A cold recycling rolling path intelligent planning system for multi-machine cooperative operation

Abstract

The application relates to the technical field of intelligent construction scheduling and path planning, and discloses a cold regeneration rolling path intelligent planning system for multi-machine cooperative operation, which comprises the following modules: a working area modeling module, which is used for acquiring the boundary of a construction area, terrain elevation data and compaction task density information of each position in the area; a metric tensor construction module, which generates a metric tensor of the construction area based on the terrain elevation data and the compaction task density information, and constructs a two-dimensional Riemann manifold model with weights; and a path cost construction module, which constructs a path cost functional based on the two-dimensional Riemann manifold model and the compaction task density information. The application realizes the construction effect of reasonable working path, efficient device scheduling, smooth and executable path and real-time adaptability by optimizing the geodesic path, dynamically allocating and scheduling the multi-machine and reconstructing the path in combination with the device kinematics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent construction scheduling and path planning, in particular to a cold regeneration rolling path intelligent planning system for multi-machine cooperative operation. BACKGROUND

[0002] In road maintenance and repair construction, cold regeneration technology has been widely used due to its energy saving, environmental protection, high construction efficiency and other advantages; especially in large-area pavement disease treatment, mine road repair and other scenes, using cold regeneration roller group cooperative operation can efficiently complete large-area construction task in a short time. In order to improve the overall construction efficiency, reduce energy consumption and cost, reasonable planning of the operation path and cooperative sequence of each roller becomes the core problem in construction organization.

[0003] In the prior art, various path planning and scheduling algorithms have been proposed to improve the operation efficiency of construction equipment; some methods use path division technology based on Euclidean distance, which can quickly generate basic path allocation results under flat working conditions. Some other technologies use static task allocation model to divide the working area according to the number of equipment, which simplifies the scheduling process. These technologies have certain application advantages in scenes with single construction condition and small terrain changes, which can effectively reduce path overlap, shorten part of the operation time, improve equipment utilization and reduce part of the empty running distance.

[0004] However, in complex construction scenes, the existing technology still has obvious shortcomings; the traditional path division method cannot consider the task density difference and terrain complexity, which may cause some equipment to be idle for a long time. The static allocation model ignores the starting position of the roller and the dynamic construction progress, which often leads to chaotic equipment operation sequence and frequent waiting on site. Some path reconstruction algorithms do not incorporate the kinematic characteristics of the roller, which often causes the equipment to face frequent turning or even cannot execute sharp turns. Many existing solutions cannot correct the path arrangement in time after the construction condition changes, which eventually causes a significant reduction in the overall construction organization efficiency. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides a cold regeneration rolling path intelligent planning system for multi-machine cooperative operation, which solves the problems of the prior art that the path planning cannot consider the terrain complexity and task density difference, the multi-machine operation sequence arrangement is chaotic, the path reconstruction does not conform to the kinematic constraints of the equipment, and the real-time dynamic adjustment capability is lacking.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a cold regeneration rolling path intelligent planning system for multi-machine cooperative operation, comprising:

[0007] a work area modeling module configured to obtain a boundary of a construction area, terrain elevation data, and compaction task density information at each position in the construction area;

[0008] a metric tensor construction module configured to generate a metric tensor of the construction area based on the terrain elevation data and the compaction task density information, and to construct a two-dimensional Riemannian manifold model with a weight metric;

[0009] a path cost construction module configured to construct a path cost functional based on the two-dimensional Riemannian manifold model and the compaction task density information, the path cost functional being used to describe a comprehensive cost required for a road roller to move in the construction area;

[0010] an optimal path calculation module configured to determine an optimal work path of the road roller based on the path cost functional through a geodesic path principle;

[0011] a numerical solution and reconstruction module configured to solve and reconstruct the optimal work path on a discrete area into a continuous smooth path to meet actual control constraints of the road roller;

[0012] a multi-machine path allocation module configured to allocate the optimal work path to different road rollers according to starting positions of the different road rollers and determine work sequences of the different road rollers to form a cooperative work scheduling scheme.

[0013] Preferably, the work area modeling module comprises:

[0014] a terrain data acquisition unit configured to obtain elevation data of the construction area and perform grid processing on the elevation data;

[0015] a task density generation unit configured to map construction compaction task requirements into density values at each position in the construction area to form a compaction task density distribution function .

