Vibration robot group walking path optimization method based on improved raccoon algorithm

Abstract

The invention relates to the technical field of vibration robot moving path optimization, in particular to a vibration robot group walking path optimization method based on an improved raccoon algorithm. According to the method, the three-dimensional environment model of the construction site and the raccoon algorithm are ingeniously combined, and accurate optimization of the walking path of the vibrating robot is achieved. The vibrating area is divided into the construction area and the to-be-constructed area, and the vibrating robots are placed in the construction area and the to-be-constructed area respectively, so that robot resources can be managed more effectively, and efficient construction is ensured. Meanwhile, the vibration outlet is used as the optimal position, and the time energy function is used as the target function, so that the path planning is more scientific and reasonable, and the construction time consumption and cost are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of vibration robot moving path optimization, in particular to a vibration robot group walking path optimization method based on an improved raccoon algorithm. Background Art

[0002] There are three main ways to vibrate concrete at construction sites. The first is manual vibration, performed by workers using handheld vibrators. While flexible, it is less efficient and is typically used for small-scale or specialized component production. The second is mechanical vibration, which uses fixed or mobile mechanical equipment. This can significantly improve vibration efficiency and meet the needs of mass production. The technology is relatively mature and widely used. The third is automated robotic vibration, a method that has emerged in recent years. By introducing intelligent control and sensing technology, precise control of the vibration process is achieved, ensuring the density and uniformity of concrete even in complex molds. This technology combines advanced international experience to make production more automated and efficient, and has now become a key area of ​​improvement and development in construction.

[0003] On modern construction sites, vibrating robots are responsible for efficiently vibrating concrete, while concrete distribution booms precisely distribute it. During the construction process, the concrete pouring range constantly changes, requiring the vibrating robot's working area to constantly adjust to accommodate the changes in pouring range caused by the precise concrete distribution boom. This requires the entire system to possess adaptive capabilities and dynamic response to ensure efficient and seamless coordination of the vibration and distribution processes, thereby achieving uniform and stable construction quality. In this context, intelligent path planning for mobile vibrating robots becomes particularly important. Through precise algorithms and technologies, it ensures that the robot can quickly respond to environmental changes and optimize vibration efficiency and workflow.

[0004] Currently, for the optimal design of walking paths for mobile vibrating robots, most optimization methods focus on the fixed path design of a single robot. However, with the growing demand for construction, a single fixed path can no longer meet the complex requirements of multi-robot collaborative work. Research on path planning for swarms of vibrating robots is relatively immature and still faces many challenges. To achieve efficient path planning for multi-robot swarms, it is necessary to consider multiple factors, such as dynamic environmental changes, coordination between robots, and avoiding path conflicts. In order to improve overall efficiency and flexibility, it is necessary to develop a dynamic path planning algorithm that can adapt to the construction site environment in real time. Therefore, researching and innovatively developing these intelligent path planning methods and multi-robot collaboration mechanisms is an important direction for the future development of vibrating robot technology, which will greatly promote the intelligence and efficiency of construction.

[0005] There are many existing intelligent optimization algorithms. Widely used and suitable for processing walking paths, each has its own shortcomings. To meet practical development needs, experts and scholars in various fields have adapted deep reinforcement learning algorithms from artificial intelligence, resulting in several new optimization algorithms. The Raccoon Optimization Algorithm is one of these. The Raccoon Optimization Algorithm simulates the hunting behavior of raccoons. Compared to traditional optimization algorithms, it boasts stronger evolutionary capabilities, faster convergence, and higher convergence accuracy. However, during the development phase of the Raccoon Algorithm, the position update formula based on the current individual optimal value was used to update the position, making it prone to local optimality.

