基于神经网络的撞球机器人控制器设计

对撞球机器人的母球控制问题展开研究,设计了一种基于神经网络(NN)的控制器,使机器人能够控制母球在击打目标球后按照预定的模式运动至目标点——即完成走位。针对该问题非线性且非光滑的特点,对坐标系进行阐述并给出机器人击球的模型;在光滑的假设下使用理论分析的方法建立母球的运动学模型与边库反弹的理想镜像模型;进而使用神经网络方法对理想模型进行修正,并对不同的轨迹模式进行分析与分类。测试结果表明:经过训练的机器人能够掌握各种模式的走位,统计结果与模型分析结果相吻合;相比于单一使用神经网络方法,本文使用理论分析与神经网络相结合的方法能够有效地提升网络的品质,降低训练的误差。

Design of neural network controller for a billiard robot

This paper focuses on the cue ball controlling problem for a billiard robot.A neural network (NN) controller is designed,and the trained robot is able to stroke the cue ball moving to the target point after colliding with objective ball and cushions.Since the problem is non-linear and non-smooth,the solution is divided into several steps.First,the stroking model and the coordinate definition are described.Second,the kinematic model for cue ball motion and the mirror model for cushion rebounds are established under the ideal smooth assumption.Then,the neural network method is used to modify the ideal models,and the pattern recognition method for trajectories is presented.In the verification test,the trained robot is able to master the cue ball controlling with each pattern.The statistic results tally with the model analysis.Compared with simply adopting neural network method,the method combined with theoretical kinematic analysis will effectively improve the network quality and reduce the training error.