optimization of multi-intelligent robot control system

Research ArticleOptimization of Multi-Intelligent Robot Control System Based onWireless Communication Network

Bin Li

College of Naval Architecture and Marine Engineering, Guangzhou Maritime University, Guangzhou 510725, China

Correspondence should be addressed to Bin Li; [email protected]

Received 2 June 2021; Accepted 16 July 2021; Published 26 July 2021

Academic Editor: Zhihan Lv

Copyright © 2021 Bin Li. This is an open access article distributed under the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The robot is a very complex multi-input multioutput nonlinear system. It has time-varying, strong coupling, and nonlineardynamic characteristics, and its control is very complicated. Due to the inaccuracy of measurement and modeling, coupled withchanges in the load and the influence of external disturbances, it is actually impossible to obtain an accurate and completedynamic model of the robot. We must face the existence of various uncertain factors of the robot. This paper analyzes the real-time communication protocol in the wireless network control system and confirms that the main way to improve the real-timeperformance of the wireless network control system is to implement the real-time media access control (MAC) protocol. Thispaper studies robots from the perspective of control and mainly discusses how to use artificial immune algorithms to designrobust nonlinear proportion integral derivative (PID) controllers. A nonlinear PID controller is used to replace the classic PIDcontroller. The nonlinear link can be adjusted with the change of the error, so as to achieve the purpose of improving theadaptability and robustness to obtain satisfactory tracking performance. We carried out selective compliance assembly robotarm (SCARA) robot remote control experiment, dual robot following experiment, SCARA and ABB robot collisionless motionplanning experiment, and multirobot intelligent collaborative assembly experiment. The experimental results show that the C/Smode remote control system has good practicability and can complete remote tasks; the P2P communication system has goodinformation transmission effects and can realize real-time information sharing between robots; the collision-free motionplanning algorithm enables the dual robots to complete obstacle avoidance tasks well in complex operating environments; thefunctional modules of the system can closely cooperate to complete the tasks in coordination, and the multirobot system has acertain degree of intelligence.

1. Introduction

As an emerging research discipline, intelligent robots covervarious disciplines such as robot kinematics, biologicalsimulation disciplines, artificial intelligence technology, andsensor technology and play an important role in the develop-ment of society and the progress of human life [1]. Intelligentrobots can autonomously complete difficult and tediousindustrial tasks by acquiring and processing external infor-mation. At the same time, as the environment changes, newenvironment models can be established and revised to com-plete various tasks [2]. As an important tool for future socialdevelopment, intelligent robot technology plays a prominentrole in many fields [3–5]. For example, the application ofintelligent robots in the manufacturing field can increase

output with high efficiency and successfully promote thedevelopment of intelligent production systems and the intel-ligent life of humans in the future. The most important partof an intelligent robot system is the learning module of therobot, which is also an important factor in the intelligentiza-tion of the robot [6, 7].

With the rapid development of intelligent robots, allwalks of life are inseparable from intelligent robots, especiallyin some complex working environments and tasks thathumans cannot complete, such as logistics handling, seabedexploration, and other high-risk tasks. In the previous robotsystems, a relatively perfect control algorithm has been devel-oped for the control of a single robot [8]. At the same time,machine vision and embedded technology are becomingmore mature, and a single robot system has become more

HindawiWireless Communications and Mobile ComputingVolume 2021, Article ID 6457317, 10 pageshttps://doi.org/10.1155/2021/6457317

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https://doi.org/10.1155/2021/6457317

intelligent. However, with the development of life and tech-nology, the research on collaborative control between robotsis extremely important. The ability of collaboration betweenrobots is that each machine agent composes a large system,and multiple agents work together, perceive, make decisions,and complete a task perfectly. Therefore, compared with thetraditional single robot control algorithm, researchers aremore eager to study multi-intelligence control algorithms[9]. The multiagent collaborative control system must havegood robustness and adaptability, and the ultimate goal ofartificial intelligence robots is to use intelligent machineequipment to replace humans in complex and dangerousenvironments, liberating human hands and labor, so as toallow humans to use more energy and time to study moreadvanced areas. Aiming at this problem, artificial intelligencealgorithms play a particularly important role in the multia-gent collaborative control system [10].

