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Abstract

In this paper, to solve the current problems of water quality pollution of rivers, such as multiple types, high toxicity and difficult degradation, we design an intelligent surface mobile robot based on the slow release of microorganisms. In the robot, the skeleton has a ship-type structure welded with stainless steel equilateral angle steel; the function module comprises a power supply part, motor part, transmission part, and release part; the wireless monitoring module is based on GPRS and EtherCAT networking technologies and consists of four submodules, including a data acquisition submodule, transmission submodule, database server and monitoring center. The field test results show stable overall transmission performance and wireless data transmission performance of the robot. When the water depth of the rotating cylinder is 0.3 m, and the speed of the rotating cylinder is 15 r/min, the pollution control ability of the robot is outstanding. The experimental results also verify the feasibility of the robot in treating water pollution. The robot can greatly improve the mechanization, automation and efficiency of water pollution control and facilitate the centralized monitoring of joint operations, so it is worth popularizing.

Keywords

Microorganism intelligence robot wireless motoring experiment

1. Introduction

Recently, most rivers in China have become increasingly polluted. According to the authoritative survey on the pollution degrees of 532 rivers by the Ministry of Water Resources of the People’s Republic of China, nearly 80% of water and 45% of the groundwater in China have been polluted [1, 2, 3]. The main pollution sources are industrial wastewater, agricultural wastewater, garbage, acid rain in cities, and overfishing. The variety, toxicity and non-biodegradability of polluted water resources have always been challenges for river pollution [4, 5, 6, 7]. Currently, throughout the world, most of the research on equipment for river pollution control is focused on surface garbage-cleaning robots. Such mechanical cleaning is not only inefficient but also not conducive to the rapid improvement of water quality [8, 9, 10, 11]. To solve this problem , we researched and developed an intelligent surface mobile robot based on microorganisms. The robot can release the water-dissolvable microorganisms into the polluted rivers slowly so that the solution can fully react with the pollutants to manage river pollution.

2. Modeling the overall structure of the robot

A module design is adopted for the intelligent surface mobile robot according to its purpose and working environment. It includes a robot function module, framework and wireless monitoring module. We use Inventor software for the intelligent surface mobile robot. Its structural modeling and function module diagram are shown in Figs 1 and 2, respectively. The function module a power supply part, motor part, transmission part and release part. To enhance the adaptability of the robot. A DC motor is adopted in the motor because it can meet the rotating speed. The solar panels charge the battery to continuously supply power to the DC motor [12]. We use sliding bearings in the transmission part.

Figure 1.

Structural modeling of the intelligent surface mobile robot.

Figure 2.

Structure diagram of the robot function module.

The release part contains a rotary cylinder with holes of 1 mm diameter evenly distributed on its surface. One end of the rotary cylinder is connected to the output shaft for positioning. The robot frame is welded with stainless steel with equal corners. The whole device includes solar panels, power boxes, motor protection measures and a floating ball positioning unit. From the point of use [13]. The wireless monitoring module is based on GPRS and EtherCAT Internet technologies. It is composed of four submodules, including a data acquisition submodule, transmission submodule, database server and monitoring center [14].

3. Theoretical calculation and selection of main parameters

3.1 Kinematic theory

The intelligent surface mobile robot can use the rotating cylinder to rotate around the output shaft in a uniform variable direction. This movement is the rotation of a rigid body around a fixed axis. When the rigid body rotates, rotation $\varphi$ is a single-valued continuous function of time $t$ , i.e., $\varphi=f(t)$ , and the rotation can determine the position of the rotating cylinder.

We can calculate the angular velocity and Angle of the rotating cylinder at a certain time by calculating the angular velocity and Angle of a particle on the rotating cylinder.

Since any point in the rotating cylinder moves in a circular motion around the axis, with the center of the circle on the axis and the plane of the circumference perpendicular to the axis, it is appropriate to use the natural method to study the motion of each point on the rotating cylinder [15].

According to the designed known conditions, the microbial density $\rho_{1}=$ 1.25 $\times$ 10³ kg/m³; the drum radius $R=$ 0.33 m; the drum length $L=$ 1 m; the depth of the drum into water $h=$ 0.4 m; drum and the mass of the output shaft and the axis of rotation is $M_{1}$ ; the microbial mass is $M_{2}$ ; the mass and volume of the microbial solution are $M_{3}$ and $V_{1}$ , respectively; the density of water at room temperature $\rho_{2}=$ 1.02 $\times$ 10³ kg/m³; the air density at 20^∘C $\rho_{3}=$ 1.293 kg/m³.

