Today, we will solve a real-world problem is to design a robotic controller. There are many techniques that can be used to solve this problem. Some of them include genetic algorithm (GA), particle swarm optimization and neural network (NN).

今天，我们要解决的一个现实问题是设计一个机器人控制器。 有许多技术可用于解决此问题。 其中一些包括遗传算法(GA)，粒子群优化和神经网络(NN)。

What we need to do is to apply an algorithm to the robotic as a method of designing robotic controllers that enable the robot to perform complex tasks and behaviors.

我们需要做的是将一种算法应用于机器人，以此作为设计机器人控制器的一种方法，从而使机器人能够执行复杂的任务和行为。

Autonomous robot is a robot that can perform certain work independently without the human help.

自主机器人是无需人工帮助即可独立执行某些工作的机器人。

One of the capabilities of robots is to move from one point to another which called is autonomous navigation. Imagine that we have built a robot that can move goods around a warehouse. In this article, we will implement this capability using the Python language. How the robot can see its local environment? Yes, we would install sensors, which allow the robot can look around and we have given it wheels so it can navigate based on input from its sensors. The biggest problem is how we can link the sensor data to motor actions so that the robot can navigate the warehouse.

机器人的功能之一是从一个点移动到另一个点，这就是自主导航。 想象一下，我们已经建造了一个可以在仓库中移动货物的机器人。 在本文中，我们将使用Python语言实现此功能。 机器人如何查看其本地环境？ 是的，我们将安装传感器，使机器人可以环顾四周，并且已经给它赋予轮子，以便它可以基于来自传感器的输入进行导航。 最大的问题是我们如何将传感器数据链接到动作，以便机器人可以导航仓库。

In general, we frequently use neural networks to successfully map robotic sensors to outputs by using reinforcement learning algorithms to the learning process. But we will use another way today, it is to use genetic algorithms. Typically, a genetic algorithm will evaluate a large population of individuals to find the best individuals for the next generation by using a fitness function that compute the performance of and individual based on certain predefined rules.

通常，我们经常使用神经网络通过在学习过程中使用强化学习算法来成功地将机器人传感器映射到输出。 但是，今天我们将使用另一种方法，即使用遗传算法。 通常，遗传算法将通过使用适应度函数评估大量个体，以找到下一代的最佳个体，该适应度函数根据某些预定义规则计算个体和个体的表现。

However, we will be facing a new challenge, physically evaluating each robot controller is not feasible for a large population due to the difficulty in physically testing each robotic controller and the time it would take to do so. For this reason, we will use our knowledge of genetic algorithms to design and implement a robotic controller and apply it to a virtual robot in a virtual environment.

但是，我们将面临一个新的挑战，因为对每个机器人控制器进行物理测试非常困难，并且需要花费大量的时间，因此对大量机器人进行物理评估是不可行的。 因此，我们将使用遗传算法的知识来设计和实现机器人控制器，并将其应用于虚拟环境中的虚拟机器人。

目标 (The target)

Our robot can take four actions: move one step forward, turn left, turn righ and do nothing.

我们的机器人可以执行以下四个动作：向前移动一步，向左转，向右转或什么都不做。

The robot also has six sensors in the figure below.

下图中机器人也有六个传感器。

• three on the front前面三个
• one on the left一个在左边
• one on the right一个在右边
• one on the back一个在后面

The maze is comprised of walls that the robot can’t cross and will have an outlined route. We are going to design a robotic controller that can use the robot sensors to navigate a robot successfully through a maze.

迷宫由机器人无法越过的墙壁组成，并具有勾勒出的路线。 我们将设计一个机器人控制器，该控制器可以使用机器人传感器在迷宫中成功导航机器人。

解决方案 (The Solution)

遗传算法伪代码 (The genetic algorithm pseudocode)

The pseudo code for a basic genetic algorithm is as follows

基本遗传算法的伪代码如下

generation = 0;population[generation] = initializePopulation(populationSize); evaluatePopulation(population[generation]);While isTerminationConditionMet() == false do     parents = selectParents(population[generation]);     population[generation+1] = crossover(parents);     population[generation+1] = mutate(population[generation+1]);    evaluatePopulation(population[generation]);    generation++;End loop;

This pseudocode demonstrates the basic process of a genetic algorithm. Next steps, we will implement them in Python.

