Devs-Based Simulation Acceleration for AI Training: Unmanned Surface Vehicle Case

Juho Choi, Jang Won Bae, Il-Chul Moon

Abstract: As demand for USV usage increases, the development of simulators for AI training is becoming crucial. This paper introduces a DEVS-based simulation acceleration technique achieved through dynamic changes in the model structure, which is enabled by DSDEVS, while considering domain-specific characteristics. In the case study, the proposed method was applied to and evaluated using USV models. Specifically, the proposed method adapts the coupling structure of the USV maneuver model based on changes in bank angle during simulations; the coupling structure of the USV sensor model is modified according to the distance from enemy units. These changes reduce unnecessary event exchanges during simulation execution, thus increasing the speed of simulation execution. Furthermore, they can lead to the dynamic control of time advances in USV models, enables the improvement of simulation speed. The case study shows the proposed method effectively accelerates simulation execution, but it involves a trade-off with simulation accuracy.

External IDs:dblp:conf/wsc/ChoiBM24