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# 计算机代写|机器学习代写Machine Learning代考|KIT315 Dagu Magician Robot Control Algorithm

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## 计算机代写|机器学习代写Machine Learning代考|Application: Bluetooth BLE Greenhouse Monitor

In this section, we provide the basic framework for the robot control algorithm. The control algorithm will read the IR sensors attached to the Arduino Nano 33 ANALOG IN (pins 0–2).
In response to the wall placement detected, the algorithm will render signals to turn the robot to avoid the maze walls. Provided in Fig. 3.16 is a truth table that shows all possibilities of maze placement that the robot might encounter. A detected wall is represented with a logic one. An asserted motor action is also represented with a logic one. As previously mentioned,due to the physical placement of the sensor array on the trailing edge of the robot, the sensor detecting maze walls to the right of the robot is physically located on the left side of the robot and vice versa.

Given the interface circuit used, the robot motors may only be moved in the forward direction. To render a left turn, the left motor is stopped and the right motor is asserted until the robot completes the turn. To render a right turn, the opposite action is required.
The task in writing the control algorithm is to take the UML activity diagram provided in Fig. 3.15 and the actions specified in the robot action truth table Fig. 3.16 and transform both into an Arduino sketch. This may seem formidable but we take it a step at a time.

The control algorithm begins with Arduino Nano 33 pin definitions. Variables are then declared for the readings from the three IR sensors. The two required Arduino functions follow: setup() and loop(). In the setup() function, Arduino Nano 33 pins are declared as output. The loop() begins by reading the current value of the three IR sensors. The 512 value corresponds to a particular IR sensor range. This value may be adjusted to change the range at which the maze wall is detected. The read of the IR sensors is followed by an eight part
if–else if statement. The statement contains a part for each row of the truth table provided in Fig. 3.16. For a given configuration of sensed walls, the appropriate wall detection LEDs are illuminated followed by commands to activate the motors (analogWrite) and illuminate the appropriate turn signals. The analogWrite command issues a signal from 0 to 3.3 VDC by sending a constant from 0 to 255 using pulse width modulation (PWM) techniques. The turn signal commands provide to actions: the appropriate turns signals are flashed and a 1.5 s total delay is provided. This provides the robot 1.5 s to render a turn. This delay may need to be adjusted during the testing phase.

## 计算机代写|机器学习代写Machine Learning代考|Overview

With the introduction to the Arduino IDE (Chap. 1), the Nano 33 BLE Sense (Chap. 2), and interface techniques (Chap. 3) complete; we concentrate on Artificial Intelligence (AI) and Machine Learning (ML) concepts and applications for microcontroller–based systems for the remainder of the book. In a recent release, the Arduino Team stated “Arduino is on a mission to make machine learning simple enough for everyone to use [https://www.blog.arduino.cc].” Those acquainted with AI and ML concepts might counter these concepts are most appropriate for more powerful computing platforms. However, recent developments have allowed certain AI and ML applications to be executed on microcontrollers once they have been trained.

We will see the training task, in certain applications, may also be accomplished on a microcontroller. Furthermore, we explore applications that lend themselves to remote, battery operated microcontroller–based AI and ML applications [4]. In the remainder of the book we limit our discussions to AI and ML techniques specifically for microcontrollers. The intent is to introduce the concepts and allow you to practice on low cost, accessible Arduino hardware and software.Figure 4.1 illustrates the relationship between Artificial Intelligence, Machine Learning, and Deep Learning. The goal of Artificial Intelligence is for computing machinery to imitate and mimic intelligent human behavior. Some trace the origins of AI back to 1300 BCE [4].We limit our historical review to AI developments within the 20th century and forward. Following a brief historical review, this chapter explores the concept of K Nearest Neighbors (KNN) and Decision Tree classification techniques.

Within the realm of AI, we explore Fuzzy Logic in Chap. 5. The overall goal of fuzzy logic is to control a system using a series of rule statements of the form “IF–THEN.” The input conditions of the “IF” statement, the antecedents, are obtained by taking precise, crisp input information from input sensors and transducers and mapping them to fuzzy input linguistic (word) variables. This is called the fuzzification step. The “THEN” portion of the rule, the consequences, are the control commands back to the system. Again, linguistic variables are used to describe the control output. The control output is defuzzified to obtain precise, crisp output control signals. The mapping of inputs to outputs via the “IF–THEN” statements are provided by the system designer.

## 计算机代写|机器学习代写Machine Learning代考|Application: Bluetooth BLE Greenhouse Monitor

if-else if 语句。该语句包含图 3.16 中提供的真值表的每一行的一部分。对于感应墙壁的给定配置，相应的墙壁检测 LED 会亮起，然后发出命令以激活电机 (analogWrite) 并点亮相应的转向信号灯。analogWrite 命令通过使用脉冲宽度调制 (PWM) 技术发送 0 到 255 之间的常数来发出 0 到 3.3 VDC 的信号。转向信号命令提供动作：闪烁适当的转向信号并提供 1.5 秒的总延迟。这为机器人提供了 1.5 秒的转弯时间。在测试阶段可能需要调整此延迟。

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