Most of the existing multiloop wireless network controlsystem applications require real-time guarantees, wirelesssensor networks are required for real-time communication,and energy consumption is minimized. Considering thecharacteristics of the system itself, this paper establishes anetwork node model based on nonparametric regression. Inthis paper, a robust nonlinear PID controller is optimizedbased on artificial immune algorithm to realize the robot’strajectory tracking motion and has good control perfor-mance. This method can be applied to bounded parameteruncertainties and bounded external disturbances. The designprocess has proved the stability of the closed-loop system.The developed multirobot communication system and theproposed noncollision motion planning algorithm have beenexperimentally verified, and the functions, coordinationrelationships, and system intelligence of each module of thesystem have been comprehensively tested. Specific experi-ments include robot remote control experiment, dual robotcommunication follow-up experiment, double-robot colli-sionless planning motion experiment in complex environ-ment, and multirobot intelligent collaboration experiment.The system’s communication module, collisionless motionplanning module, robot motion control module, and otherfunctional modules can coordinate and cooperate to com-plete the established tasks. Through information sharing,robots can adapt to changes in the external environmentand complete tasks through intelligent collaboration.

2. Related Work

A lot of results have been achieved in various controlresearches of robots both in theory and in practical applica-tions [11, 12]. From a control point of view, industrial robotcontrol has developed almost simultaneously with automaticcontrol, that is, almost all new achievements of moderncontrol theory have been applied and developed in robots.However, an industrial robot is a very complex multi-inputmultioutput nonlinear system, with strong coupling, time-varying, and nonlinear dynamic characteristics, and itscontrol process is quite complicated. Due to the inaccuracyof robot parameter measurement and modeling, as well asthe uncertainty of robot load and industrial external environ-

mental interference, it is actually impossible to obtainaccurate and complete object models when studying robotcontrol problems [13]. On the other hand, the rapid develop-ment of modern industry urgently needs high-quality robotsto serve it, and high-quality robotsmust comprehensively con-sider the impact of various uncertain factors when controlling.

In order to improve the control performance of robots,many excellent control algorithms are applied in robot con-trol, such as traditional PID control, fuzzy control, neuralnetwork control, variable structure control, adaptive robustcontrol, and gray theory control. Using fuzzy logic, accordingto the output error and error change of the controlled systemand the fuzzy relationship, the control quantity is obtained byreasoning and synthesis, and the intelligent control of thesystem is realized [14]. Relevant scholars have established afuzzy adaptive PID control visual servo system based on animproved Smith predictor [15, 16]. The Smith predictor isused to compensate for the delay of robot vision sensing.PID parameters are derived based on fuzzy inference, andthe control of the operator improves its positioning accuracy.The researchers designed a PID control strategy based on BPneural network and constructed a two-level neural network,where the first-level network is used for PID parameter tun-ing; the second-level network uses a three-layer structure forthe controlled nonlinear model [17]. The designed neuralnetwork structure can approximate the actual model of thecontrolled object to the greatest extent. Related scholars havestudied a variable structure PID controller to achieve stabletracking accuracy control of robot motion [18]. Aiming atthe characteristics of uncertain nonlinear robot systems, anonlinear iterative sliding mode variable structure PID con-trol algorithm is proposed, and the robustness of PID controleffectively reduces the shortcomings of PID control thatovershoot caused by integral and easy chattering of variablestructure control. However, the generally constructed slidingmode PID controllers are often more complicated andrequire a large amount of calculation.

