# Optimization of Multi-Intelligent Robot Control System Based on Wireless Communication Network

Authors: Bin Li
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

The robot is a very complex multi-input multioutput nonlinear system. It has time-varying, strong coupling, and nonlinear dynamic characteristics, and its control is very complicated. Due to the inaccuracy of measurement and modeling, coupled with changes in the load and the influence of external disturbances, it is actually impossible to obtain an accurate and complete dynamic 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-time performance of the wireless network control system is to implement the real-time media access control (MAC) protocol. This paper studies robots from the perspective of control and mainly discusses how to use artificial immune algorithms to design robust nonlinear proportion integral derivative (PID) controllers. A nonlinear PID controller is used to replace the classic PID controller. The nonlinear link can be adjusted with the change of the error, so as to achieve the purpose of improving the adaptability and robustness to obtain satisfactory tracking performance. We carried out selective compliance assembly robot arm (SCARA) robot remote control experiment, dual robot following experiment, SCARA and ABB robot collisionless motion planning experiment, and multirobot intelligent collaborative assembly experiment. The experimental results show that the C/S mode remote control system has good practicability and can complete remote tasks; the P2P communication system has good information transmission effects and can realize real-time information sharing between robots; the collision-free motion planning algorithm enables the dual robots to complete obstacle avoidance tasks well in complex operating environments; the functional modules of the system can closely cooperate to complete the tasks in coordination, and the multirobot system has a certain degree of intelligence.


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## Body

## 1. Introduction

As an emerging research discipline, intelligent robots cover various disciplines such as robot kinematics, biological simulation disciplines, artificial intelligence technology, and sensor technology and play an important role in the development of society and the progress of human life [1]. Intelligent robots can autonomously complete difficult and tedious industrial tasks by acquiring and processing external information. At the same time, as the environment changes, new environment models can be established and revised to complete various tasks [2]. As an important tool for future social development, intelligent robot technology plays a prominent role in many fields [3–5]. For example, the application of intelligent robots in the manufacturing field can increase output with high efficiency and successfully promote the development of intelligent production systems and the intelligent life of humans in the future. The most important part of an intelligent robot system is the learning module of the robot, which is also an important factor in the intelligentization of the robot [6, 7].With the rapid development of intelligent robots, all walks of life are inseparable from intelligent robots, especially in some complex working environments and tasks that humans cannot complete, such as logistics handling, seabed exploration, and other high-risk tasks. In the previous robot systems, a relatively perfect control algorithm has been developed for the control of a single robot [8]. At the same time, machine vision and embedded technology are becoming more mature, and a single robot system has become more intelligent. However, with the development of life and technology, the research on collaborative control between robots is extremely important. The ability of collaboration between robots 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 the traditional single robot control algorithm, researchers are more eager to study multi-intelligence control algorithms [9]. The multiagent collaborative control system must have good robustness and adaptability, and the ultimate goal of artificial intelligence robots is to use intelligent machine equipment to replace humans in complex and dangerous environments, liberating human hands and labor, so as to allow humans to use more energy and time to study more advanced areas. Aiming at this problem, artificial intelligence algorithms play a particularly important role in the multiagent collaborative control system [10].Most of the existing multiloop wireless network control system applications require real-time guarantees, wireless sensor networks are required for real-time communication, and energy consumption is minimized. Considering the characteristics of the system itself, this paper establishes a network node model based on nonparametric regression. In this paper, a robust nonlinear PID controller is optimized based on artificial immune algorithm to realize the robot’s trajectory tracking motion and has good control performance. This method can be applied to bounded parameter uncertainties and bounded external disturbances. The design process has proved the stability of the closed-loop system. The developed multirobot communication system and the proposed noncollision motion planning algorithm have been experimentally verified, and the functions, coordination relationships, and system intelligence of each module of the system have been comprehensively tested. Specific experiments include robot remote control experiment, dual robot communication follow-up experiment, double-robot collisionless planning motion experiment in complex environment, and multirobot intelligent collaboration experiment. The system’s communication module, collisionless motion planning module, robot motion control module, and other functional modules can coordinate and cooperate to complete the established tasks. Through information sharing, robots can adapt to changes in the external environment and complete tasks through intelligent collaboration.


