Robot road network planning method, system and equipment based on inevitable rules and medium

Abstract

The invention provides a robot road network planning method, system and device based on inevitable rules and a medium. The method comprises the following steps: pre-configuring rule elements of a robot, determining an optional rule group according to a distribution task, and generating atomic rules according to the rule elements; assembling the atomic rules into precise rules matched with the rule group according to the priority ranking; and generating road network sub-paths of the rule group according to the precise rule on the global map, obtaining map path constraints and path traffic state optimization road network sub-paths, and outputting a target road network.

Description

Technical Field

[0001] The present invention relates to the technical field of road network planning, and in particular to a robot road network planning method, system, equipment and medium based on mandatory rules. Background Art

[0002] In a hospital environment, the efficiency of material distribution is particularly important for the quality of medical services. Traditional medical material distribution is mainly done manually, which has problems such as low efficiency, prone to errors, and high labor costs. With the development of robotics technology, hospitals have begun to use robots for material distribution to improve distribution efficiency and accuracy. Due to the complex hospital environment and various traffic restrictions and special traffic requirements, how to plan a reasonable road network for robots has become a key issue.

[0003] Existing robot road network planning methods mainly perform path planning based on factors such as map information and sensor ranging information. In actual applications, there are unconventional areas such as elevators or special access control in hospitals, and the planned path cannot be obtained directly through ranging. In addition, the traffic environment and traffic conditions in the hospital are extremely complex. The above methods cannot flexibly adjust the hospital road network in a timely manner. If manual intervention is used, the robot's autonomous decision-making will be limited, reducing the feasibility of hospital road network planning.

[0004] Therefore, there is an urgent need for a robot road network planning method based on mandatory rules to adapt to the road network planning of the hospital environment. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a robot road network planning method, system, device and medium based on mandatory rules.

[0006] The first aspect of the present invention discloses a robot road network planning method based on mandatory rules, comprising:

[0007] Preconfigure the robot's rule elements, determine the optional rule groups based on the delivery tasks, and generate atomic rules based on the rule elements;

[0008] Assembling the atomic rules into precise rules matching the rule group according to the priority order;

[0009] Generate road network sub-paths of rule groups based on precise rules on the global map, obtain map path constraints and path traffic conditions to optimize the selection of road network sub-paths, and output the target road network.

[0010] In an optional embodiment, the rule elements of the pre-configured robot determine an optional rule group according to the delivery task, including:

[0011] Obtain an optional starting point and an optional end point in the hospital map, and configure the optional starting point and the optional end point as the start object and the end object of the rule element respectively;

[0012] The necessary areas within any path of the starting object and the ending object are configured as necessary objects of the rule element, and the rule element is composed of the starting object, the necessary objects and the ending object;

[0013] Get the target starting point and target end point of the delivery task and assign them to the rule elements, and filter the rule groups with the same starting object and the same end object.

[0014] In an optional embodiment, generating an atomic rule according to rule elements includes:

[0015] In response to the delivery demand obtained in real time, optional rule elements are queried according to the delivery demand, and at least two different rules in the rule elements are selected to generate atomic rules.

[0016] In an optional embodiment, the sorting according to priority includes:

[0017] Collect historical delivery data to obtain the urgency level of delivery tasks, and set the priority threshold of atomic rules according to the urgency level;

[0018] Using pre-order traversal and / or depth traversal atomic rules, taking the starting object in the rule element as the root node and using binary tree or graph traversal as the optional atomic rule of the branch, the association relationship between the node and the branch is obtained;

[0019] The association relationships between the nodes and branches are sorted according to the priority threshold to determine the optional order of the atomic rules.

[0020] In an optional embodiment, the assembling of atomic rules into precise rules matching the rule group includes:

[0021] According to the optional order of the atomic rules, the actual rule configuration of each rule element under different rule groups is confirmed, and a precise rule that satisfies the starting object, the necessary object and the ending object in the rule group is generated.

[0022] In an optional embodiment, generating a road network sub-path of a rule group according to precise rules on a global map includes:

[0023] Map data is obtained to generate multiple paths with optional connections according to the positions of each rule element in the precise rule, and the multiple paths are allocated to the rule group to generate road network sub-paths.

