The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Podsumowanie

We present a protocol on modular design and production of intelligent robots to help scientific and technical workers design intelligent robots with special production tasks based on personal needs and individualized design.

Streszczenie

Intelligent robots are part of a new generation of robots that are able to sense the surrounding environment, plan their own actions and eventually reach their targets. In recent years, reliance upon robots in both daily life and industry has increased. The protocol proposed in this paper describes the design and production of a handling robot with an intelligent search algorithm and an autonomous identification function.

First, the various working modules are mechanically assembled to complete the construction of the work platform and the installation of the robotic manipulator. Then, we design a closed-loop control system and a four-quadrant motor control strategy, with the aid of debugging software, as well as set steering gear identity (ID), baud rate and other working parameters to ensure that the robot achieves the desired dynamic performance and low energy consumption. Next, we debug the sensor to achieve multi-sensor fusion to accurately acquire environmental information. Finally, we implement the relevant algorithm, which can recognize the success of the robot's function for a given application.

The advantage of this approach is its reliability and flexibility, as the users can develop a variety of hardware construction programs and utilize the comprehensive debugger to implement an intelligent control strategy. This allows users to set personalized requirements based on their needs with high efficiency and robustness.

Wprowadzenie

Robots are complex, intelligent machines that combine knowledge of several disciplines, including mechanics, electronics, control, computers, sensors and artificial intelligence ^1,2. Increasingly, robots are assisting or even replacing humans in the workplace, especially in industrial production, due to the advantages robots possess in performing repetitive or dangerous tasks. The design of the intelligent robot protocol in the current study is based on a closed-loop control strategy, specifically path planning based on a genetic algorithm. Furthermore, the functional modules have been strictly divided^3,4, which may provide a solid foundation for future optimization work, so that the robots have a strong capacity for upgrades.

The modular implementation of the robotic platform is based primarily on the following methods: multi-dimensional combination control strategy in motor control module^5,6, and intelligent exploration based on a genetic algorithm in the optimization algorithm module.

We use double closed-loop control of the DC motor and four-quadrant motor operation in the motor control module. Double closed-loop speed control means that the output of the speed regulator serves as the input of the current regulator, allowing it to control the current and torque of the motor. The advantage of this system is that the torque of the motor can be controlled in real-time based on the difference between the given speed and the actual speed. When the difference between given and actual speeds is relatively large, the motor torque increases and the speed changes faster to drive the motor speed toward the given value as quickly as possible, which makes for rapid speed regulation^7,8,9. Conversely, when the speed is relatively close to the given value, it can automatically reduce the torque of the motor to avoid excessive speed, allowing the speed to achieve the given value relatively quickly with no error^6,10. Since the equivalent time constant of the electric current loop is relatively small, the four-quadrant motor^11,12 can respond more quickly to suppress the impact of interference when the system is subject to external interference. This allows it to improve the stability and anti-jamming ability of the system.

We choose a genetic intelligent optimization algorithm with the highest efficiency based on the results of a simulation run in MATLAB. A genetic algorithm is a stochastic parallel search algorithm based on the theory of natural selection in genetics. It constitutes an efficient method for finding the global optimal solution in the absence of any initial information. It regards the solution set of the problem as a population, thereby increasing the quality of the solution via continuous selection, crossover, mutation and other genetic operations. With regard to path planning by intelligent robots, difficulty arises as a result of insufficient initial information, complicated environments and nonlinearity. Genetic algorithms are better able to solve the problem of path planning because they possess global optimization ability, strong adaptability and robustness in solving nonlinear problems; there are no specific restrictions on the problem; the calculation process is simple; and there are no special requirements for the search space ^13,14.

Protokół

1. Construction of the Machine

1. Assemble the chassis as illustrated, securing mechanical components using appropriate fasteners. (Figure 1)
   NOTE: The chassis, which comprises the baseboard, motor, wheels, etc., is the primary component of the robot responsible for its motion. Thus, during assembly, keep the bracket straight.
2. Tin the wire lead and both the positive and negative electrodes. Solder two wire leads onto the two ends of the motor, connecting the red lead to the positive electrode and the black lead to the negative electrode.
3. Assemble the shaft sleeve, the motors and the wheels.
   1. Connect the motor to the shaft sleeve and secure it with a screw.
   2. Insert the shaft sleeve into the center of the wheel hub.
   3. Install the completed structure onto the chassis.
4. Drill two holes, 3 mm in diameter, in the center of chassis, to allow for installation of the motor driving module. Connect the motor to the motor driving module.
5. Drill one hole 1 cm from both the left and right edges of the chassis for installation of the bracket for the infrared sensors on the bottom.
6. Install two fasteners onto the center of the two sides of the chassis.
   NOTE: To ensure normal operation of the infrared sensors, ensure that the connecting piece is perpendicular to the chassis.
7. Drill a hole, 18 mm in diameter, through each of the two structural components for the installation of sensors. (Figure 2A)
8. Install the motor drive to the underside of chassis. (Figure 2B) Install one infrared sensor pointing at each of the four directions, respectively, of the chassis. (Figure 2C)
9. Install the steering gear in symmetry. Because of the large torque generated by the operation of the steering gear, ensure that the bolts are installed in a manner that provides a firm and impervious joint.
10. Install four infrared sensors on the center of the machine.
11. Place the 14.8 V power supply in the center of the machine, and affix the Microcontroller Unit (MCU) to the battery pack.
12. Affix four range sensors to the upper part of machine. Adjust the angle between each sensor and the ground to 60°, to guarantee detection accuracy relative to the working table.
13. Install the dual-axis tilt sensor, which is used to detect cases when the machine fails to reach its target in the working area.
14. Use a screwdriver to attach the robot arm to the front of the machine. (Figure 3)

