# Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Authors: Qinlong Hu; Yimin Fan
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to improve the efficiency of a logistics cycle robot picking up goods, a path planning algorithm based on artificial intelligence was proposed. After analyzing the particle swarm optimization algorithm, the particle swarm optimization algorithm is optimized and improved and the path planning of a single robot is obtained. On this basis, a multipopulation particle swarm optimization (CMMPPSO) algorithm is proposed. The results show that the JMPOPSO algorithm is more accurate than the BPSO algorithm and the maximum fitness optimized by the BPSO algorithm is 1.59, while the maximum fitness optimized by the JMPOPSO algorithm is 1.98. The path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, shortening by about 25% and shortening the time by about 30. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.


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## Body

## 1. Introduction

Robots are widely used in industrial production and people’s life. They can help people complete all kinds of hard, dangerous, and repetitive work, such as safety detection of nuclear equipment, complex medical diagnosis, glass exterior wall wiping, and assembly line operation [1]. The invention and popularization of robots have provided a strong driving force for the development of the world economy. Academician Zhao Lv once pointed out that “the birth of robots and the establishment and development of robotics are the most convincing achievements of automatic control in this century and the major achievements of human scientific and technological progress in the 20th century” [2]. The application of robots reflects the industrial automation level of a country to a certain extent [3]. With the continuous maturity and development of computer technology, intelligent control theory, VLSI, pattern recognition technology, sensor technology, and structure, robot technology has also entered a new development stage. Intellectualization and humanization have become the main trend of the development of the robot industry in the future [4].Based on the new herd intelligent optimization algorithm, particle swarm optimization (PSO) is a simple mathematical model, is easy to use, and requires no objective optimization function to distinguish, differentiate, and control. The algorithm can be used in most application fields that need optimization, among which the fields with great potential mainly include power system control, pattern recognition, signal processing, image classification, fuzzy controller design, and robot path planning. The improved algorithm can obtain a better path, as shown in Figure1 for the control system of the logistics handling robot. However, the particle swarm optimization algorithm has the disadvantage of easy convergence to the local optimal solution, that is, it is prone to premature convergence. This problem has prompted scientists to optimize the animal algorithm and use it to plan the path of mobile robots. When using the particle optimization algorithm to solve the problem of mobile robot planning, in general, the best way is not the best way in the world, but the right way. Therefore, good planning is a very important part of research.Figure 1
A manufacturing method of an intelligent logistics handling robot control system.


