# Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Authors: Yue Zhao
Journal: Journal of Sensors
 (2022)

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## Abstract

In this paper, warehouse logistics robot as the research object, starting from the reality of modern e-commerce logistics industry, proposed a warehousing logistics mobile robot path planning method. Ant colony algorithm is used to plan the forward path of mobile robot in static and dynamic environment of warehouse logistics. The results show that the elite-based strategy improves the global search capability of ACA and eliminates redundant nodes in the path, and the path length of the obtained plan is significantly better than that of traditional ACA, which is reduced by 3.46% and 5.90%, respectively, indicating that the elite-based strategy and the central-point-based smoothing method play their roles. The path length of the robot’s final operation is larger than that of the global path planning, which increases by 1, accounting for 6.67% of the original path length. Therefore, the storage and logistics mobile robot based on ACA-E algorithm has short driving distance and superior obstacle avoidance ability.


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## Body

## 1. Introduction

In recent years, major e-commerce websites have become popular, and the amount of online consumption has been refreshed repeatedly. In particular, the annual “Double 11 Shopping Festival” has created a miracle of online consumption. This reflects that people are more and more inclined to online consumption and logistics is an essential link. The quality of logistics services will have a direct impact on people’s online consumption experience. Therefore, the efficiency and convenience of logistics is a difficult problem that must be solved [1].However, the current logistics industry still has the disadvantages of low mechanization, high human demand, and low intelligence, which brings a series of problems such as high logistics cost and low operation efficiency, which seriously restrict the development of the logistics industry and even the progress of economy and society. In the modern logistics industry, the development of warehousing logistics, as one of the main forms of logistics, is undoubtedly the main factor affecting the above problems [2]. As the operation of warehousing logistics is inseparable from control, robotics, and other related disciplines, warehousing logistics has become more and more modern and intelligent with the progress and development of these disciplines [3].The robot industry has a huge and bright future. With more and more robot application scenarios and the emergence of various advanced robots abroad, domestic manufacturers also see business opportunities and invest human and material resources in the robot industry. However, due to the characteristics of this industry, such as high development threshold, large amount of funds required, and it is difficult to obtain high returns in the short term; domestic robot enterprises cannot gain a foothold in the high-end robot industry and can only develop and produce some products with low technical content and low added value [4, 5]. However, for some domestic companies with strong R&D strength, they have invested a lot of human and financial resources in the R&D and application of mobile robot technology. Although they have also achieved corresponding results, they have not been used in large-scale distribution centers.


## 2. Literature Review

Foreign countries have carried out robot related research earlier. The world’s first mobile robot Shakey was born in Stanford Research Institute in the United States in the 1970s. It completes various tasks through program control, which is generally divided into three types of programs: low level, medium level, and high level. The functions of these three levels of programs are interrelated and influence each other to control the low-level movement and more complex movement and complete more advanced programmed tasks of the robot [6].In today’s world, the United States is one of the most developed countries, and its scientific and technological development is also in the forefront of the world. Robot technology also originated in the United States. At present, the performance of robots studied in the United States is still at the forefront of the industry [7]. Many soldiers in the United States are physically disabled and unable to walk normally due to their participation in the war, which seriously affects their daily life. In order to help these disabled soldiers and other disabled people, the U.S. military has developed a robot, which can act as the legs of the disabled and help people with legs to walk flexibly [8].In Europe, robot research is also valued, and robot research in developed countries such as Germany, Britain, and France is also at the world advanced level [9]. In Europe, the proportion of empty nesters is more serious than that in China. Therefore, the German company has launched a companion robot, which can be used as a servant to complete housework, and it is also equipped with a camera as an observation response system [10]. In addition, it also has multiple sensors installed, which can be used as part of home intelligent services. For example, it knows whether the door of the refrigerator is open and when to turn on the coffee machine. It also has a motion detector to determine where people are in the room [11].China’s robot research began in 1986. Compared with other European and American developed countries, it started late and has a weak foundation [12]. However, China has always placed robot research in an important position and has always paid attention to robot projects in the national “863 project plan.” It is gratifying that after more than 30 years of development, China has made great progress in the field of robots, trained a number of well-known robot experts and scholars at home and abroad, and developed many robots with outstanding performance [13]. The humanoid robot independently developed by the robot laboratory of the school of control of Zhejiang University is the only robot system in the world that can receive and play a rotating ball. Its higher degrees of freedom (more than 30) can realize more complex actions [14]. Jiaotong University has developed two kinds of Hexapod disaster relief robots. One has a variety of high-precision sensors, which can transmit the video and other information of the disaster area, and it is mainly responsible for the detection task. The other robot has the advantage of large load capacity, which can carry more than 600 kg of relief materials, and has strong passing capacity, which can make the post disaster relief materials even if they are transported to the disaster area [15]. The autonomous navigation system of micro nanorobot was researched and invented by researchers of Harbin Institute of technology and scholars at the University of California, San Diego. The robot can analyze the obstacles on the path in real time in the process of moving, adjust its moving route according to relevant algorithms, and realize the timely avoidance of obstacles. It can also plan its travel route in real time according to the complex environment and find the best route, so as to safely reach the target point in the shortest time [16, 17].Based on the current research, this paper mainly takes the warehousing and logistics robot as the research object and puts forward a path planning method of warehousing and logistics mobile robot from the reality of e-commerce and modern logistics industry. In this paper, ant colony algorithm is used to plan the forward path of mobile robot in the static and dynamic environment of warehousing and logistics. Firstly, this paper expounds the development status and future development trend of the robot, studies and analyzes various optimization algorithms, and selects ACA to plan the path of the mobile robot. The information concentration in the transfer probability of the algorithm process is strengthened, and the improved algorithm is applied to the path planning of warehouse logistics mobile robot.


