Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (7): 2034-2040.doi: 10.13229/j.cnki.jdxbgxb.20250415

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Trajectory planning algorithm for mobile robot clusters based on fused graph neural networks

Wei-jiang GU1(),Qiao-lin YE1,Xing-hu WANG2   

  1. 1.College of Information Science and Technology & Artificial Intelligence，Nanjing Forestry University，Nanjing 210037，China
    2.Information Technology Office，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China
  • Received:2025-05-13 Online:2026-07-01 Published:2026-08-12

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Abstract:

Aiming at the problem of path conflicts among multiple robots running in the same environment during mobile robot cluster operations， which can lead to collisions among robots， this paper studies a trajectory planning algorithm for mobile robot clusters that integrates graph neural networks. Construct a kinematic model of a mobile robot to describe its motion characteristics； Using a fusion graph neural network-based mobile robot cluster operating environment feature extraction model， multi-dimensional features of the mobile robot cluster operating environment are extracted to comprehensively understand environmental information； Combining the artificial potential field method， based on the extracted features， the destination is set as a gravitational source， and the direction of motion that attracts the robot is towards the destination； Set obstacles as repulsive sources， control robots to avoid obstacles， and determine the direction and speed of each robot's movement based on the resultant force. Plan collision avoidance trajectories for mobile robot clusters during operation. The experiment shows that the proposed algorithm can effectively extract multidimensional features of the operating environment of mobile robot clusters， and plan collision free and risk index less than 0.1 running trajectories for multiple mobile robots.

Key words: fusion graph neural network, residual connection, mobile robots, cluster trajectory planning, artificial potential field method, anti-collision

CLC Number: 

  • TP391

Fig.1

Topology diagram of intelligent warehouse management system"

Fig.2

Scene diagram of robot operation in storage environment"

Table 1

Parameter information of mobile robots"

参数类型具体信息
额定载重/kg150
尺寸/mm805*705*605
巡点精度/mm±5.5
导引方式RFID磁带导引
充电方式电源
电源49/65 A·h铅酸蓄电池
驱动模式两轮差速

Fig.3

Visual information extraction of operating environment features for mobile robot clusters"

Fig.4

Trajectory planning results for mobile robot clusters"

Fig.5

Comparison results of risk indices for three algorithms"

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