[0016] Preferably, the metric tensor construction module comprises:

[0017] a gradient calculation unit configured to calculate a gradient based on the terrain elevation data;

[0018] a metric weight construction unit configured to construct a metric tensor on a two-dimensional area by taking the gradient and the task density as parameters ;

[0019] wherein the metric tensor satisfies:

[0020] ;

[0021] wherein, 、 is a weight coefficient; is a gradient vector of a terrain function ; and a metric tensor constructed at position a metric tensor constructed at position a compactness task density at point , representing the construction intensity or task weight of the position; a unit matrix.

[0022] Preferably, the gradient calculation unit comprises:

[0023] a first data input subunit for receiving terrain elevation data and mapping it to a regular grid coordinate system;

[0024] a derivative calculation subunit for calculating the spatial first-order derivative of each grid point using the finite difference method;

[0025] a slope vector generation subunit for generating a slope vector field according to the derivative result and outputting it to the metric weight construction unit as an input parameter.

[0026] Preferably, the path cost construction module comprises:

[0027] a path cost definition unit for constructing a functional form for calculating the path cost of the road roller, the functional form being determined by the combination of path length and task density value on the path;

[0028] a cost function input unit for extracting the density distribution and metric information required by the path from the metric tensor construction module and the work area modeling module as cost function calculation parameters;

[0029] a weight control unit for setting the weighting proportion of different parameters in the path cost and supporting the configuration mode of adjusting the parameter coefficients according to the construction needs.

[0030] Preferably, the cost function input unit comprises:

[0031] a task density extraction subunit for extracting the local density value of the path passing area from the compaction task density distribution function;

[0032] a tensor parameter extraction subunit for extracting the tensor parameters of the corresponding position of the path point from the metric tensor and performing coordinate system transformation to meet the path modeling requirements;

[0033] an interpolation processing subunit for performing bilinear interpolation between the path point column and the input data.

[0034] Preferably, the optimal path calculation module comprises:

[0035] a path optimization unit for constructing the corresponding Euler-Lagrange equation according to the path cost functional;

[0036] A path solving unit is configured to determine a differential form of the path solution based on the principle of geodesic line and the shortest path condition.

[0037] Preferably, the numerical solution and reconstruction module comprises:

[0038] A discrete solving unit is configured to solve a path propagation time field according to a fast marching algorithm.

[0039] A path backtracking unit is configured to generate a path point list by backtracking from a path end point along a gradient direction.

[0040] A path smoothing unit is configured to introduce a second-order regularization term on the path point list for curvature constraint processing.

[0041] Preferably, the path smoothing unit is configured to optimize based on the following objective function:

[0042] ;

[0043] In the formula, represents a path smoothing objective function value, and represents a path overall curvature variation metric; is a differential integral variable; represents a second-order derivative of the path, reflecting the curvature variation of the path, and the objective is to minimize the curvature of the path to ensure smooth transition of the path.

[0044] Preferably, the multi-machine path allocation module comprises:

[0045] A path task division unit is configured to divide a set of work paths.

[0046] A road roller state matching unit is configured to allocate path subsets to corresponding road rollers according to initial positions of the road rollers and allocation principles.

[0047] A work order determination unit is configured to determine a work order according to spatial distance sorting of the road rollers and the paths.

[0048] The present application provides a cold recycling compaction path intelligent planning system for multi-machine cooperative work.

[0049] 1、The present application adopts an optimal path calculation technical scheme based on a path cost functional and a geodesic path principle, achieves the technical effect of optimizing road roller work paths, and compared with a simple Euclidean distance calculation method in the prior art, the present application realizes calculation of a globally optimal path by considering comprehensive influences of a terrain and a task density, solves problems of local optimality and low work efficiency often occurring in traditional methods.

[0050] 2、The present application adopts a multi-machine cooperative path allocation technical scheme, achieves the technical effect of optimizing the road roller operation resource configuration, compared with the single-machine path planning scheme in the prior art, the present application ensures the balance and operation efficiency of multi-device operation through intelligent path division and operation order adjustment, solves the problems of resource conflict and uneven operation load in the cooperation process of multiple road rollers.

[0051] 3、The present application adopts a numerical solution and path reconstruction technical scheme, achieves the technical effect of reconstructing the discrete path into a continuous smooth path, compared with the scheme in the prior art that ignores the actual control requirements of the device during path planning, the present application introduces the control constraints of the road roller in the path reconstruction process, effectively avoids the operation difficulty problem caused by discontinuous or too steep path in actual operation of the device.