[0006] It can be seen that the existing walking path planning of vibrating robots still has technical problems such as optimal path design and unsatisfactory solution effect. Summary of the Invention

[0007] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a walking path optimization method for a group of vibrating robots based on an improved raccoon algorithm.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The walking path optimization method of the vibrating robot group based on the improved raccoon algorithm includes the following optimization steps:

[0010] S1. Construct a three-dimensional environmental model of the construction site, and divide the vibration area in the three-dimensional environmental model into a construction area and an area to be constructed with the vibration outlet as the boundary;

[0011] S2. Place some vibration robots in the construction area and place other vibration robots in the area to be constructed;

[0012] S3. Based on the raccoon algorithm, the vibration exit is taken as the optimal position and the time energy function is used as the objective function to calculate the optimal time required for the vibration robots in the construction area and the area to be constructed to drive out of the vibration exit according to the predetermined vibration path.

[0013] As a further solution of the present invention: the time energy function is specifically expressed as follows:

[0014]

[0015] Where E represents the total time energy function value in the current area; t i represents the total time that the i-th vibration robot moves in the current area; I represents the total number of vibration robots in the current area; η1 represents the time weight factor; η2 represents the energy consumption weight factor, and η1+η2=1; α represents the energy control coefficient; τ i(t) represents the driving torque of the driving motor of the i-th vibrating robot in the current area changing with time t; represents the angular acceleration of the drive motor of the i-th vibrating robot in the current area changing with time t; dt represents the time differential.

[0016] As a further solution of the present invention: the specific steps of step S3 are as follows:

[0017] S31. Based on the raccoon algorithm, the predetermined vibration paths of each vibration robot in the construction area are divided into N segments of sub-routes. The moving time of each segment is the same or different, thereby forming a time series T N ={t s,1 ,t s,2 ,…,t s,n ,…,t s,N}, where t s,1 It represents the time it takes for the vibrating robot to complete the first section of the sub-line in the construction area; t s,2 It represents the time it takes for the vibrating robot to complete the second section of the sub-line in the construction area; t s,n represents the time it takes for the vibrating robot to complete the nth sub-line in the construction area; t s,N represents the time it takes for the vibration robot to complete the Nth sub-line in the construction area, and the subscript s indicates the movement of the vibration robot in the construction area;

[0018] Similarly, the predetermined vibration paths of each vibration robot in the area to be constructed are divided into M segments, and the moving time of each segment is the same or different, thus forming a time series T M ={t d,1 ,t d,2 ,…,t d,m ,…,t d,M}, where t d,1 It represents the time it takes for the vibrating robot to complete the first section of the sub-line in the construction area; t d,2 It represents the time it takes for the vibrating robot to complete the second section of the sub-line in the construction area; t d,m represents the time it takes for the vibrating robot to complete the mth sub-line in the construction area; t d,M represents the time it takes for the vibration robot to complete the Mth sub-line in the area to be constructed, and the subscript d represents the movement of the vibration robot in the area to be constructed;

[0019] S32: According to the content of step S31, change the length of the sub-path to randomly generate multiple groups of time series T N and T M , thus forming the time optimization space of the construction area and the time optimization space of the area to be constructed;

[0020] S33. Select a set of time series T in the time optimization space of the construction area. N , and according to the set of time series T N Start to iterate the position of the vibrating robot in the area N times until the vibrating robot in the area just drives out of the vibrating exit under this set of time series; the position update formula of the vibrating robot in the area is expressed as follows:

[0021]

[0022] Where, represents the position of the i-th vibrating robot in the construction area at the n+1th iteration; represents the position of the i-th vibrating robot in the construction area at the nth iteration; σ represents the wavelet function perturbation factor; r represents a random number between 0 and 1; R is a constant with a value of 1 or 2; G0 represents the position of the vibrating exit;

[0023] S34, according to the content of step S33, each group of time series T N Perform N iterative position updates and select the time series T with the shortest total duration N And the total duration is recorded as T s,min ;

[0024] S35. Select a set of time series T in the time optimization space of the area to be constructed. M , and according to the set of time series T M Start to iterate the position of the vibrating robot in the area M times until the vibrating robot in the area just drives out of the vibrating exit under this set of time series; the position update formula of the vibrating robot in the area is expressed as follows:

[0025]

[0026] Where, represents the position of the i-th vibrating robot in the construction area at the m+1th iteration; represents the position of the i-th vibrating robot in the construction area at the time of the m-th iteration; E G Indicates the time energy function value at the vibration outlet, which is the set value; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ;

[0027] S36, according to the content of step S35, each group of time series T MPerform M iterative position updates and select the time series T that minimizes the total time energy function value in the area to be constructed. M , and the time series T M The total duration is recorded as T d,min ;

[0028] S37, the obtained T s,min and T d,min The sum is calculated, and the sum result is the optimal time consumption.

[0029] As a further solution of the present invention: the calculation formula of the wavelet function disturbance factor σ is specifically expressed as follows:

[0030]

[0031]

[0032] Where h represents the expansion factor; represents the expansion angle; g represents the upper limit of the expansion factor h, which is 10000; t represents the perturbation time; T represents the perturbation period; H represents the shape parameter; e represents a natural constant; ln represents the logarithmic function with base ; cos represents the cosine function.

[0033] As a further solution of the present invention: the preset vibration paths of each vibrating robot in the construction area and the preset vibration paths of each vibrating robot in the area to be constructed are staggered with each other. Therefore, when the moving paths of two vibrating robots in any area are staggered during the current iteration process, the two robots adopt a mutually beneficial symbiotic strategy to avoid each other, and after avoiding each other, a new position update formula is used to update the position.

[0034] As a further solution of the present invention, the mutually beneficial symbiotic strategy within the construction area is specifically expressed as follows:

[0035]

[0036]

[0037]

[0038]

[0039] Where, represents the position of the i-th vibration robot in the construction area after it avoids the l-th vibration robot in the n-th iteration; represents the position of the lth vibration robot in the construction area after it avoids the i-th vibration robot in the nth iteration; rand(0,1) represents a random number from 0 to 1; X bestIndicates the global optimal position; bf1 and bf2 both represent benefit factors, both of which are 1 or 2; Rmv s,i,l express and The interactive relationship between express and The interactive relationship between

[0040] The mutual benefit and symbiosis strategy in the area to be constructed is specifically expressed as follows:

[0041]

[0042]

[0043]

[0044]

[0045] Where, represents the position of the i-th vibration robot in the construction area after it avoids the l-th vibration robot in the m-th iteration; Rmv represents the position of the lth vibration robot in the construction area after it avoids the i-th vibration robot in the mth iteration; d,i,l express and The interactive relationship between express and The interactive relationship between them.

[0046] As a further solution of the present invention: based on the mutual benefit symbiosis strategy, the position update formula generated after avoiding the construction area is specifically expressed as follows:

[0047]

[0048]

[0049] Where lb local Indicates the lower bound of the position; ub local Indicates the previous position; bf indicates the benefit factor, which takes a value of 1 or 2; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ;

[0050] The position update formula generated after avoiding the construction area is specifically expressed as follows:

[0051]

[0052] As a further solution of the present invention: after updating the position using the position update formula after the collision, the time series T that minimizes the total time energy function value in the construction area is selected. N , and the time series T N The total duration is recorded as T s,min .

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. This invention cleverly combines a three-dimensional construction site environment model with the Raccoon algorithm to achieve precise optimization of the vibrating robot's path. By dividing the vibrating area into a construction area and an area to be constructed, and placing the vibrating robots in each area, this method more effectively manages robot resources and ensures efficient construction. Furthermore, by using the vibrating exit as the optimal location and the time-energy function as the objective function, path planning is more scientific and rational, effectively reducing construction time and costs.

[0055] 2. The introduction of the time-energy function allows for simultaneous consideration of two key factors, time and energy consumption, during path planning. By adjusting the time and energy weighting factors, a flexible balance between construction speed and energy consumption can be achieved to meet the needs of different construction scenarios. Furthermore, the definition of this function also considers the drive motor's torque and angular acceleration, further improving the accuracy and practicality of path planning.