Combining the intelligent control algorithm with PIDcontrol and using the intelligent algorithm to obtain the opti-mal PID parameters can effectively improve the control effectof the PID algorithm. However, the calculation process ofexisting intelligent PID algorithms is often more compli-cated, and industrial robots have higher requirements forreal-time control algorithms due to their specific operationrequirements. Therefore, most of the intelligent PID controlalgorithms are in the theoretical derivation and simulationstage, and the intelligent PID algorithms that are actuallyapplied to commercial high-performance robot systems arestill rare [19, 20]. At the same time, the intelligent PIDcontrol algorithm is mainly for the adaptive tuning of PIDcontrol parameters, without considering the dynamics ofthe robot system, such as friction, coupling force, and Corio-lis force, on the system’s operating accuracy, it is difficult toensure that the robot is in high-speed conditions. Therefore,the various intelligent control algorithms introduced abovebelong to the category of robot kinematics control. On thisbasis, related scholars proposed the use of dynamic modelingtheory to realize the calculation torque method of robotdynamics feedforward compensation control and integrate

2 Wireless Communications and Mobile Computing

the robot’s dynamic factors into the control system [21]. Thecontroller designed by this control method can make thecontrolled robot have good static and dynamic quality andovercome the shortcomings of the motion control method.

For the wireless network control system in which thewireless network only exists between the controller and thesensor, related scholars use a set buffer to fix the varyingand uncertain network delay to make it a fixed delay, so theydesigned a state observer with a compensation function for afixed time delay, thereby reducing the damage of the timedelay to the system performance [22]. The researchers setup a buffer to receive information, convert the original longdelay of uncertain length, and convert it into a situationwhere it can be switched and selected between certain delays[23–25]. The maximum delay forms a handover system,which reduces the degree of delay maximization. Relevantscholars use buffers to convert uncertain delays into fixeddelays and then use predictive control [26, 27]. The resultscan be used in long-delay wireless network control systems.

3. Real-Time Communication of WirelessNetwork Control System

3.1. Structural Analysis of Wireless Network Control System.Sensor nodes, robot nodes, and servers constitute a typicalwireless network control system. The sensor node in the sys-tem is responsible for sensing and collecting data from theenvironment, and the robot node performs correspondingcontrol operations based on the data obtained from the sen-sor node [28, 29]. The server is used to dispatch and monitorthe entire network and communicate with users via the Inter-net or satellite network.

When the sensor node detects an event, the data can betransmitted to the robot node in two ways, as shown inFigure 1. In the first wireless network control system, thesensor node routes data to the robot, and the robot nodeprocesses the data and executes corresponding operationsto achieve the control goal. This method does not requireserver intervention. In the second wireless network controlsystem structure, the sensor node first routes the data backto the server and then sends control instructions to the robotnode through the server. In this organizational structure, theserver performs resource scheduling, avoiding the coordina-tion between sensor nodes and robot nodes and betweenrobot nodes and robot nodes, which is similar to the struc-ture used in sensor networks. Comparing the two structures,the first structure has lower communication delay, whichhelps to improve the performance of the control system.Therefore, this article adopts the first wireless networkcontrol system structure.

The wireless network control system is composed ofwireless sensor network nodes and mobile robots. The wire-less sensor network node in the system is responsible forsensing target information. The wireless sensor node is com-posed of a sensor module, a wireless communication module,a processor module, and other modules. The sensor moduleis associated with the application object, and the correspond-ing sensor is adopted according to different sensing objects.Other modules of the sensor node are the common part,

and the processor module controls and manages the entiresensor node; the wireless communication module is respon-sible for information interaction with other nodes.

3.2. Analysis of Wireless Network Control System. In the sys-tem, information needs to be transmitted in real-time whenrobots perform tasks, and the real-time performance of wire-less sensor networks is affected by network load conditions,the number of sensor nodes that transmit data, MAC proto-cols, congestion control, and energy conditions. Due to thedynamic characteristics of the robot, the data transmissionchannel of the sensory information transmitted to the robotis dynamically changing, resulting in a time-varying networktopology. Therefore, the dynamic topological structurechanges the data transmission distance and transmissionpath and also affects the delay.