[0052] 4、The present application adopts a dynamic path updating and scheduling technical scheme, achieves the technical effect of improving the path adaptability and real-time adjustment capability, compared with the static path planning scheme in the prior art, the present application can dynamically adjust the operation path according to the construction progress and real-time feedback, ensures that the road roller can always operate with the optimal path in the complex construction environment, solves the problem that the traditional technology cannot cope with the change of construction environment. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 It is a system architecture diagram of the present application;

[0054] Figure 2 It is a work area modeling module framework diagram of the present application;

[0055] Figure 3 It is a metric tensor construction module framework diagram of the present application;

[0056] Figure 4 It is a path cost construction module framework diagram of the present application;

[0057] Figure 5 It is an optimal path calculation module framework diagram of the present application;

[0058] Figure 6 It is a numerical solution and reconstruction module framework diagram of the present application;

[0059] Figure 7 It is a multi-machine path allocation module framework diagram of the present application. DETAILED DESCRIPTION

[0060] With reference to the drawings, the technical solutions in the embodiments of the present application will be clearly and completely described in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0061] Please refer to the drawings of the present application Figure 1 -attached Figure 7 The embodiments of the present application provide a cold regeneration rolling path intelligent planning system for multi-machine cooperative operation, comprising:

[0062] The work area modeling module is configured to obtain the boundary of the construction area, the terrain elevation data, and the compaction task density information of each position in the area.

[0063] The work area modeling module is mainly configured to obtain the boundary information of the construction area, the terrain elevation data, and generate the compaction task density distribution of each position in the area. Through these information, the subsequent path cost calculation, optimal path solving and multi-machine cooperative scheduling are provided with basic data support. The work area modeling module is the first step in the entire path planning system, and provides high-quality input data for the subsequent modules through accurate data modeling. The specific implementation of the work area modeling module in the embodiment will be described in detail below.

[0064] The work area modeling module includes a terrain data acquisition unit and a task density generation unit. The terrain data acquisition unit is configured to obtain the elevation data of the construction area, and convert these data into a digital elevation model (DEM) suitable for path planning. Specifically, the terrain data acquisition unit can obtain the elevation data through different measurement methods, such as using unmanned aerial vehicle aerial survey, GPS RTK technology or laser radar scanning, etc. The elevation data obtained through these methods can be used to construct the digital elevation model of the area for use in subsequent steps.

[0065] Generally, the obtained elevation data will be divided into a regular grid format for further processing. The elevation value in each grid cell represents the height of that position. Through these elevation values, the slope and other related terrain features of the construction area can be calculated, and then input is provided for the path cost construction module.

[0066] In one possible implementation, the elevation data obtained by the terrain data acquisition unit through unmanned aerial vehicle or satellite image will be gridized. Specifically, the obtained elevation data can be converted into a uniform grid coordinate through data fitting techniques such as least squares method, forming a regular grid. The elevation value of each cell of the grid can be represented by a two-dimensional function, where the elevation value of each coordinate point is These elevation data are one of the input data of the subsequent metric tensor construction module.

[0067] As an option, the task density generation unit in the operation area modeling module is configured to map the construction compaction task requirements to a compaction density distribution at each location within the area. Specifically, the task density generation unit divides the entire construction area into a number of grids, and the compaction task density value within each grid reflects the intensity of compaction treatment required in that area. The task density is a function closely related to the terrain elevation data, which can be generated by construction design requirements, historical construction data, or field measurement data.

[0068] In some embodiments, the task density generation unit assigns a density value to each grid area according to different construction requirements. For example, for special areas such as soft soil foundation and disease repair area, the density value of the compaction task is higher, while for flat terrain and good material area, the compaction task density is lower. The specific calculation of the task density distribution function can be obtained by interpolation or fitting method. The generated task density distribution function not only depends on the boundary of the construction area, but also considers the terrain undulation within the area and the task requirements of the specific area.

[0069] In this embodiment, the compaction task density can be calculated by the following formula:

[0070] ;

[0071] In the formula, is the task density generation function; is the elevation data of the area; , is a parameter related to construction requirements; represents the compaction task density at the position point in the area.

[0072] In some embodiments, the task density generation unit can further optimize the density distribution function by combining other construction parameters such as soil type, construction time, equipment performance, etc. Through the refined task density distribution, it can ensure that the rolling intensity and effect of different areas are fully considered in actual operation, avoiding uneven construction or excessive load on equipment.

