[2S1-GS-2] Machine learning:

[2S1-GS-2-01] Trajectory Prediction of the Ball and Players using LSTM Models in the RoboCup Soccer

〇Shota Yano¹, Tomoharu Nakashima¹, Yoshifumi Kusunoki¹, Hidehisa Akiyama² (1. Graduate School of Informatics, Osaka Metropolitan University, 2. Okayama University of Science)

Keywords:RoboCup Simulation, Time Series Prediction, Multi Agent System

In real soccer, players predict the movements of the ball and opponents to construct effective offensive and defensive plays. Similarly, in the RoboCup Soccer Simulation 2D League, which is the focus of this study, such predictions are necessary to take optimal actions. This research aims to predict ball movements and opponent players’ movements, particularly those involved in goal-scoring situations, using LSTM models in the RoboCup Soccer. Numerical experiments demonstrated that predictions for one cycle ahead could be made with high accuracy.