作为任务的一部分，我必须实施一种学习方法，使扫雷艇能够避免与地雷相撞。我可以在有监督/无监督/强化学习算法之间进行选择。
我记得在我的一次讲座中，讲师提到了ALVIN。他正在教人工神经网络。
由于我正在寻找的行为与 ALVINN 的行为或多或少相似，因此我想实现一个 ANN。我之前已经实现了一个人工神经网络来解决 3 奇偶异或问题，这是我的解决方案。我从来没有真正理解过人工神经网络背后的直觉。
我想知道，我的 ANN 的输入可能是什么？在 3parity xor 问题的情况下，这是显而易见的。
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When it comes to frameworks for ANN, each person will have their own preferences. I recently used Encog framework for implementing an image processing project and found it very easy to implement.
Now, coming to your problem statement, "a learning method that enables minesweepers to avoid colliding with mines" is a very wide scope. What is indeed going to be your input to the ANN? You will have to decide your input based on whether it is going to be implemented on a real robot or in a simulation environment.
It can be clearly inferred that an unsupervised learning can be ruled out if you are trying to implement something like the ALVIN.
In a simulation environment, the best option is if you can somehow form a grid map of the environment based on the simulated sensor data. Then the occupancy grid surrounding the robot can form a good input to the robot's ANN.
If you can't form a grid map (if the data is insufficient), then you should try to feed all the available and relevant sensor data to the ANN. However, they might have to be pre-processed, depending on the modelled sensor noise given by your simulation environment. If you have a camera feed (like the ALVIN model), then you may directly follow their footsteps and train your ANN likewise.
If this is a real robot, then the choices vary considerably, depending upon the robustness and accuracy requirements. I really hope you do not want to build a robust and field-ready minesweeper single-handedly. :) For a small, controlled environment, your options will be very similar to that of a simulated environment, however sensor noise would be nastier and you would have to figure in various special cases into your mission planner. Still, it would be advisable to fuse a few other sensors (LRF, ultrasound etc.) with vision sensors and use it as an input to your planner. If nothing else is available, copy paste the ALVIN system with only a front camera input.
The ANN training methodology will be similar (if using only vision). The output will be right/left/straight etc. Try with 5-7 hidden layer nodes first, since that is what ALVIN uses. Increase it up to 8-10 max. Should work. Use activation functions properly.
于 2013-09-10T07:44:58.047 回答
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鉴于它在现实世界中的成功，ALVIN 似乎是一个很好的系统，可以作为您的基础！正如您链接到的页面所讨论的那样，ALVIN 本质上是接收前方道路的图像作为其输入。在低层次上，这是通过代表 30X32 像素图像的 960 个输入节点来实现的。每个节点的输入值是该节点所代表的像素的颜色饱和度（0 是完全白色的图像，1 是完全黑色的图像，或者类似的东西）（我很确定图片是灰度的，尽管他们现在可能正在使用颜色，例如，这可以通过每个像素使用三个输入节点来实现，一个代表红色饱和度，一个代表绿色，一个代表蓝色）。您是否有理由认为这对您的系统来说也不是一个好的输入？
有关更多低级详细信息，请参阅原始论文。
于 2013-09-10T01:08:08.883 回答