## 3. Research Methods

### 3.1. Environmental Modeling

Environmental model is a mathematical model describing the working area of mobile robot. Scholars at home and abroad have carried out a series of research on environmental model and put forward many modeling methods. The following introduces three classical modeling methods: grid method, geometric method, and topology method [18].
(1)
In order to reduce the complexity of obstacle boundary, the grid method divides the grid size according to the size of the real mobile robot and divides the robot operating environment into grids of the same size. There are both black grids representing obstacles and white grids representing free walking in the grid, which makes the robot operating environment from complex to simple, and the path planning problem is relatively simple. Therefore, for the grid method, the most important thing is to determine the size of the divided grid, which will directly affect the operation of the algorithm and the final planning effect [19](2)
Geometric method uses geometric features (such as points, lines, and surfaces) to represent objects in the scene; abstracts the environmental information collected by the sensors carried by the robot into common geometric features, such as vertices, lines, curves, and corners; and then describes and records them with coordinates [20](3)
The expression of topological graph method is more abstract. It uses graphs to represent the spatial relationship between objects in the environment, and the nodes of the graph represent the feature points in the environment [21]. This method can be used to build a large and simple environment. The topological model can usually be expressed by(1)G=V,E,whereV represents the target object in the environment and E represents the connecting path between the target objects, which together constitute the topological map G.


### 3.2. Basic Ant Colony Algorithm

Step1. Variable initialization: The starting pointS and target point E are fixed grid numbers, and the ant colony is placed at the starting point s. Initialize the number of ants m, the number of iterations N, the heuristic factor a, the expected heuristic factor β, and the tabu table (ants have walked the path) Bk to an empty setStep 2. State transition probabilitypijk: The state transition probability formula pijk and j of ants transferring from grid point to grid point j are nodes that have not been visited. It can be expressed as
(2)pijk=τijαtηijβt∑s∈allowedkτisαtηisβt,j∈allowedk0,otherwise,whereτijt is the pheromone that the mobile robot transfers from point to point which is the trajectory intensity andηijt is the heuristic degree of mobile robot transfer from point to point, and its formula is
(3)ηijt=1dij,wheredij represents the distance between the grid i and j where the mobile robot is located.allowedk represents the grid points allowed to be selected by ant k in the next step, and its formula is
(4)allowedk=D−Bk,whereD represents the optional path node, D=0,1,2,⋯n−1, and Bk represents the taboo table, that is, the grid point that ant k has passedStep 3. Modify the tabu tableBk: Every time the ant k moves, add the moved node j to Bk, and clear the tabu table after this cycleStep 4. Repeat Steps 2 and 3: Until each ant exits the cycle and reaches the end point, calculate the increment of pheromone according to the traversal quality (fitness) of each ant, calculate the path length traveled by each ant, and save itStep 5. Update pheromoneτij, as shown in the following equation:
(5)τijt+1=1−ρτijt+Δτijt,t+1,where,(6)Δτijt,t+1=∑k=1mΔτijkt,t+1,(7)Δτijk=QLk,i,j∈lk0,Otherwise,whereQ represents the information intensity element, which will affect the overall convergence speed of the algorithm andLk is the total length of ant k’s path in this cycleStep 6. Select and save the optimal path in this iteration and outputs the optimal obstacle avoidance path. Otherwise, repeat Steps 4 and 5 until the maximum number of iterations [22, 23]


