Citation format: Yang Hui, Yuan Yi. A fuzzy logic-based gas source localization system [J]. Journal of Navigation and Positioning, 2020, 8(5): 63–67. (YANG Hui, YUAN Yi. A fuzzy logic-based small odor source localization mobile robot [J]. Journal of Navigation and Positioning, 2020, 8(5): 63–67.)
A Fuzzy Logic-Based Gas Source Localization System
Yang Hui, Yuan Yi
(Lanzhou University of Arts and Science Continuous Operation Reference Station, Lanzhou 730000)
Abstract: In order to further improve the localization accuracy of gas sources in odor detection by mobile robots, a fuzzy logic-based localization scheme is proposed: gas concentration is collected through TGS 2620 gas sensors; then the gas concentration data is processed and used as input signals for the fuzzy logic system; finally, the output of the fuzzy logic system controls the movement of the mobile robot, driving the motor to enable it to quickly search for the gas source location. Experimental data shows that the designed robotic system can accurately search for gas sources.
0 Introduction
Gas source detection and localization technology is widely used in industry and daily life [1]. This technology can detect harmful gases and locate the position of the gas source. With the development of artificial intelligence technology, mobile robots can now perform some simple tasks. By installing gas sensors on mobile robots, the robots can move in the environment to detect and locate gas sources.
This gas source localization technology is referred to as dynamic gas detection technology[2-3]. Compared to dynamic gas detection technology, static gas detection technology [4] places gas sensors in fixed positions. Although static gas detection technology is simple to operate and has uncomplicated software/hardware, its detection range is limited.
In contrast to static gas detection technology, the application range of mobile gas detection technology is broader. Mobile gas detection technology uses the movement of robots carrying gas sensors to detect gases in the surrounding environment. Therefore, the detection range of the gas sensors is influenced by the robot’s movement path. In other words, the robot’s movement path becomes key to gas detection. Within a specific space, how to enable the robot to achieve the maximum detection range with the least movement distance becomes a critical issue. Currently, researchers have proposed different path planning algorithms [5-7].
Literature [8] proposes a small gas source localization robot based on insect odor tracking behavior, which sets the robot’s movement path according to insect behavior. Literature [9] designs a gas leak source localization robot based on wireless sensor networks, which moves according to a zigzag algorithm. Meanwhile, a positioning module is referenced to transmit the gas source location information to terminal devices, allowing management personnel to obtain the coordinates of the odor source. Literature [10] detects odors based on rat olfactory neuron signals and locates the odors. Literature [11] proposes a multi-robot system for gas target searching based on wireless sensor networks. This system consists of multiple olfactory robots that exchange information with each other to collaboratively detect the gas source location. At the same time, this system combines concentration gradients and wind speed information for indirect localization of the gas source.
Moreover, the robot’s movement trajectory is affected by multiple factors. Therefore, a multi-input single-output decision system is referenced to optimize the movement trajectory. The fuzzy logic system is a good choice. For example, literature [12–13] utilizes fuzzy logic to process multiple pieces of information collected by sensors and makes decisions based on this information.
Thus, a fuzzy logic-based gas source localization system is proposed. In this system, gas sensors are installed on mobile robots to expand the sensing range of gas sensors, thereby enhancing the accuracy of gas source localization.
1 Robot Hardware System
Figure 1 shows the structure of the robotic system. This system consists of a control module centered on Arduino Mega 2560, distance sensors, odor sensors, and motor drivers.
Figure1 Robot Model
As the main control module, the working voltage of Arduino Mega 2560 is 5V and it has 4 UART interfaces. The distance sensor and gas sensor use HC-SR04 and TGS 2620 respectively. Among them, the TGS 2620 sensor is a metal oxide semiconductor sensor that can detect combustible gases such as alcohol. When the gas concentration increases, the conductivity of the sensor increases. Figure 2 shows the signal control circuit of the TGS 2620 gas sensor.
Figure2 TGS 2620 Control Circuit
The motor is driven by a DC motor based on LMD18200. Figure 3 shows the control circuit of LMD18200 [9].
Figure3 Motor Drive Control Circuit
From a functional perspective, the robot can be divided into three layers: the first layer (top layer) places the gas sensor, liquid crystal display (LCD), and X-bee communication module; the second layer is the core control module and distance sensor; the third layer is the bottom layer, which houses the DC motor and driver. As shown in Figure 4.
Figure4 Robot Model
2 Fuzzy Logic System
Figure 5 shows the fuzzy logic system. The gas concentrations sensed by the three TGS 2620 sensors are used as system inputs, and the fuzzy logic system outputs control the pulse width modulation signals for the motor driver.
Figure5 Fuzzy Logic Algorithm Model
The gas concentrations sensed by the three gas sensors are represented as
、
and
. They represent the gas concentrations sensed by the right, middle, and left gas sensors, respectively. The PWM signals controlling the left and right motors are represented as
and
, respectively.
