A quadruped robot task allocation method, device, equipment and medium

Abstract

The application discloses a quadruped robot task allocation method and device, equipment and medium, relates to the task allocation technical field, the method is aimed at the terrain adaptability defect in the application of quadruped robot, is committed to solving the problem of terrain element loss when multi-machine cooperative task planning in unstructured environment, terrain evaluation matrix (TAS) is constructed, and terrain constraints are integrated into the task allocation model. On the basis of the traditional unmanned aerial vehicle model, the terrain evaluation parameter is added, the adaptability and operation efficiency of the system in the actual scene are improved, the prediction result is more close to the actual application. At the same time, a hybrid heuristic strategy is adopted, the global search ability of the genetic algorithm and the local optimization advantage of the tabu search are combined, the double tabu table mechanism is effectively used to avoid repeated search and invalid area exploration, and the search efficiency is improved. When the task scale expands, stable solving efficiency can be maintained.

Description

Technical Field

[0001] This invention relates to the field of task allocation technology, and in particular to a method, apparatus, device and medium for task allocation of a quadruped robot that incorporates terrain assessment. Background Technology

[0002] With the continuous expansion of infrastructure in energy, transportation, and industry, the demand for autonomous, high-precision, and all-weather inspections in scenarios such as power equipment inspection, oil and gas pipeline monitoring, and disaster site investigation is becoming increasingly urgent. Traditional manual inspections suffer from high costs and risks (such as operations in high-heat / high-cold regions, toxic environments, and confined spaces). In recent years, quadruped robots have demonstrated unique advantages in scenarios such as substation equipment inspection, oil and gas pipeline leak detection, and earthquake disaster site investigation due to their excellent terrain adaptability. However, a single robot cannot simultaneously meet the requirements of large-scale coverage and multi-tasking. Therefore, multi-robot collaborative systems have begun to attract attention, and the design of task allocation algorithms is a core element in improving system efficiency.

[0003] Current research on cluster task allocation mainly focuses on drone swarms and unmanned vehicle swarms, and mostly adopts task allocation frameworks based on heuristic rules, market auction mechanisms or swarm intelligence optimization. Significant progress has been made in top-level design, theoretical research, project demonstration and key technology breakthroughs.

[0004] Existing technical solutions are mostly based on the field of drone swarms. They quantify and integrate key parameters such as path length, time cost, completion benefits, and damage probability to construct a multi-dimensional weighted evaluation system. At the same time, they incorporate constraints such as drone endurance and payload capacity to establish a model for multi-drone task allocation problems.

[0005] Based on the above model, the Discrete Particle Swarm Optimization (DPSO) algorithm is then used to solve the problem. Particle encoding rules are used to represent the task's execution plan and order, and the DPSO algorithm is used for iterative optimization. An evaluation function is then used to assess the quality of the searched solutions.

[0006] It is evident that the drone-based swarm task allocation model constructs an evaluation system using general parameters such as path length and time cost. However, this system is built in an ideal airspace with no terrain or obstacles. In the working scenario of quadruped robots, it is necessary to consider the rigid constraints of the spatial distribution of obstacles in the ground environment on the movement trajectory and the influence of terrain complexity. This leads to deviations between the actual operation of quadruped robot swarms in real terrain environments and ideal predictions. Summary of the Invention

[0007] In view of the above problems, the present invention provides a quadruped robot task allocation method, apparatus, device and medium for overcoming the above problems or at least partially solving the above problems.

[0008] This invention provides the following solution: A method for task allocation in a quadruped robot includes: A raster map is constructed using discretized geographic information, abstracting complex terrain into a quantifiable matrix model. A terrain assessment matrix is ​​then built based on this matrix model, comprising a slope matrix, a surface friction coefficient matrix, and a stair height matrix. Based on the design constraints of the quadruped robot and the requirements of the task scenario, a task allocation optimization model is constructed. The task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm with the terrain evaluation matrix according to the environmental map and the coordinates of each task. A real-number encoding method based on a single chromosome is used for encoding so that the solution to the problem can be represented as a chromosome in the genetic algorithm; A hybrid heuristic optimization strategy is adopted, combining genetic algorithm and tabu search algorithm to solve the task allocation optimization model. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer to correct the solution of the genetic algorithm that is trapped in local optimum through dynamic tabu table and neighborhood perturbation mechanism.

