# Artificial Intelligence Optimization Design Analysis of Robot Control System

Authors: Haifeng Guo; Yiyang Wang; Guangwei Wang; Zhongbo Du; Rui Chen; He Sun
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to improve the accuracy of robot control system, a scheme based on artificial intelligence is proposed. On the basis of the software environment of reinforcement learning simulation platform, a kind of rounding scheme in dynamic environment is designed and simulated. The results show that when the inclination sensor is placed on an inclined plane of 300 and collected for ten times, the maximum error of measurement that can be seen from the experimental data is 0.40. The relative included angles were 30°, 45°, 60°, and 90°, respectively, by compass sensor. The measurement was carried out, and the average value of each angle was measured 5 times. It can be seen from the experimental data that the measurement error meets the requirements of the system. Therefore, it is feasible to use artificial intelligence algorithm to optimize the robot control system.


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## Body

## 1. Introduction

Intelligent control is a control mode with intelligent information processing, intelligent feedback, and intelligent control decision, which is the advanced stage of the development of control theory. It is mainly used to solve the control problems of complex systems that are difficult to be solved by traditional methods. The main characteristics of intelligent control research object are highly nonlinear with uncertain mathematical models and complex task requirements. Now the practical control methods are: multi-level hierarchical intelligent control, knowledge-based intelligent control, fuzzy control, neural networks control, rule-based humanoid intelligent control, intelligent control and chaos control based on pattern recognition, etc. Fuzzy control and neural network control are the most widely used intelligent control methods in mobile service robot. The path planning of mobile robots can be divided into two types: one is global path planning based on environmental prior information, and the other is local collision avoidance planning based on sensor information in uncertain environment. Robot will develop into a highly automated intelligent tool with its progress and wide range of applications. This not only frees one from the onerous and repetitive monotonous work but concentrates one on the work of intellect and creativity as well as interest. Beside, it also can serve as a servant of life and exploration and creation of nature, greatly improving and enriching human social life. Its technology development and wide application will bring social and life revolution. It can not only bring high productivity and huge economic benefits to social and economic construction but also contribute to the country’s space development, ocean development, nuclear energy utilization, and other new fields. See Figure1.Figure 1
Robot control system.


## 2. Literature Review

A large number of studies show that more and more roboticists and high-tech workers are willing to invest a lot of time and energy into the research of robots and further improve the quality of their products. The robot research technology is the integration of engineering, mechanical, and electronic basic knowledge, materials, artificial intelligence communication technology, and other aspects of advanced technology and research results, reflecting the highest comprehensive level and strength of the country. Common intelligent robots mainly have foot walking robot, crawler walking robot and wheel walking robot. Wheeled and tracked robots are easy to walk stably, while bipedal robots (also known as two-legged humanoid robots) have more human characteristics and can operate in special environments and occasions, such as desert storms. In flood and other cases, it can take corresponding intelligent actions through the collection and perception of sensors to easily complete corresponding tasks through the road surface and the designated position, which brings convenience to human beings [1].As early as in the 1970s, Cai and Zheng proposed the theoretical experiment and analysis of multirobot cooperative work [2]. Zheng and Cai and Qiu and Duan first proposed the concept of agent in their book The Society of Mind published in 1986. They believe that the basic idea of an agent is to simulate human cognition and social behavior, including cognitive thinking, problem-solving, evolution, mechanism, cooperation, and organization of human society. Its purpose is to enable agents to have the ability to recognize and understand the world environment, the ability to learn, the ability to adapt, and the ability to judge and reason [3, 4].On the basis of the current research, the intelligent control technology of robot workers is proposed. Autonomy: autonomy refers to the ability of an agent to control its own behavior [5]. An agent has its own behavior control mechanism and limited computing resources. Without direct guidance or interference from other agents or human beings, the agent can run continuously and control its own behavior and state according to the perceived external environment information and internal state [6]. Autonomy is an important characteristic of agent, which can be distinguished from some other abstract concepts, such as interaction of object process. An agent can sense and influence the environment. In a certain way, the agent can monitor the surrounding environment and the service requests and interactive information sent by other agents and also communicate with other agents in the system in various forms, so as to complete tasks harmoniously [7]. Initiative: the behavior of an agent is the result of some kind of goal rather than simply reacting to events in the environment. The agent can take the initiative to complete the task according to its predetermined task. Sociality: agents can cooperate with other agents in the environment. There are mutual constraints and dependence among all intelligent agents. When conflicts occur, they can negotiate with each other to solve problems. An agent can effectively perceive and act on its environment through sensors and effectors, respectively [8]. Therefore, at this time, the agent can be seen as an autonomous independent module. In the process of interacting with the environment, the agent needs to process and interpret the received information to achieve its own purpose [9]. See Figure 2.Figure 2
The abstract structure of an agent.


