Control method, device and equipment of intelligent auxiliary system for riding instead of walk and storage medium

Abstract

The invention provides a control method, device and equipment of an intelligent walking assisting system and a storage medium. The method comprises the steps that a walking robot is divided into a first robot and a second robot according to the available electricity value of the walking robot; generating a task distribution density map according to the issued task and a preset navigation map, wherein the task distribution density map comprises a plurality of task dense areas; determining available charging piles according to the task dense area and a preset navigation map, and obtaining charging position information of the available charging piles; if the number of the task dense areas is larger than the number of the first robots, first operation areas and second operation areas are determined according to the task distribution density map and the charging position information, the number of the first operation areas is equal to the number of the first robots, and the second operation areas comprise at least one available charging pile; and respectively controlling the first robot to execute tasks contained in one first operation area, and controlling the plurality of second robots to execute tasks contained in one second operation area in turn.

Description

Technical Field

[0001] The present application relates to the field of automation control technology, and in particular to a control method, device, equipment and storage medium for an intelligent mobility assistance system. Background Art

[0002] At present, robots are increasingly used in modern industrial automation and service industries, from production lines in manufacturing to customer reception in the service industry. Robots are playing an increasingly important role. With the development of robot technology, multi-robot collaborative work has become an important way to improve work efficiency and cope with complex tasks. However, in practical applications, multi-robot systems face a series of challenges, one of the most prominent of which is the contradiction between the concentrated outbreak of tasks and the power management of robots. When multiple tasks appear at the same time or emerge in large numbers in a short period of time, it is necessary to quickly deploy available robot resources to respond. In this case, how to efficiently assign tasks to different robots and ensure that each robot can maintain sufficient power while completing the task has become an urgent problem to be solved. Due to the different working intensity and time of each robot during use, there will be large differences in the remaining power, which further increases the difficulty of unified scheduling. Summary of the invention

[0003] The present application provides a control method, device, equipment and storage medium of an intelligent mobility assistance system, which are used to improve the scheduling efficiency of a mobility robot when tasks are highly bursty.

[0004] In a first aspect, an embodiment of the present application provides a control method for an intelligent mobility assistance system, the method comprising: When entering the task increment mode, obtaining an available power value of the walking robot, and dividing the walking robot into a first robot and a second robot according to the available power value; Generate a task distribution density map according to the published tasks and a preset navigation map, wherein the task distribution density map includes a plurality of task intensive areas; Determine available charging piles according to the task-intensive area and the preset navigation map, and obtain charging location information of the available charging piles; If the number of the task-intensive areas is greater than the number of the first robots, determining a first operation area and a second operation area according to the task distribution density map and the charging position information, wherein the number of the first operation areas is equal to the number of the first robots, and the second operation area includes at least one of the available charging piles; The first robots are controlled to execute the tasks included in the first operation area respectively, and the plurality of second robots are controlled to execute the tasks included in the second operation area in turn.

[0005] In a second aspect, an embodiment of the present application provides a control device for an intelligent mobility assistance system, the device comprising: A power measurement module, used for obtaining an available power value of the walking robot when entering the task increment mode, and dividing the walking robot into a first robot and a second robot according to the available power value; A task management module, used to generate a task distribution density map according to the published tasks and a preset navigation map, wherein the task distribution density map includes a plurality of task intensive areas; A charging control module, used to determine available charging piles according to the task-intensive area and the preset navigation map, and obtain charging location information of the available charging piles; an area division module, configured to determine, if the number of the task-intensive areas is greater than the number of the first robots, a first operation area and a second operation area according to the task distribution density map and the charging position information, wherein the number of the first operation areas is equal to the number of the first robots, and the second operation area includes at least one of the available charging piles; The motion control module is used to control the first robot to execute the tasks included in the first working area, and to control multiple second robots to execute the tasks included in the second working area in turn.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the control method of the intelligent mobility assistance system as described in any one of the embodiments of the present application when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements a control method for an intelligent mobility assistance system as described in any one of the embodiments of the present application.

