Abstract: Mobile robots mainly consist of three parts: a remote controller, an automatic control system, and sensors, which serve as the mobile carrier. The core technology of mobile robots is the control system, among which motion control technology is a key component. This paper takes a mobile manipulator as the research object and designs a motion control system for accurately grasping objects. Fuzzy control algorithms are used to control the mobile manipulator to accurately stop at the appropriate grasping position.
Foreword
Classical control theory is very effective for solving control problems of linear time-invariant systems; however, it is less effective for nonlinear time-varying systems. With the application and development of computers, automatic control theory has made leaps and bounds. Modern control theory based on state-variable descriptions has been widely and successfully applied to solving control problems of linear or nonlinear, time-invariant or time-varying multi-input and multi-output systems. However, regardless of whether a control system uses classical or modern control theory, it requires prior knowledge of the accurate mathematical model of the controlled object (or process). Then, based on the mathematical model and given performance indicators, an appropriate control law is selected to design the control system. However, in many cases, it is difficult to establish an accurate mathematical model of the controlled object, making automatic control of such objects or processes challenging.
In fact, for complex production processes influenced by multiple factors, even without knowing the mathematical model of the process, experienced operators can effectively control it based on long-term observation and operational experience, while the results of traditional automatic control methods are not ideal. However, is it possible to summarize human operational experience into several control rules and design a device to execute these rules, thereby effectively controlling the system? Fuzzy control theory and methods emerged from this question.
A mobile robotic arm is a simple mobile robot that can perform some simple tasks, such as queuing.
In explosive applications, it can replace humans in grabbing dangerous explosives and transporting them to designated locations. This paper uses a commercially available children's car as a base, modifying the car to build a mobile robotic arm. The mobile robotic arm operates in either remote-controlled or automatic mode. In remote-controlled mode, the movement of the mobile robotic arm is controlled wirelessly; in automatic mode, the robotic arm uses an ultrasonic distance sensor to extract the position information of the object being grasped, using this position information as input to a fuzzy control algorithm to drive the movement of the mobile robotic arm. It possesses mobility and greater maneuverability and flexibility than ordinary robots in replacing humans in dangerous and harsh environments (such as those with radiation or toxic substances) and in environments inaccessible to humans (such as outer space or underwater).
1. Overall Design
The motion control system for the mobile robotic arm designed in this paper mainly consists of a microcontroller (STM32F103), a display screen, a motor driver, a wireless remote controller, an ultrasonic ranging sensor, and a transmissive photoelectric sensor. The microcontroller manages the operation of each module and performs fuzzy control algorithm calculations. The display screen mainly shows information about the mobile robotic arm's movement, such as its operating mode, whether it is moving forward or backward, its turning angle, and the distance to the object it is grasping. The motor driver mainly drives the modified children's car. The wireless remote control module sends control commands to the motion control system, including mode selection and vehicle movement control commands. The ultrasonic ranging module detects the distance to objects and can also detect obstacles to prevent mechanical collisions during the robotic arm's movement. The transmissive photoelectric sensor records pulse counts and, when used with an encoder disk, extracts turning angle and relative displacement information of the robotic arm. The motion control system operates as follows: After initialization, the system enters the mode selection interface. If the object to be grasped is far away, the remote control mode is selected first, and the robotic arm is controlled by the remote controller to approach the object. Once the distance reaches the specified range, the remote control mode exits and enters automatic mode. A fuzzy control algorithm analyzes the object information obtained from the ultrasonic ranging sensor and automatically controls the mobile robotic arm to reach the appropriate grasping position. The block diagram of the mobile robotic arm motion control system is shown in Figure 1.
Figure 1. Block diagram of the motion control system for the mobile robot.
2 Hardware Design
2.1 Introduction to Some Hardware
A: Wireless remote control
The wireless remote control uses the RO3BS wireless remote control from Nanjing Puwen Audiovisual Electronics Co., Ltd., which includes transmitting and receiving modules. This wireless remote control is a four-channel remote control, with single or dual signal outputs, an operating frequency of 315MHz, and a maximum remote control distance of 80 meters. Figure 2 shows the actual RO3BS receiver module, which has a total of 7 pins. +5V and G are the power input terminals, and D0-D3 are the signal output terminals.