## 3. Development History and Classification of Machine Intelligence Learning

### 3.1. Intelligent Control Theory

Intelligent control is the advanced stage of the development of traditional control theory. It is an interdisciplinary research field of control theory, operation research, artificial intelligence learning theory, and modern adaptive control. From the beginning of the 20th century to its 60s, traditional control theory has experienced the development from classical control theory to modern control theory. Its characteristic is that the research work should be based on the precise model of the control object [10]. However, most of the actual industrial production systems are complex control systems with complex objects, complex tasks, and complex environments. To solve the control problem of this kind of system, we must jump out of the framework of traditional control theory based on ideal mathematical model and start from decomposing complex tasks, perceiving complex environment, and analyzing control objects [11]. Intelligent control system is put forward in this context; it is the process of driving intelligent machines to achieve their goals autonomously and has such functions as learning to adapt to the organization. In the analysis and design of the system, it is not to establish accurate mathematical model but to carry on the symbol description of the nonmathematical model to develop and design the knowledge base and inference machine [12]. After the development of recent years, intelligent control theory has become the main way to solve the control problems of complex systems, and its further research will promote the development of intelligent robot field.


### 3.2. Bionics

Bionics is the application of biological methods and systematic knowledge to solve engineering problems. In the process of biological simulation, it is by no means simple bionics, but to innovate on the basis of bionics. The basic idea of multirobot system research is inspired by the cooperation within or between biological groups. This connection makes the combination of robot technology and bionic technology inevitable [13]. It is very beneficial to study the social characteristics of insects and animals and apply them to multirobot systems. The idea that interaction produces internal motion patterns has inspired current research methods for multirobot systems. Much of the current work in collaborative robotics uses biological systems as its inspiration or validation criteria, applying simple control rules derived from biological societies to develop similar behaviors in cooperative robotic systems. The purpose of this group bionics is to enhance individual intelligence through group behavior, improve the efficiency of the system as a whole, and reduce the influence of local faults on the whole [14]. Work in this area has shown the ability of robot groups to swarm foraging and track tracking. Put forward related topics include the following: in recent years by imitation to learn new behavior, refer to the nature of competition behavior research robot soccer game, created by interaction of human machine system of group behavior, so as to show the swarm intelligence, through the research team to collect social insect food handling and cooperation behavior of the object and verify its social behavior which is the result of self-organization [15].


### 3.3. Multirobot Learning

Traditional robotics research is based on precise prior knowledge of the working environment. With the expansion of the application of robots from the early structured environment to the unstructured environment, multirobot systems have to face some new problems when completing complex tasks. It is difficult to establish an accurate model for dynamic uncertain environment, and incomplete information acquisition caused by sensor constraints leads to poor adaptability of the robot to the environment. The real-time requirements of the system require highly optimization of the robot strategy, etc. [16]. Obviously, it is unrealistic to solve the above problems through artificial design. An important goal of multirobot system development is to design a basic structure of distributed control, so that robots can perform tasks without supervision and have strong adaptive ability in unknown environment. Learning is a strategy to solve the above problems and enable the robot to adapt to the environment autonomously. By interacting with the external environment with the help of domain knowledge and sharing relevant information of other robots, the robot can understand the unknown state in the environment, realize the impact of its own behavior on the environment, and constantly adjust itself to better adapt to the environment [17]. For individual robots, this ability can improve the effectiveness of their own behavior and enhance their intelligence. For the multirobot system as a whole, it is helpful to improve the cooperation between individuals and improve the overall performance of the system.


### 3.4. Research Status and Existing Problems of Multirobot System

The development trend of today’s robot technology mainly has the following outstanding characteristics. First, the application field of robot is expanding, and its types are increasing day by day. Second, the performance of robots is improving and gradually to the direction of intelligent development. Third, in order to improve the autonomy of robots, multirobot technology is developing from the traditional artificial intelligence method to the group robot technology and evolutionary robot technology [18]. Fourthly, the research of reconfigurable robot system makes multirobot system gradually develop towards modularization. This high degree of integration and reconfigurable characteristics can make it have higher robustness and various function. Therefore, driven by the dual needs of research and application, the research of multirobot system and its related technologies has become an important direction in robotics research [19].