[0024] In an optional embodiment, the step of obtaining map path constraints and path traffic status to optimize the selection of road network sub-paths and outputting a target road network includes:

[0025] Obtain the map data and traffic data of the hospital, generate the access constraints, access control constraints and action constraints of the optional paths according to the map data, and filter the optional road network sub-paths of different rule groups according to the constraints of the optional paths;

[0026] The congestion threshold of each road network sub-path is generated in real time according to the traffic data, the optional road network sub-path is secondary screened according to the congestion threshold, and a target road network that meets the distribution requirements of all rule groups is output.

[0027] A second aspect of the present invention discloses a robot road network planning system based on mandatory rules, the system comprising:

[0028] The rule configuration module is used to pre-configure the rule elements of the robot, determine the optional rule groups according to the delivery tasks, and generate atomic rules according to the rule elements;

[0029] A rule assembly module, used for assembling the atomic rules into precise rules matching the rule group according to priority sorting;

[0030] The road network planning module is used to generate road network sub-paths of rule groups based on precise rules on the global map, obtain map path constraints and path traffic conditions to optimize the selection of road network sub-paths, and output the target road network.

[0031] In an optional embodiment, the rule configuration module includes:

[0032] An element configuration module, used for obtaining an optional starting point and an optional end point in the hospital map, and configuring the optional starting point and the optional end point as the start object and the end object of the rule element respectively;

[0033] The necessary areas within any path of the starting object and the ending object are configured as necessary objects of the rule element, and the rule element is composed of the starting object, the necessary objects and the ending object;

[0034] Get the target starting point and target end point of the delivery task and assign them to the rule elements, and filter the rule groups with the same starting object and the same end object.

[0035] In an optional embodiment, the rule configuration module includes:

[0036] The atomic rule module is used to respond to the delivery demand obtained in real time, query the optional rule elements according to the delivery demand, and select at least two different rules from the rule elements to generate atomic rules.

[0037] In an optional embodiment, the rule assembly module includes:

[0038] A rule sorting module is used to collect historical delivery data to obtain the urgency level of delivery tasks, and set the priority threshold of the atomic rule according to the urgency level; use pre-order traversal and / or depth traversal of the atomic rule, take the starting object in the rule element as the root node through a binary tree or graph traversal as the optional atomic rule of the branch, and obtain the association relationship between the node and the branch; sort the association relationship between the node and the branch according to the priority threshold to determine the optional order of the atomic rule.

[0039] In an optional embodiment, the rule assembly module includes:

[0040] The precise rule module is used to confirm the actual rule configuration of each rule element under different rule groups according to the optional order of the atomic rules, and generate precise rules that meet the starting object, the required object and the ending object in the rule group.

[0041] In an optional embodiment, the road network planning module includes:

[0042] The road network planning submodule is used to obtain map data and generate multiple paths with optional connections according to the positions of each rule element in the precise rule, and allocate the multiple paths to the rule group to generate road network subpaths.

[0043] In an optional embodiment, the road network planning module includes:

[0044] The road network output module is used to obtain the map data and traffic data of the hospital, generate the access constraints, access control constraints and action constraints of the optional paths according to the map data, and screen the optional road network sub-paths of different rule groups according to the constraints of the optional paths; generate the congestion threshold of each road network sub-path in real time according to the traffic data, perform secondary screening on the optional road network sub-paths according to the congestion threshold, and output the target road network that meets the distribution requirements of all rule groups.

[0045] The third aspect of the present invention discloses a robot road network planning device based on mandatory rules, comprising:

[0046] at least one processor, and

[0047] a memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the robot road network planning method based on necessary rules as described in any one of the first aspects of the present invention.

[0049] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the robot road network planning method based on necessary rules as described in any one of the first aspect of the present invention.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] The present invention proposes a robot road network planning method, system, device and medium based on necessary rules. The method includes pre-configuring the rule elements of the robot, determining the optional rule group according to the delivery task, generating atomic rules according to the rule elements; assembling the atomic rules into precise rules matching the rule group according to the priority sorting; generating the road network sub-path of the rule group according to the precise rules on the global map, obtaining the map path constraints and the path traffic status to optimize the selection of the road network sub-path, and outputting the target road network. The configuration method of the necessary objects and rules for the hospital environment reasonably plans the path, reduces the time waste of the robot in the delivery process to improve the delivery efficiency, determines the road network application in the hospital delivery scene by the rule group and priority, considers the specific areas and equipment of the hospital, ensures that the robot can accurately reach the destination, improves the delivery accuracy, can flexibly meet the needs of priority delivery and coordinated delivery in different situations, combines the hospital map data and the real-time traffic congestion situation to perform path planning and secondary screening, and through the collaborative work of the perception system, navigation system and motion system in hospital delivery, can reduce human intervention so that the robot can autonomously plan the path according to the actual situation, improve the intelligence of the robot, and has a high application value in the field of robot road network planning in the hospital scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 A flowchart of a robot road network planning method based on mandatory rules of the present invention;