## 2. Related Work

A lot of results have been achieved in various control researches of robots both in theory and in practical applications [11, 12]. From a control point of view, industrial robot control has developed almost simultaneously with automatic control, that is, almost all new achievements of modern control theory have been applied and developed in robots. However, an industrial robot is a very complex multi-input multioutput nonlinear system, with strong coupling, time-varying, and nonlinear dynamic characteristics, and its control process is quite complicated. Due to the inaccuracy of robot parameter measurement and modeling, as well as the uncertainty of robot load and industrial external environmental interference, it is actually impossible to obtain accurate and complete object models when studying robot control problems [13]. On the other hand, the rapid development of modern industry urgently needs high-quality robots to serve it, and high-quality robots must comprehensively consider 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 control, such as traditional PID control, fuzzy control, neural network control, variable structure control, adaptive robust control, and gray theory control. Using fuzzy logic, according to the output error and error change of the controlled system and the fuzzy relationship, the control quantity is obtained by reasoning and synthesis, and the intelligent control of the system is realized [14]. Relevant scholars have established a fuzzy adaptive PID control visual servo system based on an improved Smith predictor [15, 16]. The Smith predictor is used to compensate for the delay of robot vision sensing. PID parameters are derived based on fuzzy inference, and the control of the operator improves its positioning accuracy. The researchers designed a PID control strategy based on BP neural network and constructed a two-level neural network, where the first-level network is used for PID parameter tuning; the second-level network uses a three-layer structure for the controlled nonlinear model [17]. The designed neural network structure can approximate the actual model of the controlled object to the greatest extent. Related scholars have studied a variable structure PID controller to achieve stable tracking accuracy control of robot motion [18]. Aiming at the characteristics of uncertain nonlinear robot systems, a nonlinear iterative sliding mode variable structure PID control algorithm is proposed, and the robustness of PID control effectively reduces the shortcomings of PID control that overshoot caused by integral and easy chattering of variable structure control. However, the generally constructed sliding mode PID controllers are often more complicated and require a large amount of calculation.Combining the intelligent control algorithm with PID control and using the intelligent algorithm to obtain the optimal PID parameters can effectively improve the control effect of the PID algorithm. However, the calculation process of existing intelligent PID algorithms is often more complicated, and industrial robots have higher requirements for real-time control algorithms due to their specific operation requirements. Therefore, most of the intelligent PID control algorithms are in the theoretical derivation and simulation stage, and the intelligent PID algorithms that are actually applied to commercial high-performance robot systems are still rare [19, 20]. At the same time, the intelligent PID control algorithm is mainly for the adaptive tuning of PID control parameters, without considering the dynamics of the robot system, such as friction, coupling force, and Coriolis force, on the system’s operating accuracy, it is difficult to ensure that the robot is in high-speed conditions. Therefore, the various intelligent control algorithms introduced above belong to the category of robot kinematics control. On this basis, related scholars proposed the use of dynamic modeling theory to realize the calculation torque method of robot dynamics feedforward compensation control and integrate the robot’s dynamic factors into the control system [21]. The controller designed by this control method can make the controlled robot have good static and dynamic quality and overcome the shortcomings of the motion control method.For the wireless network control system in which the wireless network only exists between the controller and the sensor, related scholars use a set buffer to fix the varying and uncertain network delay to make it a fixed delay, so they designed a state observer with a compensation function for a fixed time delay, thereby reducing the damage of the time delay to the system performance [22]. The researchers set up a buffer to receive information, convert the original long delay of uncertain length, and convert it into a situation where 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. Relevant scholars use buffers to convert uncertain delays into fixed delays and then use predictive control [26, 27]. The results can be used in long-delay wireless network control systems.


## 3. Real-Time Communication of Wireless Network Control System

### 3.1. Structural Analysis of Wireless Network Control System

Sensor nodes, robot nodes, and servers constitute a typical wireless network control system. The sensor node in the system is responsible for sensing and collecting data from the environment, and the robot node performs corresponding control operations based on the data obtained from the sensor node [28, 29]. The server is used to dispatch and monitor the entire network and communicate with users via the Internet or satellite network.When the sensor node detects an event, the data can be transmitted to the robot node in two ways, as shown in Figure1. In the first wireless network control system, the sensor node routes data to the robot, and the robot node processes the data and executes corresponding operations to achieve the control goal. This method does not require server intervention. In the second wireless network control system structure, the sensor node first routes the data back to the server and then sends control instructions to the robot node through the server. In this organizational structure, the server performs resource scheduling, avoiding the coordination between sensor nodes and robot nodes and between robot nodes and robot nodes, which is similar to the structure used in sensor networks. Comparing the two structures, the first structure has lower communication delay, which helps to improve the performance of the control system. Therefore, this article adopts the first wireless network control system structure.Figure 1
Structure diagram of wireless network control system.The wireless network control system is composed of wireless sensor network nodes and mobile robots. The wireless sensor network node in the system is responsible for sensing target information. The wireless sensor node is composed of a sensor module, a wireless communication module, a processor module, and other modules. The sensor module is associated with the application object, and the corresponding 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 entire sensor node; the wireless communication module is responsible for information interaction with other nodes.