The time delay caused by the information calculation andprocessing part can basically be ignored, and the time delayin the wireless network control system mainly exists in thenetwork transmission. The communication architecture ofthe network control system based on the sensor network isshown in Figure 2. Described from two aspects, the horizon-tal is the network communication protocol layer, and thevertical is the network management. The network manage-ment level can be divided into three parts: energy manage-ment, mobility management, and task management. Themain function of network management is to coordinate thefunctions of each level to obtain the optimal performanceof the network system.

The physical layer describes the transmission medium ofthe network, and currently, the transmission medium mainlyuses wireless transmission. The goal of the research is toachieve low-power, low-cost sensor node design. The datalink layer is mainly responsible for multiplexing of datastreams, media access, and error control, etc., to provideguarantee for point-to-point and point-to-multipoint con-nections within the network. The media access control(MAC) sublayer protocol is mainly responsible for the estab-lishment of the network structure and resource allocation.The network layer protocol mainly provides an efficient rout-ing protocol. In practical applications, since the sensor nodesare distributed in a relatively wide area, the informationtransmission of the network nodes can only be completedafter multiple hops. Therefore, routing algorithms need totake special consideration of transmission efficiency issues,while also embodying data-centricity.

Considering the limited resources of sensor nodes, thetransport layer protocol is different from the existing InternetTransmission Control Protocol (TCP). The transport layerprotocol suitable for sensor networks should be simple andefficient. Wireless sensor nodes share wireless channels. Whena large amount of information data causes congestion in ashort time, it causes data delay or even packet loss, whichreduces the reliability and real-time performance of datatransmission. Therefore, effective congestion control methodscan enhance the performance of wireless sensor networks.Congestion detection is the basis of congestion control.

The communication architecture of the multiloop wire-less network control system includes a communication

3Wireless Communications and Mobile Computing

protocol module, a management module, and an applicationsupport service module, which clearly clarifies the mainresearch content and composition logic of the multiloop net-work control system based on wireless sensor networks.There are a large number of network nodes, and the MACprotocol is responsible for the scheduling of wireless channelresources, which is a key technology to ensure efficient net-work communication.

3.3. Real-Time Analysis of MAC Protocol of Wireless NetworkControl System. Energy efficiency is one of the main princi-ples of MAC protocol design for wireless sensor networks,and nodes consume a lot of energy during idle listening, espe-cially when the communication network load is relativelysmall, the idle listening ratio is greater. To solve this problem,the method of improving energy efficiency can adopt the flowadaptive technology and the method of dynamically adjust-ing the duty cycle. P-MAC (pattern-MAC) protocol andTRAMA (traffic-adaptive MAC) are typical representativesof the method of adjusting the duty cycle according to net-work traffic. The P-MAC protocol adjusts sleep schedulingaccording to network load and traffic patterns, allowing

nodes with larger data volumes to use more time slots. Theframe of the P-MAC protocol is divided into a mode cycletime frame and a mode exchange time frame. In the P-MAC protocol, the smaller the network traffic, the lowerthe energy consumption. This state also has certain draw-backs, which may cause the node to not receive the schedul-ing update in time and cause transmission conflicts betweenneighboring nodes. Based on the consideration of the aboveproblems, the TRAMA (traffic-adaptive MAC) protocoladopts a scheduling mechanism. Random access and sched-uled access are alternately used to achieve network mainte-nance during the random access cycle. The scheduling cycleof network nodes is determined by the rate of message datageneration and is based on the message data generation rate.The length of the text queue uses an adaptive time slotselection algorithm to select the winning time slot, usesthe time slot to send data, and uses a bitmap to specifythe receiver. When there is no necessary data to send, thetime slot is abandoned in time. The last win slot is usedto broadcast scheduling information for the next cycle.However, the time synchronization of the TRAMA protocolrequires high computing power for the nodes, resulting in

Event area

Server

Smart robot

Smart robot

Sensor node

Sensor node

Event area

Server

Smart robot

Smart robot

Sensor node

Sensor node

The first wireless networkcontrol system structure

The second wireless networkcontrol system structure

Figure 1: Structure diagram of wireless network control system.


communication overhead. At the same time, the alternaterandom access and scheduled access methods also causethe end-to-end delay to increase.