[0056] 3. Through meticulous sub-route division and time series generation, a temporal optimization space is constructed for the construction area and the area to be constructed. Through multiple iterative position updates, the optimal path is gradually approached, allowing the vibrating robot to efficiently exit the vibrating exit along the predetermined path. Furthermore, this method considers the differences in travel time between different sub-routes, making path planning more flexible and adaptable.

[0057] 4. The introduction of the wavelet function perturbation factor adds randomness and diversity to the iterative position update process, helping to escape local optimal solutions and find the global optimal path. The various parameters in this calculation formula work together to generate the perturbation factor, making the iterative process more stable and efficient.

[0058] 5. When the vibrating robots' paths intersect during the iteration process, a mutually beneficial symbiotic strategy is employed to avoid collisions and conflicts, ensuring smooth construction. This strategy not only improves construction safety but also reduces delays and costs caused by collisions.

[0059] 6. The mutually beneficial symbiotic strategy formula enables the vibrator robot to more intelligently select new positions during avoidance, minimizing the impact of path adjustments on overall construction efficiency while ensuring safety. This strategy applies not only to the construction area but also to areas awaiting construction, effectively ensuring the smooth progress of the entire construction process.

[0060] 7. The position update formula based on the mutualistic symbiosis strategy combines the mutualistic symbiosis strategy with the time energy function, enabling the vibrator robot to converge to the optimal position more quickly after avoiding obstacles. Furthermore, by constraining the upper and lower bounds of the position update, the stability and convergence of the iterative process are ensured. This formula is applicable not only to construction areas but also to areas to be constructed, providing strong support for the accuracy and efficiency of path planning.

[0061] 8. After avoiding obstacles and updating the position, the method further optimizes the vibrator robot's path by selecting the time series that minimizes the total time energy function within the construction area. This strategy not only improves construction efficiency but also ensures the stability and reliability of the entire construction process. Furthermore, this step provides strong data support and decision-making basis for subsequent construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Design a flow chart for the present invention.

[0063] Figure 2 It is a schematic diagram of the three-dimensional environment model structure of the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] See also Figures 1 and 2 In an embodiment of the present invention, a walking path optimization method for a vibrating robot group based on an improved raccoon algorithm includes the following optimization steps:

[0066] 1. Divide the area

[0067] A three-dimensional environmental model of the construction site is constructed, and the vibration area in the three-dimensional environmental model is divided into a construction area and an area to be constructed based on the vibration outlet.

[0068] The terrain, obstacles and boundary data of the area are collected through sensors at the construction site. These data are integrated and converted into a dynamically updated three-dimensional environmental model to mark the walkable and dangerous areas. The three-dimensional environmental model is dynamically updated to reflect construction changes, integrating information input from multiple aspects to provide an accurate and reliable basis for the path optimization of the vibrating robot. This precise environmental information modeling not only improves the effectiveness of path planning, but also improves construction efficiency and safety. Concrete pouring information is obtained through the infrared scanner on the concrete placing boom. The vibration intensity and compliance status are detected by the vibration sensor installed on the robot. The real-time position, speed and acceleration information of the vibrating robot on the walking path are obtained in real time through the position sensor installed on the vibrating robot. Figure 2 As shown, BIM software was used to create a 3D model of the construction site, integrating information such as the target vibration area, obstacle locations, terrain, and material properties. This provided a comprehensive digital environment to optimize the vibrating robot's path planning and operation strategy. The vibration area was divided into a construction area and an area to be constructed.

[0069] 2. Place the vibrating robot

[0070] A portion of the vibrating robots are placed in the construction area, and another portion of the vibrating robots are placed in the area to be constructed. Figure 2 As described above, the No. 1 and No. 2 vibrating robots are located in the construction area, and the No. 3 and No. 4 are located in the area to be constructed. Under different working backgrounds, the vibrating robots will form different walking paths to improve construction efficiency and accuracy.