## 2. Literature Review

A mobile robot is a kind of robot with high intelligence, and it is also the focus and frontier field of intelligent robot research. As an important group in the robot family, mobile robots have made many remarkable achievements in the past decades [5]. Developed countries such as Europe, America, and Japan have studied mobile robot technology earlier, and the mobile robot technology of these countries represents the highest level in the world to a certain extent [6]. In 1966, the artificial intelligence center at the Stanford Research Center (ACSRC) successfully developed the world’s first mobile machine—Shakey. It has certain observation ability to the environment and independent modeling ability, but it needs to use multiple large computers to control it [7]. Domestic scholars study mobile robot technology later than developed countries such as Europe and the United States, but after more than 40 years of unremitting efforts, they have also made many great achievements [8]. Zhao and others developed a new type of special robot—independent 4WD all-terrain explosive disposal robot. The independent 4WD all-terrain explosive disposal robot is mainly used in harsh environments such as wild mountains and can check, transport, and detonate suspected explosives [9]. Foroughi and others proposed the Voronoi diagram. At first, the Voronoi diagram was used to solve the proximity problem of plane points in mathematics but some scholars soon introduced it into robot path planning. In this method, the Voronoi diagram can be used to describe the networked structure of feasible areas in the robot working environment [10]. Cai and Zheng proposed robot path planning based on the grid method, which was proposed relatively early and widely used. They further tested and analyzed the reliability of applying the grid method to solve path planning problems [11]. Zheng and Cai proposed a real-time robot path planning method based on the concept of the artificial potential field. This method can avoid collision with obstacles in real time in a complex environment. This method has been successfully applied to the cosmos system of a PUMA robot [12]. Sennan and others proved that Hopfield-type nonlinear simulation neurons are very effective for robot path planning and obstacle avoidance. From any starting position to any target position, the artificial neural network system can successfully avoid static and dynamic obstacles of arbitrary shape and quickly provide an appropriate path [13]. Kaya and others introduced the fuzzy control theory into mobile robot path planning and proposed a robot path planning method based on multiple motion instruction sets. This method uses fuzzy rules to guide the motion of the robot, avoids the disadvantage of poor real-time performance of mobile robot path planning due to large amount of calculation, and achieved good simulation results [14]. Pahnehkolaei and others combined the improved genetic algorithm with the Dijkstra algorithm (DA) to optimize the robot path and designed a heuristic way to generate the initial population. As a swarm intelligence optimization algorithm, the ant colony algorithm is also widely used in robot path planning [15]. Wu and Song combined the ant colony algorithm with a probabilistic roadmap planner (PRM) to plan the robot path, which can reduce the path planning time to an acceptable range [16].Robot navigation technology is one of the most important technologies in mobile robots. Robot navigation technology mainly includes the following three aspects: robot positioning, task planning, and path planning. Robot positioning is the key technology to realize autonomous navigation. This technology requires a mobile robot to determine its position and direction when its initial position is known or unknown. Task planning refers to the mobile robot completing some specific tasks in a specific time and space. Path planning requires the mobile robot to reach the target position from the starting position at the least cost (such as the shortest walking path, the least walking time, and the lowest walking energy consumption). In this process, the collision between the robot and obstacles and between the robot and the robot should be avoided [17].The research on robot path planning technology can reduce the working time of the robot, improve the working efficiency of the robot, and reduce some unnecessary wear and consumption of the robot, so as to achieve the purpose of saving resources and reducing costs [18]. In some special environments, a good robot path planning technology is particularly important, such as flood fighting and rescue, explosive disposal, rescue, and military reconnaissance. The use of good path planning technology can make the mobile robot quickly reach the designated target position. In order to save and ensure the safety of people’s lives and property and win valuable time, the research on path planning technology can also reduce the uncertainty of the robot in the movement process and enhance the flexibility of the mobile robot, so as to enhance the intelligent level of the mobile robot and lay a solid foundation for the further wide use of the mobile robot in industrial production and people’s life [19]. Robot path planning technology is the basis for the development of various high-performance mobile robots. Therefore, the research on robot path planning technology has very important theoretical and practical significance [20].


## 3. Research Methods

### 3.1. Particle Swarm Optimization Algorithm

The particle swarm optimization algorithm is a swarm intelligence evolutionary method proposed by Dr. Kennedy and Professor Eberhart [21, 22]. Like the genetic algorithm, evolutionary programming (EP), evolutionary strategies (ES), and other evolutionary algorithms, the algorithm is a direct search algorithm based on population evolution and does not need to rely on gradient, curvature, and other information.In the particle swarm optimization algorithm, the calculation formula ofk+1 iteration of each particle i in the d dimension is as follows (1):
(1)vidk+1=vidk+c1·rand·pidk−xidk+c2·rand·pgdk−xidk,xidk+1=xidk+vidk+1.Among them,c1 and c2 are called learning factors, which are used to adjust the step length of particles flying towards the optimal value of the individual position and the optimal value of the group position, respectively [2, 23]. pidk represents the optimal value of individual position of the particle, pgdk represents the optimal value of the group position, d1≤d≤D represents the dimension of the position vector and velocity vector, and rand is a random number between (0,1).When generating a new particle position, the constraint conditions of the particle velocity vector must also be met, as shown in formula (2):
(2)vidk+1≤Vmax.In formula (2), Vmax represents the limiting constant of the velocity vector. The velocity vector constraint condition can also be expressed as equation (3):
(3)xidk+1−xidk≤Vmax.This is also the Lipschitz condition of the dynamic system [3].In the particle swarm optimization algorithm, the update function of the optimal value of the individual position of the particle is defined as equation (4):
(4)pidk=xik,fxik≤fpidk−1,pidk−1,fxik>fpidk−1.In the particle swarm optimization algorithm, the update function of the optimal position of the whole population is defined as equation (5):
(5)pgdk∈p1dk,p2dk,⋯,pmdk∣fpidk=minfp1dk,fp2dk,⋯,fpmdk,In formulas (4) and (5), f represents the fitness function value.The steps of the particle swarm optimization algorithm are shown in Figure2.Figure 2
Flow chart of the particle swarm optimization algorithm.