### 3.2. Analysis of Wireless Network Control System

In the system, information needs to be transmitted in real-time when robots perform tasks, and the real-time performance of wireless sensor networks is affected by network load conditions, the number of sensor nodes that transmit data, MAC protocols, congestion control, and energy conditions. Due to the dynamic characteristics of the robot, the data transmission channel of the sensory information transmitted to the robot is dynamically changing, resulting in a time-varying network topology. Therefore, the dynamic topological structure changes the data transmission distance and transmission path and also affects the delay.The time delay caused by the information calculation and processing part can basically be ignored, and the time delay in the wireless network control system mainly exists in the network transmission. The communication architecture of the network control system based on the sensor network is shown in Figure2. Described from two aspects, the horizontal is the network communication protocol layer, and the vertical is the network management. The network management level can be divided into three parts: energy management, mobility management, and task management. The main function of network management is to coordinate the functions of each level to obtain the optimal performance of the network system.Figure 2
The communication architecture of the wireless network control system.The physical layer describes the transmission medium of the network, and currently, the transmission medium mainly uses wireless transmission. The goal of the research is to achieve low-power, low-cost sensor node design. The data link layer is mainly responsible for multiplexing of data streams, media access, and error control, etc., to provide guarantee for point-to-point and point-to-multipoint connections within the network. The media access control (MAC) sublayer protocol is mainly responsible for the establishment of the network structure and resource allocation. The network layer protocol mainly provides an efficient routing protocol. In practical applications, since the sensor nodes are distributed in a relatively wide area, the information transmission of the network nodes can only be completed after multiple hops. Therefore, routing algorithms need to take special consideration of transmission efficiency issues, while also embodying data-centricity.Considering the limited resources of sensor nodes, the transport layer protocol is different from the existing Internet Transmission Control Protocol (TCP). The transport layer protocol suitable for sensor networks should be simple and efficient. Wireless sensor nodes share wireless channels. When a large amount of information data causes congestion in a short time, it causes data delay or even packet loss, which reduces the reliability and real-time performance of data transmission. Therefore, effective congestion control methods can enhance the performance of wireless sensor networks. Congestion detection is the basis of congestion control.The communication architecture of the multiloop wireless network control system includes a communication protocol module, a management module, and an application support service module, which clearly clarifies the main research content and composition logic of the multiloop network control system based on wireless sensor networks. There are a large number of network nodes, and the MAC protocol is responsible for the scheduling of wireless channel resources, which is a key technology to ensure efficient network communication.


### 3.3. Real-Time Analysis of MAC Protocol of Wireless Network Control System

Energy efficiency is one of the main principles of MAC protocol design for wireless sensor networks, and nodes consume a lot of energy during idle listening, especially when the communication network load is relatively small, the idle listening ratio is greater. To solve this problem, the method of improving energy efficiency can adopt the flow adaptive technology and the method of dynamically adjusting the duty cycle. P-MAC (pattern-MAC) protocol and TRAMA (traffic-adaptive MAC) are typical representatives of the method of adjusting the duty cycle according to network traffic. The P-MAC protocol adjusts sleep scheduling according to network load and traffic patterns, allowing nodes with larger data volumes to use more time slots. The frame of the P-MAC protocol is divided into a mode cycle time frame and a mode exchange time frame. In the P-MAC protocol, the smaller the network traffic, the lower the energy consumption. This state also has certain drawbacks, which may cause the node to not receive the scheduling update in time and cause transmission conflicts between neighboring nodes. Based on the consideration of the above problems, the TRAMA (traffic-adaptive MAC) protocol adopts a scheduling mechanism. Random access and scheduled access are alternately used to achieve network maintenance during the random access cycle. The scheduling cycle of network nodes is determined by the rate of message data generation and is based on the message data generation rate. The length of the text queue uses an adaptive time slot selection algorithm to select the winning time slot, uses the time slot to send data, and uses a bitmap to specify the receiver. When there is no necessary data to send, the time slot is abandoned in time. The last win slot is used to broadcast scheduling information for the next cycle. However, the time synchronization of the TRAMA protocol requires high computing power for the nodes, resulting in communication overhead. At the same time, the alternate random access and scheduled access methods also cause the end-to-end delay to increase.


## 3.1. Structural Analysis of Wireless Network Control System

Sensor nodes, robot nodes, and servers constitute a typical wireless network control system. The sensor node in the system is responsible for sensing and collecting data from the environment, and the robot node performs corresponding control operations based on the data obtained from the sensor node [28, 29]. The server is used to dispatch and monitor the entire network and communicate with users via the Internet or satellite network.When the sensor node detects an event, the data can be transmitted to the robot node in two ways, as shown in Figure1. In the first wireless network control system, the sensor node routes data to the robot, and the robot node processes the data and executes corresponding operations to achieve the control goal. This method does not require server intervention. In the second wireless network control system structure, the sensor node first routes the data back to the server and then sends control instructions to the robot node through the server. In this organizational structure, the server performs resource scheduling, avoiding the coordination between sensor nodes and robot nodes and between robot nodes and robot nodes, which is similar to the structure used in sensor networks. Comparing the two structures, the first structure has lower communication delay, which helps to improve the performance of the control system. Therefore, this article adopts the first wireless network control system structure.Figure 1
Structure diagram of wireless network control system.The wireless network control system is composed of wireless sensor network nodes and mobile robots. The wireless sensor network node in the system is responsible for sensing target information. The wireless sensor node is composed of a sensor module, a wireless communication module, a processor module, and other modules. The sensor module is associated with the application object, and the corresponding 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 entire sensor node; the wireless communication module is responsible for information interaction with other nodes.