[0073] As an option, the processing method of the terrain data acquisition unit and the task density generation unit in the operation area modeling module is flexible and adjustable, which can be adaptively adjusted according to different types of construction tasks. The design of this module has good expansibility and adaptability, which can meet the needs of different construction environments.

[0074] A metric tensor construction module generates a metric tensor of the construction area based on the terrain elevation data and the compaction task density information, and constructs a two-dimensional Riemannian manifold model with weighted metrics;

[0075] The metric tensor construction module is used to generate the metric tensor for the construction area and construct a weighted metric two-dimensional Riemannian manifold model based on terrain elevation data and compaction task density information. Through this module, the system can accurately reflect the terrain characteristics and compaction task requirements of the construction area, thereby providing more accurate input data for subsequent path planning and cost calculation. The construction of the metric tensor is a critical step in the entire path planning process, as it affects the calculation of path costs and the determination of the optimal path. The metric tensor model can organically combine terrain and task density information to generate a path planning solution that better meets actual operational needs.

[0076] In this embodiment, the metric tensor construction module includes a gradient calculation unit and a metric weight construction unit. The gradient calculation unit calculates the slope of each location based on the terrain elevation data of the work area, while the metric weight construction unit combines task density information with slope information to generate a metric tensor for each location in the construction area.

[0077] In some embodiments, the workflow of the metric tensor building module is as follows: First, the gradient calculation unit uses the terrain elevation data obtained from the work area modeling module to calculate the , calculate the slope by finite difference method, that is, calculate the elevation function This calculation method obtains slope information at each grid point, representing the rate of elevation change per unit horizontal distance. Slope information is an important indicator of the terrain characteristics of the construction area. It reflects the steepness of the terrain and has a significant impact on factors such as energy consumption and construction difficulty in path planning.

[0078] Specifically, the gradient calculation unit is based on the elevation data , use the following formula to calculate each position Gradient:

[0079] ;

[0080] Where, and Elevation functions exist and Through these partial derivatives, the system can obtain the slope size and direction information of each position, thus providing basic data for the subsequent metric tensor construction.

[0081] As an alternative, the metric weight construction unit combines the slope information with the compaction task density information to construct a metric tensor. In this implementation, the metric tensor not only reflects the terrain slope, but also takes into account the compaction task density at different positions in the region , so as to make more accurate calculation of the path cost. Specifically, the construction of the metric tensor takes the following form:

[0082] ;

[0083] wherein, , is a weight coefficient; is the gradient vector of the terrain function ; is the metric tensor constructed at position ; is the compaction task density at point , representing the construction intensity or task weight of the position; is the unit matrix.

[0084] In general, the construction of the metric tensor can accurately describe the construction difficulty and task demand of each regional position. When the regional slope is large or the task density is high, the metric tensor value of the corresponding position will be large, indicating that the path cost of the position is high. Conversely, the metric tensor value of a position with a flat slope or low task density is small, and the path cost is low. In this way, the metric tensor model not only takes into account the construction terrain, but also takes into account the compaction intensity demand of different regions, thereby providing a reliable basis for subsequent path cost calculation.

[0085] Specifically, in this embodiment, the construction model of the metric tensor is a two-dimensional Riemannian manifold with weighted metric. Riemannian manifold is a smooth surface that defines the distance between any two points in space, while the weighted metric allows the distance of different positions to be weighted to reflect the construction difficulty of different regions. In this way, the metric tensor construction module combines the terrain information of the construction region with the task demand information to generate a metric model that meets the actual operation demand.

[0086] Through the metric tensor construction module, the system can take into account the terrain and task density of different regions during path planning, ensuring that the planned path not only guarantees construction efficiency, but also reduces the working burden of the equipment, achieving energy saving and uniform rolling effect.

[0087] The path cost construction module constructs a path cost functional based on the two-dimensional Riemannian manifold model and the compaction task density information, wherein the path cost functional is used to describe the comprehensive cost required for the road roller to move in the construction region.

[0088] A construction area modeling method based on two-dimensional Riemannian manifold has been constructed to represent the variation characteristics of complex terrain or structural surface in space, and to provide multi-source heterogeneous data support in combination with compaction task density information. On this basis, in order to further realize path optimization scheduling, a path cost functional capable of quantifying the cost required by different paths needs to be designed. The path cost functional should comprehensively consider the terrain resistance, compaction task intensity distribution and equipment operating state in the operation of the road roller, so as to provide accurate quantitative basis for subsequent path optimization.