### 3.3. Improved Ant Colony Algorithm Based on Elite Strategy (ACA-E)

For the disadvantage that traditional ACA is easy to fall into local optimization, elite strategy is introduced to improve it. Elitist strategy means that after a cycle, each ant can find the optimal solution by using traditional ACA and elitist strategy gives additional compensation to its pheromone. The ant colony mainly relies on pheromone to transmit information, and the pheromone corresponding to the found optimal solution is enhanced. The purpose is that the optimal solution found in this cycle will be more attractive to the ants passing by after the pheromone enhancement operation in the next cycle of the ant, which solves the disadvantage that the ant colony algorithm is easy to enter the local optimization in the iterative process, resulting in unnecessary spikes in the path [24, 25]. The pheromone update formula is shown in the following equation:
(8)τijt+1=ρ.τijt+Δτij+Δτij∗,where,(9)Δτij∗=αQL∗,Ifedgei,jispartoftheoptimalsolution0,0,Otherwise,whereΔτij∗ represents the increase of pheromone of elite ants on path i,j;α represents elite ants and their number; andL∗ represents the total length of the path corresponding to the optimal solution found after the end of the cycle.


### 3.4. Improvement of Path Smoothing Machine Method for Mobile Robot (ACA-S)

The improved ant colony algorithm (ACA-S) based on the center point smoothing method in this paper is realized by adding new nodes at the inflection point of the path. The new nodes replace the old nodes to complete the path smoothing. The selection and addition of new nodes have a direct impact on the improvement of path smoothness and the efficiency of overall path planning. Figure1 is the operation diagram of the smoothing method based on the center point. The included angle of the original path segment is called α2. Selecting and adding new nodes and removing old nodes are related to the angle expected value α1 of the two path corners set, and the angle expected value α1 is set to 155°. If the actual corner α2 of two adjacent lines is less than or equal to α1, take the midpoint x−new1,y−new1 and x−new2,y−new2 between the feasible regions of the two line segments, and then judge whether the corner angle formed by the new node and both sides meets or is greater than the expected angle α. If not, continue the above transformation to find new inflection points x−new1,y−new1 and x−new2,y−new2. After meeting the angle conditions, delete the old inflection point, and replace it with a new inflection point. Then, judge whether other inflection points in the path meet the conditions, and repeat them continuously so that the corners of the whole path are greater than the expected angle, as shown in the following equations:
(10)x−new1=xold1+xold22y−new1=yold1+yold22,x−new1=xold2+xold32y−new2=yold2+yold32,Figure 1
Schematic diagram of smoothing method.


### 3.5. ACA-ES Algorithm

The steps of the improved ant colony algorithm (ACA-ES) based on the smoothing method of elite strategy and central point are shown in Figure2.Figure 2
Improved algorithm flow.


### 3.6. Dynamic Path Planning Process

Step 1: Modeling. The grid method is used to model the information in the environmentStep 2: Using the improved ACA to carry out global static path planning without considering the dynamic and only considering the static obstaclesStep 3: Give priority to environmental prediction by using the rolling window. If there are no obstacles in the current rolling window, move to the next according to the path. If there are dynamic obstacles, collect and sort out the detected information, and make prediction judgmentStep 4: Analyze the predicted information, and judge and select the corresponding collision avoidance strategy according to the characteristicsStep 5: Under the theoretical guidance of the corresponding collision avoidance strategy, plan a local path to ensure that the robot does not collide with dynamic obstaclesStep 6: Continue to refresh the scrolling window, and repeat Steps 3 to 5 until the robot reaches the specified target point. The flow chart is shown in Figure3.Figure 3
Flow chart of dynamic obstacle avoidance.


