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A Q-learning algorithm to generate shots for walking robots in soccer simulations

Credit: C M, Unsplash

RoboCup, initially named the J-League, is an annual robotics and synthetic intelligence (AI) competitors organized by the Worldwide RoboCup Federation. Throughout RoboCup, robots compete with different robots soccer tournaments.

The thought for the competitors originated in 1992, when Professor Alan Mackworth at University of British Columbia in Canada wrote a paper entitled “On Seeing Robots.” In 1993, a analysis staff in Japan drew inspiration from this paper to prepare the primary competitors.

Whereas RoboCup will be extremely entertaining, its principal goal is to showcase developments in robotics and AI in a real-world setting. The robotic methods collaborating within the competitors are the results of intensive analysis efforts by many researchers worldwide.

Along with the real-world competitors, pc scientists and roboticists can take a look at their computational instruments for robotic soccer on the the RoboCup 3D soccer simulation league. That is primarily a platform that replicates the RoboCup atmosphere in simulation, serving as a digital “gym” for AI strategies and robotic methods designed to play soccer.

Researchers at Yantai Institute of Know-how in China and University of Rahjuyan Danesh Borazjan in Iran have just lately developed a brand new method that would improve the power of robots collaborating in soccer video games to shoot the ball whereas strolling. This system, offered in a paper printed in Springer Hyperlink’s Journal of Ambient Intelligence and Humanized Computing, is predicated on a computational strategy generally known as the Q-learning algorithm.

“One of the most important goals of the teams participating in the RoboCup3D league is the ability to increase the number of shots,” Yun Lin, Yibin Track and Amin Rezaeipanah, the three researchers who developed the method, wrote of their paper. “The reason for this importance is that superiority over the opponent requires a powerful and precise shot.”

Most strategies to generate pictures in simulation are based mostly on two approaches known as inverse kinematics (IK) and level evaluation. These are that can be utilized each to create pc animations and in robotics to foretell the joint parameters required for a robotic to realize a given place or full an motion.

“The assumption of these methods is that the positions of the robot and the ball are fixed,” the researchers defined of their paper. “However, this is not always the case for shooting.”

To beat the restrictions of beforehand proposed strategies, Lin and his colleagues created a brand new taking pictures technique based mostly on a Q-learning algorithm, which might improve the power of robots to shoot the ball whereas strolling. Q-learning algorithms are model-free computational approaches based mostly on reinforcement studying. These algorithms are significantly helpful in situations the place brokers are trying to learn to optimally navigate their atmosphere or carry out complicated actions.

“A curved path is designed to move the robot towards the ball, so that it will eventually have an optimal position to shoot,” the researchers wrote of their paper. “In general, the vision preceptor in RoboCup3D has noise. Hence, robot movement paramenters such as speed and angle are more precisely adjusted by the Q-learning algorithm. Finally, when the robot is in the optimal position relative to the ball and the goal, the IK module is applied to the shooting strategy.”

Lin, Track and Rezaeipanah evaluated their Q-learning algorithm in a collection of experiments and simulations. Remarkably, they discovered that it allowed robots to shoot the ball whereas strolling significantly better than robots in most groups collaborating within the RoboCupSoccer league and in Iran’s RoboCup3D league. Finally, it might thus considerably enhance the efficiency of robots throughout RoboCup soccer video games.

A heuristic search algorithm to plan attacks in robotic football

Extra data:
Era a taking pictures on the strolling for soccer simulation 3D league utilizing Q-learning algorithm. Journal of Ambient Intelligence and Humanized Computing(2021). DOI: 10.1007/s12652-021-03551-9

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A Q-learning algorithm to generate pictures for strolling robots in soccer simulations (2021, November 25)
retrieved 25 November 2021

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