Multi-AGV Task Allocation with Attention Based on Deep Reinforcement Learning

Author:

Yin Zuozhong12ORCID,Liu Jihong1,Wang Dianpeng1

Affiliation:

1. School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, P. R. China

2. Beijing Research Institute of Automation for Machinery Industry Co., Ltd., Beijing 100120, P. R. China

Abstract

Automated guided vehicle (AGV) is an important transportation equipment, which is widely used in warehouses and factories. In the scenarios of multi-AGVs application, an efficient AGVs task assignment strategy is beneficial for transportation costs, balance of workload and increasing distribution efficiency. The traditional method usually depends on a powerful scheduling system, which solves the task assignment problem in a regular way. In this paper, we present a decentralized framework of multi-task allocation with attention (MTAA) in deep reinforcement learning, which combines with the methods of task assignment in balance and path planning in cooperation for distribution application. As for task assignment balance, we adopt DNN network to achieve task assignment equilibrium. In multi-AGVs path planning, methods of A3C are embedded in MTAA framework, which are instrumental in improving the stationarity and performance in deep reinforcement learning application. In experiments, we designed two different scenarios under different obstacles to verify the performance of MTAA-A3C and MTAA-DQN methods. The experiments show that the proposed approach has feasibility and effectiveness used in multi-AGVs application.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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