## 3.2. Analysis of Wireless Network Control System

In the system, information needs to be transmitted in real-time when robots perform tasks, and the real-time performance of wireless sensor networks is affected by network load conditions, the number of sensor nodes that transmit data, MAC protocols, congestion control, and energy conditions. Due to the dynamic characteristics of the robot, the data transmission channel of the sensory information transmitted to the robot is dynamically changing, resulting in a time-varying network topology. Therefore, the dynamic topological structure changes the data transmission distance and transmission path and also affects the delay.The time delay caused by the information calculation and processing part can basically be ignored, and the time delay in the wireless network control system mainly exists in the network transmission. The communication architecture of the network control system based on the sensor network is shown in Figure2. Described from two aspects, the horizontal is the network communication protocol layer, and the vertical is the network management. The network management level can be divided into three parts: energy management, mobility management, and task management. The main function of network management is to coordinate the functions of each level to obtain the optimal performance of the network system.Figure 2
The communication architecture of the wireless network control system.The physical layer describes the transmission medium of the network, and currently, the transmission medium mainly uses wireless transmission. The goal of the research is to achieve low-power, low-cost sensor node design. The data link layer is mainly responsible for multiplexing of data streams, media access, and error control, etc., to provide guarantee for point-to-point and point-to-multipoint connections within the network. The media access control (MAC) sublayer protocol is mainly responsible for the establishment of the network structure and resource allocation. The network layer protocol mainly provides an efficient routing protocol. In practical applications, since the sensor nodes are distributed in a relatively wide area, the information transmission of the network nodes can only be completed after multiple hops. Therefore, routing algorithms need to take special consideration of transmission efficiency issues, while also embodying data-centricity.Considering the limited resources of sensor nodes, the transport layer protocol is different from the existing Internet Transmission Control Protocol (TCP). The transport layer protocol suitable for sensor networks should be simple and efficient. Wireless sensor nodes share wireless channels. When a large amount of information data causes congestion in a short time, it causes data delay or even packet loss, which reduces the reliability and real-time performance of data transmission. Therefore, effective congestion control methods can enhance the performance of wireless sensor networks. Congestion detection is the basis of congestion control.The communication architecture of the multiloop wireless network control system includes a communication protocol module, a management module, and an application support service module, which clearly clarifies the main research content and composition logic of the multiloop network control system based on wireless sensor networks. There are a large number of network nodes, and the MAC protocol is responsible for the scheduling of wireless channel resources, which is a key technology to ensure efficient network communication.


## 3.3. Real-Time Analysis of MAC Protocol of Wireless Network Control System

Energy efficiency is one of the main principles of MAC protocol design for wireless sensor networks, and nodes consume a lot of energy during idle listening, especially when the communication network load is relatively small, the idle listening ratio is greater. To solve this problem, the method of improving energy efficiency can adopt the flow adaptive technology and the method of dynamically adjusting the duty cycle. P-MAC (pattern-MAC) protocol and TRAMA (traffic-adaptive MAC) are typical representatives of the method of adjusting the duty cycle according to network traffic. The P-MAC protocol adjusts sleep scheduling according to network load and traffic patterns, allowing nodes with larger data volumes to use more time slots. The frame of the P-MAC protocol is divided into a mode cycle time frame and a mode exchange time frame. In the P-MAC protocol, the smaller the network traffic, the lower the energy consumption. This state also has certain drawbacks, which may cause the node to not receive the scheduling update in time and cause transmission conflicts between neighboring nodes. Based on the consideration of the above problems, the TRAMA (traffic-adaptive MAC) protocol adopts a scheduling mechanism. Random access and scheduled access are alternately used to achieve network maintenance during the random access cycle. The scheduling cycle of network nodes is determined by the rate of message data generation and is based on the message data generation rate. The length of the text queue uses an adaptive time slot selection algorithm to select the winning time slot, uses the time slot to send data, and uses a bitmap to specify the receiver. When there is no necessary data to send, the time slot is abandoned in time. The last win slot is used to broadcast scheduling information for the next cycle. However, the time synchronization of the TRAMA protocol requires high computing power for the nodes, resulting in communication overhead. At the same time, the alternate random access and scheduled access methods also cause the end-to-end delay to increase.