## 3.1. Intelligent Control Theory

Intelligent control is the advanced stage of the development of traditional control theory. It is an interdisciplinary research field of control theory, operation research, artificial intelligence learning theory, and modern adaptive control. From the beginning of the 20th century to its 60s, traditional control theory has experienced the development from classical control theory to modern control theory. Its characteristic is that the research work should be based on the precise model of the control object [10]. However, most of the actual industrial production systems are complex control systems with complex objects, complex tasks, and complex environments. To solve the control problem of this kind of system, we must jump out of the framework of traditional control theory based on ideal mathematical model and start from decomposing complex tasks, perceiving complex environment, and analyzing control objects [11]. Intelligent control system is put forward in this context; it is the process of driving intelligent machines to achieve their goals autonomously and has such functions as learning to adapt to the organization. In the analysis and design of the system, it is not to establish accurate mathematical model but to carry on the symbol description of the nonmathematical model to develop and design the knowledge base and inference machine [12]. After the development of recent years, intelligent control theory has become the main way to solve the control problems of complex systems, and its further research will promote the development of intelligent robot field.


## 3.2. Bionics

Bionics is the application of biological methods and systematic knowledge to solve engineering problems. In the process of biological simulation, it is by no means simple bionics, but to innovate on the basis of bionics. The basic idea of multirobot system research is inspired by the cooperation within or between biological groups. This connection makes the combination of robot technology and bionic technology inevitable [13]. It is very beneficial to study the social characteristics of insects and animals and apply them to multirobot systems. The idea that interaction produces internal motion patterns has inspired current research methods for multirobot systems. Much of the current work in collaborative robotics uses biological systems as its inspiration or validation criteria, applying simple control rules derived from biological societies to develop similar behaviors in cooperative robotic systems. The purpose of this group bionics is to enhance individual intelligence through group behavior, improve the efficiency of the system as a whole, and reduce the influence of local faults on the whole [14]. Work in this area has shown the ability of robot groups to swarm foraging and track tracking. Put forward related topics include the following: in recent years by imitation to learn new behavior, refer to the nature of competition behavior research robot soccer game, created by interaction of human machine system of group behavior, so as to show the swarm intelligence, through the research team to collect social insect food handling and cooperation behavior of the object and verify its social behavior which is the result of self-organization [15].


## 3.3. Multirobot Learning

Traditional robotics research is based on precise prior knowledge of the working environment. With the expansion of the application of robots from the early structured environment to the unstructured environment, multirobot systems have to face some new problems when completing complex tasks. It is difficult to establish an accurate model for dynamic uncertain environment, and incomplete information acquisition caused by sensor constraints leads to poor adaptability of the robot to the environment. The real-time requirements of the system require highly optimization of the robot strategy, etc. [16]. Obviously, it is unrealistic to solve the above problems through artificial design. An important goal of multirobot system development is to design a basic structure of distributed control, so that robots can perform tasks without supervision and have strong adaptive ability in unknown environment. Learning is a strategy to solve the above problems and enable the robot to adapt to the environment autonomously. By interacting with the external environment with the help of domain knowledge and sharing relevant information of other robots, the robot can understand the unknown state in the environment, realize the impact of its own behavior on the environment, and constantly adjust itself to better adapt to the environment [17]. For individual robots, this ability can improve the effectiveness of their own behavior and enhance their intelligence. For the multirobot system as a whole, it is helpful to improve the cooperation between individuals and improve the overall performance of the system.


## 3.4. Research Status and Existing Problems of Multirobot System

The development trend of today’s robot technology mainly has the following outstanding characteristics. First, the application field of robot is expanding, and its types are increasing day by day. Second, the performance of robots is improving and gradually to the direction of intelligent development. Third, in order to improve the autonomy of robots, multirobot technology is developing from the traditional artificial intelligence method to the group robot technology and evolutionary robot technology [18]. Fourthly, the research of reconfigurable robot system makes multirobot system gradually develop towards modularization. This high degree of integration and reconfigurable characteristics can make it have higher robustness and various function. Therefore, driven by the dual needs of research and application, the research of multirobot system and its related technologies has become an important direction in robotics research [19].