## 3.1. Environmental Modeling

Environmental model is a mathematical model describing the working area of mobile robot. Scholars at home and abroad have carried out a series of research on environmental model and put forward many modeling methods. The following introduces three classical modeling methods: grid method, geometric method, and topology method [18].
(1)
In order to reduce the complexity of obstacle boundary, the grid method divides the grid size according to the size of the real mobile robot and divides the robot operating environment into grids of the same size. There are both black grids representing obstacles and white grids representing free walking in the grid, which makes the robot operating environment from complex to simple, and the path planning problem is relatively simple. Therefore, for the grid method, the most important thing is to determine the size of the divided grid, which will directly affect the operation of the algorithm and the final planning effect [19](2)
Geometric method uses geometric features (such as points, lines, and surfaces) to represent objects in the scene; abstracts the environmental information collected by the sensors carried by the robot into common geometric features, such as vertices, lines, curves, and corners; and then describes and records them with coordinates [20](3)
The expression of topological graph method is more abstract. It uses graphs to represent the spatial relationship between objects in the environment, and the nodes of the graph represent the feature points in the environment [21]. This method can be used to build a large and simple environment. The topological model can usually be expressed by(1)G=V,E,whereV represents the target object in the environment and E represents the connecting path between the target objects, which together constitute the topological map G.


## 3.2. Basic Ant Colony Algorithm

Step1. Variable initialization: The starting pointS and target point E are fixed grid numbers, and the ant colony is placed at the starting point s. Initialize the number of ants m, the number of iterations N, the heuristic factor a, the expected heuristic factor β, and the tabu table (ants have walked the path) Bk to an empty setStep 2. State transition probabilitypijk: The state transition probability formula pijk and j of ants transferring from grid point to grid point j are nodes that have not been visited. It can be expressed as
(2)pijk=τijαtηijβt∑s∈allowedkτisαtηisβt,j∈allowedk0,otherwise,whereτijt is the pheromone that the mobile robot transfers from point to point which is the trajectory intensity andηijt is the heuristic degree of mobile robot transfer from point to point, and its formula is
(3)ηijt=1dij,wheredij represents the distance between the grid i and j where the mobile robot is located.allowedk represents the grid points allowed to be selected by ant k in the next step, and its formula is
(4)allowedk=D−Bk,whereD represents the optional path node, D=0,1,2,⋯n−1, and Bk represents the taboo table, that is, the grid point that ant k has passedStep 3. Modify the tabu tableBk: Every time the ant k moves, add the moved node j to Bk, and clear the tabu table after this cycleStep 4. Repeat Steps 2 and 3: Until each ant exits the cycle and reaches the end point, calculate the increment of pheromone according to the traversal quality (fitness) of each ant, calculate the path length traveled by each ant, and save itStep 5. Update pheromoneτij, as shown in the following equation:
(5)τijt+1=1−ρτijt+Δτijt,t+1,where,(6)Δτijt,t+1=∑k=1mΔτijkt,t+1,(7)Δτijk=QLk,i,j∈lk0,Otherwise,whereQ represents the information intensity element, which will affect the overall convergence speed of the algorithm andLk is the total length of ant k’s path in this cycleStep 6. Select and save the optimal path in this iteration and outputs the optimal obstacle avoidance path. Otherwise, repeat Steps 4 and 5 until the maximum number of iterations [22, 23]


## 3.3. Improved Ant Colony Algorithm Based on Elite Strategy (ACA-E)

For the disadvantage that traditional ACA is easy to fall into local optimization, elite strategy is introduced to improve it. Elitist strategy means that after a cycle, each ant can find the optimal solution by using traditional ACA and elitist strategy gives additional compensation to its pheromone. The ant colony mainly relies on pheromone to transmit information, and the pheromone corresponding to the found optimal solution is enhanced. The purpose is that the optimal solution found in this cycle will be more attractive to the ants passing by after the pheromone enhancement operation in the next cycle of the ant, which solves the disadvantage that the ant colony algorithm is easy to enter the local optimization in the iterative process, resulting in unnecessary spikes in the path [24, 25]. The pheromone update formula is shown in the following equation:
(8)τijt+1=ρ.τijt+Δτij+Δτij∗,where,(9)Δτij∗=αQL∗,Ifedgei,jispartoftheoptimalsolution0,0,Otherwise,whereΔτij∗ represents the increase of pheromone of elite ants on path i,j;α represents elite ants and their number; andL∗ represents the total length of the path corresponding to the optimal solution found after the end of the cycle.