此伪代码演示了遗传算法的基本过程。 下一步，我们将在Python中实现它们。

将传感器值映射到动作 (Mapping the sensor values to actions)

As mentioned in the previous section, the robot will have four actions that can be represented in binary as follows:

如上一节所述，机器人将具有四个可以用二进制表示的动作，如下所示：

• “00” — do nothing;“ 00”-不执行任何操作；
• “01” — move forward;“ 01”-前进；
• “10” — turn left;“ 10”-向左转；
• and “11” — turn right.和“ 11” —向右转。

We also have six different sensors. To simplify the representation we limit the measurements to binary encoding, that is, values less than a threshold indicate obstacle detection and larger than threshold indicate a clear path. 6 sensors are giving us 2⁶ = 64 possible combinations of sensor inputs. Since an action requires 2 bits, our controller requires 64*2 = 128 bits of storage to represent any possible input. Given that 128 bits are our requirement for representing instructions different combinations. But how should we organize the chromosome so we can encode and decode it?

我们还有六个不同的传感器。 为了简化表示，我们将测量值限制为二进制编码，即，小于阈值的值表示障碍物检测，大于阈值的值表示畅通路径。 6个传感器为我们提供2⁶= 64种可能的传感器输入组合。 由于一个动作需要2位，因此我们的控制器需要64 * 2 = 128位存储来表示任何可能的输入。 鉴于我们需要128位来表示不同组合的指令。 但是，我们应该如何组织染色体，以便对它进行编码和解码？

We have a human readable list of inputs and outputs as follows:

我们有一个易于理解的输入和输出列表，如下所示：

• Sensor #1 (front): on传感器＃1(正面)：开
• Sensor #2 (front-left): off传感器＃2(左前)：关闭
• Sensor #3 (front-right): on传感器＃3(右前)：开
• Sensor #4 (left): off传感器＃4(左)：关闭
• Sensor #5 (right): off传感器＃5(右)：关闭
• Sensor #6 (back): off传感器＃6(背面)：关闭

We also have an instruction that is “turn left” with a value of 10 in binary. Our next is to take the six sensor values and encode those further.

我们还有一条指令“向左转”，二进制值为10。 我们的下一步是获取六个传感器值，并对它们进行进一步编码。

000101 => 10

If we now convert the sensor values’ bit string to decimal, we get the following:

如果现在将传感器值的位字符串转换为十进制，则会得到以下信息：

5 => 10

So, we can use the sensor’s decimal value as the position in the chromosome that represents a combination of sensor inputs as follows.

因此，我们可以使用传感器的十进制值作为代表传感器输入组合的染色体位置，如下所示。

xx xx xx xx xx 10 xx xx xx xx (… 54 more pairs…)

As can be seen in the following figure, a combination of sensor values produces a binary output that describes how a typical chromosome can map the robot’s sensor values to actions.

如下图所示，传感器值的组合产生二进制输出，该输出描述典型染色体如何将机器人的传感器值映射到动作。

This encoding scheme may seem obtuse at first and the chromosome is not human-readable but it has a couple of helpful properties.

这种编码方案乍一看似乎很钝，并且染色体不是人类可读的，但是它具有几个有用的属性。

• First, the chromosome can be manipulated as an array of bits which makes crossover, mutation, and other manipulations much easier.首先，可以将染色体按位阵列进行操作，从而使交叉，突变和其他操作变得更加容易。
• Secondly, every 128-bit value is a valid solution.其次，每个128位值都是有效的解决方案。

实作 (Implementation)

Firstly, we need to create and initialize a maze to run the robot in (world.py file). The maze object we’ve created uses integers to represent different terrain types:

首先，我们需要创建并初始化一个迷宫来运行( world.py文件)中的机器人。 我们创建的迷宫对象使用整数表示不同的地形类型：

• 1 defines a wall;1定义墙；
• 2 is the starting position;2是起始位置；
• 3 traces the best route through the maze;3追踪穿过迷宫的最佳路线；
• 4 is the goal position;4是目标位置；
• and 0 is an empty position that the robot can travel over but isn’t on the route to the goal.0是机器人可以越过但未到达目标路线的空位置。

We write a constructor to create a new maze from a double int array and implement public methods to get the start position, score a route through the maze.

我们编写了一个构造函数，以便从double int数组创建一个新的迷宫，并实现公共方法来获取起始位置，在迷宫中刻一条路线。

个人 (Individual)

An individual is represented by a single chromosome composed of multiple genes.

一个个体由一个由多个基因组成的染色体代表。

机器人 (Robot)

Next, we need to create a Robot that can follow instructions and generate a route by executing those instructions.

接下来，我们需要创建一个可以遵循指令并通过执行这些指令生成路线的机器人。

人口 (Population)

The population refers to a group of chromosomes.

种群是指一组染色体。

遗传算法 (Genetic Algorithm)

We implement methods to calculate fitness, select individuals, crossover and do mutation.

我们实现了计算适应度，选择个体，交叉和变异的方法。

机器人控制器 (Robotic Controller)

Finally, we can write a class that actually executes the algorithm. Create another new file called main.py as follows.

最后，我们可以编写一个实际执行算法的类。 如下创建另一个名为main.py新文件。

The output.

输出。

Easy, right?

容易吧？