## 4. Experimental Principles and Methods

### 4.1. Analysis of Simulation Result

Considering that the robot is always on the move, the coverage of the map is used as an evaluation index, and the coverage of the map is expressed asMocc, as shown in
(1)Mocc=1−Uunexp−UexpUexp∗100%.When you have three robots that are basically done searching after 100 iterations, the method is proved to be effective for multirobot systems. Still, there are some areas that need to be improved. First, there is waste when the robot looks for the minimum parameter (cell value); that is, if there are more than two identical minimum values, the last one is chosen [20]. Second, when costs and benefits contain the same value, the robot can be stuck in one location in the search space, even if the other area has not been searched. It is therefore necessary to find solutions that enable robots to constantly seek out unknown areas. So we optimized the mapping process for a group of mobile robots. As an optimization technique, an improved GWO heuristic algorithm is used for sensor coverage task, which generates random parameters for the maximum position of changing order [21]. Therefore, the hierarchical optimizer has reformed the robot’s position selection, which is a deterministic search method. In the case that it is impossible to optimize in the real-time process, random technique is adopted as one of the solutions of region coverage of mobile sensor system without environmental prior information. GWO was used to optimize the position of three robots in search, and different search effects were achieved according to different set parameters. In the case of one hundred iterations, the coverage results are shown in Figure 3.Figure 3
The improved algorithm has 20 search coverages.


### 4.2. Design of Roundup Scheme in Dynamic Environment

Bacterial chemotaxis is used to guide the robot towards the target it has found. Bacterial chemotaxis refers to the phenomenon that the bacteria follow the chemical concentration gradient around them to guide the robot towards [22], as shown in
(2)C=A0.Initialize the robot’s position and the concentration of each cell.A is the initial concentration. When each robot reaches all the grids in its cell, the robot is considered to have covered the cell. After initialization, robot i becomes A. The concentration of the base moves in the direction of the nearest grid. If the robot has multiple nearest targets to guide, the distance calculation method is used to calculate the direction S to guide the next target, as shown in
(3)dNitdt=bδ+aDi.NIt is the position of robot i,s is the position of the target, andb is the velocity coefficient of the robot and is the constant coefficient of D. The collision between robots D is the sum of the interactions between the i th robot and other robots, so as to avoid the problem as shown in
(4)Di=∑j=1biDij.B is the number of neighbors around robot i, and D is the interaction force between them, which is determined by the relative distance between them compared to the absolute distance between them. The energy cost of a robot moving in a straight line is less than that of turning, so the robot will not change direction unless they are stuck in a local optimum [23]. In order to avoid local optimization, the orientation of the robot is determined as shown in
(5)PX=δ=PV=1PX=δ•V=1.


### 4.3. Construction of Software Environment of Reinforcement Learning Simulation Platform

Reinforcement learning agent in the environment needs to continue to try and learn, in order to avoid the consumption of real robots and improve the feasibility of reinforcement learning method. If the training and testing of the algorithm are transferred to the computer, carried out in the simulation environment, and the learned model is transplanted to the real robot, the learning cost will be greatly reduced. After the development of recent years, a variety of simulation platforms have emerged in deep reinforcement learning, but most of them are not convenient to modify the simulation environment according to their own needs, so a good experimental environment is very important. Considering that subsequent experimental models can interact with physical robots, we choose OpenAlGym for environment construction. Gym is a toolbox for developing RL algorithm, combined with TensorFlow machine learning platform, which is convenient for improving development efficiency [24]. The simulation platform software is shown in Table 1.Table 1
Software record used to build simulation platform environment.
Development toolsPyCharmOperating systemWindows 10Development languagePython 4.1Robot learning platformTensorFlowReinforcement learning platformGym


### 4.4. Applicability Analysis of Hybrid Control Structure

The hybrid control structure is analyzed from the simulation results.


#### 4.4.1. Conduct Cooperation

Hybrid control structure can make the movement of the robots much more regular than purely behavior-based movements, which makes a certain guarantee of behavioral cooperation.


#### 4.4.2. Dynamic Environment

Hybrid control draws on the idea of behavior-based, and independent behavior processes directly process sensor information to ensure fast response to the environment.


