Dynamic Motion Planning Model for Multirobot Using Graph Neural Network and Historical Information

Abstract

In order to effectively improve the path‐finding capability of a multirobot system in a decentralized control approach, a dynamic motion planning model based on graph neural networks and historical information (GNNHIM) is proposed. Due to the limited sensing range of the robot's onboard sensors, GNNHIM uses convolutional neural networks to extract features from the heterogeneous environmental information collected and analyzes the motion trends of other robots in conjunction with the historical path information stored locally. After finishing feature fusion, each robot exchanges its feature vectors with other robots still within sensing range to obtain the next action by the graph neural network model processing. Herein, imitation learning is used to train robots to choose behavioral strategies to maximize team benefit. The experimental results show that GNNHIM can make path planning for multirobot systems more efficient and reliable, enabling all robots to reach their own goal within a limited time in 95.1% of the experimental dataset. Moreover, GNNHIM still has great generalization when applied to unseen scenarios and larger scale robot systems. GNNHIM in unseen scenarios has improved the success rate by 6%‐34% and reduced the detour percentage by 3.8% on average compared to the other scheduling model.