## 3.4. Improvement of Path Smoothing Machine Method for Mobile Robot (ACA-S)

The improved ant colony algorithm (ACA-S) based on the center point smoothing method in this paper is realized by adding new nodes at the inflection point of the path. The new nodes replace the old nodes to complete the path smoothing. The selection and addition of new nodes have a direct impact on the improvement of path smoothness and the efficiency of overall path planning. Figure1 is the operation diagram of the smoothing method based on the center point. The included angle of the original path segment is called α2. Selecting and adding new nodes and removing old nodes are related to the angle expected value α1 of the two path corners set, and the angle expected value α1 is set to 155°. If the actual corner α2 of two adjacent lines is less than or equal to α1, take the midpoint x−new1,y−new1 and x−new2,y−new2 between the feasible regions of the two line segments, and then judge whether the corner angle formed by the new node and both sides meets or is greater than the expected angle α. If not, continue the above transformation to find new inflection points x−new1,y−new1 and x−new2,y−new2. After meeting the angle conditions, delete the old inflection point, and replace it with a new inflection point. Then, judge whether other inflection points in the path meet the conditions, and repeat them continuously so that the corners of the whole path are greater than the expected angle, as shown in the following equations:
(10)x−new1=xold1+xold22y−new1=yold1+yold22,x−new1=xold2+xold32y−new2=yold2+yold32,Figure 1
Schematic diagram of smoothing method.


## 3.5. ACA-ES Algorithm

The steps of the improved ant colony algorithm (ACA-ES) based on the smoothing method of elite strategy and central point are shown in Figure2.Figure 2
Improved algorithm flow.


## 3.6. Dynamic Path Planning Process

Step 1: Modeling. The grid method is used to model the information in the environmentStep 2: Using the improved ACA to carry out global static path planning without considering the dynamic and only considering the static obstaclesStep 3: Give priority to environmental prediction by using the rolling window. If there are no obstacles in the current rolling window, move to the next according to the path. If there are dynamic obstacles, collect and sort out the detected information, and make prediction judgmentStep 4: Analyze the predicted information, and judge and select the corresponding collision avoidance strategy according to the characteristicsStep 5: Under the theoretical guidance of the corresponding collision avoidance strategy, plan a local path to ensure that the robot does not collide with dynamic obstaclesStep 6: Continue to refresh the scrolling window, and repeat Steps 3 to 5 until the robot reaches the specified target point. The flow chart is shown in Figure3.Figure 3
Flow chart of dynamic obstacle avoidance.


## 4. Result Analysis

### 4.1. ACA-ES Application Test

In order to verify the feasibility and effectiveness of the improved algorithm (ACA-ES) applied to the path planning of mobile robot, the path planning simulation is carried out in the17∗17 grid environment. The parameter settings are consistent with the 20∗20 grid. The coordinates of the starting point and the ending point of the robot are (0.5, 16.5) and (16.5, 0.5). Table 1 and Figure 4 summarize and compare the path length and simulation time.Table 1
Comparison of path length.
Algorithm namePlanning path lengthTraditional ACA33.8995ACA-E32.7279ACA-ES31.8995Figure 4
Comparison of average simulation time.The elite strategy improves the global search ability of ACA and eliminates redundant nodes in the path. As shown in Table1, the path length is shortened from 33.8995 to 32.7279, 3.46%, and the number of iterations is reduced by half from 70 to 35. Secondly, the smoothing method based on the center point is introduced to further optimize the inflection point, making the path more smooth and in line with the motion characteristics of the solid robot. At the same time, the path length is shortened to 31.8995, 5.90%, and the number of iterations is reduced to 25.The data in Table1 and Figure 4 show that after the path planning passes the elite strategy (ACA-E) and adds the center point based smoothing method (ACA-ES), the planned path length is significantly better than the traditional ACA, reducing by 3.46% and 5.90%, respectively, indicating that the elite strategy and the center point based smoothing method have played their role. However, the shortening of the path is at the cost of time consumption. The improved ACA-E and ACA-ES increase the planning time by 5.48% and 4.51%, respectively.


### 4.2. Obstacle Avoidance Performance of ACA-ES

In order to verify the performance comparison of the proposed improved method with dynamic obstacles moving in a straight line in the robot operating environment, this chapter uses Matlab2012a as the algorithm development platform to conduct simulation experiments under the environment of Windows 10 operating system.The robot working environment is divided into20∗20 grids for path planning simulation. The simulation parameters are set as follows: The number of ants M is 50, the number of iterations N is 200, the heuristic factor α is 1, the expected heuristic factor β is 7, the pheromone evaporation coefficient ρ is 0.7, and the pheromone increase intensity coefficient is 1. The coordinates of the starting point and the ending point of the robot are determined to be (0.5, 19.5) and (19.5, 0.5). In this experiment, it is assumed that the moving speed of dynamic obstacles in the environment is consistent with the forward speed of the robot. The results are shown in Table 2.Table 2
Final operation of mobile robot.
Starting point positionTarget point locationInitial path lengthPath lengthPause time(0.5, 19.5)(19.5, 0.5)15.020816.02081It can be seen from the table that the length of the planned path is 67.2% longer than that of the original path, which accounts for another 67.2% of the total path length. The results in the table fully show that in the environment with dynamic obstacles, the mobile robot increases the motion cost in the process of path planning, but the performance optimization is at the cost of path length.