[0071] In intelligent construction site management, the integration of multiple sensing technologies not only improves the efficiency and accuracy of the vibrating robot but also facilitates data collection. An infrared scanner on the concrete placing boom captures concrete pouring information, while a vibration sensor mounted on the robot detects vibrated concrete. A position sensor obtains the vibrating robot's real-time position. This feedback is used to adjust the speed of the drive motor within the robot's motion system to maintain the optimal time sequence calculated for the robot's movement along its path.

[0072] 3. Calculation time

[0073] Based on the Raccoon algorithm, the optimal location of the vibration exit is taken as the optimal position, and the time energy function is used as the objective function to calculate the optimal time required for the vibration robots in the construction area and the area to be constructed to drive out of the vibration exit according to the predetermined vibration path. The details are as follows:

[0074] S31. Based on the raccoon algorithm, the predetermined vibration paths of each vibration robot in the construction area are divided into N segments of sub-routes. The moving time of each segment is the same or different, thereby forming a time series T N={t s,1 ,t s,2 ,…,t s,n ,…,t s,N}, where t s,1 It represents the time it takes for the vibrating robot to complete the first section of the sub-line in the construction area; t s,2 represents the time it takes for the vibrating robot to complete the second section of the sub-line in the construction area; t s,n represents the time it takes for the vibrating robot to complete the nth sub-line in the construction area; t s,N It represents the time it takes for the vibration robot to complete the Nth sub-line in the construction area, and the subscript s indicates the movement of the vibration robot in the construction area.

[0075] Similarly, the predetermined vibration paths of each vibration robot in the area to be constructed are divided into M segments, and the moving time of each segment is the same or different, thus forming a time series T M ={t d,1 ,t d,2 ,…,t d,m ,…,t d,M}, where t d,1 It represents the time it takes for the vibrating robot to complete the first section of the sub-line in the construction area; t d,2 It represents the time it takes for the vibrating robot to complete the second section of the sub-line in the construction area; t d,m represents the time it takes for the vibrating robot to complete the mth sub-line in the construction area; t d,M It represents the time it takes for the vibration robot to complete the Mth sub-line in the area to be constructed, and the subscript d represents the movement of the vibration robot in the area to be constructed.

[0076] S32: According to the content of step S31, change the length of the sub-path to randomly generate multiple groups of time series T N and T M , thus forming the time optimization space of the construction area and the time optimization space of the area to be constructed;

[0077] S33. Select a set of time series T in the time optimization space of the construction area. N , and according to the set of time series T N Start to iterate the position of the vibrating robot in the area N times until the vibrating robot in the area just drives out of the vibrating exit under this set of time series; the position update formula of the vibrating robot in the area is expressed as follows:

[0078]

[0079] Where, represents the position of the i-th vibrating robot in the construction area at the n+1th iteration; represents the position of the i-th vibrating robot in the construction area at the nth iteration; σ represents the wavelet function perturbation factor; r represents a random number between 0 and 1; R is a constant with a value of 1 or 2; G0 represents the position of the vibrating exit;

[0080] The calculation formula of the wavelet function perturbation factor σ is specifically expressed as follows:

[0081]

[0082]

[0083] Where H represents the shape parameter; e represents the natural constant; ln represents the logarithmic function with base ; cos represents the cosine function.

[0084] S34, according to the content of step S33, each group of time series T N Perform N iterative position updates and select the time series T with the shortest total duration N And the total duration is recorded as T s,min .

[0085] S35. Select a set of time series T in the time optimization space of the area to be constructed. M , and according to the set of time series T M Start to iterate the position of the vibrating robot in the area M times until the vibrating robot in the area just drives out of the vibrating exit under this set of time series; the position update formula of the vibrating robot in the area is expressed as follows:

[0086]

[0087] Where, represents the position of the i-th vibrating robot in the construction area at the m+1th iteration; represents the position of the i-th vibrating robot in the construction area at the time of the m-th iteration; E G Indicates the time energy function value at the vibration outlet, which is the set value; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; Indicates that the i-th vibrating robot in the construction area is The time energy function value at .