## 4. Optimization of Multi-Intelligent Robot Motion Control Strategy Based on Artificial Immune Algorithm

### 4.1. PID Control of Multi-Intelligent Robot

PID control is one of the earliest control strategies developed. Because of its simple algorithm, good robustness, and high reliability, it is widely used in process control and motion control. It is a simple and effective control algorithm based on the estimation of deviation information. The PID controller is a linear controller, which forms a control deviation according to the given valueXt and the actual output value Yt:(1)Et=Yt−Xt.The deviation is proportional, integral, and differential to form a control variable through a linear combination to control the controlled object. The control law is(2)ut=kp⋅Et−1+TDdt⋅dEt+1TI−1⋅∫0tEtdt.The transfer function form is(3)Gs=kpT1−T1⋅TD⋅s+1T1⋅s.Among them,kp is the proportional coefficient, TI is the integral time constant, and TD is the derivative time constant. The value of these three parameters will directly affect the control effect of the control system.Generally speaking, the proportional link proportionally reflects the error signal error (t) of the control system. Once the error occurs, the controller immediately takes control to reduce the error. In the case of a large deviation, in order to speed up the response speed and reduce the transition process time, the Kp value of the proportional gain parameter should be kept large. At the same time, in order to avoid excessive system overshoot, the Kp value should also follow when the deviation is gradually reduced. We construct the following nonlinear function:(4)kpept=ap−bp−1⋅tan2hcpept+2.The differential link reflects the rate of change of the deviation signal and can introduce an effective correction signal into the system before the deviation signal becomes too large, thereby speeding up the system’s action speed and reducing the adjustment time. Without affecting the speed,Kd should be gradually increased from the start of the system response to the peak time, but at the same time, Kd should be limited to suppress overshoot. And we construct the nonlinear function of the differential coefficient:(5)kdept=ad−bd⋅cd⋅expep−1t⋅dd−1.The integral link is mainly used to eliminate static error, increase system damping, reduce transition process time, and improve system error-freeness. When the error is large,Ki should not be too large to prevent the response from oscillating and help reduce the overshoot; when the error is small, Ki should not be too small to eliminate the steady-state error of the system. The nonlinear function of the integral gain coefficient can be expressed as:(6)kiept=ai⋅bd⋅tan2heit−1⋅ci−ai⋅bd.Therefore, the nonlinear PID controller can be expressed as:(7)ut=kp⋅ept+kd⋅dept−1dt⋅evt−ki⋅∫0t−1ept−1dt.Among them,ki is the integral coefficient, kd is the differential coefficient, and a and b are constant terms.


### 4.2. The Mathematical Model of the Robot

Robot is a kind of complex multi-input and multioutput nonlinear time-varying system. To control the robot system, its mathematical model must be studied in depth. In this section, we give the application of the Lagrangian method without proof. The established robot dynamics model and its dynamic characteristics are given.Without considering friction and external disturbances, for ann-degree-of-freedom tandem robot, when there is force/torque acting on each joint, the robot dynamics force range established by the Lagrangian method is as follows:(8)Mθθ′′+Cθ,θ′θ−Gθ′=τ.In the formula,θ∈Rn is the n×1 dimensional angular displacement, τ∈Rn is the n×1 dimensional force/torque vector acting on the joint, Mθ is the n×n-dimensional symmetric positive definite inertia matrix of the manipulator, Cθ,θ is the n×1-dimensional centrifugal force torque and Coriolis torque vector, and Gθ is the n×1-dimensional gravity vector.However, in the actual system, due to the existence of the robot’s uncertain factors, it is difficult to obtain the precise dynamic model represented by the above formula. The so-called uncertainty refers to factors that have not been considered or deliberately ignored when establishing the mathematical model of the accused object.If the control system is designed according to the conventional linear control method, the desired effect will not be obtained. The robot system must be analyzed and researched according to the nonlinear system theory. When designing the actual robot dynamic control system, these uncertain factors must be considered to control the quality. The robustness of the system is realized, thereby improving the working performance of the robot. If these uncertain factors are fully considered, the complete dynamics model of the robot is as follows:(9)Mθθ′′−Cθ,θ′θ+Gθ+fθ,θ′,t−1=τ,(10)fθ,θ′,t=Fθ′−dθ,θ′,t−1.In the formula,fθ,θ′,t represents the nonparametric small certainty, and τ is the n×1 dimensional force/torque vector acting on the joint, which is the same as in the formula in the physical sense, but due to the uncertainty of the robot, Mθ, Cθ,θ′θ, and Gθ are time variables.


### 4.3. Design of Nonlinear PID Controller for Multi-Intelligent Robots

Suppose the desired trajectory isθd, the actual output is θ, and the dynamic characteristics of the error satisfy the characteristic equation:(11)e′′−ae′+be=0.In the formula,a and b are positive constants. According to the algebraic stability criterion, for a second-order system, when the coefficients of the closed-loop characteristic equation of the system are all greater than zero, the system is stable and the tracking error et converges to zero. General PD+ feedforward compensation control, its control law is(12)τ=G0θd′−kpe+kve′+C0θd,θd′θd′−M0θdθ′′d.The program flow chart is shown in Figure3, and the specific steps are as follows:(1)
We initialize the parameters of the simulation program(2)
The initial population of the immune algorithm is randomly generated, and the fitness of the individual is calculated and sorted. The clone selection algorithm selects the following optimal indicators as the fitness function:(13)J=∫0∞w1et−1+w2u2t−1−w3tudt.Figure 3
Flow chart of nonlinear PID parameter optimization program.In order to avoid overshoot, a penalty function is adopted, that is, once overshoot occurs, the amount of overshoot is added to the optimal index:(14)J=∫0∞w1et+w2u2t−w3tudt+w4⋅yt−yt−1.In the formula,et is the system error, ut is the controller output, tu is the rise time, yt is the output of the controlled object, and w2, w2, w3, and w4 are the weights.(3)
We use the optimized control parameters to simulate the robot system. When the system error has a large oscillation, it will return to the clone selection algorithm to automatically adjust the control parameters. Among them,ts is the sampling time