## 4.1. ACA-ES Application Test

In order to verify the feasibility and effectiveness of the improved algorithm (ACA-ES) applied to the path planning of mobile robot, the path planning simulation is carried out in the17∗17 grid environment. The parameter settings are consistent with the 20∗20 grid. The coordinates of the starting point and the ending point of the robot are (0.5, 16.5) and (16.5, 0.5). Table 1 and Figure 4 summarize and compare the path length and simulation time.Table 1
Comparison of path length.
Algorithm namePlanning path lengthTraditional ACA33.8995ACA-E32.7279ACA-ES31.8995Figure 4
Comparison of average simulation time.The elite strategy improves the global search ability of ACA and eliminates redundant nodes in the path. As shown in Table1, the path length is shortened from 33.8995 to 32.7279, 3.46%, and the number of iterations is reduced by half from 70 to 35. Secondly, the smoothing method based on the center point is introduced to further optimize the inflection point, making the path more smooth and in line with the motion characteristics of the solid robot. At the same time, the path length is shortened to 31.8995, 5.90%, and the number of iterations is reduced to 25.The data in Table1 and Figure 4 show that after the path planning passes the elite strategy (ACA-E) and adds the center point based smoothing method (ACA-ES), the planned path length is significantly better than the traditional ACA, reducing by 3.46% and 5.90%, respectively, indicating that the elite strategy and the center point based smoothing method have played their role. However, the shortening of the path is at the cost of time consumption. The improved ACA-E and ACA-ES increase the planning time by 5.48% and 4.51%, respectively.


## 4.2. Obstacle Avoidance Performance of ACA-ES

In order to verify the performance comparison of the proposed improved method with dynamic obstacles moving in a straight line in the robot operating environment, this chapter uses Matlab2012a as the algorithm development platform to conduct simulation experiments under the environment of Windows 10 operating system.The robot working environment is divided into20∗20 grids for path planning simulation. The simulation parameters are set as follows: The number of ants M is 50, the number of iterations N is 200, the heuristic factor α is 1, the expected heuristic factor β is 7, the pheromone evaporation coefficient ρ is 0.7, and the pheromone increase intensity coefficient is 1. The coordinates of the starting point and the ending point of the robot are determined to be (0.5, 19.5) and (19.5, 0.5). In this experiment, it is assumed that the moving speed of dynamic obstacles in the environment is consistent with the forward speed of the robot. The results are shown in Table 2.Table 2
Final operation of mobile robot.
Starting point positionTarget point locationInitial path lengthPath lengthPause time(0.5, 19.5)(19.5, 0.5)15.020816.02081It can be seen from the table that the length of the planned path is 67.2% longer than that of the original path, which accounts for another 67.2% of the total path length. The results in the table fully show that in the environment with dynamic obstacles, the mobile robot increases the motion cost in the process of path planning, but the performance optimization is at the cost of path length.


## 5. Conclusion

In the research field of mobile robot technology, the path planning of mobile robot in the storage and logistics environment has become a hot research topic for researchers from all walks of life because of the rapid rise of e-commerce industry. This paper mainly analyzes and simulates the path planning method of mobile robot in the storage and logistics environment. The research work and results completed in this paper are summarized as follows:(1)
In this paper, the existing ant colony path planning algorithm is improved, and an improved ACA with elite strategy is proposed. It is applied to the path planning of mobile robot in the storage and logistics environment, which enhances the pheromone concentration in the transfer probability of ants choosing the path in the process of moving forward. The addition of elite strategy solves the disadvantage that the traditional ACA is easy to fall into the local optimal solution, resulting in the stagnation of algorithm search, and improves the global understanding(2)
On the basis of introducing the improved ACA of elite strategy, a smoothing method based on the center point is added. This method makes the small corner in the path more smooth, reduces the energy loss of the robot at the turn of the path, ensures the stability of the robot entity in the process of moving, and solves the practical problems that the mobile robot in warehousing and logistics will encounter in the process of moving forward(3)
Aiming at the path planning of mobile robot with dynamic obstacles in the environment, the method of combining global static path planning with local dynamic collision avoidance planning is adopted, and the improved ACA is used to obtain a global optimal path



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*Source: 5757719-2022-07-13.xml*