
Vehicles
Working hours
ISSN:2468-2322, 2468-6557, 2468-2322
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Shrnutí:This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks. The graph of welding spots distribution is encoded using the graph attention network. Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks. The policy network is used to convert the large scale welding spots allocation problem to multiple small scale single‐robot welding path planning problems, and the path planning problem is quickly solved through existing methods. Then, the model is trained through reinforcement learning. In addition, the task balancing method is used to allocate tasks to multiple stations. The proposed algorithm is compared with classical algorithms, and the results show that the algorithm based on DRL can produce higher quality solutions.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2468-2322
2468-6557
2468-2322
DOI:10.1049/cit2.12394