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Multi-robot systems are widely used in spatially distributed tasks, and theircollaborative path planning is of great significance for working efficiency.Currently, different multi-robot collaborative path planning methods have beenproposed, but how to process the sensory information of neighboring robots atdifferent locations from a local perception perspective in real environment tomake better decisions is still a major difficulty. To address this problem,this paper proposes a multi-robot collaborative path planning method based ongeometric graph neural network (GeoGNN). GeoGNN introduces the relativeposition information of neighboring robots into each interaction layer of thegraph neural network to better integrate neighbor sensing information. Anexpert data generation method is designed for the robot to advance in a singlestep, by which expert data are generated in ROS to train the network.Experimental results show that the accuracy of the proposed method is improvedby about 5% compared to the model based only on CNN on the expert data set. InROS simulation environment path planning test, the success rate is improved byabout 4% compared to CNN and flowtime increase is reduced about 8%, whichoutperforms other graph neural network models.

Collaborative Goal Tracking of Multiple Mobile Robots Based on Geometric Graph Neural Network