[0009] Preferably, the terrain assessment matrix is ​​calculated using the following formula:

[0010] in These are the weights for slope, ground friction coefficient, and step height, respectively. Indicates the slope of the terrain within the unit. This indicates the maximum slope that the quadruped robot can traverse. The standard coefficient of friction of the terrain material. Indicates the height of the steps. This indicates the maximum height of a step that a quadruped robot can traverse.

[0011] Preferably: the slope matrix The calculation methods include: Surface curvature is calculated using spatial difference based on a digital elevation model; The Sobel operator is used to perform convolution operations on the elevation matrix to obtain the horizontal and vertical gradients, and the average slope angle of each grid cell is calculated. The friction coefficient matrix Calculation methods include: Preset base friction values ​​for different materials in a dry state The friction coefficient is corrected by combining the motion data of the quadruped robot during its movement. The stepped height matrix Calculation methods include: Based on prior terrain information, a stair height matrix is ​​established for the environmental map.

[0012] Preferably, the A* algorithm combined with TAS parameters is expressed as:

[0013] In the formula: It is the valuation of each possible testing point. This represents the cost from the starting search point to the current point. This represents the estimated distance from the current node to the target node. This indicates the parameters that affect the terrain.

[0014] Preferably, the allocation scheme is optimized during the solution process using the following objective function:

[0015] In the formula: Indicates the path distance cost. This indicates the time and cost involved. This represents the cost of the distance from the standard deviation. This represents the cost of the standard deviation of the probability of failure. This indicates the cost of terrain assessment.

[0016] Preferably, the constraints in the task allocation optimization model include endurance constraints and load capacity constraints.

[0017] Preferably, when the tabu algorithm is combined with the genetic algorithm, the search process is optimized through a double tabu table mechanism, including: The list of valid solutions to taboos is dynamically recorded and updated according to the first-in, first-out principle. The illegal solution taboo table permanently stores all illegal solutions, forming a global interception; After a new solution is generated, the two tables are compared first. If the new solution is already in the illegal table or the legal table, the new solution is discarded and regenerated. Only new solutions that are not in the taboo table are subject to constraint checks and fitness calculations. The algorithm terminates when the termination condition is met, outputting the solution with the best fitness, which is then decoded as the optimal task allocation scheme.

[0018] A quadruped robot task allocation device for executing the above-described quadruped robot task allocation method, the device comprising: The terrain assessment matrix acquisition unit is used to construct a raster map through discretized geographic information, abstract complex terrain into a matrix model that can be quantified and analyzed; and to construct a terrain assessment matrix based on the matrix model; the terrain assessment matrix includes a slope matrix, a surface friction coefficient matrix, and a stair height matrix; The task allocation optimization model construction unit is used to construct a task allocation optimization model based on the design constraints of the quadruped robot body and the requirements of the task scenario. The task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm with the terrain evaluation matrix according to the environmental map and the coordinates of each task. The encoding unit is used to encode using a real number encoding method based on a single chromosome, so that the solution to the problem can be represented as a chromosome in the genetic algorithm; A hybrid heuristic optimization unit is used to solve the task allocation optimization model by employing a hybrid heuristic optimization strategy, combining genetic algorithm and tabu search algorithm. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer to correct the solution of the genetic algorithm that is trapped in local optimum through dynamic tabu table and neighborhood perturbation mechanism.

[0019] A task assignment device for a quadruped robot, the device comprising a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the above-described quadruped robot task allocation method according to the instructions in the program code.

[0020] A computer-readable storage medium for storing program code for executing the above-described quadruped robot task allocation method.