### 3.2. Path Planning of a Single Machine Robot Based on the Improved Particle Swarm Optimization (JMPOPSO) Algorithm

When using the particle optimization algorithm to improve the performance of the robot cell, the method can be estimated according to the physical function. Creating an outflow directly affects whether the algorithm can find a good way. The energy function is designed to improve the flow. For the optimization index method, the long road is the first index. Of course, other performance measures such as road safety and road smoothness should be determined. The performance force described in this paper includes three performance measures.(1)
Path lengthfit1(6)Fit1=∑j=0Npjpj+1=d+ys−y12+∑j=1N−1d+yj+1−yj2+d+yg−yN2.In formula (6), d=xs−xg/N+12 is the fixed value, which represents the length of each horizontal axis in the newly established coordinate system, N+1 is the number of segments evenly divided by the vertical line, and xs,ys and xg,yg represent the coordinates of the starting point and target point in the robot path planning, respectively
(2)
Path securityfit2The safety performance of the path can be expressed by the number of segments intersecting the obstacle in a complete path, as shown in equation (7).
(7)Fit1=n,numberofsectionswherethepathintersectstheobstacle,thepathisnotfeasible,0,thepathdoesnotintersectwiththeobstacle,thepathisfeasible.(3)
Smoothness of pathfit3The walking distance between mobile robots should be as small as possible and the walkway should be as smooth as possible. Only in this way can the wear and tear caused by the robot rotation be reduced. Therefore, as shown in equation (8), the design of the robot method must take into account the optimized method uniformity.
(8)Fit3=∑j=0Nβlj,lj+1N.In formula (8), βlj,lj+1 represents the included angle between two path segments lj and lj+1 and N represents the number of included angles. The greater the included angle between path segments, the smoother the optimized path.Considering the performance indexes of the abovementioned three path optimizations, the fitness functionf1p is designed as equation (9):
(9)f1p=N+1−ω2·fit2+ω3·fit3ω1·fit1.In formula (9), ω1, ω2, and ω3 are the adjustment parameters of path length fit1, path security fit2, and path smoothness fit3, respectively, so that the three performance indexes can be unified in the order of magnitude. When the fitness function value f1p is larger, the optimized solution is better, that is, the optimized path quality is better.