[0008] An embodiment of the present application provides a control method for an intelligent mobility assistance system, the method comprising: when entering a task increment mode, obtaining an available power value of a mobility robot, and dividing the mobility robot into a first robot and a second robot according to the available power value; generating a task distribution density map according to published tasks and a preset navigation map, the task distribution density map including multiple task intensive areas; determining available charging piles according to the task intensive areas and the preset navigation map, and obtaining charging location information of the available charging piles; if the number of task intensive areas is greater than the number of first robots, determining a first operating area and a second operating area according to the task distribution density map and the charging location information, wherein the number of first operating areas is equal to the number of first robots, and the second operating area includes at least one available charging pile; respectively controlling the first robot to execute a task included in a first operating area, and controlling multiple second robots to execute a task included in a second operating area in turn. Through the above method, the robots are divided into the first robot (high power) and the second robot (low power) based on the power value, and the task distribution density map and the charging pile location information are combined to reasonably divide the operation area. When multiple task-intensive areas are identified, the task territories are divided according to the number of task-intensive areas and the number of first robots. The task-intensive areas close to the charging piles are preferentially set as the second operation area to ensure that they can charge conveniently while performing tasks. The task-intensive areas far from the charging piles are divided into the first operation area. Therefore, not only the resource allocation is optimized and the invalid movement is reduced, but also the service interruption caused by insufficient power is avoided. Even in the face of a large number of concurrent task requests, the tasks can be completed efficiently and stably, thereby greatly improving the overall service quality and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0010] Figure 1 A schematic flow chart of a control method of an intelligent mobility assistance system provided in an embodiment of the present application; Figure 2 A schematic block diagram of a control device of an intelligent mobility assistance system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0013] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0014] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0015] See also Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of a control method of an intelligent mobility assistance system provided by an embodiment of the present application. Figure 1 As shown, the specific steps of the control method of the intelligent travel assistance system include: S101-S105.

[0016] S101. When entering a task increment mode, obtaining an available power value of a walking robot, and dividing the walking robot into a first robot and a second robot according to the available power value.

[0017] Exemplarily, in the control system of the walking robot, if the number of tasks received is greater than the preset increment threshold, the task increment mode will be entered. The preset increment threshold can be determined according to the number of walking robots or according to the execution of historical tasks. The scenarios of the task increment mode include: factories, restaurants and airports. Even in routine operations, due to unforeseen factors (weather changes, holiday effects, etc.), more task requests than usual may appear in certain time periods. The use of the task increment mode can make the walking assistance system more flexible to respond to these fluctuations and ensure that the service is always efficient and reliable. Before task reallocation, it is necessary to obtain the available power value of the walking robot. The available power value is the difference between the current circuit value of the walking robot and the return power value. The return power value is the power value required to pass the longest path between the task point and the charging pile.

[0018] When classifying robot types, a preset power threshold is needed as the basis for classification. The preset power threshold can be set manually and determined based on historical data. For example, in the task increment mode, the initial power values ​​of the walking robots that need to be charged are counted, and the largest one of the above initial power values ​​is used as the preset power threshold.

[0019] First Robot (High Power): First Robots can take on multi-floor or cross-regional food delivery tasks because they have enough power to support longer operations. For example, during the lunch rush, these robots can be responsible for delivering food from the first-floor restaurant to the upper-floor guest rooms.

[0020] Second Robot (Low Battery): Second robots are more suitable for tasks on the same floor or near available charging stations. For example, they can serve rooms near elevator entrances, so that even if the battery is low, they can quickly return to the available charging station to recharge and ensure continuous service capabilities.

[0021] The classification strategy of mobility robots based on power value is one of the key factors to improve the scheduling efficiency in the task increment mode. It ensures that an efficient and stable service level can be maintained regardless of daily operations or sudden high-demand situations.

[0022] S102: Generate a task distribution density map according to the published tasks and a preset navigation map, where the task distribution density map includes a plurality of task intensive areas.

[0023] For example, a task distribution density map is created based on the published tasks and the preset navigation map. The task distribution density map will indicate the task density of each area, that is, which areas have more task requirements. Usually, this involves a comprehensive analysis of factors such as task location, time requirements, and task type.

