Generating Motion Patterns Using Evolutionary Computation in Digital Soccer

TL;DR

This paper presents a hybrid approach combining computational geometry and evolutionary algorithms to generate motion trajectories for dribbling in digital soccer, improving online performance by offline planning.

Contribution

It introduces a novel hybrid method that reduces online computation by precomputing trajectories offline using evolutionary computation and geometric techniques.

Findings

Offline trajectory planning enhances online dribbling performance.

Hybrid approach reduces real-time computational costs.

Trajectory plans effectively guide agent movement in digital soccer.

Abstract

Dribbling an opponent player in digital soccer environment is an important practical problem in motion planning. It has special complexities which can be generalized to most important problems in other similar Multi Agent Systems. In this paper, we propose a hybrid computational geometry and evolutionary computation approach for generating motion trajectories to avoid a mobile obstacle. In this case an opponent agent is not only an obstacle but also one who tries to harden dribbling procedure. One characteristic of this approach is reducing process cost of online stage by transferring it to offline stage which causes increment in agents' performance. This approach breaks the problem into two offline and online stages. During offline stage the goal is to find desired trajectory using evolutionary computation and saving it as a trajectory plan. A trajectory plan consists of nodes which approximate information of each trajectory plan. In online stage, a linear interpolation along with Delaunay triangulation in xy-plan is applied to trajectory plan to retrieve desired action.