[0088] The time energy function is specifically expressed as follows:

[0089]

[0090] Where E represents the total time energy function value in the current area; t irepresents the total time that the i-th vibration robot moves in the current area; I represents the total number of vibration robots in the current area; η1 represents the time weight factor; η2 represents the energy consumption weight factor, and η1+η2=1; α represents the energy control coefficient; τ i (t) represents the driving torque of the driving motor of the i-th vibrating robot in the current area changing with time t; represents the angular acceleration of the drive motor of the i-th vibrating robot in the current area changing with time t; dt represents the time differential.

[0091] S36, according to the content of step S35, each group of time series T M Perform M iterative position updates and select the time series T that minimizes the total time energy function value in the area to be constructed. M , and the time series T M The total duration is recorded as T d,min .

[0092] S37, the obtained T s,min and T d,min The sum is calculated, and the sum result is the optimal time consumption.

[0093] The preset vibration paths of each vibrating robot in the construction area and the preset vibration paths of each vibrating robot in the area to be constructed are all intertwined with each other. Therefore, when the moving paths of two vibrating robots in any area intersect during the current iteration, the two robots adopt a mutually beneficial symbiotic strategy to avoid each other, and after avoiding each other, they use a new position update formula to update their positions.

[0094] The mutual benefit and symbiosis strategy within the construction area is specifically expressed as follows:

[0095]

[0096]

[0097]

[0098]

[0099] Where, represents the position of the i-th vibration robot in the construction area after it avoids the l-th vibration robot in the n-th iteration; represents the position of the lth vibration robot in the construction area after it avoids the i-th vibration robot in the nth iteration; rand(0,1) represents a random number from 0 to 1; X best Indicates the global optimal position; bf1 and bf2 both represent benefit factors, both of which are 1 or 2; Rmv s,i,l express and The interactive relationship between express and The interactive relationship between

[0100] The mutual benefit and symbiosis strategy in the area to be constructed is specifically expressed as follows:

[0101]

[0102]

[0103]

[0104]

[0105] Where, represents the position of the i-th vibration robot in the construction area after it avoids the l-th vibration robot in the m-th iteration; Rmv represents the position of the lth vibration robot in the construction area after it avoids the i-th vibration robot in the mth iteration; d,i,l express and The interactive relationship between express and The interactive relationship between them.

[0106] Based on the mutually beneficial symbiotic strategy, the position update formula generated after avoiding the construction area is specifically expressed as follows:

[0107]

[0108] Where lb local Indicates the lower bound of the position; ub local Indicates the previous position; bf indicates the benefit factor, which takes a value of 1 or 2; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ;

[0109] The position update formula generated after avoiding the construction area is specifically expressed as follows:

[0110]

[0111] After updating the position using the position update formula after the collision, select the time series T that minimizes the total time energy function value in the construction area. N, and the time series T N The total duration is recorded as T s,min .

[0112] In this embodiment, r=0.5, R=1, t=1,T=10,g=10000,H=5,substituting into the calculation formula we can get h=e 9 , substituting into the calculation formula of the wavelet function perturbation factor σ, we can get σ=0.0019. The coordinates of G0 are (15,15,5), Figure 2 In the figure, the position of vibration robot No. 1 is (1,1,2), and the position of vibration robot No. 4 is (10,1,1). The corresponding position update formula is:

[0113] X2=(7.0019,7.0019,1.5038);

[0114] X3=(4.01235,4.01235,1.75095);

[0115] X4=(5.5014, 5.5014, 1.6278).