## 4.1. PID Control of Multi-Intelligent Robot

PID control is one of the earliest control strategies developed. Because of its simple algorithm, good robustness, and high reliability, it is widely used in process control and motion control. It is a simple and effective control algorithm based on the estimation of deviation information. The PID controller is a linear controller, which forms a control deviation according to the given valueXt and the actual output value Yt:(1)Et=Yt−Xt.The deviation is proportional, integral, and differential to form a control variable through a linear combination to control the controlled object. The control law is(2)ut=kp⋅Et−1+TDdt⋅dEt+1TI−1⋅∫0tEtdt.The transfer function form is(3)Gs=kpT1−T1⋅TD⋅s+1T1⋅s.Among them,kp is the proportional coefficient, TI is the integral time constant, and TD is the derivative time constant. The value of these three parameters will directly affect the control effect of the control system.Generally speaking, the proportional link proportionally reflects the error signal error (t) of the control system. Once the error occurs, the controller immediately takes control to reduce the error. In the case of a large deviation, in order to speed up the response speed and reduce the transition process time, the Kp value of the proportional gain parameter should be kept large. At the same time, in order to avoid excessive system overshoot, the Kp value should also follow when the deviation is gradually reduced. We construct the following nonlinear function:(4)kpept=ap−bp−1⋅tan2hcpept+2.The differential link reflects the rate of change of the deviation signal and can introduce an effective correction signal into the system before the deviation signal becomes too large, thereby speeding up the system’s action speed and reducing the adjustment time. Without affecting the speed,Kd should be gradually increased from the start of the system response to the peak time, but at the same time, Kd should be limited to suppress overshoot. And we construct the nonlinear function of the differential coefficient:(5)kdept=ad−bd⋅cd⋅expep−1t⋅dd−1.The integral link is mainly used to eliminate static error, increase system damping, reduce transition process time, and improve system error-freeness. When the error is large,Ki should not be too large to prevent the response from oscillating and help reduce the overshoot; when the error is small, Ki should not be too small to eliminate the steady-state error of the system. The nonlinear function of the integral gain coefficient can be expressed as:(6)kiept=ai⋅bd⋅tan2heit−1⋅ci−ai⋅bd.Therefore, the nonlinear PID controller can be expressed as:(7)ut=kp⋅ept+kd⋅dept−1dt⋅evt−ki⋅∫0t−1ept−1dt.Among them,ki is the integral coefficient, kd is the differential coefficient, and a and b are constant terms.


## 4.2. The Mathematical Model of the Robot

Robot is a kind of complex multi-input and multioutput nonlinear time-varying system. To control the robot system, its mathematical model must be studied in depth. In this section, we give the application of the Lagrangian method without proof. The established robot dynamics model and its dynamic characteristics are given.Without considering friction and external disturbances, for ann-degree-of-freedom tandem robot, when there is force/torque acting on each joint, the robot dynamics force range established by the Lagrangian method is as follows:(8)Mθθ′′+Cθ,θ′θ−Gθ′=τ.In the formula,θ∈Rn is the n×1 dimensional angular displacement, τ∈Rn is the n×1 dimensional force/torque vector acting on the joint, Mθ is the n×n-dimensional symmetric positive definite inertia matrix of the manipulator, Cθ,θ is the n×1-dimensional centrifugal force torque and Coriolis torque vector, and Gθ is the n×1-dimensional gravity vector.However, in the actual system, due to the existence of the robot’s uncertain factors, it is difficult to obtain the precise dynamic model represented by the above formula. The so-called uncertainty refers to factors that have not been considered or deliberately ignored when establishing the mathematical model of the accused object.If the control system is designed according to the conventional linear control method, the desired effect will not be obtained. The robot system must be analyzed and researched according to the nonlinear system theory. When designing the actual robot dynamic control system, these uncertain factors must be considered to control the quality. The robustness of the system is realized, thereby improving the working performance of the robot. If these uncertain factors are fully considered, the complete dynamics model of the robot is as follows:(9)Mθθ′′−Cθ,θ′θ+Gθ+fθ,θ′,t−1=τ,(10)fθ,θ′,t=Fθ′−dθ,θ′,t−1.In the formula,fθ,θ′,t represents the nonparametric small certainty, and τ is the n×1 dimensional force/torque vector acting on the joint, which is the same as in the formula in the physical sense, but due to the uncertainty of the robot, Mθ, Cθ,θ′θ, and Gθ are time variables.


