Planning to Score a Goal in Robotic Football with Heuristic Search

TL;DR

This paper applies heuristic search, specifically A*, to plan ball trajectories in robotic football, demonstrating its effectiveness through simulation comparisons with baseline methods.

Contribution

It introduces a discretized environment model and tailored cost and heuristic functions for applying A* in RoboCup attack planning.

Findings

Heuristic search outperforms baseline methods in simulation.

The approach effectively plans attack trajectories in robotic football.

The method integrates game state information into the search process.

Abstract

This paper considers a problem of planning an attack in robotic football (RoboCup). The problem is reduced to finding a trajectory of the ball from its current position to the opponents goals. Heuristic search algorithm, i.e. A*, is used to find such a trajectory. For this algorithm to be applicable we introduce a discretized model of the environment, i.e. a graph, as well as the core search components: cost function and heuristic function. Both are designed to take into account all the available information of the game state. We extensively evaluate the suggested approach in simulation comparing it to a range of baselines. The result of the conducted evaluation clearly shows the benefit of utilizing heuristic search within the RoboCup context.