2. Debugging the Steering Engine and Driver Module

1. Double-click to open the debugging software (e.g., Robot Servo Terminal2010). Connect computer to the debug board with a Universal Serial Bus (USB) converting cable. (Figure 4)
2. Set the steering engine's baud rate to 9600 bits/s, the rate limitation to 521 rad/min, the angular limitation to 300° and voltage limitation to 9.6 V in the working interface.
3. Set the working mode of the robot steering gear to "steering engine mode."
4. Apply asynchronous half-duplex communication as the connection between the controller and steering engines. This way, the controller can control more than 255 steering engines from a single Universal Asynchronous Receiver/Transmitter (UART) interface. (Figure 5)
   Caution: There may be, at most, 6 steering engines connected to a single wire. Too many steering engines will lead to overheating and large voltage drop, resulting in unusual behavior such as resetting and abnormal data communication, etc. (Figure 6)
5. Apply asynchronous half-duplex communication as the connection between the controller and the motor driving module. (Figure 7)
6. Set the ID number of the two driving modules and the four steering engines. ID3 and ID4 are left blank for future updating purposes. (Figure 8)
   NOTE: ID1: leftward driving module; ID2: rightward driving module; ID5: left-front steering engine; ID6: right-front steering engine; ID7: left-rear steering engine; ID8: right-rear steering engine.
7. Cascade the steering engines one by one and connect the cascade to the controller.
8. Connect the sensors to their respective controller interfaces. It should be noted that the sensor whose connector bears a triangular mark is the ground (GND).
   NOTE: AD1: front infrared photoelectric sensor on underside; AD2: right infrared photoelectric sensor on underside; AD3: rear infrared photoelectric sensor on underside; AD4: left infrared sensor on underside; AD5: front infrared distance measuring sensor; AD6: right infrared distance measuring sensor; AD7: rear infrared measuring sensor; AD8: left infrared distance measuring sensor; AD9: left-front anti-fall infrared photoelectric sensor; AD10: right-front anti-fall infrared photoelectric sensor; AD11: right-rear anti-fall infrared photoelectric sensor; AD12: left-rear anti-fall infrared photoelectric sensor.

3. Debugging the Sensors

1. Rotate the regulating knob on the tail of the infrared sensors to adjust the detection range of the sensors. When the robot is positioned in the center of the working table, the logic level of the top four infrared sensors is 1. When the machine moves to the edge of the working table, the logic level of the infrared sensor on the corresponding side will be 0. (Figure 9A)
   NOTE: The robot can determine its location in the working table by analyzing the logic level of the infrared sensors. For example, if the logic levels of the left and front sensors are 0, the robot must be in the upper-left region of the working table.
2. Compare the measured values of the distance sensor to their baseline values for calibration. (Figure 9B)
   NOTE: The distance sensor is an analog sensor. As the distance varies, the sensor's signal strength feedback and corresponding measured values will also vary. The measured values will be relayed to the host machine via digital sensors so that the robot can identify changes in its surroundings.
3. Debug the tilt-angle sensor.
   1. Position the tilt-angle sensor horizontally and record its measured values.
   2. Incline the sensor toward two different directions and record its measured values. If the measured values are within the error range, the sensor can be regarded as being in regular operation.

4. Control Scheme

1. Construct the simulation model of the DC motor, based on the DC motor voltage balance equation, flux linkage equation and torque balance equation.
   1. Establish voltage balance equation given by
      
      where u_d is direct axis voltage, u_q is quadrature axis voltage. R_d and R_q denote direct axis resistance and quadrature axis resistance respectively. ,,,, represent direct axis current, direct axis current, direct axis flux and quadrature axis flux.
   2. Establish flux linkage equation given by
      