[0116] The present invention obtains concrete pouring information through an infrared scanner on a concrete placing boom and analyzes the data to determine the pouring area. The vibration intensity and compliance status are detected by a vibration sensor installed on the robot to determine whether the work is completed. The real-time position, speed, acceleration information of the vibrating robot in the path movement, as well as the time information between adjacent nodes are obtained by the position sensor on the vibrating robot. Based on this information, the power of the driving motor in the robot mobile system is adjusted in real time, the real-time acceleration of the vibrating robot is dynamically adjusted, and the dynamic optimization of the walking path of the vibrating robot group is completed through the improved raccoon algorithm, ultimately achieving a mobile vibrating robot group system with short overall time and low energy consumption, shortening the construction period to a certain extent and improving construction efficiency.

[0117] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A walking path optimization method for a group of vibrating robots based on an improved raccoon algorithm, characterized in that: The optimization steps include: S1. Construct a three-dimensional environmental model of the construction site, and divide the vibration area in the three-dimensional environmental model into a construction area and an area to be constructed with the vibration outlet as the boundary; S2. Place some vibration robots in the construction area and place other vibration robots in the area to be constructed; S3. Based on the raccoon algorithm, the vibration exit is taken as the optimal position and the time energy function is used as the objective function to calculate the optimal time required for the vibration robots in the construction area and the area to be constructed to drive out of the vibration exit according to the predetermined vibration path.

2. The walking path optimization method of a vibrating robot group based on the improved raccoon algorithm according to claim 1 is characterized in that: The time energy function is specifically expressed as follows: Where E represents the total time energy function value in the current area; t i represents the total time that the i-th vibration robot moves in the current area; I represents the total number of vibration robots in the current area; η1 represents the time weight factor; η2 represents the energy consumption weight factor, and η1+η2=1; α represents the energy control coefficient; τ i (t) represents the driving torque of the driving motor of the i-th vibrating robot in the current area changing with time t; represents the angular acceleration of the drive motor of the i-th vibrating robot in the current area changing with time t; dt represents the time differential.

3. The walking path optimization method of a vibration robot group based on the improved raccoon algorithm according to claim 1 or 2, characterized in that: The specific steps of step S3 are as follows: S31. Based on the raccoon algorithm, the predetermined vibration paths of each vibration robot in the construction area are divided into N segments of sub-routes. The moving time of each segment is the same or different, thereby forming a time series T N ={t s,1 ,t s,2 ,…,t s,n ,…,t s,N }, where t s,1 It represents the time it takes for the vibrating robot to complete the first section of the sub-line in the construction area; t s,2 represents the time it takes for the vibrating robot to complete the second section of the sub-line in the construction area; t s,n represents the time it takes for the vibrating robot to complete the nth sub-line in the construction area; t s,N represents the time it takes for the vibration robot to complete the Nth sub-line in the construction area, and the subscript s indicates the movement of the vibration robot in the construction area; Similarly, the predetermined vibration paths of each vibration robot in the area to be constructed are divided into M segments, and the moving time of each segment is the same or different, thus forming a time series T M ={t d,1 ,t d,2 ,…,t d,m ,…,t d,M }, where t d,1 It represents the time it takes for the vibrating robot to complete the first section of the sub-line in the construction area; t d,2 It represents the time it takes for the vibrating robot to complete the second section of the sub-line in the construction area; t d,m represents the time it takes for the vibrating robot to complete the mth sub-line in the construction area; t d,M represents the time it takes for the vibration robot to complete the Mth sub-line in the area to be constructed, and the subscript d represents the movement of the vibration robot in the area to be constructed; S32: According to the content of step S31, change the length of the sub-path to randomly generate multiple groups of time series T N and T M , thus forming the time optimization space of the construction area and the time optimization space of the area to be constructed; S33. Select a set of time series T in the time optimization space of the construction area. N , and according to the set of time series T N Start to iterate the position of the vibrating robot in the area N times until the vibrating robot in the area just drives out of the vibrating exit under this set of time series; the position update formula of the vibrating robot in the area is expressed as follows: Where, represents the position of the i-th vibrating robot in the construction area at the n+1th iteration; represents the position of the i-th vibrating robot in the construction area at the nth iteration; σ represents the wavelet function perturbation factor; r represents a random number between 0 and 1; R is a constant with a value of 1 or 2; G0 represents the position of the vibrating exit; S34, according to the content of step S33, each group of time series T N Perform N iterative position updates and select the time series T with the shortest total duration N And the total duration is recorded as T s,min ; S35. Select a set of time series T in the time optimization space of the area to be constructed. M , and according to the set of time series T M Start to iterate the position of the vibrating robot in the area M times until the vibrating robot in the area just drives out of the vibrating exit under this set of time series; the position update formula of the vibrating robot in the area is expressed as follows: Where, represents the position of the i-th vibrating robot in the construction area at the m+1th iteration; represents the position of the i-th vibrating robot in the construction area at the time of the m-th iteration; E G Indicates the time energy function value at the vibration outlet, which is the set value; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; S36, according to the content of step S35, each group of time series T M Perform M iterative position updates and select the time series T that minimizes the total time energy function value in the area to be constructed. M , and the time series T M The total duration is recorded as T d,min ; S37, the obtained T s,min and T d,min The sum is calculated, and the sum result is the optimal time consumption.