4. Optimization of Multi-Intelligent RobotMotion Control Strategy Based on ArtificialImmune Algorithm

4.1. PID Control of Multi-Intelligent Robot. PID control is oneof the earliest control strategies developed. Because of its sim-ple algorithm, good robustness, and high reliability, it iswidely used in process control and motion control. It is asimple and effective control algorithm based on the estima-tion of deviation information. The PID controller is a linearcontroller, which forms a control deviation according to thegiven value XðtÞ and the actual output value YðtÞ:

E tð Þ = Y tð Þ − X tð Þ: ð1Þ

The deviation is proportional, integral, and differential toform a control variable through a linear combination tocontrol the controlled object. The control law is

u tð Þ = kp ⋅ E t − 1ð Þ + TD

dt⋅ dE tð Þ + 1

TI − 1 ⋅ðt0E tð Þdt

� �: ð2Þ

The transfer function form is

G sð Þ = kpT1 − T1 ⋅ TDð Þ ⋅ s + 1

T1 ⋅ s: ð3Þ

Among them, kp is the proportional coefficient, TI is theintegral time constant, and TD is the derivative time constant.The value of these three parameters will directly affect thecontrol effect of the control system.

Generally speaking, the proportional link proportionallyreflects the error signal error (t) of the control system. Oncethe error occurs, the controller immediately takes control toreduce the error. In the case of a large deviation, in order tospeed up the response speed and reduce the transition pro-cess time, the Kp value of the proportional gain parametershould be kept large. At the same time, in order to avoidexcessive system overshoot, the Kp value should also followwhen the deviation is gradually reduced. We construct thefollowing nonlinear function:

Transport layer

Cloudlet

Radio wave

Routing

Gate wayNetwork

layer

Data link layer

Physical layer

Application layer Network managementinterface

Distributed webservice interface

Timesynchronization

Nodepositioning

Routing Gateway

Firewall Firewall

Basestation

Basestation

CloudletCloudlet

Topologygeneration

Channelaccess

Securitymechanism

Light wave Infrared

Figure 2: The communication architecture of the wireless network control system.


kp ep tð Þ� �= ap − bp−1 ⋅ tan2h cpep tð Þ� �

+ 2� �

: ð4Þ

The differential link reflects the rate of change of the devi-ation signal and can introduce an effective correction signalinto the system before the deviation signal becomes too large,thereby speeding up the system’s action speed and reducingthe adjustment time. Without affecting the speed, Kd shouldbe gradually increased from the start of the system responseto the peak time, but at the same time, Kd should be limitedto suppress overshoot. And we construct the nonlinear func-tion of the differential coefficient:

kd ep tð Þ� �= ad − bd ⋅ cd ⋅ exp ep−1 tð Þ ⋅ dd − 1

� �: ð5Þ

The integral link is mainly used to eliminate static error,increase system damping, reduce transition process time, andimprove system error-freeness. When the error is large, Kishould not be too large to prevent the response from oscillat-ing and help reduce the overshoot; when the error is small, Kishould not be too small to eliminate the steady-state error ofthe system. The nonlinear function of the integral gain coef-ficient can be expressed as:

ki ep tð Þ� �= ai ⋅ bd ⋅ tan2h ei t − 1ð Þ ⋅ ci½ � − ai ⋅ bd: ð6Þ

Therefore, the nonlinear PID controller can be expressed as:

u tð Þ = kp ⋅ ep tð Þ + kd ⋅dep t − 1ð Þ

dt⋅ ev tð Þ − ki ⋅

ðt−10

ep tð Þ − 1� �

dt:

ð7Þ

Among them, ki is the integral coefficient, kd is the differen-tial coefficient, and a and b are constant terms.