[0021] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This application provides a method, apparatus, device, and medium for task allocation in a quadruped robot. The method incorporates terrain constraints into the task allocation model by constructing a Terrain Assessment Matrix (TAS). Based on traditional UAV models, terrain assessment parameters are added to improve the system's adaptability and operational efficiency in real-world scenarios, making the prediction results more closely reflect practical applications. Simultaneously, a hybrid heuristic strategy is employed, combining the global search capability of genetic algorithms with the local optimization advantages of tabu search. A dual tabu table mechanism effectively avoids redundant searches and exploration of invalid regions, improving search efficiency and maintaining stable solution efficiency even as the task scale increases.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0024] Figure 1 This is a flowchart of a task allocation method for a quadruped robot provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of terrain information provided in an embodiment of the present invention; Figure 3 This is a terrain assessment heatmap provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of real number encoding based on a single chromosome provided in an embodiment of the present invention; Figure 5 This is a flowchart of the hybrid optimization strategy algorithm provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of a task allocation device for a quadruped robot provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of a quadruped robot task allocation device provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0026] See Figure 1 This invention provides a method for task allocation in a quadruped robot, as exemplified by [example of the invention]. Figure 1 As shown, the method may include: S101: Construct a raster map by discretizing geographic information, and abstract complex terrain into a matrix model that can be quantified and analyzed; construct a terrain evaluation matrix based on the matrix model; the terrain evaluation matrix includes a slope matrix, a surface friction coefficient matrix, and a stair height matrix; In a specific implementation, the terrain assessment matrix can be calculated using the following formula:

[0027] in These are the weights for slope, ground friction coefficient, and step height, respectively. Indicates the slope of the terrain within the unit. This indicates the maximum slope that the quadruped robot can traverse. The standard coefficient of friction of the terrain material. Indicates the height of the steps. This indicates the maximum height of a step that a quadruped robot can traverse.

[0028] Furthermore, the slope matrix The calculation methods include: Surface curvature is calculated using spatial difference based on a digital elevation model; The Sobel operator is used to perform convolution operations on the elevation matrix to obtain the horizontal and vertical gradients, and the slope angle of each grid cell is calculated. The friction coefficient matrix Calculation methods include: Preset base friction values ​​for different materials in a dry state The friction coefficient is corrected by combining the motion data of the quadruped robot during its movement. The stepped height matrix Calculation methods include: Based on prior terrain information, a stair height matrix is ​​established for the environmental map.

[0029] S102: Based on the design constraints of the quadruped robot body and the requirements of the task scenario, a task allocation optimization model is constructed; the task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm of TAS according to the environmental map and the coordinates of each task. In a specific implementation, the A* path planning method combined with TAS parameters can be represented as follows:

[0030] In the formula: It is the valuation of each possible testing point. This represents the cost from the starting search point to the current point. This represents the estimated distance from the current node to the target node. This indicates the parameters that affect the terrain.

[0031] S103: The encoding method based on real number encoding of single chromosome is used to encode the solution to the problem as a chromosome in the genetic algorithm; S104: A hybrid heuristic optimization strategy is adopted, combining genetic algorithm and tabu search algorithm to solve the task allocation optimization model. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer so as to correct the solution of the genetic algorithm that is trapped in local optimum through dynamic tabu table and neighborhood perturbation mechanism.

[0032] The allocation scheme is optimized using the following objective function during the solution process:

[0033] In the formula: Indicates the path distance cost. This indicates the time and cost involved. This represents the cost of the distance from the standard deviation. This represents the cost of the standard deviation of the probability of failure. This indicates the cost of terrain assessment.

[0034] When the tabu algorithm is combined with the genetic algorithm, the search process is optimized through a double tabu table mechanism, including: The list of valid solutions to taboos is dynamically recorded and updated according to the first-in, first-out principle. The illegal solution taboo table permanently stores all illegal solutions, forming a global interception; After a new solution is generated, the two tables are compared first. If the new solution is already in the illegal table or the legal table, the new solution is discarded and regenerated. Only new solutions that are not in the taboo table are subject to constraint checks and fitness calculations. The algorithm terminates when the termination condition is met, outputting the solution with the best fitness, which is then decoded as the optimal task allocation scheme.

[0035] The quadruped robot task allocation method provided in this application addresses the terrain adaptability deficiencies of existing swarm task allocation technologies in quadruped robot applications, aiming to solve the problem of missing terrain elements during multi-robot collaborative task planning in unstructured environments. It incorporates terrain evaluation parameters into the traditional evaluation system framework, combining environmental elements such as slope matrix, surface friction coefficient matrix, and stair height matrix. A hybrid heuristic optimization strategy, combining genetic algorithms and tabu search algorithms, is employed to enable the traditional swarm task allocation model to adapt to the unique terrain adaptation needs of quadruped robots, making the model more suitable for specific scenarios.