4. The walking path optimization method of a vibrating robot group based on the improved raccoon algorithm according to claim 3 is characterized in that: The calculation formula of the wavelet function perturbation factor σ is specifically expressed as follows: Where h represents the expansion factor; represents the telescopic deflection angle; g represents the upper limit of the telescopic factor h; t represents the disturbance time; T represents the disturbance period; H represents the shape parameter; e represents the natural constant; ln represents the logarithmic function with base ; cos represents the cosine function.

5. The walking path optimization method of a vibrating robot group based on the improved raccoon algorithm according to claim 4 is characterized in that: The preset vibration paths of each vibrating robot in the construction area and the preset vibration paths of each vibrating robot in the area to be constructed are all intertwined with each other. Therefore, when the moving paths of two vibrating robots in any area intersect during the current iteration, the two robots adopt a mutually beneficial symbiotic strategy to avoid each other, and after avoiding each other, they use a new position update formula to update their positions.

6. The walking path optimization method of a vibrating robot group based on the improved raccoon algorithm according to claim 5 is characterized in that: The mutual benefit and symbiosis strategy within the construction area is specifically expressed as follows: Where, represents the position of the i-th vibration robot in the construction area after it avoids the l-th vibration robot in the n-th iteration; represents the position of the lth vibration robot in the construction area after it avoids the i-th vibration robot in the nth iteration; rand(0,1) represents a random number from 0 to 1; X best Indicates the global optimal position; bf1 and bf2 both represent benefit factors, both of which are 1 or 2; Rmv s,i,l express and The interactive relationship between express and The interactive relationship between The mutual benefit and symbiosis strategy in the area to be constructed is specifically expressed as follows: Where, represents the position of the i-th vibration robot in the construction area after it avoids the l-th vibration robot in the m-th iteration; Rmv represents the position of the lth vibration robot in the construction area after it avoids the i-th vibration robot in the mth iteration; d,i,l express and The interactive relationship between express and The interactive relationship between them.

7. The walking path optimization method of a vibrating robot group based on the improved raccoon algorithm according to claim 6 is characterized in that: Based on the mutually beneficial symbiotic strategy, the position update formula generated after avoiding the construction area is specifically expressed as follows: Where lb local Indicates the lower bound of the position; ub local Indicates the previous position; bf indicates the benefit factor, which takes a value of 1 or 2; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; Indicates that the i-th vibrating robot in the construction area is The time energy function value at ; The position update formula generated after avoiding the construction area is specifically expressed as follows:

8. The walking path optimization method of a vibrating robot group based on the improved raccoon algorithm according to claim 7 is characterized in that: After updating the position using the position update formula after the collision, select the time series T that minimizes the total time energy function value in the construction area. N , and the time series T N The total duration is recorded as T s,min .

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