Work

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

Year: 2024

Type: article

Abstract: 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.

Source: CAAI Transactions on Intelligence Technology

DOI: 10.1049/cit2.12394

Authors: Junnan Zhang, Ke Wang, Chaoxu Mu

Institution: Tianjin University

Language: English

Topic: Advanced Manufacturing and Logistics Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,680

Funders: China Postdoctoral Science Foundation, Postdoctoral Research Foundation of China, National Key Research and Development Program of China