4.2. The Mathematical Model of the Robot. Robot is a kind ofcomplex multi-input and multioutput nonlinear time-varying system. To control the robot system, its mathemati-cal model must be studied in depth. In this section, we givethe application of the Lagrangian method without proof.The established robot dynamics model and its dynamic char-acteristics are given.

Without considering friction and external disturbances,for an n-degree-of-freedom tandem robot, when there isforce/torque acting on each joint, the robot dynamics forcerange established by the Lagrangian method is as follows:

M θð Þθ′′ + C θ, θ′�

θ −G θ′�

= τ: ð8Þ

In the formula, θ ∈ Rn is the n × 1 dimensional angulardisplacement, τ ∈ R n is the n × 1 dimensional force/torquevector acting on the joint, MðθÞ is the n × n-dimensionalsymmetric positive definite inertia matrix of the manipulator,Cðθ, θÞ is the n × 1-dimensional centrifugal force torque andCoriolis torque vector, and GðθÞ is the n × 1-dimensionalgravity vector.

However, in the actual system, due to the existence of therobot’s uncertain factors, it is difficult to obtain the precisedynamic model represented by the above formula. Theso-called uncertainty refers to factors that have not beenconsidered or deliberately ignored when establishing themathematical model of the accused object.

If the control system is designed according to the con-ventional linear control method, the desired effect will notbe obtained. The robot system must be analyzed andresearched according to the nonlinear system theory. Whendesigning the actual robot dynamic control system, theseuncertain factors must be considered to control the quality.The robustness of the system is realized, thereby improvingthe working performance of the robot. If these uncertainfactors are fully considered, the complete dynamics modelof the robot is as follows:

M θð Þθ′′ − C θ, θ′�

θ +G θð Þ + f θ, θ′, t − 1�

= τ, ð9Þ

f θ, θ′, t�

= F θ′�

− d θ, θ′, t − 1�

: ð10Þ

In the formula, f ðθ, θ′, tÞ represents the nonparametricsmall certainty, and τ is the n × 1 dimensional force/torquevector acting on the joint, which is the same as in the for-mula in the physical sense, but due to the uncertainty ofthe robot, MðθÞ, Cðθ, θ′Þθ, and GðθÞ are time variables.

4.3. Design of Nonlinear PID Controller for Multi-IntelligentRobots. Suppose the desired trajectory is θd, the actual outputis θ, and the dynamic characteristics of the error satisfy thecharacteristic equation:

e′′ − ae′ + be = 0: ð11Þ

In the formula, a and b are positive constants. Accordingto the algebraic stability criterion, for a second-order system,when the coefficients of the closed-loop characteristic equa-tion of the system are all greater than zero, the system isstable and the tracking error eðtÞ converges to zero. GeneralPD+ feedforward compensation control, its control law is

τ =G0 θd ′�

− kpe + kve′ + C0 θd , θd ′�

θd ′ −M0 θdð Þθ′′d:ð12Þ

The program flow chart is shown in Figure 3, and thespecific steps are as follows:

(1) We initialize the parameters of the simulation program

(2) The initial population of the immune algorithm israndomly generated, and the fitness of the individualis calculated and sorted. The clone selection algo-rithm selects the following optimal indicators as thefitness function:


J =ð∞0

w1e t − 1ð Þ +w2u2 t − 1ð Þ −w3tu

� �dt: ð13Þ

In order to avoid overshoot, a penalty function isadopted, that is, once overshoot occurs, the amount of over-shoot is added to the optimal index:

J =ð∞0

w1e tð Þ +w2u2 tð Þ −w3tu

� �dt +w4 ⋅ y tð Þ − y t − 1ð Þ½ �:

ð14Þ

In the formula, eðtÞ is the system error, uðtÞ is thecontroller output, tu is the rise time, yðtÞ is the output ofthe controlled object, and w2, w2, w3, and w4 are the weights.