[0089] Generally, the selection of the moving path of the road roller in the construction area directly affects the work efficiency and construction quality, therefore, a cost function model with global constraint ability and geographical adaptability needs to be constructed. As an option, the non-Euclidean geometric structure of the construction surface is introduced into the path evaluation framework by introducing the Riemannian metric tensor, and a path cost functional is constructed to describe the moving resistance of the road roller on the diversified terrain.

[0090] In the embodiment, the path cost construction module is based on the two-dimensional Riemannian manifold model and the compaction task density function , and defines the path cost functional as follows:

[0091] ;

[0092] In the formula, is the path cost functional; represents the path of the road roller in the construction area; represents the density distribution function of the compaction task at the coordinate point ; represents the Riemannian metric tensor defined on the two-dimensional manifold of the construction area; represents the first component of the first-order derivative of the path at the time ; , , are weight coefficients respectively controlling the influence of the task density and the path length on the overall cost; is the velocity component in the th direction; is a differential integral variable.

[0093] Specifically, the first term in the path cost functional is used to reflect the density intensity of the compaction task in the area passed by the path, and the greater the value, the higher the compaction load that the path needs to bear; the second term reflects the geometric length of the path on the Riemannian manifold, considering the influence factors of terrain undulation and ground resistance.

[0094] In one possible implementation, to improve the accuracy and adaptability of the model, the above metric tensor may further consist of the following expression:

[0095]

[0096] wherein, is a unit matrix component; represents the slope information of the construction terrain in the direction of the ; is the component of the Riemann metric tensor at the two-dimensional coordinate point; is a weight coefficient, which controls the strength of the tensor perturbation term composed of the terrain slope.

[0097] is a terrain influence factor, which is used to adjust the proportion of terrain information in the overall path cost.

[0098] In some embodiments, the compaction task density function may be obtained based on historical compaction data, construction blueprints, and foundation feedback information obtained by sensors. The function takes a larger value in high task areas, reflecting that the area has heavy compaction work tasks and high operation frequency, and puts forward higher requirements for path selection.

[0099] In addition, after constructing the path cost functional, the module can perform path search through the shortest path algorithm. This algorithm can quickly approximate the optimal path under the Riemannian manifold structure, significantly improving the real-time response capability of the path planning module.

[0100] In further extended applications, the path cost construction module can also introduce a dynamic updating mechanism. When the compaction density changes as the construction progresses, the path cost functional can also be adjusted in real time, thereby realizing adaptive re-optimization of the path. This dynamic feedback mechanism is particularly effective in large construction areas.

[0101] The optimal path calculation module determines the optimal operation path of the road roller based on the path cost functional and the geodesic path principle;

[0102] The optimal path calculation module determines the optimal operation path of the road roller in a complex construction area based on the path cost functional and the geodesic path principle. This module closely cooperates with the operation area modeling module, the metric tensor construction module, and the path cost construction module to realize path optimization. The cost defined by the path cost functional contains the geometric characteristics of the terrain and the density distribution information of the compaction task, and through the geodesic path principle, the operation path of the road roller is optimized, ensuring the minimization of the path cost and the optimization of the operation efficiency.

[0103] ​Generally, the construction area has complex terrain and compaction task density distribution. In order to meet the requirements of the efficiency and quality of the road roller, the path planning must consider the influence of the terrain change and the task density. Therefore, in the path cost construction module, a two-dimensional Riemannian manifold model with weighted measurement has been constructed by the terrain elevation data and the compaction task density information, which provides the necessary cost calculation basis for the subsequent optimal path calculation module.

[0104] Specifically, the working principle of the optimal path calculation module is based on the theory of geodesic path. By using the Euler-Lagrange equation to describe the moving track of the road roller in the construction area, the optimization objective given by the path cost functional is used. The solution of the geodesic path ensures the minimization of the path cost, which meets the multiple requirements of the path optimization of the construction task. In this process, the two important components of the path cost functional, the task density and the terrain resistance, will be considered together to ensure the optimal driving route of the road roller in complex terrain.