### 3.3. Multiswarm Particle Swarm Optimization (CMMPPSO) Algorithm Based on the Collaborative Mechanism

Previously, the use of single-robot planning based on particle optimization algorithms has been shown, but not the use of multirobot planning. To solve the problem of multiple robotic methods, a synergistic mechanism is integrated into the PSO algorithm and multiple public particle optimization based on the synergy (CMPPSO) algorithm is planned. Because multiple robots work in the same environment, there is a relationship of competition and cooperation between robots. Firstly, the multirobot path planning problem is decomposed into several subproblems for solution and the initial population is divided according to the number of robots. Each subpopulation corresponds to a robot’s individual evolutionary optimization. Each robot is optimized separately by the corresponding subpopulation, and then, the representative individual group is selected according to the fitness evaluation function. The selected representative individual group is operated by collaborative information interaction, and the combination with the best fitness value is selected as the elite individual, so that each optimized path meets the objective constraints and outputs the final optimal combination. The flow of CMMPPSO algorithm is shown in Figure3.Figure 3
Flow chart of the CMMPPSO algorithm.In the process of multirobot path planning, because there are multiple mobile robots in the same working environment and the starting point and target point of each robot are different, the path optimized by the algorithm may cross in the same space at the same time. When the robot moves along the optimized path, there will be a collision between the robot and the robot.If there are two robots moving from the starting point to the starting point at the same speed, it can be judged whether there is a collision between the two robots at the same time:(10)dcross=cross−beg.In formula (10), cross represents the position of the intersection between the optimized two paths, beg is the starting point of the robot, and dcross represents the distance from the starting point to the intersection of the two paths moving along the optimized path.If thedcross of the two robots are the same, it means that the two robots will collide when moving along the optimized path and the number of collisions between paths will be counted; otherwise, there will be no collision.Here, a collision evaluation functionfit4 is designed to evaluate the collision between each group of paths optimized in the multirobot system. The specific design is shown in formula (11):
(11)Fit4=1,nocollisionbetweenrobots,10∗Num_cross,collisionbetweenrobots.In formula (11), Num_cross represents the number of collisions between each group of paths optimized in the multirobot system.The construction of fitness functionf1p is the same as the fitness function introduced in the previous article. The construction of fitness function f2p for collaborative information interaction is shown in formula (12):
(12)f2p=f11p+f12p+⋯+f1ip+⋯+f1np·1fit4.The fitness evaluation functionf1ip is the same as the previously established fitness evaluation function f1p, where 1<i≤n, n is the number of robots, and fit4 is the collision evaluation function between paths of the multirobot system. In collaborative information interaction, the larger the value of fitness function f2p, the better the representative individual of the combination and the best representative individual combination is set as the elite individual.


## 3.1. Particle Swarm Optimization Algorithm

The particle swarm optimization algorithm is a swarm intelligence evolutionary method proposed by Dr. Kennedy and Professor Eberhart [21, 22]. Like the genetic algorithm, evolutionary programming (EP), evolutionary strategies (ES), and other evolutionary algorithms, the algorithm is a direct search algorithm based on population evolution and does not need to rely on gradient, curvature, and other information.In the particle swarm optimization algorithm, the calculation formula ofk+1 iteration of each particle i in the d dimension is as follows (1):
(1)vidk+1=vidk+c1·rand·pidk−xidk+c2·rand·pgdk−xidk,xidk+1=xidk+vidk+1.Among them,c1 and c2 are called learning factors, which are used to adjust the step length of particles flying towards the optimal value of the individual position and the optimal value of the group position, respectively [2, 23]. pidk represents the optimal value of individual position of the particle, pgdk represents the optimal value of the group position, d1≤d≤D represents the dimension of the position vector and velocity vector, and rand is a random number between (0,1).When generating a new particle position, the constraint conditions of the particle velocity vector must also be met, as shown in formula (2):
(2)vidk+1≤Vmax.In formula (2), Vmax represents the limiting constant of the velocity vector. The velocity vector constraint condition can also be expressed as equation (3):
(3)xidk+1−xidk≤Vmax.This is also the Lipschitz condition of the dynamic system [3].In the particle swarm optimization algorithm, the update function of the optimal value of the individual position of the particle is defined as equation (4):
(4)pidk=xik,fxik≤fpidk−1,pidk−1,fxik>fpidk−1.In the particle swarm optimization algorithm, the update function of the optimal position of the whole population is defined as equation (5):
(5)pgdk∈p1dk,p2dk,⋯,pmdk∣fpidk=minfp1dk,fp2dk,⋯,fpmdk,In formulas (4) and (5), f represents the fitness function value.The steps of the particle swarm optimization algorithm are shown in Figure2.Figure 2
Flow chart of the particle swarm optimization algorithm.


