Nowadays, humanoid soccer serves as a benchmark for artificial intelligence and robotic problems. The factors such as the kicking speed and the number of kicks by robot soccer players are the most significant aims that the participating teams are pursued in the RoboCup 3D Soccer Simulation League. The proposed method presents a kicking strategy during walking for humanoid soccer robots. Achieving an accurate and powerful kicking while robots are moving requires a dynamic optimization of the speed and motion parameters of the robot. In this paper, a curved motion path has been designed based on the robot position relative to the ball and the goal. Ultimately, the robot will be able to kick at the goal by walking along this curve path. The speed and angle of the walking robot are set towards the ball with regard to the robots curved motion path. After the final step of the robot, the accurate and effective adjustment of these two parameters ensures that the robot is located in the ideal position to perform the perfect kick. Due to the noise and walking condition of the robot, it is essential that the speed and angle of motion to be measured more accurately. For this purpose, we use a reinforcement learning model to adjust the robots step size and so does achieve the optimal value of two abovementioned parameters. Using reinforcement learning, robot would learn to pursue an optimal policy to correctly kick towards designated points. Therefore, the proposed method is a model-free and based on dynamic programming. The experiments reveal that the proposed method has significantly improved the team overall performance and robots ability to kick. Our proposed method has been 9.32% successful on average and outperformed the UTAustinVilla agent in terms of goal-scoring time in a non-opponent simulator.

如今，类人足球已经成为人工智能和机器人问题的基准。机器人足球运动员的踢脚速度和踢脚次数等因素是参加RoboCup 3D足球模拟联赛的参赛队追求的最重要目标。所提出的方法提出了类人足球机器人在行走过程中的踢脚策略。为了在机器人移动时实现准确而有力的脚踢，需要动态优化机器人的速度和运动参数。本文根据机器人相对于球和球门的位置设计了弯曲的运动路径。最终，机器人将能够通过沿着这条曲线路径行走来踢球。相对于机器人弯曲的运动路径，步行机器人的速度和角度设置为朝向球。在机器人完成最后一步之后，对这两个参数进行准确而有效的调整可确保机器人位于理想位置以执行完美的踢球。由于机器人的噪音和行走状况，必须更准确地测量运动的速度和角度。为此，我们使用强化学习模型来调整机器人的步长，因此可以实现上述两个参数的最优值。借助强化学习，机器人将学会追求最佳策略，以正确地踢向指定点。因此，所提出的方法是无模型的并且基于动态规划。实验表明，该方法大大提高了团队的整体表现和机器人的踢球能力。我们提出的方法是9。