[0054] Figure 2 Schematic diagram of the robot road network planning system based on mandatory rules of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0056] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0057] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0058] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0059] Example 1

[0060] See also Figure 1 The embodiment of the present invention discloses a robot road network planning method based on necessary rules, including:

[0061] 101. Pre-configure the robot's rule elements, determine the optional rule group according to the delivery task, and generate atomic rules based on the rule elements;

[0062] In an optional embodiment, the rule elements of the pre-configured robot determine an optional rule group according to the delivery task, including:

[0063] Obtain an optional starting point and an optional end point in the hospital map, and configure the optional starting point and the optional end point as the start object and the end object of the rule element respectively;

[0064] The necessary areas within any path of the starting object and the ending object are configured as necessary objects of the rule element, and the rule element is composed of the starting object, the necessary objects and the ending object;

[0065] Get the target starting point and target end point of the delivery task and assign them to the rule elements, and filter the rule groups with the same starting object and the same end object.

[0066] In an optional embodiment, generating an atomic rule according to rule elements includes:

[0067] In response to the delivery demand obtained in real time, optional rule elements are queried according to the delivery demand, and at least two different rules in the rule elements are selected to generate atomic rules.

[0068] It should be noted that the rule elements include the starting point, passing point and end point of any task performed by the robot. It can be understood that the various element points related to distribution in the hospital environment are atomized, and the element points are configured as path generation rules that need to be considered in the road network path executed by the robot through rule configuration. For example, in task A, medical supplies need to be taken from the material storage area (starting point) and then delivered to the corresponding department (end point). The material storage area is configured as the starting object in the rule element, and the department that may need to receive the materials is configured as the end object in the rule element. It should be noted that any starting object can also be an end object. If the end object of task A in the two tasks is the gynecology department, and the starting object of task B is also the gynecology department, then the gynecology department in the road network satisfies the rules as the starting point and end point at the same time, so that the robot can respond quickly and continuously perform delivery tasks.

[0069] Specifically, due to the complex hospital environment, path planning has its own particularities, especially since the starting and ending objects often need to shuttle through narrow hospital corridors, elevators, special access control areas, etc. The corridor area is often equipped with mobile beds or medical equipment, and the robot needs to perform special avoidance actions. The elevator area requires the robot to take the elevator and get off the elevator to complete the movement between floors. For special access control areas, it is necessary to request the access control to be opened or wait for the access control to be opened. The actual hospital environment includes but is not limited to the above special situations. In actual path planning, if the robot's control input is generated only based on map information and sensor ranging feedback to complete path planning, it is not suitable for the hospital environment. The road network planned by the above method This will cause the robot to be hindered due to special circumstances during the delivery process, making it impossible for the robot to reach the destination accurately. For several robots that perform delivery tasks at the same time, the paths corresponding to each sub-delivery task in the road network of the hospital environment are interconnected and related to each other. In particular, the hospital environment has high requirements for the efficiency of delivery tasks. If the robot encounters obstacles or fails to reach the specified precise position due to poor road network planning results, it can only be solved through manual control. In the time-sensitive hospital environment, such adjustment methods are not allowed. Therefore, it is necessary to configure each possible starting point, must-pass point and end point as a rule element. The atomized delivery task is easier for the robot to understand and no manual intervention is required.

[0070] Furthermore, in order to avoid the impact of special areas and special situations in the above-mentioned hospital environment on the hospital road network planning, the special areas and special situations that may exist between the paths of any starting object and the ending object are configured as must-pass objects. These must-pass objects include but are not limited to elevators, electric doors of special departments and other areas that must be passed. The starting object, must-pass objects and end objects can be used as the starting point, passing point and end point that can be selected for any delivery task in the hospital road network. The feasibility of road network planning is further restricted through rule elements, so that the generated road network path can meet the needs of the hospital environment and avoid the robot being affected by special moments and special situations, thereby improving delivery efficiency and accuracy.