2.1 Fuzzy Representation
First, the gas concentrations sensed by the TGS 2620 are fuzzily represented using low (low), medium (medium), and high (high). Specifically, as shown in Table 1, the sensed gas concentrations are divided, and then slow (slow), moderate (medium), and fast (fast) are used to describe the output variable, as shown in Table2.
Table1 Input of Gas Sensors
|
Gas Concentration |
Language Variable |
Symbol |
|
0~449 |
Low |
L |
|
50~849 |
Medium |
M |
|
450~900 |
High |
H |
Table2 Output Variable
|
PWM |
Language Variable |
Symbol |
|
50 |
Slow |
S |
|
150 |
Medium |
M |
|
250 |
Fast |
F |
2.2 Rule Base
The inference system outputs the PWM signal to control the motor driver based on gas concentration information and the rule base (as shown in Table3).
PWM is a very effective technique for controlling analog circuits using the digital output of a microprocessor. By adjusting the PWM information, the speed and direction of the motor can be controlled. Ultimately, the robot moves based on gas concentrations to find the location of the gas source.
Table3 Rule Base
|
Gas Concentration Detected by Sensor 1 |
Gas Concentration Detected by Sensor 2 |
Gas Concentration Detected by Sensor 3 |
Input Signal for Left Motor |
Input Signal for Right Motor |
Condition |
|
L |
L |
L |
S |
S |
Go forward slowly |
|
M |
M |
S |
Turn right slowly |
||
|
H |
F |
S |
Turn right fast |
||
|
M |
L |
M |
M |
Go forward moderately |
|
|
M |
M |
S |
Turn right slowly |
||
|
H |
F |
S |
Turn right fast |
||
|
H |
L |
F |
Go forward fast |
||
|
M |
F |
F |
Go forward fast |
||
|
H |
F |
S |
Turn right fast |
||
|
M |
L |
L |
S |
M |
Turn left slowly |
|
M |
S |
M |
Turn left slowly |
||
|
H |
S |
F |
Turn left fast |
||
|
M |
L |
S |
M |
Turn left slowly |
|
|
M |
M |
M |
Go forward moderately |
||
|
H |
F |
M |
Turn right moderately |
||
|
H |
L |
M |
M |
Go forward moderately |
|
|
M |
M |
M |
Go forward moderately |
||
|
H |
F |
M |
Turn right moderately |
||
|
H |
L |
L |
S |
F |
Turn left fast |
|
M |
S |
F |
Turn left fast |
||
|
H |
S |
F |
Turn left fast |
||
|
M |
L |
S |
F |
Turn left fast |
|
|
M |
M |
F |
Turn right moderately |
||
|
H |
M |
F |
Turn left fast |
||
|
H |
L |
S |
F |
Turn left fast |
|
|
M |
M |
F |
Turn left fast |
||
|
H |
F |
F |
Go forward fast |
3 Experimental Results and Analysis
3.1 Experimental Environment
Consider a single gas leak experimental scenario. The gas source is placed at the center of the experimental site, and a fan is set behind the gas source to disperse the gas. The three gas sensors on the robot monitor the gas concentration and drive the robot to move, as shown in Figure 6.
Figure6 Experimental Principle
In the laboratory, the gas source and robot are placed at the position of (0, -5 m), and the initial position of the robot is (0.5m, 100m).
3.2 Data Analysis
The experiment is conducted using the Monte Carlo method [14]. Each experiment is independently repeated 20 times, and the average value is taken as the final experimental data.
Figure 7 shows the experimental process of the robot searching for the gas source. Initially, a global search is conducted to detect whether there are abnormal gases in the air. When a gas source is detected, a local search is performed to accurately estimate the gas source location.
Figure7 Experimental Process of Robot Searching for Gas Source
From Figure 8, it can be seen that initially the robot deviates significantly from the direction of the gas source, but upon discovering the gas source, it quickly approaches it, thereby accurately estimating the gas source location.
Figure 9 shows the time consumed by the robot in three experiments to find the gas source. The initial positions of the robot in these three experiments are different. However, as shown in Figure 9, the time consumed by the mobile robot to find the gas source is similar. This also fully demonstrates that the mobile robot can quickly search for gas sources and has a certain robustness.
Figure9 Time Consumed to Find Gas Source
4 Conclusion
In response to the problem of locating harmful gas sources, a fuzzy logic-based gas source localization system is proposed. This system utilizes the convenience of mobile robot movement by installing TGS 2620 gas sensors on the robot, which collects environmental data, and then these data are used as input for the fuzzy logic system. The fuzzy logic system then decides the robot’s movement path, enabling it to quickly search for the location of the gas source. Experimental data confirms the effectiveness of this system.
Funding Project: Gansu Province Higher Education Innovation Capability Improvement Project (2020A-160); 2020 Gansu Provincial Department of Education Teaching Achievement Cultivation Project.
First Author Profile: Yang Hui (1983—), female, from Baoji, Shaanxi, master’s degree, associate professor, research direction in control theory and control engineering.
Corresponding Author Profile: Yuan Yi (1979—), male, from Anping, Hebei, master’s degree, professor, research direction in control engineering and electronic information.