[0036] Based on the traditional UAV model, terrain assessment parameters are added to improve the system's adaptability and operational efficiency in real-world scenarios, making the prediction results more closely reflect actual applications. Simultaneously, a hybrid heuristic strategy is employed, combining the global search capability of genetic algorithms with the local optimization advantages of tabu search. A dual tabu table mechanism effectively avoids redundant searches and exploration of invalid regions, improving search efficiency and maintaining stable solution efficiency even as the task scale increases.

[0037] The task allocation method for quadruped robots provided in the embodiments of this application will be described in detail below.

[0038] The method provided in this application introduces terrain assessment parameters, including a slope matrix, a surface friction coefficient matrix, and a stair height matrix, to construct a task allocation model for quadruped robots adapted to ground-based scenarios. Through the quantitative fusion and optimization strategies of terrain elements, efficient collaborative task planning for quadruped robots in complex terrain environments is achieved, improving the system's adaptability and operational efficiency in real-world scenarios.

[0039] Meanwhile, a hybrid heuristic optimization strategy is adopted, combining genetic algorithm and tabu search algorithm. Genetic algorithm is used for global search, while tabu search is introduced as a local optimizer: dynamic tabu table and neighborhood perturbation mechanism are used to correct the solution of genetic algorithm getting trapped in local optimum and improve convergence accuracy.

[0040] 1. Topographical assessment By constructing raster maps from discretized geographic information, complex terrain can be abstracted into a matrix model that can be quantified and analyzed. For example... Figure 2 As shown, based on elevation data and surface material, the system first identifies non-passable areas such as rivers, trees, buildings, and areas where parameters exceed safety thresholds, marks them as obstacle units, and generates a basic binary matrix.

[0041] Based on this, the Terrain Assessment Matrix (TAS) integrates the following multi-dimensional physical features: (1) Slope matrix .

[0042] Based on a digital elevation model (DEM), surface curvature is calculated using spatial differencing. The elevation matrix is ​​convolved using the Sobel operator to obtain the horizontal and vertical gradients, and then the average slope angle of each grid cell is calculated.

[0043] (2) Friction coefficient matrix .

[0044] Preset the base friction values ​​of different materials (such as asphalt, grass, and sand) in a dry state. The friction coefficient is corrected by combining the motion data of the robot during its movement.

[0045] (3) Staircase height matrix .

[0046] Based on prior terrain information, a stair height matrix is ​​established for the environmental map.

[0047] The above parameters are weighted and fused to form a terrain assessment matrix. The calculation method is as follows:

[0048] in, These are the slope weight, friction coefficient weight, and stair height weight, respectively. This matrix can represent an ideal passageway that is flat, non-slip, and has continuous footholds.

[0049] like Figure 3 As shown, through A matrix heatmap can visually display the degree of terrain structure, presented as a color gradient. Ideal passages (L1) represent flat and safe areas, while treacherous passages (L2) are dangerous areas requiring avoidance or special measures. Black (L3) represents impassable areas. Embedding the terrain assessment matrix into the task assignment model can significantly improve the efficiency and safety of task execution.

[0050] 2. Model System Construction The research problem is described as follows: n quadruped robots need to collaboratively complete m inspection tasks within a region. To efficiently complete these tasks, it is necessary to rationally allocate the tasks to each quadruped robot under certain constraints, ensuring timely task execution, optimal resource utilization, and improved overall execution efficiency.

[0051] (1) Variable design Define the set of quadruped robots as The task set is . No. Task Represented by a quinary vector group ,in For the task The number, For the task The location of For the task The estimated time, For the task Resource consumption, This indicates the probability of damage to the quadruped robot while performing this task.

[0052] No. A quadruped robot Represented by a six-element vector group ,in For quadruped robots The number, For quadruped robots coordinates For quadruped robots speed of travel For quadruped robots Battery life, For quadruped robots The load capacity, For quadruped robots The task sequence.