3.1.1 Calculation of the new centroid system

Analysis process: when the rotating cylinder is empty, for a homogeneous rotating cylinder, its center of mass $O$ should be at the center of the rotating cylinder, i.e., on the center line of the output shaft.

If a uniform microbial solution fills the rotating cylinder, the center of mass of the whole rotating cylinder should also be on the center line of the output shaft.

At this time, the microbial solution is required not to fill the rotating cylinder, leaving a certain height of free space. When the rotating cylinder rotates, the overall center of gravity inevitably shifts from the original center of mass to form a new center-of-mass system.

If only the microbial solution in the rotating cylinder is considered, the microbial solution should also have its center of mass $O^{\prime}$ .

According to the analysis, the new mass system must be between the center of mass $O$ of the rotating cylinder and the center of mass $O^{\prime}$ of the microbial solution.

The analysis of the centroid of the transverse section of the rotating cylinder is shown in Fig. 3, and the quantitative analysis of the centroid of the transverse section is shown in Fig. 4.

Figure 3.

Barycenter analysis of the rotating cylinder.

Figure 4.

Quantitative analysis of the barycenter.

It can be seen from Figs 3 and 4 that the distance between two centers of mass can be expressed by

$\displaystyle H=R-H/2$ (1)

According to the principle of centroid balance,

$\displaystyle x+y=H$ (2) $\displaystyle M_{1}\times x=M_{3}\times y$ (3)

3.1.2 Calculation of the mass of the microbial solution

Analysis process: 100 kg of microbial particles are put into the drum, and the drum is placed into water. Due to holes in the external surface of the drum (the hole diameter is less than the particle length), water can enter the drum and mix with microbial particles. Due to the effect of the floating ball, the drum can float on the water surface. Under the action of atmospheric pressure, the level of the solution in the drum can remain at the same level as the water surface.

It is assumed that $\left|{O{O}^{\prime\prime}}\right|={h}^{\prime}$ , $\angle\textit{AOO}^{\prime\prime}=\theta$ According to Pythagoras’ theorem,

$\displaystyle\left|\textit{AO}^{\prime\prime}\right|=\sqrt{R^{2}-{h}^{\prime 2}}$ (4) $\displaystyle S_{\Delta\textit{AOB}}=\left|\textit{AO}^{\prime\prime}\right|% \times{h}^{\prime}$ (5)

Then, the transverse surface area of the microbial solution in the rotating cylinder can be expressed by

$\displaystyle S=2S_{\textit{AOC}}+S_{\Delta\textit{AOB}}+S_{\textit{COQ}}$ $\displaystyle S=\pi R^{2}{\theta}^{\prime}/180+{h}^{\prime}\times\sqrt{R^{2}-{% h}^{\prime 2}}+\pi R^{2}/2$ (6) $\displaystyle{\theta}^{\prime}=90^{\circ}-\theta$ $\displaystyle\sin{\theta}^{\prime}={h}^{\prime}/R$ (7)

The mass of the microbial solution in the rotating cylinder during mixing can be estimated by

$\displaystyle M_{3}=\rho_{2}\times V_{1}+M_{2}=\rho_{2}\times S\times L+M_{2}$ (8)

Through the above analysis, the values of x and y can be solved. The value of $|x|$ is new center of mass and the center of massg, the deviation of the center of mass when the rotating cylinder rotates.

3.2 Dynamics analysis

3.2.1 The momentum theorem

The rotating cylinder moves around the output shaft in a uniformly variable circle. The external force acting on the particle system can be calculated using the momentum theorem of the particle system. The momentum of the particle system is $P=\sum m_{i}V_{i}$ , and the differential form of the momentum theorem is dp $=\sum F_{i}\textit{dt}$ .

Since the momentum theorem is of a vector form, the projection form should be used in the application. The projection form of the differential form of the momentum theorem of the particle system in the two-dimensional Cartesian coordinate system is dp_x /dt = F_x and dp_y /dt=F_y.

3.2.2 Calculation of buoyancy of a floating ball

Analysis process: the robot should float on the surface with a water depth of about 0.4 m. Based on the analysis of the working force of the robot on the surface, using the volume of the drum water as the drainage volume when the robot is working, we can calculate the minimum pressure that the floating ball should bear under the condition of bearing the maximum buoyancy to determine the size of the floating ball.

It is assumed that the gravity of the robot at work is $G$ , the gravity of the floating ball is $G^{\prime}$ , the overall mass of the robot is $M_{0}$ , the mass of the floating ball is $M_{4}$ , the buoyancy of the robot at work is $F_{1}$ , the minimum pressure of the floating ball is $F_{2}$ , the volume of the boiling water of the robot is $V_{2}$ , and the volume of the floating ball is $V_{3}$ .