## 3.2. Path Planning of a Single Machine Robot Based on the Improved Particle Swarm Optimization (JMPOPSO) Algorithm

When using the particle optimization algorithm to improve the performance of the robot cell, the method can be estimated according to the physical function. Creating an outflow directly affects whether the algorithm can find a good way. The energy function is designed to improve the flow. For the optimization index method, the long road is the first index. Of course, other performance measures such as road safety and road smoothness should be determined. The performance force described in this paper includes three performance measures.(1)
Path lengthfit1(6)Fit1=∑j=0Npjpj+1=d+ys−y12+∑j=1N−1d+yj+1−yj2+d+yg−yN2.In formula (6), d=xs−xg/N+12 is the fixed value, which represents the length of each horizontal axis in the newly established coordinate system, N+1 is the number of segments evenly divided by the vertical line, and xs,ys and xg,yg represent the coordinates of the starting point and target point in the robot path planning, respectively
(2)
Path securityfit2The safety performance of the path can be expressed by the number of segments intersecting the obstacle in a complete path, as shown in equation (7).
(7)Fit1=n,numberofsectionswherethepathintersectstheobstacle,thepathisnotfeasible,0,thepathdoesnotintersectwiththeobstacle,thepathisfeasible.(3)
Smoothness of pathfit3The walking distance between mobile robots should be as small as possible and the walkway should be as smooth as possible. Only in this way can the wear and tear caused by the robot rotation be reduced. Therefore, as shown in equation (8), the design of the robot method must take into account the optimized method uniformity.
(8)Fit3=∑j=0Nβlj,lj+1N.In formula (8), βlj,lj+1 represents the included angle between two path segments lj and lj+1 and N represents the number of included angles. The greater the included angle between path segments, the smoother the optimized path.Considering the performance indexes of the abovementioned three path optimizations, the fitness functionf1p is designed as equation (9):
(9)f1p=N+1−ω2·fit2+ω3·fit3ω1·fit1.In formula (9), ω1, ω2, and ω3 are the adjustment parameters of path length fit1, path security fit2, and path smoothness fit3, respectively, so that the three performance indexes can be unified in the order of magnitude. When the fitness function value f1p is larger, the optimized solution is better, that is, the optimized path quality is better.


## 3.3. Multiswarm Particle Swarm Optimization (CMMPPSO) Algorithm Based on the Collaborative Mechanism

Previously, the use of single-robot planning based on particle optimization algorithms has been shown, but not the use of multirobot planning. To solve the problem of multiple robotic methods, a synergistic mechanism is integrated into the PSO algorithm and multiple public particle optimization based on the synergy (CMPPSO) algorithm is planned. Because multiple robots work in the same environment, there is a relationship of competition and cooperation between robots. Firstly, the multirobot path planning problem is decomposed into several subproblems for solution and the initial population is divided according to the number of robots. Each subpopulation corresponds to a robot’s individual evolutionary optimization. Each robot is optimized separately by the corresponding subpopulation, and then, the representative individual group is selected according to the fitness evaluation function. The selected representative individual group is operated by collaborative information interaction, and the combination with the best fitness value is selected as the elite individual, so that each optimized path meets the objective constraints and outputs the final optimal combination. The flow of CMMPPSO algorithm is shown in Figure3.Figure 3
Flow chart of the CMMPPSO algorithm.In the process of multirobot path planning, because there are multiple mobile robots in the same working environment and the starting point and target point of each robot are different, the path optimized by the algorithm may cross in the same space at the same time. When the robot moves along the optimized path, there will be a collision between the robot and the robot.If there are two robots moving from the starting point to the starting point at the same speed, it can be judged whether there is a collision between the two robots at the same time:(10)dcross=cross−beg.In formula (10), cross represents the position of the intersection between the optimized two paths, beg is the starting point of the robot, and dcross represents the distance from the starting point to the intersection of the two paths moving along the optimized path.If thedcross of the two robots are the same, it means that the two robots will collide when moving along the optimized path and the number of collisions between paths will be counted; otherwise, there will be no collision.Here, a collision evaluation functionfit4 is designed to evaluate the collision between each group of paths optimized in the multirobot system. The specific design is shown in formula (11):
(11)Fit4=1,nocollisionbetweenrobots,10∗Num_cross,collisionbetweenrobots.In formula (11), Num_cross represents the number of collisions between each group of paths optimized in the multirobot system.The construction of fitness functionf1p is the same as the fitness function introduced in the previous article. The construction of fitness function f2p for collaborative information interaction is shown in formula (12):
(12)f2p=f11p+f12p+⋯+f1ip+⋯+f1np·1fit4.The fitness evaluation functionf1ip is the same as the previously established fitness evaluation function f1p, where 1<i≤n, n is the number of robots, and fit4 is the collision evaluation function between paths of the multirobot system. In collaborative information interaction, the larger the value of fitness function f2p, the better the representative individual of the combination and the best representative individual combination is set as the elite individual.


