论文标题
用数据驱动和基于物理的方法进行自动高尔夫球
Autonomous Golf Putting with Data-Driven and Physics-Based Methods
论文作者
论文摘要
我们正在使用基于数据驱动的方法和基于物理学的方法开发一个自我学习的机电高尔夫机器人，以使机器人自主学习从绿色的任意点将球推杆。除了机器人的机电控制设计外，此任务是通过具有图像识别的摄像头系统和一个神经网络来完成的，用于预测成功的孔 - 一对一孔所需的冲程速度矢量。为了最大程度地减少与真实系统的耗时相互作用的数量，通过评估模型上的基本物理定律来鉴定神经网络，该定律以数据驱动的方式近似于绿色表面上的高尔夫球动力学。因此，我们证明了在高尔夫机器人上作为机电蛋白酶示例系统的基于数据驱动的方法和基于物理的方法的协同组合。
We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitrary point on the green. Apart from the mechatronic control design of the robot, this task is accomplished by a camera system with image recognition and a neural network for predicting the stroke velocity vector required for a successful hole-in-one. To minimize the number of time-consuming interactions with the real system, the neural network is pretrained by evaluating basic physical laws on a model, which approximates the golf ball dynamics on the green surface in a data-driven manner. Thus, we demonstrate the synergetic combination of data-driven and physics-based methods on the golf robot as a mechatronic example system.