B: Motor drive module
The miniIBT motor driver is a high-performance DC brushed motor full-bridge driver designed specifically for smart cars, model cars, and industrial products. The input terminals include a power input port, a motor connection terminal, and a signal control terminal. Its control signals are simple, consisting only of chip select (EN), forward rotation (RPWM), and reverse rotation (LPWM) signals. The forward and reverse rotation control signals can be either high-level or PWM signals, and the PWM signal duty cycle can be freely adjusted between 0% and 100%.
Figure 2. Physical image of the RO3BS receiver module.
C: Ultrasonic Differential Module
The HC-SR04 ultrasonic ranging module provides non-contact distance sensing from 2cm to 400cm with a measurement accuracy of up to 3mm. It has four pins: +5V, GND, trigger signal input, and echo signal output. Its control is relatively simple; a trigger signal (at least a 10µs high-level signal) will trigger an output signal. The measurement distance is calculated as follows:
Test distance = High-level time * Speed of sound / 2 (where the speed of sound is 340m/s)
D: Transmission-type photoelectric sensor
The GK102 is a transmissive photoelectric sensor, and its circuit diagram is shown in Figure 3. The left side is a light-emitting diode (LED), and the right side is a phototransistor. When a forward voltage is applied to the left side, the right side conducts; when a reverse voltage is applied to the left side, the right side is cut off. Used in conjunction with an encoder disk, it can be used to detect turning angle signals and robotic arm displacement signals. Its signal acquisition principle is as follows: First, the encoder disk is a disc with evenly spaced small holes along its edge. The edge of the disc is placed in the groove of the GK102. When the disc rotates, due to the presence of the small holes, the light emitted by the GK102 LED sometimes passes through the holes and hits the phototransistor, causing the phototransistor to conduct and generate a signal.
Figure 3 GK102 Circuit Diagram
2.2 Hardware Connection
Based on the children's car's dual rear-wheel drive and independent turning modes, it's known that the car requires three motors to drive. Therefore, the motion control system for the mobile robotic arm needs three mini-IBTs. All three mini-IBTs are controlled by PWM signals. The STM32F103 microcontroller has four timers, each with four channels, and each channel can easily generate PWM waves. In this design, channels 1 and 4 are selected as the RPWM and LPWM of the mini-IBTs, respectively; TIM2 controls the turning motor, TIM3 controls the left wheel motor, and TIM4 controls the right wheel motor. The EN signals of the three mini-IBTs are controlled by PD0-PD2 respectively; the D0-D3 signals of the wireless remote control are connected to PC0-PC3 respectively. Since it's necessary to determine the relative position of the object being grasped, which includes distance and direction information, the distance information can be directly obtained from the feedback signal of the HC-SR04 ultrasonic ranging module. However, the direction information cannot be obtained solely from the HC-SR04. Therefore, the design uses three HC-SR04 sensors, installed in the middle, left, and right of the front of the car. The direction information can be obtained by comparing the signals returned by the three HC-SR04 sensors. For example, if the object being grasped is to the left of the vehicle, the distance information returned by the left HC-SR04 will be shorter than that returned by the middle and right HC-SR04. The HC-SR04 uses pulse triggering, sending a pulse every 60ms. The echo signal is captured using the input capture function of a timer; each timer channel 2 of the STM32F103 has input capture functionality. The trigger signal is connected to timer channel 3, and the echo signal is connected to timer channel 2. TIM2-TIM4 correspond to the left, middle, and right HC-SR04 ultrasonic ranging modules, respectively. In the motion control system, turning angle and relative displacement need to be controlled, so two GK102 transmissive photoelectric sensors are used in the design. One encoder is mounted on the steering wheel, and the other on the wheel, to extract turning angle and displacement signals. The output pins of the two GK102 sensors are connected to PC8 and PC9; the one connected to PC8 is used to obtain the turning angle, and the one connected to PC9 is used to obtain the relative displacement.
3. Design of Fuzzy Control Algorithm
Fuzzy control is a rule-based control that directly adopts linguistic control rules. Its starting point is the control experience of on-site operators or the knowledge of relevant experts. It does not require the establishment of an accurate mathematical model of the controlled object in the design. The motion system of the mobile manipulator is very similar to the car driving system. Car driving is a relatively complex problem, and it is difficult to establish an accurate mathematical model and describe it with mathematical analytical expressions [2]. Experienced drivers can drive cars very well, which mainly relies on their experience. Based on this idea, using fuzzy control algorithm to solve the motion of the mobile manipulator is a very good solution.