[0071] Furthermore, the atomic rule can be understood as all optional task object points in the hospital are atomized and fuzzily combined according to the distribution requirements. For example, when receiving a distribution request from Department A, the robot needs to move to storage area B to pick up the corresponding materials and then deliver them to Department A. If the robot has completed the previous distribution task at this time, then storage area B will be used as the starting object and department A will be used as the ending object in the distribution task at this time. Then, an atomic rule is generated with the starting object being storage area B and the ending object being department A. Subsequently, the road network sub-path corresponding to the robot is generated through the restriction of the atomic rule on the distribution task. It is easy to know that the atomic rules can be combined. For example, if the robot has not completed the previous distribution task at this time, storage area B is configured as the ending object in the atomic rule in the previous distribution task, and storage area B is configured as a must-pass object in this task. Therefore, the generated atomic rule is that the must-pass object is storage area B and the ending object is department A, so that any possible distribution task is expressed through atomic rules to be applicable to the subsequent road network planning for the robot, thereby improving accuracy and distribution efficiency.

[0072] 102. Assemble the atomic rules into precise rules matching the rule group according to the priority order;

[0073] In an optional embodiment, the sorting according to priority includes:

[0074] Collect historical delivery data to obtain the urgency level of delivery tasks, and set the priority threshold of atomic rules according to the urgency level;

[0075] Using pre-order traversal and / or depth traversal atomic rules, taking the starting object in the rule element as the root node and using binary tree or graph traversal as the optional atomic rule of the branch, the association relationship between the node and the branch is obtained;

[0076] The association relationships between the nodes and branches are sorted according to the priority threshold to determine the optional order of the atomic rules.

[0077] Specifically, in actual road network planning, it is necessary to consider the comprehensive efficiency of each path in the global map to complete the delivery task. Therefore, a trade-off optimization needs to be made between different delivery tasks, and priority sorting can be used to determine the order in which the rules are used. For example, in an emergency, the rule that passes through a smaller number of must-pass objects and reaches the end object in the shortest time or the shortest path is given priority to plan the hospital road network. The setting of the priority threshold can be processed by a classification model, such as inputting historical data and priority constraints of actual delivery as training corpus, training the classification model to output the urgency level of delivery tasks in different scenarios, and setting thresholds for each level. It can be understood that the priority threshold can be adjusted through real-time updated delivery events to ensure the rationality of the priority sorting as much as possible, and to ensure the distribution efficiency and accuracy of the generated hospital road network.

[0078] Furthermore, the binary tree in the pre-order traversal is usually used to select the starting objects and the associated necessary objects and the end objects of multiple delivery tasks in turn, so as to connect the configured atomic rules in series to generate selectable road network sub-paths one by one. If the pre-order traversal cannot effectively complete the sorting work, the deep traversal can be further used to search for the associated atomic rules. It can be understood that the pre-order traversal is a special deep traversal. When considering the computational loss, using the pre-order traversal first can reduce the computational loss and improve the computational efficiency.

[0079] In an optional embodiment, the assembling of atomic rules into precise rules matching the rule group includes:

[0080] According to the optional order of the atomic rules, the actual rule configuration of each rule element under different rule groups is confirmed, and a precise rule that satisfies the starting object, the necessary object and the ending object in the rule group is generated.

[0081] It can be understood that the various starting points, necessary points and end points in the hospital map are taken as atomic rule elements. The rule group is first classified to determine its starting object and ending object, so as to facilitate the subsequent generation of the paths of each atomic rule element in the intermediate path between the starting object and the ending object. Since the atomic rules can be sorted according to priority, the path planning is improved by adding the atomic rules to the rule group to generate precise rules with the robot as the moving unit, which can provide clear control information for the robot. It should be noted that in the actual road network planning, the paths of various delivery tasks in the hospital environment are dynamically planned. Therefore, the optional atomic rules can flexibly adjust the paths executed by the actual rule group. Therefore, several different precise rules can be generated to control the robot to complete the corresponding delivery tasks of multiple atomic rules. Based on the above rule configuration, human intervention can be reduced, and the path planning that follows the rule configuration can increase rationality and reduce computing loss, thereby improving delivery efficiency and delivery accuracy.

