rf_f , rf_l , rf_r, rf_60, rf_320 , turn
0 0 0 0 0 0 0 // we go directly, no obstacles detected
0 0 0 0 0 0 0 // we go directly, , no obstacles detected
1.0 0 0 0 0 0 0 // We see a wall in forward far away.
0.9 1 0 0 0 0 0.2 // We see a wall in forward and left,
therefore turn right slightly etc.
0.8 0.8 0 0 0 0 0.4 // We see a wall in forward and left,
therefore turn right slightly etc.
将这样的训练数据集提供给神经网络后，您就可以对其进行训练。
It seems that this is a supervised learning problem. In this type of problem you NEED to provide some answers BEFORE to train your NN.
You can try following approach
Create a simple maze for your car.
Drive your car manually in this maze.
Collect your turning information
Lets assume you have following car.
rf = rangefinder
rf_f = rangefinder_forward
rf_r = rangefinder_right
rf_l = rangefinder_left
rf_60 = rangefinder_60 degree
rf_320 = rangefinder_320 degree
Below is your rf diagram
320 f 60
\ | /
\ | /
\ |/
l--------------r
|
|
|
Your train set should be like below.
rf_f , rf_l , rf_r, rf_60, rf_320 , turn
0 0 0 0 0 0 0 // we go directly, no obstacles detected
0 0 0 0 0 0 0 // we go directly, , no obstacles detected
1.0 0 0 0 0 0 0 // We see a wall in forward far away.
0.9 1 0 0 0 0 0.2 // We see a wall in forward and left,
therefore turn right slightly etc.
0.8 0.8 0 0 0 0 0.4 // We see a wall in forward and left,
therefore turn right slightly etc.
After you have given such a training dataset to your NN you may train it.
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评论（1）
看来这是一个监督学习问题。在此类问题中，您需要在训练神经网络之前提供一些答案。
您可以尝试以下方法
假设您有跟随的汽车。
下面是你的 rf 图
你的火车组应该如下所示。
将这样的训练数据集提供给神经网络后，您就可以对其进行训练。
It seems that this is a supervised learning problem. In this type of problem you NEED to provide some answers BEFORE to train your NN.
You can try following approach
Lets assume you have following car.
Below is your rf diagram
Your train set should be like below.
After you have given such a training dataset to your NN you may train it.