As the hardware circuit design shows, the distance signal from the HC-SR04 can be used to determine the distance and direction information of the grasped object, while the turning angle and relative displacement of the moving robot are used to control the relative position of the moving robot and the grasped object. This design employs two independent fuzzy controllers to control the turning angle and relative displacement, with the inputs being distance and direction information, respectively. For simplicity and speed, a two-dimensional fuzzy controller structure is used in this system, consisting of an input quantity E and a rate of change Ec. The fuzzy controller controlling the turning angle sets the fuzzy subsets of the input variables direction (E1) and direction change rate (E1c) linguistic values as {negative large, negative small, zero, positive small, positive large} (negative represents left, positive represents right), and abbreviated as {NB, NS, Z, PS, PB}. The fuzzy subset of the output quantity turning angle (K1) is {NB, NM, NS, Z, PS, PM, PB}. Similarly, the fuzzy controller controlling the relative displacement sets the fuzzy subsets of the input variables relative displacement (E2) and relative displacement change rate (E2c) linguistic values as {negative large, negative small, zero, positive small, positive large} (negative represents far, positive represents near), and abbreviated as {NB, NS, Z, PS, PB}. The fuzzy subset of the output quantity relative displacement (K2) is {NB, NM, NS, Z, PS, PM, PB}. The universe of discourse for the membership functions of the input variables is defined as [-2, 2], and the universe of discourse for the membership functions of the output variables is defined as [-3, 3]. Membership functions are all selected as triangular functions with high sensitivity and uniform distribution and equal spacing within the universe of discourse.
Based on driving experience, E1, E1c, and K1 should meet the following rules:
(1) When |E1| is large, and at the same time |E1c| is large, the larger K1 should be taken;
(2) When |E1| is equal, an appropriate K1 should be chosen;
(3) When |E1| is small, and at the same time |E1c| is small, the smaller K1 should be taken.
E2, E2c, and K2 should satisfy the following rules:
(1) When |E2| is large, and at the same time |E2c| is large, the larger K1 should be taken;
(2) When |E2| is moderate, an appropriate K1 should be selected;
(3) When |E2| is small, and at the same time |E1c| is small, the smaller K1 should be taken.
Based on the above considerations, E and the rate of change Ec are used as inputs to the fuzzy controller, and the fuzzy control rules for K1 and K2 are shown in Table 1 and Table 3, respectively.
Table 3K1 Fuzzy Control Rules Table
Ec E | NB | NS | Z | PS | PB |
NB | PB | PB | PB | PB | PM |
NS | PM | PM | PS | PS | PS |
Z | PS | Z | Z | Z | NS |
PS | NS | NS | NS | NM | NM |
PB | NM | NB | NB | NB | NB |
Table 3K2 Fuzzy Control Rules
Ec E | NB | NS | Z | PS | PB |
NB | PB | PB | PB | PM | PM |
NS | PM | PM | PS | PS | PS |
Z | PS | Z | Z | Z | NS |
PS | NS | NS | NS | NM | NM |
PB | NM | NM | NB | NB | NB |
4. System Software Design
According to the motion control system of the mobile robotic arm, the system software needs to control devices such as the display screen, miniIBT motor driver, RO3BS wireless receiver module, HC-SR04 ultrasonic ranging module, and GK102 transmissive photoelectric sensor (including initialization, control signal output, and return signal acquisition); it also includes information display, sensor signal processing, and fuzzy control algorithm completion. The system software design adopts a modular design method, with the entire system mainly composed of a main program and various functional subroutines. The main program primarily handles system initialization, interface display (including welcome screen, mode selection screen, and mode information screen), switching between modes, and receiving remote control signals. The functional subroutines mainly include remote control drive, reading remote control signals, acquiring the relative position of the grasped object, fuzzy control algorithm, and automatic drive subroutines. The main program flowchart is shown in Figure 4.
Figure 4 Main Program Flowchart
5. Conclusion
After multiple tests on object grasping at different distances and in different environments, the mobile robotic arm was able to successfully dock at the grasping position under normal conditions, meeting the design requirements. In remote control mode, the motion control system operated stably and responded to control commands normally. In automatic mode, if the target object was in an open environment, the system could quickly locate it and successfully dock at the grasping position. However, in more complex environments, locating the target object was slower or even failed, indicating that the motion control system needs improvement in this aspect. Overall, the mobile robotic arm's motion control system meets the design requirements and can handle object grasping tasks under normal conditions.
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