[0105] In one possible implementation, the optimal path calculation module solves the optimal form of the path based on the path cost functional through the geodesic path principle by numerical solution. Specifically, the module first calculates the local variation of the path, and by solving the Euler-Lagrange equation, the differential form of the path is obtained. At this time, the solution of the differential equation will give the optimal distribution of the path, that is, the shortest cost path from the starting point to the end point in the construction area.

[0106] In some embodiments, in order to improve the calculation accuracy and efficiency, the optimal path calculation module can use numerical integration method to update the position of the path point at each time step, so as to continuously approach the optimal path. These calculation steps finally determine the optimal operation path of the road roller according to the aforementioned metric tensor model and compaction task density information, combined with the geometric constraints of the geodesic line.

[0107] Through the optimal path calculation module, the system can effectively avoid the local optimal solution problem in the traditional path planning method, thereby realizing the calculation of the global optimal path and significantly improving the operation efficiency and construction quality of the road roller. For large-scale construction projects, this path planning method provides strong support, ensures the optimal allocation of construction resources, and reduces the operation time and energy consumption.

[0108] As an option, the embodiment can further introduce a path smoothing processing module to optimize the calculated optimal path to reduce the burden on the road roller caused by excessive curvature of the path during operation. In this process, the second derivative of the path will be used as the basis for smoothing optimization to ensure the continuity and smoothness of the path, further improving the operability of the path and the stability of the operation.

[0109] In some embodiments, the path optimization module can also dynamically adjust the path according to real-time feedback information to adapt to different construction environments and task requirements. During the construction process, as the terrain changes and the compaction task density updates, the optimal path calculation module can achieve dynamic optimization of the path to respond to the changing construction conditions.

[0110] The numerical solution and reconstruction module is used to solve the optimal work path in discrete areas and reconstruct it into a continuous smooth path, meeting the actual control constraints of the roller;

[0111] The numerical solution and reconstruction module is used to solve the optimal work path in discrete areas and reconstruct it into a continuous smooth path, ensuring that the actual control constraints of the roller are met. The function of this module is to convert the discrete representation of the optimal path into a smooth continuous path suitable for actual operation, avoiding the difficulty of roller operation due to discontinuous or excessively steep paths, and ensuring the smoothness and operability of the path. The numerical solution and reconstruction module is closely linked with the aforementioned optimal path calculation module, relying on the optimal path data calculated by the path cost functional, and combining the actual control requirements for path reconstruction and smoothing.

[0112] Generally, the path given by the optimal path calculation module is represented on a discrete point set, which needs to be further processed by the numerical solution and reconstruction module to convert it into a continuous and smooth path. This process is to meet the driving needs of the actual roller in complex construction environments, avoiding the situation that the path is too tortuous or changes sharply, causing the equipment to be unable to travel smoothly.

[0113] Specifically, the numerical solution and reconstruction module mainly consists of two steps: discrete solution and path reconstruction. The discrete solution step calculates the propagation time field of the path on the discrete grid through the fast marching algorithm or similar methods to ensure that each part of the path can complete the compaction task within a reasonable time. The path backtracking unit traces the path from the end point to the starting point along the gradient direction according to the propagation time field information to determine the specific point list of the path.

[0114] In one possible implementation, the path backtracking unit starts from the end point of the path and gradually traces the path points in reverse, until it reaches the starting point of the path, based on the fastest propagation path algorithm. At this time, the path point list will be generated according to the path cost and the terrain information of the construction area. In this way, the geometric shape of the optimal path can be accurately restored and further meet the control requirements of the roller operation.

[0115] In some embodiments, to ensure the smoothness of the path, the numerical solution and reconstruction module also introduces a path smoothing process. During the reconstruction of the path point list, the path smoothing unit will perform curvature optimization on the path based on the smoothing objective function, reducing unnecessary sharp turns or excessive curvature on the path. This smoothing process optimizes by introducing the second derivative of the path (i.e., the curvature of the path), with the goal of reducing the curvature variation of the path, thereby making the path smoother and facilitating the actual control of the road roller. Specifically, the path smoothing objective function can be expressed as:

[0116] ;

[0117] wherein, represents the path smoothing objective function value, represents the path overall curvature variation measure of the path; is a differential integral variable; represents the second derivative of the path, reflecting the curvature variation of the path, with the goal of minimizing the curvature of the path to ensure smooth transition of the path.

[0118] In some embodiments, the path smoothing process can adjust the degree of smoothing of the path according to specific control constraints of the road roller (such as maximum turning radius or device stability requirements). If the terrain of the area through which the path passes is complex, a weighting factor can be added to the smoothing process to adaptively adjust the path under specific terrain conditions.