## 4.3. Design of Nonlinear PID Controller for Multi-Intelligent Robots

Suppose the desired trajectory isθd, the actual output is θ, and the dynamic characteristics of the error satisfy the characteristic equation:(11)e′′−ae′+be=0.In the formula,a and b are positive constants. According to the algebraic stability criterion, for a second-order system, when the coefficients of the closed-loop characteristic equation of the system are all greater than zero, the system is stable and the tracking error et converges to zero. General PD+ feedforward compensation control, its control law is(12)τ=G0θd′−kpe+kve′+C0θd,θd′θd′−M0θdθ′′d.The program flow chart is shown in Figure3, and the specific steps are as follows:(1)
We initialize the parameters of the simulation program(2)
The initial population of the immune algorithm is randomly generated, and the fitness of the individual is calculated and sorted. The clone selection algorithm selects the following optimal indicators as the fitness function:(13)J=∫0∞w1et−1+w2u2t−1−w3tudt.Figure 3
Flow chart of nonlinear PID parameter optimization program.In order to avoid overshoot, a penalty function is adopted, that is, once overshoot occurs, the amount of overshoot is added to the optimal index:(14)J=∫0∞w1et+w2u2t−w3tudt+w4⋅yt−yt−1.In the formula,et is the system error, ut is the controller output, tu is the rise time, yt is the output of the controlled object, and w2, w2, w3, and w4 are the weights.(3)
We use the optimized control parameters to simulate the robot system. When the system error has a large oscillation, it will return to the clone selection algorithm 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 the effectiveness of the multi-intelligent robot remote control system, the complex trajectory movement is realized by remotely controlling the robot—drawing the “butterfly pattern” as shown in Figure4. We perform image enhancement processing on the original butterfly pattern shown in Figure 4(a), and the effect of the pattern is shown in Figure 4(b). After processing by nearest neighbor algorithm and colony ant algorithm, the optimized processing path as shown in Figure 4(c) is obtained, which is stored as discrete point data at a certain interval.Figure 4
Butterfly pattern.
(a)
Original butterfly pattern(b)
Image enhancement effect(c)
Processing path diagramAccording to the control information application-layer communication protocol, the butterfly pattern discrete points are compiled into control instructions by linear interpolation. After the robot controller receives the remote control information, it parses the byte stream into robot control instructions according to the communication protocol. The inverse kinematics analysis is transformed into robot joint angle information, and then the robot movement is controlled. The trajectory error of the robot end is shown in Figure5.Figure 5
The trajectory error of the remote control robot end.It can be seen from Figure5 that the end trajectory of the robot running is very similar to the processing path planned by the client, and there is no jumping phenomenon during operation, indicating that there is no packet loss or disorder in the network transmission process of the remote control command. The remote control system has better control effect. The robot trajectory is a bit jerky when drawing, which is caused by the internal control algorithm of the robot.


### 5.2. P2P Communication Experiment between Robots

In order to verify the effectiveness of the developed P2P-based information communication system between robots, a three-robot “instant follow” experiment was carried out. Each robot shares its own operating mode, position, and speed information with other robots through the P2P communication system in real-time, and other robots automatically follow to do the same movement. The running track is shown in Figure6.Figure 6
The running trajectory of the three robots.Figure6 shows the trajectory of the three robots. There are two different points in the figure, which is a kind of tracking error. It can be seen from Figure 6 that the end motion trajectories of the three robots are almost coincident. This shows that the follow-up effect of the SCARA robot is very ideal, which means that the running instructions shared by the robots can be received in real-time and responded in time. This proves that the constructed communication system between robots based on the P2P mode has a good communication effect.


### 5.3. Experiment of Collisionless Motion Planning Algorithm

The robot works in an environment with obstacles. During the movement, the three robots have overlapping working spaces. The position information of the three robots is shared in real-time in the local area network through the communication system between the robots based on the P2P mode, and the swept ball bounding box is used to simplify the robot and obstacle models and calculate the closest distance between the robots and the environmental obstacles. The running speed of the robot in the operating space is planned by the collision-free motion planning algorithm. The inverse Jacobian matrix is used to convert the operating speed of the operating space into the operating speed of each joint of the robot and send it to the robot controller in real-time as a control command of the robot to control the robot’s movement. The projection error of the trajectory of the robot moving obstacle on theXY plane is shown in Figure 7. It can be clearly seen from Figure 7 that the projection errors of the three robots on the XY plane are all small, all below 4%, which meets the requirements.Figure 7
The projection error of the trajectory of the robot moving obstacle on theXY plane.


### 5.4. Multirobot Intelligent Collaboration Experiment

In order to test the coordination between the various modules of the system and the overall operation effect of the system, a multirobot collaboration experiment was carried out. The system consists of a remote client, an intelligent center (server), and three robots. The remote client, robot controller, and server (intelligent center) construct a C/S mode communication system to realize remote monitoring, and a P2P mode communication system between robots to realize information sharing. When the moving trolley transports the cylinder, there are 4 stationary obstacles in the scene, and the repulsion vector field radius of the obstacles is 10 cm. The running track is shown in Figure8. It can be seen from Figure 8 that the collision avoidance trajectory of the multi-intelligent robot is relatively smooth, and there is no vibration phenomenon. The running speed of the multi-intelligent robot is shown in Figure 9.Figure 8
The running track of the moving trolley.Figure 9
Running speed of multi-intelligent robot.