#### 4.4.3. Complex Task

Tasks are more constraints on the robot movement. Behavior management and behavior synthesis can better optimize the behavior of the robot and complete the upper monitoring of complex tasks. The upper monitoring ensures the combination of human control and autonomous command. When the robot cannot achieve autonomous planning, it often needs the participation of human thought [25]. To sum up, the comparative analysis of hybrid control structure and behavior-based structure is shown in Table 2.Table 2
Structural analysis.
Conduct cooperationDynamic tasksComplex taskThe upper monitoringBased on behaviorOrdinaryExcellentOrdinaryPoorHybrid structureOrdinaryExcellentExcellentExcellent


### 4.5. Experimental Analysis of Inclination Sensor Acquisition

In the experiment, the inclination sensor was placed on an inclined plane of 300 and collected for ten times. It can be seen from the experimental data that the maximum error of measurement is 0.40, which can meet the requirements of the system, as shown in Table3.Table 3
Experimental data of inclination sensor.
The serial number of collection12345678910Measurements28.627.428.328.528.428.928.228.428.328.7Deviation0.20.50.10.40.30.60.50.60.40.3


### 4.6. Experimental Analysis of Compass Sensor Acquisition

In the experiment, the compass sensor was used to measure the relative included angles of 30, 45, 60, and 90, respectively, and the average value of each angle was measured for 5 times. It can be seen from the experimental data that the measurement error meets the requirements of the system, as shown in Table4.Table 4
Compass sensor experimental data.
The data collected29445989Measurements29.343.658.889.2Deviation0.30.30.20.2


## 4.1. Analysis of Simulation Result

Considering that the robot is always on the move, the coverage of the map is used as an evaluation index, and the coverage of the map is expressed asMocc, as shown in
(1)Mocc=1−Uunexp−UexpUexp∗100%.When you have three robots that are basically done searching after 100 iterations, the method is proved to be effective for multirobot systems. Still, there are some areas that need to be improved. First, there is waste when the robot looks for the minimum parameter (cell value); that is, if there are more than two identical minimum values, the last one is chosen [20]. Second, when costs and benefits contain the same value, the robot can be stuck in one location in the search space, even if the other area has not been searched. It is therefore necessary to find solutions that enable robots to constantly seek out unknown areas. So we optimized the mapping process for a group of mobile robots. As an optimization technique, an improved GWO heuristic algorithm is used for sensor coverage task, which generates random parameters for the maximum position of changing order [21]. Therefore, the hierarchical optimizer has reformed the robot’s position selection, which is a deterministic search method. In the case that it is impossible to optimize in the real-time process, random technique is adopted as one of the solutions of region coverage of mobile sensor system without environmental prior information. GWO was used to optimize the position of three robots in search, and different search effects were achieved according to different set parameters. In the case of one hundred iterations, the coverage results are shown in Figure 3.Figure 3
The improved algorithm has 20 search coverages.


## 4.2. Design of Roundup Scheme in Dynamic Environment

Bacterial chemotaxis is used to guide the robot towards the target it has found. Bacterial chemotaxis refers to the phenomenon that the bacteria follow the chemical concentration gradient around them to guide the robot towards [22], as shown in
(2)C=A0.Initialize the robot’s position and the concentration of each cell.A is the initial concentration. When each robot reaches all the grids in its cell, the robot is considered to have covered the cell. After initialization, robot i becomes A. The concentration of the base moves in the direction of the nearest grid. If the robot has multiple nearest targets to guide, the distance calculation method is used to calculate the direction S to guide the next target, as shown in
(3)dNitdt=bδ+aDi.NIt is the position of robot i,s is the position of the target, andb is the velocity coefficient of the robot and is the constant coefficient of D. The collision between robots D is the sum of the interactions between the i th robot and other robots, so as to avoid the problem as shown in
(4)Di=∑j=1biDij.B is the number of neighbors around robot i, and D is the interaction force between them, which is determined by the relative distance between them compared to the absolute distance between them. The energy cost of a robot moving in a straight line is less than that of turning, so the robot will not change direction unless they are stuck in a local optimum [23]. In order to avoid local optimization, the orientation of the robot is determined as shown in
(5)PX=δ=PV=1PX=δ•V=1.


## 4.3. Construction of Software Environment of Reinforcement Learning Simulation Platform

Reinforcement learning agent in the environment needs to continue to try and learn, in order to avoid the consumption of real robots and improve the feasibility of reinforcement learning method. If the training and testing of the algorithm are transferred to the computer, carried out in the simulation environment, and the learned model is transplanted to the real robot, the learning cost will be greatly reduced. After the development of recent years, a variety of simulation platforms have emerged in deep reinforcement learning, but most of them are not convenient to modify the simulation environment according to their own needs, so a good experimental environment is very important. Considering that subsequent experimental models can interact with physical robots, we choose OpenAlGym for environment construction. Gym is a toolbox for developing RL algorithm, combined with TensorFlow machine learning platform, which is convenient for improving development efficiency [24]. The simulation platform software is shown in Table 1.Table 1
Software record used to build simulation platform environment.
Development toolsPyCharmOperating systemWindows 10Development languagePython 4.1Robot learning platformTensorFlowReinforcement learning platformGym


## 4.4. Applicability Analysis of Hybrid Control Structure

The hybrid control structure is analyzed from the simulation results.