The analysis of the buoyancy of the robot is as follows:

$\displaystyle F_{2}=G-F_{1}$ (9) $\displaystyle G=(M_{0}+M_{3})g$ (10) $\displaystyle F_{1}=\rho_{2}gV_{2}=\rho_{2}gSL$ (11)

According to Eqs (9), (10) and (11), we can calculate the maximum buoyancy to be borne by the robot in the floating state $F_{2}$ .

The analysis of the buoyancy of the floating ball is as follows:

$\displaystyle G^{\prime}=F_{2}$ (12) $\displaystyle G^{\prime}=m_{4}g=\rho_{3}V_{3}g$ (13)

The mass $M_{4}$ and volume $V_{3}$ of the required floating ball can be calculated by Eqs (12) and (13).

3.2.3 Calculation of the output shaft diameter

Analysis process: $M$ is the torque of the drum when it rotates in water, and $P_{0}$ is the output power of the drum when it rotates in water.

According to the equation $P_{0}=M\cdot\omega$ , we can obtain

$\displaystyle P_{0}=\left|F\right|\bullet x\bullet\omega$ (14)

For a solid circular shaft, the output shaft diameter $D$ is estimated according to the condition of torsion strength, and the calculation equation is

$\displaystyle d\geqslant c\sqrt[3]{P_{0}/n}$ (15)

where $C$ is a constant. Since the material of the output shaft is Q235-A, $C=$ 149 can be found in the table of “Mechanical Design”.

Since the keyways on the output shaft can weaken its strength, the value of the actual diameter of the output shaft ${d}^{\prime}\geqslant d\times(1+5\%\times a)$ , where $a$ is the number of the keyways on the shaft.

3.3 Comprehensive data of the structural sizes of the robot

According to the requirement that rotating cylinder is 1–20 r/min, rotating speed of the rotating cylinder can be initially determined to be $n=$ 10 r/min during the design.

According to the two indexes of $n=$ 10 r/min and $M_{1}=$ 56 kg, combining the above kinematics with dynamics analyses of the robot, we can calculate the comprehensive size data of the robot.

The comprehensive size data of the intelligent surface mobile robot is shown in Table 1.

Table 1
Size integration data ( $n=$ 10 r/min, $M_{1}=$ 56 kg)

Performance index	Size data	Performance index	Size data
Robot weight $M_{0}$ /kg	107.24	Output shaft diameter ${d}^{\prime}$ /mm	20
Microbial solution weight $M_{3}$ /kg	316.81	Rotation of external force F/N	16.17
Core distance $\left\|x\right\|$ /m	0.11	Output power $P_{0}$ /kW	1.12 $\times$ 10³
Floating ball weight $M_{4}$ /kg	207.20	Bearing power $P_{0}$ /kW	1.25 $\times$ 10³
Floating ball volume $V_{3}$ /m³	0.17	Floating ball buoyancy $F_{2}$ /N	2.07 $\times$ 10³

Figure 5.

Working principle of wireless monitoring data transmission.

3.4 Selection of the main components

The power supply part, motor part and transmission part are important components, and the power supply, motor, coupling, bearings and the sprocket chain are the main components.

According to the comprehensive size data of the intelligent surface mobile robot, the models and performance parameters of the main components can be reasonably selected through calculation. The comprehensive analysis of the performance of the robot indicates that the normal operation of the intelligent surface mobile robot can be satisfied. The selection of the main components in Table 2.

Table 2
Selection of the main components

Component index	Motor	Coupling	Bearings		Sprocket chain	Power supply
Type	DD speed-down	The elastic	Bearing I	Bearing II	Roller chain	The solar panels	Battery
Model	ASLONG JGB37-3530	DR6-20 $\times$ 25	FZF-1		10A-1 $\times$ 74 GB/T 1243.1-1983	Photosynthesis brand poly crystal 60W & Z07	Power card HGY6003
Material	Copper and steel	Copper and steel	Teflon		Stainless steel	Silicon crystal	Copper and plastic
Installation size/mm	External diameter: 33; Length: 55	6 $\times$ 10 $\times$ 25	10 $\times$ 30 $\times$ 10	20 $\times$ 40 $\times$ 20	Small diameter: 6; Large sprocket: 20	676 $\times$ 735 $\times$ 30 126 $\times$ 50	151 $\times$ 65 $\times$ 95
Performance parameter	Rated voltage: 12 V; Output power: 4.8 W; Load speed: 9 r/min; Load current: 400 mA; Load current: 30 kg.cm; Motor shaft diameter: 6 mm.	Good coaxiality, maintenance-free, no back clearance.	High-temperature resistance, thermal oxidation resistance, radiation resistance, corrosion resistance, self-lubricating or insulating properties.		Numbers of sprocket teeth: $Z_{1}=$ 10, $Z_{2}=$ 15, $a=$ 487.98 mm, $F_{a}=$ 211.65 N $\varphi=$ 75^∘C	Working voltage: 12 V; Working current: 10 A	Rated voltage: 12 V; Rated capacity: 7 Ah.

