Dynamic navigation: Integrating GL-STGCNN and MPC for collision avoidance with future Awareness

Abstract

Existing ship dynamic collision avoidance methods mostly rely on the instantaneous motion information of surrounding ships to make decisions. This makes it difficult to adapt to changes in the motion states of surrounding ships, which may lead to collisions between ships. To improve the safety of dynamic collision avoidance methods, this paper combines the multi-ship trajectory prediction model GL-STGCNN with model predictive control for ship dynamic collision avoidance tasks. Firstly, the interaction between ships is extracted through GL-STGCNN to predict the future trajectories of surrounding ships. Then, the objective function based on the artificial potential field method and the velocity obstacle method is optimized to control the ship to complete the dynamic collision avoidance task. The performance of the dynamic collision avoidance method is verified and analyzed in the ship navigation scenario simulated by AIS data. The experiments show that the new ship dynamic collision avoidance method not only complies with the COLREGs, but also can flexibly select the collision avoidance method according to different scenarios. In addition, the theoretical collision avoidance threshold distance based on the MPC objective function shows a high degree of fit with the actual collision avoidance trigger distance observed in the simulation verification.