[0024] It should be noted that the task distribution density map can be two-dimensional or three-dimensional. For example, a two-dimensional task distribution density map is established in a flat factory, and a three-dimensional task distribution density map is established in a multi-story hotel. Combined with the current visual neural network, processing based on the task distribution density map can efficiently divide the task area.

[0025] It should also be noted that the task-intensive area must meet the following conditions: 1. The area of ​​the task-intensive area is larger than the preset area; 2. The task path density in the task-intensive area is larger than the preset density threshold. In this way, after a fully charged walking robot completes the task in the task-intensive area, the remaining power value is 15%-25%.

[0026] Presenting it in a visual way can help decision makers intuitively see the hot spots where tasks are concentrated, providing a basis for the next step of task allocation.

[0027] S103: Determine available charging piles according to the task-intensive area and a preset navigation map, and obtain charging location information of the available charging piles.

[0028] For example, after determining the task-intensive areas, it is also necessary to identify the available charging piles in and around these task-intensive areas and collect the specific location information of the available charging piles. This is crucial for making a charging plan, especially for the second robots with low power.

[0029] It should be noted that the available charging piles are those in an idle state. There may be multiple charging piles in the scene, and other mobility robots may be charging. The charging piles in an idle state (no mobility robot is charging) are available charging piles.

[0030] By adding the specific location information of available charging stations, the work area can be planned more reasonably, allowing the mobility robot to replenish energy in time while completing the task, ensuring service continuity. In addition, this also provides an important reference point for the subsequent division of the work area.

[0031] S104. If the number of task-intensive areas is greater than the number of first robots, determine the first operating area and the second operating area according to the task distribution density map and the charging position information, wherein the number of first operating areas is equal to the number of first robots, and the second operating area includes at least one available charging pile.

[0032] For example, if it is found that the number of task-intensive areas exceeds the number of first robots (high-power robots), the second robot is required to assist in completing the corresponding work. Two types of work areas are defined based on the task distribution density map and the location information of available charging piles: the first work area and the second work area. The number of first work areas is exactly equal to the number of first robots, which means that each such area will be solely responsible for a robot with sufficient power. For the second work area, its design includes at least one available charging pile so that multiple second robots with lower power can take turns to perform tasks and charge in time.

[0033] It should be noted that when it is detected that the number of task-intensive areas exceeds the number of first robots (high-power robots), the second operating area is planned first to ensure that the second operating area is closest to the available charging pile in the task-intensive area, and then the first operating area is planned.

[0034] When it is confirmed that there is a task-intensive area with more robots than the first one, the second operation area is divided based on whether it is close to an available charging pile. A key feature of the second operation area is that it contains at least one available charging pile. The reason for this is to solve the energy replenishment problem of the second robot. By arranging these second robots close to the charging piles, it is convenient for them to rotate and charge between tasks, thereby extending their working cycle. The available charging piles in the second operation area are only allowed to be used by the robots corresponding to the second operation area in the task increment mode.

[0035] In some embodiments, the first operation area can also be prioritized while ensuring that there are enough available charging piles. These first operation areas are usually assigned to robots with sufficient power to perform tasks. The selection of standard first operation areas may be based on multiple factors, such as the urgency of the task, the expected completion time, the complexity of the tasks in the area, etc. The goal is to ensure that each first operation area can be efficiently processed by a first robot.

[0036] Through the above scheme, the intelligent mobility assistance system can realize the effective allocation of resources and ensure good operating performance even in the face of complex and changing working environments. It not only improves the utilization rate of a single mobility robot, but also enhances the ability of the entire system to deal with emergencies.

[0037] S105, respectively controlling the first robots to execute a task included in a first operation area, and controlling the plurality of second robots to execute a task included in a second operation area in turn.

[0038] Exemplarily, instructions are issued to the first robots respectively, so that the first robots each undertake all tasks in a first operation area. For the second operation area, a rotation system is adopted, and multiple second robots perform tasks in the area in sequence or as needed.