## 4. Result Analysis

### 4.1. Path Planning of a Single Machine Robot Based on the Improved Particle Swarm Optimization (JMPOPSO) Algorithm

In order to verify the performance of the JMPOPSO algorithm, this paper compares the JMPOPSO algorithm with the BPSO algorithm and makes 100 experiments on the two algorithms, respectively. Table1 records the maximum, average, minimum, and average running time of the fitness optimized by the two particle swarm optimization algorithms.Table 1
Comparison of path planning performance based on the BPSO algorithm and JMPOPSO algorithm.
AlgorithmMaximumAverage valueMinimum valueAverage time (S)BPSO algorithm1.591.431.177.56JMPOPSO algorithm1.981.761.349.39As shown in Figure4, from the comparison, we can see that the BPSO algorithm searches for a more accurate fitness value than the BPSO algorithm. The maximum fitness value optimized by the BPSO algorithm is 1.59, while the maximum fitness value optimized by the JMPOPSO algorithm is 1.98. And in 100 experiments, the average value obtained by the JMPOPSO algorithm is higher than that of the BPSO algorithm. The average value obtained by the BPSO algorithm is 1.43, while the average value obtained by the JMPOPSO algorithm is 1.76, indicating that the JMPOPSO algorithm has stronger global search ability. The average path length of the JMPOPSO algorithm is about 7% shorter than that of the BPSO algorithm, and the running time is about 20%.Figure 4
Average length of path planning based on the BPSO algorithm and JMPOPSO algorithm.


### 4.2. Multiswarm Particle Swarm Optimization (CMMPPSO) Algorithm Based on the Collaborative Mechanism

In order to verify the effectiveness of the algorithm, simulation experiments are carried out in this paper. Three mobile robots R1, R2, and R3 are set in the working environment, and five circular obstacles with radiusr=2 are set. In the simulation environment, the starting position and target position coordinates of the three mobile robots are set as shown in Table 2.Table 2
Setting of the starting point and target point of the mobile robot in the simulation environment.
RobotStarting pointTarget pointSimulation environment settingsR1S1(13,12)T1(26,28)R2S2(18,11)T2(18,29)R3S3(23,12)T3(10,28)Figures5 and 6 show the time and average path length of multirobot path planning based on the CMMPPSO algorithm based on BPSO and the CMMPPSO algorithm based on JMPOPSO, respectively.Figure 5
Algorithm time.Figure 6
Average path length.The simulation results show that the path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, which is shortened by about 25% and the time is shortened by about 30%. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.