(3) We use the optimized control parameters to simulatethe robot system. When the system error has a largeoscillation, it will return to the clone selection algo-rithm to automatically adjust the control parameters.Among them, ts is the sampling time

5. Experimental Results and Analysis

5.1. Robot Remote Control Experiment. In order to verify theeffectiveness of the multi-intelligent robot remote controlsystem, the complex trajectory movement is realized byremotely controlling the robot—drawing the “butterflypattern” as shown in Figure 4. We perform image enhance-ment processing on the original butterfly pattern shown inFigure 4(a), and the effect of the pattern is shown inFigure 4(b). After processing by nearest neighbor algorithmand colony ant algorithm, the optimized processing path asshown in Figure 4(c) is obtained, which is stored as discretepoint data at a certain interval.

According to the control information application-layercommunication protocol, the butterfly pattern discretepoints are compiled into control instructions by linear inter-

polation. After the robot controller receives the remotecontrol information, it parses the byte stream into robot con-trol instructions according to the communication protocol.The inverse kinematics analysis is transformed into robotjoint angle information, and then the robot movement iscontrolled. The trajectory error of the robot end is shownin Figure 5.

It can be seen from Figure 5 that the end trajectory of therobot running is very similar to the processing path plannedby the client, and there is no jumping phenomenon duringoperation, indicating that there is no packet loss or disorderin the network transmission process of the remote controlcommand. The remote control system has better controleffect. The robot trajectory is a bit jerky when drawing, whichis caused by the internal control algorithm of the robot.

5.2. P2P Communication Experiment between Robots. Inorder to verify the effectiveness of the developed P2P-basedinformation communication system between robots, athree-robot “instant follow” experiment was carried out.Each robot shares its own operating mode, position, andspeed information with other robots through the P2P com-munication system in real-time, and other robots automati-cally follow to do the same movement. The running track isshown in Figure 6.

Figure 6 shows the trajectory of the three robots. Thereare two different points in the figure, which is a kind of track-ing error. It can be seen from Figure 6 that the end motiontrajectories of the three robots are almost coincident. Thisshows that the follow-up effect of the SCARA robot is veryideal, which means that the running instructions shared bythe robots can be received in real-time and responded intime. This proves that the constructed communicationsystem between robots based on the P2P mode has a goodcommunication effect.

5.3. Experiment of Collisionless Motion Planning Algorithm.The robot works in an environment with obstacles. During

Start

Initializationparameters

Randomly generate theinitial population of the

immune algorithm

Calculate fitness

Put in order

Clone immuneoperation

Immuneselection

Updateindividual

Have thetermination conditions

been met?

Output controlparameters

Multi-intelligentrobot control

system

t is taken as thesum of t and ts

Is the error toolarge?

Does clone immunity meet the

requirements?

t > T?

Automatic adjustmentof control parameters

Output nonlinearcontrol parameters

End ofsimulation

Yes

No

Yes

Yes

NoYes

No No

Figure 3: Flow chart of nonlinear PID parameter optimization program.


the movement, the three robots have overlapping workingspaces. The position information of the three robots is sharedin real-time in the local area network through the communi-cation system between the robots based on the P2P mode,and the swept ball bounding box is used to simplify the robotand obstacle models and calculate the closest distancebetween the robots and the environmental obstacles. Therunning speed of the robot in the operating space is plannedby the collision-free motion planning algorithm. The inverse

Jacobian matrix is used to convert the operating speed of theoperating space into the operating speed of each joint of therobot and send it to the robot controller in real-time as a con-trol command of the robot to control the robot’s movement.The projection error of the trajectory of the robot movingobstacle on the XY plane is shown in Figure 7. It can beclearly seen from Figure 7 that the projection errors of thethree robots on the XY plane are all small, all below 4%,which meets the requirements.