[0053] Terrain assessment matrix The environment map serves as the input for the task scenario. Based on the environment map and the coordinates of each task, path planning is performed using the A* algorithm of TAS, incorporating the influence of terrain evaluation values ​​when calculating distance costs. The terrain influence parameter is as follows:

[0054] In the formula, It is the valuation of each possible testing point. This represents the cost from the starting search point to the current point (usually expressed as the depth of a node in the search tree). This represents the estimated distance from the current node to the target node. The actual distance cost and path between tasks are then calculated, and this process is repeated to obtain the distance cost matrix. and path matrix .

[0055] (2) Model Design The allocation scheme is optimized through an objective function during the solution process. The objective function consists of five parts:

[0056] The results are obtained from the following formulas (where...) (For the parameters of the corresponding item): A. Path distance cost It is obtained by calculation using the following formula.

[0057]

[0058] In the formula, This represents the path distance cost.

[0059] B. Time-consuming cost It is obtained by calculation using the following formula.

[0060]

[0061] In the formula, The result was derived from the group that took the longest time among the quadruped robots. To control the movement speed of the quadruped robot, For the task The time it takes.

[0062] C. Cost of Distance Standard Deviation It is obtained by calculation using the following formula.

[0063]

[0064] In the formula, The standard deviation of the path distance for each group of tasks. This can make the path distances of each quadruped robot as balanced as possible.

[0065] D. Cost of standard deviation of damage probability It is obtained by calculation using the following formula.

[0066]

[0067] In the formula, To carry out the mission The probability of damage, The average failure probability for all tasks. This can balance the damage probability of each quadruped robot, preventing a situation where one group has an exceptionally high damage probability.

[0068] E. Cost of Topographic Assessment It is obtained by calculation using the following formula.

[0069]

[0070] The constraints defined in the model are as follows: The endurance constraint is that the total time spent on all tasks cannot exceed the endurance time of the quadruped robot, expressed as:

[0071] The load capacity constraint stipulates that the total resource consumption of each task group shall not exceed the load capacity of the quadruped robot, expressed as:

[0072] 3. Hybrid Heuristic Optimization Strategy (1) Encoding method Encoding is the process of representing a problem solution as a chromosome in a genetic algorithm. Using a single-chromosome-based real-number encoding method, any task assignment scheme can be represented by a one-dimensional sequence, derived from... 0 and 1 to The integer sequence consists of 0, where 0 represents the dividing point, and 1 to 1... The integer sequence represents the tasks and their order, such as Figure 4 As shown.

[0073] (2) Hybrid optimization strategy A hybrid heuristic optimization strategy is adopted, combining genetic algorithm and tabu search algorithm. Genetic algorithm is used for global search, while tabu search is introduced as a local optimizer: dynamic tabu table and neighborhood perturbation mechanism are used to correct the solution of genetic algorithm getting trapped in local optimum and improve convergence accuracy.

[0074] Genetic Algorithm (GA) is a search heuristic algorithm that simulates the principles of natural selection and genetics. It iteratively searches for the optimal solution through selection, crossover, and mutation to solve optimization and search problems. In a genetic algorithm, each candidate solution is an individual, and a group of candidate solutions is called a population. The process of continuously generating new individuals is the process of searching for the optimal solution.

[0075] When combining the tabu algorithm with the genetic algorithm, a dual tabu table mechanism optimizes the search process: the legal solution tabu table dynamically records explored valid solutions and updates them according to the first-in-first-out principle to avoid redundant searches; the illegal solution tabu table permanently stores all illegal solutions, forming a global interception. After a new solution is generated, it is first compared with the dual tables. If the new solution is already in either the illegal or legal table, it is discarded and regenerated. Only new solutions not in the tabu tables undergo constraint checks and fitness calculations. When the termination condition is met (reaching the maximum number of iterations or the fitness of the best individual in the population does not change after multiple consecutive iterations, i.e., the population stops evolving), the algorithm terminates, outputs the solution with the best fitness, and decodes it in a manner opposite to encoding, as a task allocation scheme. This strategy can significantly reduce redundant searches in complex task allocation, maintaining population diversity while avoiding invalid regions, achieving a dual improvement in efficiency and solution quality. The algorithm flow is as follows: Figure 5 As shown.