[0082] 103. Generate road network sub-paths of rule groups based on precise rules on the global map, obtain map path constraints and path traffic conditions to optimize the selection of road network sub-paths, and output the target road network.

[0083] In an optional embodiment, generating a road network sub-path of a rule group according to precise rules on a global map includes:

[0084] Map data is obtained to generate multiple paths with optional connections according to the positions of each rule element in the precise rule, and the multiple paths are allocated to the rule group to generate road network sub-paths.

[0085] In an optional embodiment, the step of obtaining map path constraints and path traffic status to optimize the selection of road network sub-paths and outputting a target road network includes:

[0086] Obtain the map data and traffic data of the hospital, generate the access constraints, access control constraints and action constraints of the optional paths according to the map data, and filter the optional road network sub-paths of different rule groups according to the constraints of the optional paths;

[0087] The congestion threshold of each road network sub-path is generated in real time according to the traffic data, the optional road network sub-path is secondary screened according to the congestion threshold, and a target road network that meets the distribution requirements of all rule groups is output.

[0088] Specifically, when actually generating the sub-paths of the road network, the control input of the robot between the specific atomic rule elements can be planned through the navigation system, decision-making system and hospital map, especially when moving across floors or between access control areas. The planned theoretical walking path results include but are not limited to the elevator information, walking constraint information, special action mechanism information of the path, etc. that need to be passed through when walking on the multi-layer map. The control quantities such as communication constraints, access control constraints and action constraints can further optimize the feasibility of the sub-paths of the road network and improve the efficiency of the robot in completing the delivery task. Especially in the case of traffic congestion, the theoretical walking path generation is executed cyclically, and combined with the real-time traffic congestion situation (such as the position of other robots, pedestrian flow, etc.), secondary screening is performed to finally obtain the most ideal theoretical walking path.

[0089] The present invention proposes a robot road network planning method, system, device and medium based on necessary rules. The method includes pre-configuring the rule elements of the robot, determining the optional rule group according to the delivery task, generating atomic rules according to the rule elements; assembling the atomic rules into precise rules matching the rule group according to the priority sorting; generating the road network sub-path of the rule group according to the precise rules on the global map, obtaining the map path constraints and the path traffic status to optimize the selection of the road network sub-path, and outputting the target road network. The configuration method of the necessary objects and rules for the hospital environment reasonably plans the path, reduces the time waste of the robot in the delivery process to improve the delivery efficiency, determines the road network application in the hospital delivery scene by the rule group and priority, considers the specific areas and equipment of the hospital, ensures that the robot can accurately reach the destination, improves the delivery accuracy, can flexibly meet the needs of priority delivery and coordinated delivery in different situations, combines the hospital map data and the real-time traffic congestion situation to perform path planning and secondary screening, and through the collaborative work of the perception system, navigation system and motion system in hospital delivery, can reduce human intervention so that the robot can autonomously plan the path according to the actual situation, improve the intelligence of the robot, and has a high application value in the field of robot road network planning in the hospital scene.

[0090] like Figure 2 As shown, the second aspect of the present invention discloses a robot road network planning system based on necessary rules, the system comprising:

[0091] The rule configuration module is used to pre-configure the rule elements of the robot, determine the optional rule groups according to the delivery tasks, and generate atomic rules according to the rule elements;

[0092] A rule assembly module, used for assembling the atomic rules into precise rules matching the rule group according to priority sorting;

[0093] The road network planning module is used to generate road network sub-paths of rule groups based on precise rules on the global map, obtain map path constraints and path traffic conditions to optimize the selection of road network sub-paths, and output the target road network.

[0094] In an optional embodiment, the rule configuration module includes:

[0095] An element configuration module, used for obtaining an optional starting point and an optional end point in the hospital map, and configuring the optional starting point and the optional end point as the start object and the end object of the rule element respectively;

[0096] The necessary areas within any path of the starting object and the ending object are configured as necessary objects of the rule element, and the rule element is composed of the starting object, the necessary objects and the ending object;

[0097] Get the target starting point and target end point of the delivery task and assign them to the rule elements, and filter the rule groups with the same starting object and the same end object.

[0098] In an optional embodiment, the rule configuration module includes:

[0099] The atomic rule module is used to respond to the delivery demand obtained in real time, query the optional rule elements according to the delivery demand, and select at least two different rules from the rule elements to generate atomic rules.