[0119] As an option, the present embodiment can also dynamically introduce actual control parameters of the road roller during path reconstruction, such as the maximum turning angle of the device, the minimum turning radius, and other information, to ensure that the final path can complete the work task without exceeding the operating capacity of the device. The smoothing and reconstruction of the path not only considers geometric constraints, but also combines the physical performance of the road roller, making the path more in line with the actual operating requirements of the device.

[0120] In further applications, the numerical solution and reconstruction module can also dynamically optimize the path in combination with real-time feedback information. For example, as the construction area changes gradually, the work path required by the road roller may change. By updating the path cost function in real time and reconstructing and smoothing the path, the system can adaptively adjust the path to always maintain the optimal state and meet the needs of different construction stages.

[0121] The multi-machine path allocation module is used to allocate the optimal work path to different road rollers and determine the work order of each road roller according to its starting position, forming a collaborative work scheduling scheme.

[0122] The multi-roller path allocation module is responsible for allocating tasks to different rollers according to the optimal work path and determining the work order according to the starting position of each roller, thereby forming an efficient collaborative work scheduling scheme. This module is closely connected with the aforementioned optimal path calculation module and numerical calculation and reconstruction module, relies on the calculation results of the optimal path, reasonably allocates the work paths of multiple rollers and schedules the order, so as to achieve optimal allocation of resources and improve overall work efficiency.

[0123] Generally, in multi-roller collaborative work, the work path of the roller needs to be reasonably allocated according to the complexity of the construction task and the geographical conditions of the area to ensure that each device can perform the task in the most suitable area and avoid resource waste and path repetition. The design purpose of the multi-roller path allocation module is to allocate path tasks to multiple rollers according to the optimal path allocation scheme and improve construction efficiency through reasonable scheduling order to maximize work quality.

[0124] Specifically, the multi-roller path allocation module in this embodiment reasonably divides the path into multiple sub-paths based on the discrete point set of the optimal work path. According to the starting position, work capacity, and predetermined task density of each roller, the module allocates these sub-paths to different rollers. When dividing the path, the work capacity, starting point, and overlapping area between adjacent paths of the roller are considered to ensure that each roller maximizes its work capacity in the construction area.

[0125] In one possible implementation, the path task division unit first divides the path into multiple smaller sub-paths according to the total length of the optimal work path and the task density distribution information. Then, according to the starting position and operable range of each roller, a reasonable path segment is divided. At this time, by calculating the spatial distance between the roller and the path, it is ensured that there is no path crossing and resource conflict when each device works.

[0126] For example, in some cases, if the construction area is large and the task density varies greatly, some rollers may be allocated to areas with high task density, while other rollers are allocated to areas with low task density. This allocation method can effectively reduce the burden during work and avoid repetitive work.

[0127] In some embodiments, the roller state matching unit further determines the priority of path allocation by evaluating the distance between the starting position of each roller and the path segment. Specifically, if a roller is close to the starting point of a path segment, the path segment is preferentially allocated to that device. In this way, the idle waiting time between devices can be reduced, and the work efficiency can be improved.

[0128] In some embodiments, the system further considers the relative positions of the rollers and the balance of the workloads to further optimize the path allocation and the work order. For example, when multiple rollers are working in the same construction area, the module can intelligently adjust the path allocation and the work order by considering the work progress and the task density of each roller to avoid excessive concentration of rollers on the path, thereby ensuring the work quality and efficiency during the construction process.

[0129] The work order determination unit then calculates the relative distances between the paths according to the work starting positions of each roller and the predetermined paths, and determines the work order of each roller according to a certain sorting algorithm (such as the greedy algorithm or the shortest path algorithm). This method can ensure that each device does not interfere with each other during actual work, and the work order is reasonable, thereby avoiding resource conflicts and time waste.

[0130] As an option, the determination of the work order can also be adjusted in combination with real-time feedback information. For example, if the work progress of a roller is ahead of or delayed, its work order can be adjusted in time, and the remaining paths can be re-allocated to ensure that the overall work plan is not affected. This flexible scheduling strategy can ensure that the system can adapt to unforeseen changes during the construction process, and improve the adaptability and robustness of the system.

[0131] The system of the present embodiment can be used to execute the above-mentioned algorithm embodiments, and the principles and technical effects are similar, which will not be described here.