[0076] In summary, the quadruped robot task allocation method provided in this application incorporates terrain constraints into the task allocation model by constructing a Terrain Evaluation Matrix (TAS). Compared with traditional UAV models, this improves the model's terrain adaptability and enhances task safety. The hybrid heuristic strategy combines the global search capability of genetic algorithms with the local optimization advantages of tabu search. Through a double tabu table mechanism, it effectively avoids redundant searches and exploration of invalid regions, improving search efficiency and maintaining stable solution efficiency even as the task scale increases.

[0077] See Figure 6 This application embodiment can also provide a task allocation device for a quadruped robot, such as... Figure 6 As shown, the apparatus for performing the above-described quadruped robot task allocation method may include: The terrain assessment matrix acquisition unit 601 is used to construct a raster map through discretized geographic information, abstract complex terrain into a matrix model that can be quantified and analyzed; and to construct a terrain assessment matrix based on the matrix model; the terrain assessment matrix includes a slope matrix, a surface friction coefficient matrix, and a stair height matrix. The task allocation optimization model construction unit 602 is used to construct a task allocation optimization model based on the design constraints of the quadruped robot body and the requirements of the task scenario. The task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm of TAS according to the environmental map and the coordinates of each task. The encoding unit 603 is used to encode using a real number encoding method based on a single chromosome, so that the solution to the problem can be represented as a chromosome in a genetic algorithm; The hybrid heuristic optimization unit 604 is used to solve the task allocation optimization model by employing a hybrid heuristic optimization strategy, combining genetic algorithm and tabu search algorithm. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer to correct the solution of the genetic algorithm that is trapped in local optimum through dynamic tabu table and neighborhood perturbation mechanism.

[0078] This application embodiment can also provide a quadruped robot task allocation device, the device including a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the steps of the quadruped robot task allocation method described above according to the instructions in the program code.

[0079] like Figure 7 As shown in the figure, a quadruped robot task allocation device provided in this application embodiment may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, memory 11, and communication interface 12 all communicate with each other through the communication bus 13.

[0080] In this embodiment, the processor 10 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0081] The processor 10 can call programs stored in the memory 11. Specifically, the processor 10 can execute operations in the embodiments of the quadruped robot task allocation method.

[0082] The memory 11 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment, the memory 11 stores at least a program for implementing the following functions: A raster map is constructed by discretizing geographic information, and complex terrain is abstracted into a matrix model that can be quantified and analyzed. A terrain assessment matrix is ​​then constructed based on the matrix model. The terrain assessment matrix includes a slope matrix, a surface friction coefficient matrix, and a stair height matrix. Based on the design constraints of the quadruped robot and the requirements of the task scenario, a task allocation optimization model is constructed. The task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm of TAS according to the environmental map and the coordinates of each task. A real-number encoding method based on a single chromosome is used for encoding so that the solution to the problem can be represented as a chromosome in the genetic algorithm; A hybrid heuristic optimization strategy is adopted, combining genetic algorithm and tabu search algorithm to solve the task allocation optimization model. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer to correct the solution of the genetic algorithm that is trapped in local optimum through dynamic tabu table and neighborhood perturbation mechanism.

[0083] In addition, memory 11 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0084] Communication interface 12 can be an interface for the communication module, used to connect with other devices or systems.

[0085] Of course, it should be noted that, Figure 7 The structure shown does not constitute a limitation on the quadruped robot task allocation device in the embodiments of this application. In practical applications, the quadruped robot task allocation device may include more than Figure 7 More or fewer components as shown, or combinations of certain components.