## 4.1. Path Planning of a Single Machine Robot Based on the Improved Particle Swarm Optimization (JMPOPSO) Algorithm

In order to verify the performance of the JMPOPSO algorithm, this paper compares the JMPOPSO algorithm with the BPSO algorithm and makes 100 experiments on the two algorithms, respectively. Table1 records the maximum, average, minimum, and average running time of the fitness optimized by the two particle swarm optimization algorithms.Table 1
Comparison of path planning performance based on the BPSO algorithm and JMPOPSO algorithm.
AlgorithmMaximumAverage valueMinimum valueAverage time (S)BPSO algorithm1.591.431.177.56JMPOPSO algorithm1.981.761.349.39As shown in Figure4, from the comparison, we can see that the BPSO algorithm searches for a more accurate fitness value than the BPSO algorithm. The maximum fitness value optimized by the BPSO algorithm is 1.59, while the maximum fitness value optimized by the JMPOPSO algorithm is 1.98. And in 100 experiments, the average value obtained by the JMPOPSO algorithm is higher than that of the BPSO algorithm. The average value obtained by the BPSO algorithm is 1.43, while the average value obtained by the JMPOPSO algorithm is 1.76, indicating that the JMPOPSO algorithm has stronger global search ability. The average path length of the JMPOPSO algorithm is about 7% shorter than that of the BPSO algorithm, and the running time is about 20%.Figure 4
Average length of path planning based on the BPSO algorithm and JMPOPSO algorithm.


## 4.2. Multiswarm Particle Swarm Optimization (CMMPPSO) Algorithm Based on the Collaborative Mechanism

In order to verify the effectiveness of the algorithm, simulation experiments are carried out in this paper. Three mobile robots R1, R2, and R3 are set in the working environment, and five circular obstacles with radiusr=2 are set. In the simulation environment, the starting position and target position coordinates of the three mobile robots are set as shown in Table 2.Table 2
Setting of the starting point and target point of the mobile robot in the simulation environment.
RobotStarting pointTarget pointSimulation environment settingsR1S1(13,12)T1(26,28)R2S2(18,11)T2(18,29)R3S3(23,12)T3(10,28)Figures5 and 6 show the time and average path length of multirobot path planning based on the CMMPPSO algorithm based on BPSO and the CMMPPSO algorithm based on JMPOPSO, respectively.Figure 5
Algorithm time.Figure 6
Average path length.The simulation results show that the path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, which is shortened by about 25% and the time is shortened by about 30%. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.


## 5. Conclusion

With in-depth study of herd intelligent technology, mobile robot planning technology has reached unprecedented heights. The particle herd optimization algorithm is a new bionic optimization algorithm. This line only focuses on improving the particle optimization algorithm, using optimization algorithms to improve the robotics process, and achieving positive results. This sentence focuses on the following issues.First, we recommend the particle flock optimization (JMPOPSO) algorithm based on the jump mechanism and traction operation based BPSO algorithm. The JMPOPSO algorithm is more powerful in the world of search engine and integration faster.The JMPOPSO algorithm was then used in the design of the same robotic method, with the disadvantage that it was difficult to improve the global process using the BPSO algorithm. First, the robot’s office is modeled, and then, the fitness program is decided in order to improve the process. Finally, the method has been improved by the JMPOPSO algorithm based on the robust design. The simulation results show that the JMPOPSO algorithm is capable of better global research, more exploration, and better methods.Finally, when studying the multirobot path planning problem, this paper proposes a multiswarm particle swarm optimization (CMMPPSO) algorithm based on the collaborative mechanism. In this algorithm, the multirobot path planning problem is decomposed into multiple single robot path planning problems and each subpopulation optimizes a robot separately. Select representative individuals from each subpopulation to coordinate information interaction, so as to select elite individuals as the path of multirobot system optimization. In the optimization process, not only the collision between robots and obstacles but also the collision between robots are considered and the collision evaluation function between paths is established. Simulation results show that the algorithm can better realize multirobot path planning.



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*Source: 4268589-2022-07-19.xml*