Robot Soccer Robots: An Analysis Of Simulation In Soccer Games

Abstract— Simulation in robotics research is the one of most beneficial ways of evaluating robot motion before implementation in real platform. The motion planning is one of important things to win a robotic soccer game and A* algorithm is the one of best algorithm for path planning. This paper discusses about robot soccer simulator for robot soccer player motion for monitoring feasibility of using A* algorithm in dynamic system for generate best path for robot soccer player to avoid it’s opponent in different scenario.
Keywords— soccer, robot, path planning, simulator
I. Introduction
Automation and artificial intelligence in soccer robots can be used to solve some problems during a game, such as how to find the fastest track ball, avoiding
Path planning is the determination of the route that must be passed by the robot to pass each point on the area of a certain area to the desired target point. Generally, robot motion route planning is used to solve problems when the robot is in the labyrinth area. Planning of the motion route can also be used to determine the strategy used in the match.
In this paper, we only present a scenario where the robot, starting from a position far from a ball and player’s opponent, has to approach it using the best possible path with avoiding player’s opponent. Our algorithm takes for granted that the position and goal of all opponents are correctly perceived. We simulated it with our own simulator to evaluate the planner performance.
In the first part of this paper, an overview of A* algorithm is presented. We review the concepts of A* algorithm and propose our modification to allow A* algorithm to be used in dynamic environment. The second part of this paper present our simulation in different scenarios, and we discuss how does the performance of our algorithm varies with different scenarios. Finally, we give a briefly conclusion of our
This can be proved by the following explanation. Suppose that point is the target point with the price of line and is the suboptimal goal state, which means goal state with cost . It can be imagined that the A * algorithm selects from the queue. Since is a goal state, it will end the search with a suboptimal solution. This is not possible because node n is the node in the optimal path to . Then, there must be some more nodes, unless the path has been completely expanded and the algorithm returns the value of . Furthermore, since the h function is received, then . In addition, if n is not selected to expand , then . So,. But since is a goal state, . Thus . Therefore, it can be assumed that . This is contrary to the assumption that is suboptimal, so it can be known that the A * algorithm does not select a suboptimal goal for expansion. Therefore, Algorithm A* is the optimal algorithm because it will only return the value of the solution after selecting the goal state for