[0039] This approach not only ensures that limited resources can be used efficiently even during peak hours, but also takes into account the endurance of the mobility robots, avoiding the overall efficiency drop caused by overuse of individual robots. Through this refined management, the entire intelligent mobility assistance system can maintain efficient and stable operation when facing a large number of concurrent requests.

[0040] In order to more clearly introduce the technical solution of the present application, the technical solution of the present application will be introduced through specific embodiments below. It should be noted that the specific embodiments are used to expand the technical solution of the present application, but are not intended to limit the present application.

[0041] In some embodiments, determining available charging piles according to the task-intensive area and a preset navigation map includes: determining working charging piles and non-working charging piles according to the preset navigation map. Determining an expansion area according to the available power value of the second robot, and determining an expansion area according to the expansion area and the task-intensive area. If the number of working charging piles in the expansion area is greater than a preset difference, setting the non-working charging pile corresponding to the preset difference as an available charging pile.

[0042] Exemplarily, according to the preset navigation map, all known charging pile locations are first identified. These charging piles are further distinguished into two states: "working" and "non-working". "Working charging pile" is a charging pile currently in use; while "non-working charging pile" is an idle charging pile. Based on the available power value of the second robot, a reasonable "expansion area" is calculated. This area is estimated based on the maximum range of movement that can be covered by the remaining power of the robot. The specific value of the expansion area can be calculated based on empirical data in actual applications or a pre-set formula. For example, the average feasible distance under different power levels can be obtained through historical data analysis, and then the corresponding area can be calculated. Combined with the information of the task-intensive area, according to the expansion area calculated above, an "expansion area" surrounding the task-intensive area is determined. This area is intended to cover all potential task points that may require additional charging pile support. The boundary of the expansion area should take into account the maximum feasible distance of the robot from the task point to the nearest charging pile, ensuring that the robot can return to charge even in the most unfavorable circumstances. A non-working charging pile with a preset difference is selected and set as an available charging pile, and the preset difference is the difference between the task-intensive area and the first robot. The selection criteria may be factors such as these charging stations are close to task-intensive areas and easy to reach.

[0043] By dynamically allocating work areas, all available resources can be utilized more efficiently during peak mission periods, avoiding service interruptions due to insufficient charging stations. This provides more charging opportunities for low-power robots, allowing them to maintain sufficient power while performing tasks, thereby extending the battery life of the entire team.

[0044] In some embodiments, a task distribution density map is generated based on published tasks and a preset navigation map, and the task distribution density map includes multiple task dense areas, including: determining the task starting position information based on the published tasks. A task path planning map is generated based on a preset path planning algorithm, the task starting position information and a preset navigation map, and the task path planning map includes multiple task paths. The task path planning map is divided into multiple first grid units, and the task path density in the first grid unit is determined based on the multiple task paths and the first grid units. The first grid unit whose task path density is greater than the preset first density threshold is set as the second grid unit. The task dense area is determined based on the preset density clustering algorithm, the preset distance weighting parameter and the second grid unit.

[0045] Exemplarily, based on the published tasks, the starting position of each published task is obtained. These location information is based on user needs or pre-set task points. Using a preset path planning algorithm, combined with the task starting position and the preset navigation map, one or more possible paths will be generated for each task. These paths take into account factors such as obstacles and terrain, and optimize the route to reduce time and energy consumption. The generated task path planning map includes multiple task paths, each of which represents a feasible route from the starting point to the end point of one or more tasks. The entire navigation map is divided into a series of first grid cells of the same size. For each first grid cell, its task path density is calculated according to the number of task paths passing through the cell. The task path density reflects the concentration of tasks in a specific area. A first density threshold is set as a standard, and those first grid cells whose task path density is greater than the threshold are marked as second grid cells. The second grid cell represents an area with a high task density. All second grid cells are processed using a preset density clustering algorithm (such as DBSCAN). Algorithm parameters include a neighborhood radius and a minimum number of neighborhood points, which are used to identify closely connected high-density areas. The result is that some initial task areas and third grid cells that are not included are preliminarily defined. For each second grid cell in the initial task area, the first weighted distance value between them is calculated using a preset distance weighting function. Based on the first weighted distance value, the initial task area is further subdivided into oversized areas and undersized areas. The second grid cells on the edge of the oversized area are reclassified as third grid cells for subsequent adjustment. The second weighted distance value between the third grid cell and the undersized area is calculated. According to the second weighted distance value, the third grid cell is assigned to the closest undersized area, thereby updating the initial task area to the first update area and the second update area. Finally, the Voronoi diagram technology is used to construct the boundaries of each area and smooth them to obtain the final distribution of task-intensive areas.