      where and denote the coefficient of direct axis self-inductance and quadrature axis self-inductance respectively. and are coefficient of mutual inductance. , represent electromagnetic torque and load torque.
   3. Establish torque balance equation calculated by .
   4. Build simulation model of the DC motor. (Figure 10)
2. Apply double closed-loop control of the DC motor. Utilize the output of the speed regulator as the input to the current regulator to regulate the motor's torque and current.
   Note: Diagram of the structure of the current regulation system. (Figure 11)
   The transfer function of the PI current regulator is shown as , where is the proportional coefficient of the current regulator and is the lead time constant of the current regulator. It can be obtained by the scale coefficient , and the integral coefficient .
   1. Apply double closed-loop control of the DC motor. (Figure 12)
3. Apply four-quadrant motion control of the DC motor. (Figure 13)
   1. Utilize an H-bridge driving circuit to achieve four-quadrant motion of the DC motor by modulating the on-off of Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET). (Figure 14)
      Note: When Q1 and Q4 are turned on and the motor is in the forward electric state and the first quadrant running state. When Q3 and Q4 are turned on, the motor is in the energy braking state and the second quadrant running state. When Q2 and Q3 are turned on, the motor is in the reverse electric state and the third quadrant motion state. When Q1 and Q2 are turned on, the motor is in the reverse energy braking state and the fourth quadrant motion state.
4. Apply pulse width modulation (PWM) to regulate the speed of the DC motor. Modulate the DC voltage pulse width (duty cycle) applied to the motor armature by controlling the on-off of the electric switch when the voltage of DC motor power supply remains essentially unchanged, thus modulating the average value and the rotation speed input to armature voltage of the motor.

5. Write the Program

1. Use the USB download line to import a Binary (BIN) file generated by KEIL5 into the controller.
2. Select the program to be executed.

6. Application Scenario

1. Apply color recognition to categorize cargo in a factory. (Figure 15)
   1. Use an optical camera to collect images and verify the scanned color using the number of the bounced two-dimensional array.
   2. Lift the object with the mechanical arms.
   3. Issue a command to transport the object to the designated location using the camera and driving motor of the robot.
2. Search quickly to clear the designated areas. (Figure 16)
   1. Use the four optical sensors on the robot to detect the locations of surrounding obstacles.
   2. Command the steering engine to lift the mechanical shovel and clear obstacles in the designated areas.
   3. Use the genetic algorithm to determine the most effective search path.
3. Use self-recognition to prevent falling from the workbench to separate workers from the machine working area and ensure worker safety.
   1. Modify signals based on the difference in altitude between the four upper optical sensors, which recognize the workbench and the ground.
   2. Analyze the mutable signals to determine the location of the edges of the workbench.
   3. Command the machine to avoid the edges of the workbench.

Wyniki

In the diagram of the double closed-loop motion control program, purple represents a given speed signal and yellow represents the value of the control system output. Figure 17 clearly shows that the double closed-loop control system is significantly more effective than an open-loop system. The actual overshoot of the output of the double closed-loop system is relatively small and the dynamic performance of the system is better. ( Figure 1...

Dyskusje

In this paper, we designed a type of intelligent robot that can be built autonomously. We implemented the proposed intelligent search algorithm and autonomous recognition by integrating several software programs with hardware. In the protocol, we introduced basic approaches for configuring the hardware and debugging the intelligent robot, which may help users design a suitable mechanical structure of their own robot. However, during actual operation, it is necessary to pay attention to stability of the structure, its ope...

Materiały

Name	Company	Catalog Number	Comments
structural parts	UPTECMONYH HAR	L1-1
structural parts	UPTECMONYH HAR	L2-1
structural parts	UPTECMONYH HAR	L3-1
structural parts	UPTECMONYH HAR	L4-1
structural parts	UPTECMONYH HAR	L5-1
structural parts	UPTECMONYH HAR	L5-2
structural parts	UPTECMONYH HAR	U3A
structural parts	UPTECMONYH HAR	U3B
structural parts	UPTECMONYH HAR	U3C
structural parts	UPTECMONYH HAR	U3F
structural parts	UPTECMONYH HAR	U3G
structural parts	UPTECMONYH HAR	U3H
structural parts	UPTECMONYH HAR	U3J
structural parts	UPTECMONYH HAR	I3
structural parts	UPTECMONYH HAR	I5
structural parts	UPTECMONYH HAR	I7
structural parts	UPTECMONYH HAR	CGJ
link component	UPTECMONYH HAR	LM1
link component	UPTECMONYH HAR	LM2
link component	UPTECMONYH HAR	LM3
link component	UPTECMONYH HAR	LM4
link component	UPTECMONYH HAR	LX1
link component	UPTECMONYH HAR	LX2
link component	UPTECMONYH HAR	LX3
link component	UPTECMONYH HAR	LX4
Steering gear structure component	UPTECMONYH HAR	KD
Steering gear structure component	UPTECMONYH HAR	DP
Infrared sensor	UPTECMONYH HAR	E18-B0	Digital sensor
Infrared Range Finder	SHARP	GP2D12
Gray level sensor	SHARP	GP2Y0A02YK0F
proMOTION CDS	SHARP	CDS 5516	The robot steering gear
motor drive module	Risym	HG7881
solder wire	ELECALL	63A
terminal	Bright wire	5264
motor	BX motor	60JX
camera	Logitech	C270
Drilling machine	XIN XIANG	16MM	Please be careful
Soldering station	YIHUA	8786D	Be careful to be burn
screwdriver	EXPLOIT	043003
Tweezers	R`DEER	RST-12