4. Working principle of wireless monitoring data transmission

In the intelligent surface mobile robot, a modular design is adopted. The upper computer communication is based on GPRS DTU and TCP/IP data communication function, the real-time communication of the OPC server and GPRS DTU is achieved by the TCP/IP protocol, the OPC server has fixed IP addresses, and COMcommunication is used between the wireless OPC server [16, 17, 18, 19].

The speed adjustment of the drum, the traction speed adjustment, the online feedback of the anti-pollution area, the electronic control system and motor fault alarm, the monitoring objects through the position sensor signal, the pulling speed, the detection signals of the drum speed, the visual conduction, the button control signals and the servo motor working parameters on the BECKHOFF CX2030 controller and module under the action of the real-time data transmission [20]. The EtherCAT control bus and GPRS networking technologies are used for real-time wireless monitoring of the robot [21]. The working principle of the wireless monitoring data transmission is shown in Fig. 5.

5. Field test

5.1 The test environment

The field test of the intelligent surface mobile robot was performed at the experimental base in Xialing District, Huangshi City. The tested object was the polluted water in the river. The microorganisms were released slowly to fully react with the polluted water so as to achieve the goal of governance and improvement of water quality.

The test area is about 1000 m² and has been polluted seriously with an average pH value of $<$ 2. The traction speed is 0.1 m/s. The field test of the intelligent surface mobile robot is shown in Fig. 6.

Figure 6.

Field test of the intelligent surface mobile robot.

5.2 Test preparation and monitoring process

In order to prevent excessive rotary torque caused by too large traction speed or rotary depth, the traction speed and rotary depth need to be tested to the maximum depth into water. The intelligent surface mobile robot starts to work driven by the traction speed. In case of a fault alarm, to prompt to stop the operation. The workflow of the wireless monitoring is shown in Fig. 7.

Table 3
Test results of the robot

Test group	Depth into water/m	Drum speed/r/min	pH	System performance
I	0.2	10	About 3.6	Normal
		15	About 4.8	Normal
		20	About 4.6	Normal
II	0.3	10	About 5.4	Normal
		15	About 6.8	Normal
		20		Alarm when abnormal
III	0.4	10	About 5.7	Normal
		15	About 6.2	Normal
		20		Alarm when abnormal

Figure 7.

Workflow of the wireless monitoring of the robot.

5.3 Test plan

To pollution control ability of the intelligent surface mobile robot in the field, the whole test process can be divided into three groups.

Test I: the depth of the rotating cylinder into water is 0.2 m, and the drum speed is 10, 15, 20 r/min.

Test II: the depth of the rotating cylinder into water is 0.3 m, and the drum speed is 10, 15, 20 r/min.

Test III: the depth of the rotating cylinder into the water is 0.4 m, and the drum speed is 10, 15, 20 r/min.

In the test process, the depth into water and drum speed are automatically adjusted online, and the test periods of the three tests are the same. The ability and working efficiency are comprehensively reflected through the comparison of water pollution and the average pH values.

The capability of the system can be alerted by the drum rotation.

5.4 Test results

After repeated tests, the normal distribution of each test result was obtained. Pollution control test results in Table 3.

The test results indicate the stable performance of designed intelligent surface mobile robot.

When the depth of the rotating cylinder into water was 0.3 m, and the drum speed was 15 r/min, the pollution control ability of the robot was outstanding.

When the depth of the rotating cylinder into water was 0.4 m, and the drum speed was 20 r/min, monitoring system of the robot raised an abnormal alarm. This phenomenon shows that the operation torque resistance of the, surface needs to be improved, designed structure of the robot still has anti-pollution ability, and the overall transmission performance and wireless monitoring data transmission performance are normal.

6. Conclusion

In the case of the increasing pollution of rivers, the successful development of the robot contributes to the effectiveness of microorganism treatment of polluted water.

Based on 3D modeling, theoretical analysis, calculation and selection, wireless monitoring data transmission and automatic control, we designed and developed the intelligent surface mobile robot based on microorganisms.

The test results show that the intelligent surface mobile robot can greatly improve mechanization and automation, thus greatly improving the efficiency of water surface pollution control operation and facilitating joint operations.