[0046] By effectively planning the task path, unnecessary moving distances are reduced, thereby reducing the robot's energy consumption and extending the operating time after a single charge. By scientifically analyzing the distribution of tasks, areas with a high concentration of tasks can be identified more quickly and accurately, which helps to formulate more reasonable robot scheduling strategies.

[0047] In some embodiments, according to a preset clustering algorithm, a preset distance weighting parameter and a second grid unit, a task intensive area is determined, including: according to a preset neighborhood radius and a preset minimum number of neighborhood points, a density clustering process is performed on the second grid unit to obtain an initial task area and a third grid unit outside the initial task area. According to a preset distance weighting function, a first weighted distance value of each second grid unit in the initial task area is determined respectively. According to the first weighted distance value, the initial task area is divided into an oversized area and an undersized area. The second grid unit at the edge of the oversized area is set as a third grid unit. The second weighted distance value between the third grid unit and the undersized area is calculated. According to the second weighted distance value, the third grid unit is assigned to the corresponding undersized area, the oversized area is updated to the first update area, and the undersized area is updated to the second update area. According to the first update area and the second update area, a Voronoi diagram is constructed, and boundary smoothing is performed to generate a task intensive area.

[0048] Exemplarily, the second grid unit is subjected to density clustering using a preset neighborhood radius and a minimum number of neighborhood points. The neighborhood radius defines the range considered around each unit, while the minimum number of neighborhood points specifies the minimum number of units required to form a cluster. Through a density clustering algorithm (such as DBSCAN), the areas with higher density are identified as the initial task area, and the third grid units that are not in these areas are determined. For each second grid unit in the initial task area, a preset distance weighting function is applied to calculate the first weighted distance value between it and other units in the area. The distance weighting function can be based on Euclidean distance, Manhattan distance or other suitable distance metrics, and combined with a weight factor to reflect the relative importance or closeness between units. According to the first weighted distance value, the initial task area is divided into an oversized area and an undersized area. An oversized area refers to an area where the distance between internal units is large and the distribution is sparse; while an oversized area refers to an area where the distance between internal units is small and the distribution is more concentrated. The specific division criteria can be set according to actual needs, for example, by setting a threshold to distinguish between oversized areas and oversized areas. Reclassify the second grid cell on the edge of the oversized area into a third grid cell to reduce the area of ​​the oversized area and make it more compact. Calculate the second weighted distance value between the third grid cell and the undersized area, and then assign the third grid cell to the nearest undersized area based on these distance values, thereby updating the boundaries of the oversized area and the undersized area. Construct a Voronoi diagram based on the first updated area and the second updated area. The Voronoi diagram is a spatial segmentation method that divides a plane into several regions, each of which contains all locations that are closer to a specific point than to any other point. The generated Voronoi diagram is subjected to boundary smoothing to eliminate unnecessary sharp corners and irregular shapes, making the boundaries of the task-intensive area more natural and smooth. After the above steps, the optimized distribution of task-intensive areas is obtained. These task-intensive areas not only reflect the actual distribution of tasks, but also take into account the efficiency of path planning and robot scheduling.

[0049] In some embodiments, if the number of task-intensive areas is less than the number of first robots, the method further includes: detecting the task path density of the task-intensive area. Setting the task-intensive area where the task path density is greater than a preset second density threshold as the first operating area, and setting the task-intensive area where the task path density is less than or equal to the preset second density threshold as the second operating area. Setting the first robot as the third robot and the fourth robot, respectively, wherein the number of the third robots is equal to the number of the second operating areas. Controlling the third robots to perform a task included in the second operating area respectively. Controlling multiple fourth robots to jointly perform a task included in the first operating area, or controlling multiple second robots to take turns with the third robot to jointly perform a task included in the first operating area.