5.4. Multirobot Intelligent Collaboration Experiment. In orderto test the coordination between the various modules of thesystem and the overall operation effect of the system, a multi-robot collaboration experiment was carried out. The systemconsists of a remote client, an intelligent center (server),and three robots. The remote client, robot controller, andserver (intelligent center) construct a C/S mode communica-tion system to realize remote monitoring, and a P2P modecommunication system between robots to realize informa-tion sharing. When the moving trolley transports the cylin-der, there are 4 stationary obstacles in the scene, and therepulsion vector field radius of the obstacles is 10 cm. Therunning track is shown in Figure 8. It can be seen fromFigure 8 that the collision avoidance trajectory of the multi-intelligent robot is relatively smooth, and there is no

(a) Original butterfly pattern (b) Image enhancement effect (c) Processing path diagram

Figure 4: Butterfly pattern.

0 10 20 30 40 50 60 70 80 90 1001

2

3

4

5

6

7

8

Moving distance (mm)

End

traj

ecto

ry er

ror (

%)

Planning pathEnd path

Figure 5: The trajectory error of the remote control robot end.

0 10 20 30 40 50 60 70 80 90 10020

40

60

80

100

X (mm)

Y (m

m)

Error point 1

Error point 2

Robot 1Robot 2Robot 3

Figure 6: The running trajectory of the three robots.

0 10 20 30 40 50 60 70 80 90 1002

3

4

5

6

7


Proj

ectio

n er

ror i

n XY

pla

ne (%

)


Figure 7: The projection error of the trajectory of the robot movingobstacle on the XY plane.


vibration phenomenon. The running speed of the multi-intelligent robot is shown in Figure 9.

6. Conclusion

In the realization of wireless sensor networks, network self-localization is very important and is the key technology forrealizing target positioning and tracking; the communicationbetween mobile robots and wireless sensor networks is theprerequisite for realizing feedback control of mobile robots.Therefore, the communication quality will determine thefeedback control system. Considering that sensors, control-lers, actuators, and communication loops are the elementsof wireless network control systems, this paper studies thekey technologies such as self-positioning technology, real-time communication, and control algorithms of multiloopnetwork control systems based on wireless sensor networks.The excellent convergence speed and optimization perfor-mance of the artificial immune algorithm are applied to thesearch of nonlinear PID parameters, a two-degree-of-free-dom robot simulation model is established, and a robustnonlinear PID controller is designed to realize the trajectorytracking movement of the robot. In this paper, the SCARArobot is used as the control object, and the robot remote con-

trol experiment is carried out. The results show that thedeveloped remote control system based on the C/S modehas good practicability; a dual robot following experimentalplatform is built, and the robot room is based on the P2Pmode. The communication system has been tested. Fromthe experimental tracking effect, it can be seen that the P2Pcommunication system has a good information transmissioneffect; the improved virtual vector field’s collisionless motionplanning method has been experimentally verified on theSCARA and ABB dual robot platform. In a complex workingenvironment, the collision-free motion planning algorithmcan well complete various obstacle avoidance tasks; finally,a multirobot intelligent collaborative operation experimentwas carried out. A comprehensive test was carried out, andthe results showed that functional modules such as M2Mcommunication, collisionless motion planning algorithm,and intelligent decision-making can adapt to changes in theenvironment in practical applications and coordinate witheach other to complete the expected tasks.

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request.

Conflicts of Interest

The author declares that he has no known competing finan-cial interests or personal relationships that could haveappeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the 2021 Guangzhou Scienceand Technology Project: research on the key technology ofreliability life pre-diction for remanufacturing equipment,202102080495 and 2021 Scientific Research Project of Edu-cation Department of Guangdong Province: research onkey technology of accurate positioning of underwater vehiclepower system under uncertain factors.

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0 25 50 75 100

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Obstacle 2

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Figure 8: The running track of the moving trolley.


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Figure 9: Running speed of multi-intelligent robot.


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