### 4.4.1. Conduct Cooperation

Hybrid control structure can make the movement of the robots much more regular than purely behavior-based movements, which makes a certain guarantee of behavioral cooperation.


### 4.4.2. Dynamic Environment

Hybrid control draws on the idea of behavior-based, and independent behavior processes directly process sensor information to ensure fast response to the environment.


### 4.4.3. Complex Task

Tasks are more constraints on the robot movement. Behavior management and behavior synthesis can better optimize the behavior of the robot and complete the upper monitoring of complex tasks. The upper monitoring ensures the combination of human control and autonomous command. When the robot cannot achieve autonomous planning, it often needs the participation of human thought [25]. To sum up, the comparative analysis of hybrid control structure and behavior-based structure is shown in Table 2.Table 2
Structural analysis.
Conduct cooperationDynamic tasksComplex taskThe upper monitoringBased on behaviorOrdinaryExcellentOrdinaryPoorHybrid structureOrdinaryExcellentExcellentExcellent


## 4.4.1. Conduct Cooperation

Hybrid control structure can make the movement of the robots much more regular than purely behavior-based movements, which makes a certain guarantee of behavioral cooperation.


## 4.4.2. Dynamic Environment

Hybrid control draws on the idea of behavior-based, and independent behavior processes directly process sensor information to ensure fast response to the environment.


## 4.4.3. Complex Task

Tasks are more constraints on the robot movement. Behavior management and behavior synthesis can better optimize the behavior of the robot and complete the upper monitoring of complex tasks. The upper monitoring ensures the combination of human control and autonomous command. When the robot cannot achieve autonomous planning, it often needs the participation of human thought [25]. To sum up, the comparative analysis of hybrid control structure and behavior-based structure is shown in Table 2.Table 2
Structural analysis.
Conduct cooperationDynamic tasksComplex taskThe upper monitoringBased on behaviorOrdinaryExcellentOrdinaryPoorHybrid structureOrdinaryExcellentExcellentExcellent


## 4.5. Experimental Analysis of Inclination Sensor Acquisition

In the experiment, the inclination sensor was placed on an inclined plane of 300 and collected for ten times. It can be seen from the experimental data that the maximum error of measurement is 0.40, which can meet the requirements of the system, as shown in Table3.Table 3
Experimental data of inclination sensor.
The serial number of collection12345678910Measurements28.627.428.328.528.428.928.228.428.328.7Deviation0.20.50.10.40.30.60.50.60.40.3


## 4.6. Experimental Analysis of Compass Sensor Acquisition

In the experiment, the compass sensor was used to measure the relative included angles of 30, 45, 60, and 90, respectively, and the average value of each angle was measured for 5 times. It can be seen from the experimental data that the measurement error meets the requirements of the system, as shown in Table4.Table 4
Compass sensor experimental data.
The data collected29445989Measurements29.343.658.889.2Deviation0.30.30.20.2


## 5. Conclusion

Although with the development of robot technology, the ability of robot is constantly improved, and the field and scope of robot application are also constantly expanding, but for some complex tasks, a better solution is composed of multiple robots. Therefore, multirobot system has become an important field of robot research. Multirobot navigation technology is the core technology to realize intelligent system, and path planning is one of the key links. Based on the research of multi-robot-related technology, this paper conducts in-depth theoretical research and simulation analysis on multirobot path planning method. A series of theoretical research work should be done, the focus of the work lies in the innovation and feasibility of theoretical research to realize various methods, and the relevant verification has been done on the self-developed simulation platform. However, some related problems need to be further studied, the performance of the proposed algorithm needs to be further improved, and the effectiveness of each technology needs to be tested in a practical application environment. Therefore, there are still many problems and work to be studied, which will be gradually realized in the future research work. Aiming at the control system architecture of intelligent mobile robot, this paper analyzes the function of robot system and control system control form, designs the control system, and realizes the scheme and principle block diagram.



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*Source: 2235042-2022-07-25.xml*