[0050] Exemplarily, when the number of task-intensive areas is less than the number of first robots, each task-intensive area needs to be evaluated first. The actual workload of the task-intensive area is determined by calculating the task path density of each grid unit in the task path planning diagram. Task-intensive areas whose task path density is higher than the preset second density threshold are defined as the first operation area. These areas need to be processed first because of the large amount of tasks. On the contrary, task-intensive areas whose task path density is lower than or equal to the second density threshold are defined as the second operation area. These areas may contain fewer tasks or be sparsely distributed. Sufficient robots (whose number is equal to the number of first operation areas) are selected from the existing first robots as third robots. The third robot will be responsible for performing tasks in the second operation area. The remaining first robots are assigned as fourth robots. The fourth robot can jointly perform tasks in a first operation area, or collaborate with the second robot to complete tasks. The third robot will independently go to its corresponding second operation area and perform all tasks assigned to them in the area. For the first operation area, you can choose to let multiple fourth robots work together to improve efficiency, or you can let the fourth robot and the second robot take turns to perform tasks, so that the charging needs of the robots can be taken into account while ensuring the completion of the task. In actual operation, the configuration of the third and fourth robots can be adjusted according to the real-time situation. For example, if a task in a second operation area is completed ahead of schedule, the third robot originally responsible for that area can be reallocated to other task-intensive areas or assist in handling tasks in the first operation area. Similarly, for the second operation area, the number and combination of participating robots are dynamically adjusted according to the changes in tasks and robot status to ensure maximum resource utilization.

[0051] By dividing the task-intensive area into the first operation area and the second operation area according to the task path density, and reassigning the robot roles to the third and fourth robots accordingly, this strategy optimizes the robot's work distribution, ensuring that high-density task areas can be processed first, while low-density task areas can also be effectively covered. This not only improves the overall work efficiency, but also ensures the effective use of the power of each robot through reasonable scheduling, and enhances the flexibility and adaptability of the system.

[0052] In some embodiments, controlling multiple second robots to perform tasks included in a second operating area in turn includes: setting a second robot with a larger available power value as an attendance robot, and setting another second robot as a standby robot. Controlling the attendance robot to perform the tasks included in the second operating area, and controlling the standby robot to charge at the available charging pile corresponding to the second operating area. When the available power value of the attendance robot is lower than the preset first power threshold, or the available power value of the standby robot is greater than the preset second power threshold, the standby robot is controlled to perform the tasks included in the second operating area, and the attendance robot is controlled to charge at the available charging pile corresponding to the second operating area.

[0053] Exemplarily, in the second operation area, multiple second robots are divided into attendance robots and standby robots according to the available power value. Specifically, one with a larger power value is selected as the attendance robot, which is responsible for performing the task; and the other with a lower power value is selected as the standby robot, ready to charge or standby. The attendance robot starts to perform the task in the second operation area. During the execution process, its available power value is continuously monitored to ensure that it can successfully complete the assigned task. At the same time, the standby robot is controlled to charge at an available charging pile in or near the second operation area. This ensures that the standby robot can replace the attendance robot in time when needed. Monitor whether the available power value of the attendance robot is lower than the preset first power threshold, or whether the available power value of the standby robot is higher than the preset second power threshold. If the power of the attendance robot is lower than the first power threshold, it indicates that it may not be able to continue to complete the remaining tasks, and it needs to be switched. If the power of the standby robot is higher than the second power threshold, it indicates that it has been charged and can take over the work of the attendance robot. When any of the above conditions is met, the role switch is performed immediately. The standby robot becomes the new attendance robot and starts to perform the unfinished task. The original on-duty robot becomes the new standby robot and goes to the charging station to charge and prepare for the next mission. The status of the two robots is continuously monitored and their roles are adjusted according to the actual situation to ensure the continuity and efficiency of the mission.

[0054] In some embodiments, dividing the walking robot into a first robot and a second robot according to the available power value includes: setting the walking robot whose available power value is within a preset first power value interval as the first robot, and setting the walking robot whose available power value is within a preset second power value interval as the second robot.