The robot has stable transmission performance, a stable structure, outstanding pollution control ability It is convenient to install and has a wide prospect of popularization and application.

Footnotes

Acknowledgments

This work was supported by the Key Projects of Hubei Provincial Natural Science Joint Innovation Fund (No. 2023AFD002).

References

. Discussion on the present situation, causes and countermeasures of water resource pollution in China. Science and Technology Innovation Herald. 2015; (7): 113.

Liang

Hou

, et al. Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments. Ocean Engineering. 2020; 205: 107328.

Zhou

Wen

, et al. A motion planning method for unmanned surface vehicle in restricted waters. Proceedings of the Institution of Mechanical Engineers. 2020; 332-345.

Gao

. Current situation and countermeasures of water pollution in China. Resources and Human Settlement Environment. 2018; 11: 44-51.

Sumroengrit

Ruangpayoongsak

. Economic Floating Waste Detection for Surface Cleaning Robots. 2017: 08001.

Karami

Moubayed

Outbib

. General review and classification of different MPPT Techniques. Renewable & Sustainable Energy Reviews. 2017; 68: 1-18.

Sun

Zhang

. The Present Status of Environmental Energy Harvesting and Utilization Technologyof Marine Robots. Robot. 2018; 40(01): 89-101.

Fan

Wang

Huang

. Heterogeneous Information Fusionand Visualizationfora LargeScale Intelligent Video Surveillance System. IEEE Transaction Systems Man & Cybernetics Systems. 2017; 47(3): 593-604.

Liu

. Research on Electrical and Control System and Object Recognition Algorithm for Water Area Garbage Cleaning Robot. Shaanxi: Shaanxi University of Science and Technology, 2019.

10.

Shen

. Design and Implementation of Remote Monitoring System for Water Surface Cleaning Robot. Hangzhou: Hangzhou University of Electronic Science and Technology, 2019.

11.

Sun

. Development of Small-Scale Waters Garbage Cleaning Robot. Kunming: Kunming University of Technology, 2018.

12.

Cheng

Tang

, et al. Design and Research of Small Independent Solar Power System Based on Actual Demand. China Test. 2018; 44(4): 125-129.

13.

Zhang

Wang

Gao

. Design Simulation Study of Hybrid Power System for Photovoltaic Ship. Computer Simulation. 2017; 34(9): 114-119.

14.

Wang

Zhou

, et al. Design and Test of Intelligent Residual Film Recovery Device Based on EtherCAT & GPRS. Transactions of the Chinese Society for Agricultural Machinery. 2016; 47(9): 50-55.

15.

Tan

Zou

Zhuang

, et al. Fast marching square method based intelligent navigation of the unmanned surface vehicle swarm in restricted waters. Applied Ocean Research. 2020; 95: 10208.

16.

Liu

Lin

, et al. Analysis on Research Status and Development Trend of Intelligent Control Technology for Agricultural Equipment. Transactions of the Chinese Society for Agricultural Machinery. 2020; 51(1): 1-18.

17.

Sundareswaran

Vigneshkumar

Sankar

, et al. Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System. IEEE Transactions on Industrial Informatics. 2016; 12(1): 187-200.

18.

Huang

Wang

, et al. A study on robot compliance control based on force sensor gravity compensation. Transactions of the Chinese Society for Agricultural Machinery. 2020; 51(3): 386-393.

19.

Zhao

Huang

. Study on Automatic Welding Control System Based on EtherCAT. Welding Technology. 2015; 44(4): 59-63.

20.

Han

Zhang

, et al. Feature extraction and image recognition of typical grassland forage based on color moment. Transactions of the CSAE. 2016; 32(23): 168-175.

21.

Zhang

Hernandez

, et al. An Optimal Path Planning Method for Low Energy Consumption of Mobile Robot. Transactions of the Chinese Society for Agricultural Machinery. 2018; 49(9): 19-26.

Design and experiment of an intelligent surface mobile robot for the microbial slow-release control of pollution

Abstract

Keywords

1. Introduction

2. Modeling the overall structure of the robot

3.1 Kinematic theory

3.1.1 Calculation of the new centroid system

3.2.1 The momentum theorem

3.2.2 Calculation of buoyancy of a floating ball

Table 1 Size integration data ( n = 10 r/min, M 1 = 56 kg)

Table 2 Selection of the main components

5. Field test

5.1 The test environment

Table 3 Test results of the robot

5.4 Test results

6. Conclusion

Footnotes

Acknowledgments

References

Table 1
Size integration data ( $n=$ 10 r/min, $M_{1}=$ 56 kg)

Table 2
Selection of the main components

Table 3
Test results of the robot