[0132] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A multi-machine collaborative cold regeneration rolling path intelligent planning system, characterized by: include: The work area modeling module is used to obtain the boundaries of the construction area, terrain elevation data, and compaction task density information at each location in the area; A metric tensor construction module generates a metric tensor of the construction area based on the terrain elevation data and the compaction task density information, and constructs a two-dimensional Riemannian manifold model with weighted metrics; a path cost construction module, which constructs a path cost functional based on the two-dimensional Riemannian manifold model and the compaction task density information, wherein the path cost functional is used to describe the comprehensive cost required for the roller to move in the construction area; an optimal path calculation module, which determines the optimal operating path of the roller based on the path cost functional and the geodesic path principle; The numerical solution and reconstruction module is used to solve the optimal operation path on the discrete area and reconstruct it into a continuous smooth path to meet the actual control constraints of the roller; The multi-machine path allocation module is used to allocate work to different rollers according to the optimal working path, and determine the working order of different rollers according to their starting positions to form a collaborative work scheduling plan; The metric tensor building block includes: A gradient calculation unit for calculating slope based on terrain elevation data; Metric weight construction unit, used to construct a metric tensor on a two-dimensional area using slope and task density as parameters ; The metric tensor satisfies: ； Where, 、 is the weight coefficient; is the terrain function The gradient vector of For the location The metric tensor constructed at ; for point The compaction task density at the location indicates the construction intensity or task weight at that location; is the identity matrix.

2. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 1 is characterized in that: The operation area modeling module includes: Terrain data acquisition unit, used to obtain elevation data of the construction area and perform grid processing on it; The task density generation unit is used to map the construction compaction task requirements to the density values ​​of each location in the area to form a compaction task density distribution function .

3. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 1 is characterized in that: The gradient calculation unit includes: a first data input subunit for receiving terrain elevation data and mapping it to a regular grid coordinate system; The derivative calculation subunit uses the finite difference method to calculate the spatial first-order derivative of each grid point; The slope vector generating subunit is used to generate a slope vector field according to the derivative result and output it to the metric weight building unit as an input parameter.

4. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 1 is characterized in that: The path cost construction module includes: A path cost definition unit, used for constructing a functional form for calculating the path cost of the road roller operation, wherein the functional form is determined by a combination of the path length and the task density value on the path; A cost function input unit is used to extract the density distribution and metric information required for the path from the metric tensor building module and the operation area modeling module, and use them as cost function calculation parameters; The weight control unit is used to set the weighted ratio of different parameters in the path cost and supports the configuration method of adjusting the parameter coefficient according to construction needs.

5. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 4 is characterized in that: The cost function input unit includes: A task density extraction subunit is used to extract the local density value of the path passing area from the compaction task density distribution function; The tensor parameter extraction subunit is used to extract the tensor parameters corresponding to the path points from the metric tensor and perform coordinate system transformation to meet the path modeling requirements; The interpolation processing subunit is used to perform bilinear interpolation between the path point sequence and the input data.

6. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 1 is characterized in that: The optimal path calculation module includes: A path optimization unit, used to construct the corresponding Euler-Lagrange equation based on the path cost functional; The path solving unit is used to determine the differential form of the path solution through the shortest path condition based on the geodesic principle.

7. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 1 is characterized in that: The numerical solution and reconstruction module includes: a discrete solution unit for solving the path propagation time field according to a fast marching algorithm; A path reverse tracking unit is used to reversely track along the gradient direction from the end point of the path to generate a path point sequence; The path smoothing unit is used to introduce a second-order regularization term on the path point sequence to perform curvature constraint processing.

8. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 7 is characterized in that: The path smoothing unit is optimized based on the following objective function: ； Where, represents the path smoothing objective function value, represents the path A measure of the overall curvature change of ; is the differential and integral variable; It represents the second-order derivative of the path, reflecting the change in the curvature of the path. The goal is to minimize the curvature of the path and ensure a smooth transition of the path.

9. The multi-machine collaborative cold regeneration rolling path intelligent planning system according to claim 1 is characterized in that: The multi-machine path allocation module includes: A path task division unit is used to divide the set of operation paths; A roller state matching unit is used to allocate the path subset to the corresponding roller according to the initial position and allocation principle of each roller; The operation sequence determination unit is used to determine the operation sequence according to the spatial distance between the roller and the path.

Images