[0055] Exemplarily, when entering the task increment mode, the current available power data of all walking robots to be assigned tasks are first collected. This step is the basis for classifying robot types. Two preset power value intervals are defined: a first power value interval and a second power value interval. These two intervals are set according to actual needs and the working performance of the robot. The first power value interval generally represents a higher power level, which is suitable for tasks of longer time or distance. The second power value interval represents a lower but still sufficient power level, which is suitable for tasks of shorter time or distance, and is convenient for fast charging rotation.

[0056] According to the current task requirements and the specific conditions of each operation area, the ratio of the first robot and the second robot can be flexibly adjusted to ensure that the overall work efficiency is maximized while maintaining good energy management.

[0057] See also Figure 2 , Figure 2 2 is a schematic block diagram of a control device of an intelligent walking assistance system provided in an embodiment of the present application, wherein the control device 200 of the intelligent walking assistance system is used to execute the control method of the intelligent walking assistance system described above. The control device 200 of the intelligent walking assistance system can be configured in a server.

[0058] Among them, the server can be an independent server or a server cluster, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN) and big data and artificial intelligence platforms.

[0059] like Figure 2 As shown, the control device 200 of the intelligent mobility assistance system includes: a power measurement module 201, a task management module 202, a charging control module 203, a region division module 204 and a motion control module 205.

[0060] The power measurement module 201 is used to obtain the available power value of the walking robot when entering the task increment mode, and divide the walking robot into a first robot and a second robot according to the available power value.

[0061] The task management module 202 is used to generate a task distribution density map according to the published tasks and the preset navigation map, and the task distribution density map includes a plurality of task dense areas.

[0062] The charging control module 203 is used to determine the available charging piles according to the task-intensive area and the preset navigation map, and obtain the charging location information of the available charging piles.

[0063] The area division module 204 is used to determine the first working area and the second working area according to the task distribution density map and the charging position information if the number of task-intensive areas is greater than the number of first robots, wherein the number of first working areas is equal to the number of first robots, and the second working area includes at least one available charging pile.

[0064] The motion control module 205 is used to control the first robot to perform a task included in a first working area, and control multiple second robots to perform a task included in a second working area in turn.

[0065] An embodiment of the present application provides an electronic device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement a control method of an intelligent mobility assistance system as described in any one of the embodiments of the present application when executing the computer program.

[0066] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements a control method for an intelligent mobility assistance system as described in any one of the embodiments of the present application.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A control method for an intelligent mobility assistance system, characterized in that: The method comprises: When entering the task increment mode, obtaining an available power value of the walking robot, and dividing the walking robot into a first robot and a second robot according to the available power value; Generate a task distribution density map according to the published tasks and a preset navigation map, wherein the task distribution density map includes a plurality of task intensive areas; Determine available charging piles according to the task-intensive area and the preset navigation map, and obtain charging location information of the available charging piles; If the number of the task-intensive areas is greater than the number of the first robots, determining a first operation area and a second operation area according to the task distribution density map and the charging position information, wherein the number of the first operation areas is equal to the number of the first robots, and the second operation area includes at least one of the available charging piles; The first robots are controlled to execute the tasks included in the first operation area respectively, and the plurality of second robots are controlled to execute the tasks included in the second operation area in turn.

2. The control method of the intelligent mobility assistance system according to claim 1, characterized in that: The determining of available charging piles according to the task-intensive area and the preset navigation map includes: Determine the working charging pile and the non-working charging pile according to the preset navigation map; Determine an expansion area according to the available power value of the second robot, and determine an expansion region according to the expansion area and the task-intensive area; If the number of working charging piles in the extended area is greater than a preset difference, the non-working charging piles corresponding to the preset difference are set as the available charging piles.

