Collaborative Navigation of Multiple Autonomous Mobile Robots via Geometric Graph Neural Network

Abstract

Multiple mobile robots play a crucial role in spatially distributed tasks. In unknown and nonrepetitive scenarios, reconstructing a global map is time-consuming and often unnecessary. Hence, research has focused on real-time collaborative planning without relying on a global map. This article introduces a novel multirobot collaborative path planning method based on geometric graph neural network (MRPP-GeoGNN). With all robots connected via local communication devices, the features of neighboring robot’s sensory data are transmitted to the ego-robot for reference. The GeoGNN layers acquire local surrounding features by integrating these sensory features with positional embeddings. We use a geometric function to project positional information into a high-dimensional space, which ensures the rotation invariance of different nodes in a relative coordinate system. An action mapper finally maps the integrated feature to multiple forward directions to guide the robot’s movement. Simulation results demonstrate an approximate 5% improvement in accuracy on expert datasets compared to convolutional neural network (CNN), along with a 4% increase in success rate and an 18% reduction in flowtime increase in robot operating system (ROS) tests, outperforming other GNN models. Real-world experiments further validate its ability to effectively leverage neighbor information, significantly improving path efficiency in practical applications.