## 5.1. Robot Remote Control Experiment

In order to verify the effectiveness of the multi-intelligent robot remote control system, the complex trajectory movement is realized by remotely controlling the robot—drawing the “butterfly pattern” as shown in Figure4. We perform image enhancement processing on the original butterfly pattern shown in Figure 4(a), and the effect of the pattern is shown in Figure 4(b). After processing by nearest neighbor algorithm and colony ant algorithm, the optimized processing path as shown in Figure 4(c) is obtained, which is stored as discrete point data at a certain interval.Figure 4
Butterfly pattern.
(a)
Original butterfly pattern(b)
Image enhancement effect(c)
Processing path diagramAccording to the control information application-layer communication protocol, the butterfly pattern discrete points are compiled into control instructions by linear interpolation. After the robot controller receives the remote control information, it parses the byte stream into robot control instructions according to the communication protocol. The inverse kinematics analysis is transformed into robot joint angle information, and then the robot movement is controlled. The trajectory error of the robot end is shown in Figure5.Figure 5
The trajectory error of the remote control robot end.It can be seen from Figure5 that the end trajectory of the robot running is very similar to the processing path planned by the client, and there is no jumping phenomenon during operation, indicating that there is no packet loss or disorder in the network transmission process of the remote control command. The remote control system has better control effect. The robot trajectory is a bit jerky when drawing, which is caused by the internal control algorithm of the robot.


## 5.2. P2P Communication Experiment between Robots

In order to verify the effectiveness of the developed P2P-based information communication system between robots, a three-robot “instant follow” experiment was carried out. Each robot shares its own operating mode, position, and speed information with other robots through the P2P communication system in real-time, and other robots automatically follow to do the same movement. The running track is shown in Figure6.Figure 6
The running trajectory of the three robots.Figure6 shows the trajectory of the three robots. There are two different points in the figure, which is a kind of tracking error. It can be seen from Figure 6 that the end motion trajectories of the three robots are almost coincident. This shows that the follow-up effect of the SCARA robot is very ideal, which means that the running instructions shared by the robots can be received in real-time and responded in time. This proves that the constructed communication system between robots based on the P2P mode has a good communication effect.


## 5.3. Experiment of Collisionless Motion Planning Algorithm

The robot works in an environment with obstacles. During the movement, the three robots have overlapping working spaces. The position information of the three robots is shared in real-time in the local area network through the communication system between the robots based on the P2P mode, and the swept ball bounding box is used to simplify the robot and obstacle models and calculate the closest distance between the robots and the environmental obstacles. The running speed of the robot in the operating space is planned by the collision-free motion planning algorithm. The inverse Jacobian matrix is used to convert the operating speed of the operating space into the operating speed of each joint of the robot and send it to the robot controller in real-time as a control command of the robot to control the robot’s movement. The projection error of the trajectory of the robot moving obstacle on theXY plane is shown in Figure 7. It can be clearly seen from Figure 7 that the projection errors of the three robots on the XY plane are all small, all below 4%, which meets the requirements.Figure 7
The projection error of the trajectory of the robot moving obstacle on theXY plane.


## 5.4. Multirobot Intelligent Collaboration Experiment

In order to test the coordination between the various modules of the system and the overall operation effect of the system, a multirobot collaboration experiment was carried out. The system consists of a remote client, an intelligent center (server), and three robots. The remote client, robot controller, and server (intelligent center) construct a C/S mode communication system to realize remote monitoring, and a P2P mode communication system between robots to realize information sharing. When the moving trolley transports the cylinder, there are 4 stationary obstacles in the scene, and the repulsion vector field radius of the obstacles is 10 cm. The running track is shown in Figure8. It can be seen from Figure 8 that the collision avoidance trajectory of the multi-intelligent robot is relatively smooth, and there is no vibration phenomenon. The running speed of the multi-intelligent robot is shown in Figure 9.Figure 8
The running track of the moving trolley.Figure 9
Running speed of multi-intelligent robot.


## 6. Conclusion

In the realization of wireless sensor networks, network self-localization is very important and is the key technology for realizing target positioning and tracking; the communication between mobile robots and wireless sensor networks is the prerequisite for realizing feedback control of mobile robots. Therefore, the communication quality will determine the feedback control system. Considering that sensors, controllers, actuators, and communication loops are the elements of wireless network control systems, this paper studies the key technologies such as self-positioning technology, real-time communication, and control algorithms of multiloop network control systems based on wireless sensor networks. The excellent convergence speed and optimization performance of the artificial immune algorithm are applied to the search of nonlinear PID parameters, a two-degree-of-freedom robot simulation model is established, and a robust nonlinear PID controller is designed to realize the trajectory tracking movement of the robot. In this paper, the SCARA robot is used as the control object, and the robot remote control experiment is carried out. The results show that the developed remote control system based on the C/S mode has good practicability; a dual robot following experimental platform is built, and the robot room is based on the P2P mode. The communication system has been tested. From the experimental tracking effect, it can be seen that the P2P communication system has a good information transmission effect; the improved virtual vector field’s collisionless motion planning method has been experimentally verified on the SCARA and ABB dual robot platform. In a complex working environment, the collision-free motion planning algorithm can well complete various obstacle avoidance tasks; finally, a multirobot intelligent collaborative operation experiment was carried out. A comprehensive test was carried out, and the results showed that functional modules such as M2M communication, collisionless motion planning algorithm, and intelligent decision-making can adapt to changes in the environment in practical applications and coordinate with each other to complete the expected tasks.



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*Source: 6457317-2021-07-26.xml*