3. The control method of the intelligent mobility assistance system according to claim 1, characterized in that: The task distribution density map is generated according to the published tasks and the preset navigation map, and the task distribution density map includes multiple task dense areas, including: Determine the task starting position information according to the published task; Generate a task path planning map according to a preset path planning algorithm, the task starting position information and the preset navigation map, wherein the task path planning map includes multiple task paths; Dividing the task path planning graph into a plurality of first grid units, and determining the task path density in the first grid units according to the plurality of task paths and the first grid units; Setting a first grid unit in which the task path density is greater than a preset first density threshold as a second grid unit; The task intensive area is determined according to a preset density clustering algorithm, a preset distance weighting parameter and the second grid unit.

4. The control method of the intelligent mobility assistance system according to claim 3, characterized in that: The step of determining the task intensive area according to a preset clustering algorithm, a preset distance weighting parameter and the second grid unit includes: According to a preset neighborhood radius and a preset minimum number of neighborhood points, density clustering processing is performed on the second grid unit to obtain an initial task area and a third grid unit outside the initial task area; According to a preset distance weighting function, respectively determining a first weighted distance value of each of the second grid cells in the initial task area; Dividing the initial task area into an overly large area and an overly small area according to the first weighted distance value; Setting the second grid unit at the edge of the oversized area as the third grid unit; Calculating a second weighted distance value between the third grid unit and the too-small area; Allocating the third grid unit to the corresponding too-small area according to the second weighted distance value, updating the too-large area to a first updated area, and updating the too-small area to a second updated area; A Voronoi diagram is constructed based on the first update region and the second update region, and boundary smoothing is performed to generate the task intensive area.

5. The control method of the intelligent mobility assistance system according to claim 1, characterized in that: If the number of the task-intensive areas is less than the number of the first robots, the method further includes: Detecting the task path density in the task-intensive area; The task-intensive area where the task path density is greater than a preset second density threshold is set as a first operation area, and the task-intensive area where the task path density is less than or equal to the preset second density threshold is set as a second operation area; Setting the first robots as third robots and fourth robots respectively, wherein the number of the third robots is equal to the number of the second working areas; respectively controlling the third robots to perform the tasks included in the one of the second working areas; Control the plurality of fourth robots to jointly execute a task included in the first operation area, or control the plurality of second robots to take turns with the third robot to jointly execute a task included in the first operation area.

6. The control method of the intelligent mobility assistance system according to claim 1, characterized in that: The controlling the plurality of the second robots to perform a task in the second operation area in turn comprises: The second robot with a larger available power value is set as an on-duty robot, and the other second robot is set as a standby robot; Control the on-duty robot to perform the tasks included in the second operation area, and control the standby robot to charge at the available charging pile corresponding to the second operation area; When the available power value of the attendance robot is lower than the preset first power threshold, or the available power value of the standby robot is greater than the preset second power threshold, the standby robot is controlled to perform the tasks included in the second working area, and the attendance robot is controlled to charge at the available charging pile corresponding to the second working area.

7. The control method of the intelligent mobility assistance system according to claim 1, characterized in that: The step of dividing the walking robot into a first robot and a second robot according to the available power value comprises: The mobility robot whose available power value is within a preset first power value interval is set as the first robot; The mobility robot whose available power value is within a preset second power value interval is set as the second robot.

8. A control device for an intelligent mobility assistance system, characterized in that: The control device of the intelligent walking assistance system comprises: A power measurement module, used for obtaining an available power value of the walking robot when entering the task increment mode, and dividing the walking robot into a first robot and a second robot according to the available power value; A task management module, used to generate a task distribution density map according to the published tasks and a preset navigation map, wherein the task distribution density map includes a plurality of task intensive areas; A charging control module, used to determine available charging piles according to the task-intensive area and the preset navigation map, and obtain charging location information of the available charging piles; an area division module, configured to determine, if the number of the task-intensive areas is greater than the number of the first robots, a first operation area and a second operation area according to the task distribution density map and the charging position information, wherein the number of the first operation areas is equal to the number of the first robots, and the second operation area includes at least one of the available charging piles; The motion control module is used to control the first robot to execute the tasks included in the first working area, and to control multiple second robots to execute the tasks included in the second working area in turn.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the control method of the intelligent mobility assistance system as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the control method of the intelligent mobility assistance system according to any one of claims 1 to 7.

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