[0086] This application embodiment may also provide a computer-readable storage medium for storing program code for executing the steps of the quadruped robot task allocation method described above.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0088] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0089] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A task allocation method for a quadruped robot, characterized in that, include: By constructing raster maps from discretized geographic information, complex terrain can be abstracted into a matrix model that can be quantified and analyzed. A terrain assessment matrix is ​​constructed based on the aforementioned matrix model. The terrain assessment matrix includes a slope matrix, a surface friction coefficient matrix, and a stair height matrix; Based on the design constraints of the quadruped robot and the requirements of the task scenario, a task allocation optimization model is constructed. The task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm with the terrain evaluation matrix according to the environmental map and the coordinates of each task. A real-number encoding method based on a single chromosome is used for encoding so that the solution to the problem can be represented as a chromosome in the genetic algorithm; A hybrid heuristic optimization strategy is adopted, combining genetic algorithm and tabu search algorithm to solve the task allocation optimization model. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer to correct the genetic algorithm from getting stuck in local optima through dynamic tabu list and neighborhood perturbation mechanism. The terrain assessment matrix is ​​calculated as follows: in These are the weights for slope, ground friction coefficient, and step height, respectively. Indicates the slope of the terrain within the unit. This indicates the maximum slope that the quadruped robot can traverse. The standard coefficient of friction of the terrain material. Indicates the height of the steps. This indicates the maximum height of a step that the quadruped robot can traverse. The slope matrix The calculation methods include: Surface curvature is calculated using spatial difference based on a digital elevation model; The Sobel operator is used to perform convolution operations on the elevation matrix to obtain the horizontal and vertical gradients, and the average slope angle of each grid cell is calculated. The friction coefficient matrix Calculation methods include: Preset base friction values ​​for different materials in a dry state The friction coefficient is corrected by combining the motion data of the quadruped robot during its movement. The stepped height matrix Calculation methods include: Based on prior terrain information, a stair height matrix for the environmental map is established; The A* algorithm combined with TAS parameters is expressed as: In the formula: It is the valuation of each possible testing point. This represents the cost from the starting search point to the current point. This represents the estimated distance from the current node to the target node. This indicates the parameters that affect the terrain.

2. The task allocation method for a quadruped robot according to claim 1, characterized in that, The allocation scheme is optimized using the following objective function during the solution process: In the formula: Indicates the path distance cost. This indicates the time and cost involved. This represents the cost of distance from the standard deviation. This represents the cost of the standard deviation of the probability of failure. This indicates the cost of terrain assessment.

3. The task allocation method for a quadruped robot according to claim 1, characterized in that, The constraints in the task allocation optimization model include range constraints and payload capacity constraints.

4. The task allocation method for a quadruped robot according to claim 1, characterized in that, When the tabu search algorithm is combined with the genetic algorithm, the search process is optimized through a double tabu table mechanism, including: The list of valid solutions to taboos is dynamically recorded and updated according to the first-in, first-out principle. The illegal solution taboo table permanently stores all illegal solutions, forming a global interception; After a new solution is generated, the two tables are compared first. If the new solution is already in the illegal table or the legal table, the new solution is discarded and regenerated. Only new solutions that are not in the taboo table are subject to constraint checks and fitness calculations. The algorithm terminates when the termination condition is met, outputting the solution with the best fitness, which is then decoded as the optimal task allocation scheme.

5. A task allocation device for a quadruped robot, characterized in that, The apparatus for performing the quadruped robot task assignment method according to any one of claims 1-4, the apparatus comprising: The terrain assessment matrix acquisition unit is used to construct a raster map through discretized geographic information, abstract complex terrain into a matrix model that can be quantified and analyzed; and to construct a terrain assessment matrix based on the matrix model; the terrain assessment matrix includes a slope matrix, a surface friction coefficient matrix, and a stair height matrix; The task allocation optimization model construction unit is used to construct a task allocation optimization model based on the design constraints of the quadruped robot body and the requirements of the task scenario. The task allocation optimization model uses the terrain evaluation matrix as the environmental map input of the task scenario and performs path planning by combining the A* algorithm with the terrain evaluation matrix according to the environmental map and the coordinates of each task. The encoding unit is used to encode using a real number encoding method based on a single chromosome, so that the solution to the problem can be represented as a chromosome in the genetic algorithm; A hybrid heuristic optimization unit is used to solve the task allocation optimization model by employing a hybrid heuristic optimization strategy, combining genetic algorithm and tabu search algorithm. The optimal solution obtained under the maximum number of iterations is the best task allocation scheme for the quadruped robot. The genetic algorithm is used for global search, and the tabu search algorithm is used as a local optimizer to correct the solution of the genetic algorithm that is trapped in local optimum through dynamic tabu table and neighborhood perturbation mechanism.

6. A task allocation device for a quadruped robot, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the quadruped robot task allocation method according to any one of claims 1-4 according to the instructions in the program code.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the quadruped robot task allocation method according to any one of claims 1-4.

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