[0100] In an optional embodiment, the rule assembly module includes:

[0101] A rule sorting module is used to collect historical delivery data to obtain the urgency level of delivery tasks, and set the priority threshold of the atomic rule according to the urgency level; use pre-order traversal and / or depth traversal of the atomic rule, take the starting object in the rule element as the root node through a binary tree or graph traversal as the optional atomic rule of the branch, and obtain the association relationship between the node and the branch; sort the association relationship between the node and the branch according to the priority threshold to determine the optional order of the atomic rule.

[0102] In an optional embodiment, the rule assembly module includes:

[0103] The precise rule module is used to confirm the actual rule configuration of each rule element under different rule groups according to the optional order of the atomic rules, and generate precise rules that meet the starting object, the required object and the ending object in the rule group.

[0104] In an optional embodiment, the road network planning module includes:

[0105] The road network planning submodule is used to obtain map data and generate multiple paths with optional connections according to the positions of each rule element in the precise rule, and allocate the multiple paths to the rule group to generate road network subpaths.

[0106] In an optional embodiment, the road network planning module includes:

[0107] The road network output module is used to obtain the map data and traffic data of the hospital, generate the access constraints, access control constraints and action constraints of the optional paths according to the map data, and screen the optional road network sub-paths of different rule groups according to the constraints of the optional paths; generate the congestion threshold of each road network sub-path in real time according to the traffic data, perform secondary screening on the optional road network sub-paths according to the congestion threshold, and output the target road network that meets the distribution requirements of all rule groups.

[0108] The third aspect of the present invention discloses a robot road network planning device based on mandatory rules, comprising:

[0109] at least one processor, and

[0110] a memory communicatively connected to the at least one processor; wherein,

[0111] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the robot road network planning method based on necessary rules as described in any one of the first aspects of the present invention.

[0112] The computer device may be a terminal, and the computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a robot road network planning method based on necessary rules is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0113] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the robot road network planning method based on necessary rules as described in any one of the first aspect of the present invention.

[0114] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of the robot road network planning method based on the necessary rules. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0115] Alternatively, if the above modules of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, RAM, ROM, magnetic disks or optical disks.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robot road network planning method based on must-pass rules, characterized in that: The method comprises: Preconfigure the robot's rule elements, determine the optional rule groups based on the delivery tasks, and generate atomic rules based on the rule elements; Assembling the atomic rules into precise rules matching the rule group according to the priority order; Generate road network sub-paths of rule groups based on precise rules on the global map, obtain map path constraints and path traffic conditions to optimize the selection of road network sub-paths, and output the target road network.

2. The robot road network planning method based on the must-pass rule according to claim 1 is characterized in that: The rule elements of the pre-configured robot determine the optional rule group according to the delivery task, including: Obtain an optional starting point and an optional end point in the hospital map, and configure the optional starting point and the optional end point as the start object and the end object of the rule element respectively; The necessary areas within any path of the starting object and the ending object are configured as necessary objects of the rule element, and the rule element is composed of the starting object, the necessary objects and the ending object; Get the target starting point and target end point of the delivery task and assign them to the rule elements, and filter the rule groups with the same starting object and the same end object.

3. The robot road network planning method based on must-pass rules according to claim 2 is characterized in that: The step of generating atomic rules according to rule elements includes: In response to the delivery demand obtained in real time, optional rule elements are queried according to the delivery demand, and at least two different rules in the rule elements are selected to generate atomic rules.

4. The robot road network planning method based on must-pass rules according to claim 1 is characterized in that: The priorities are as follows: Collect historical delivery data to obtain the urgency level of the delivery task, and set the priority threshold of the atomic rule according to the urgency level; Using pre-order traversal and / or depth traversal atomic rules, taking the starting object in the rule element as the root node and using binary tree and / or graph traversal as the optional atomic rule of the branch to obtain the association relationship between the node and the branch; The association relationships between the nodes and branches are sorted according to the priority threshold to determine the optional order of the atomic rules.

5. The robot road network planning method based on must-pass rules according to claim 1 is characterized in that: The assembling of atomic rules into precise rules matching the rule group includes According to the optional order of the atomic rules, the actual rule configuration of each rule element under different rule groups is confirmed, and accurate rules that meet the starting object, the necessary object and the ending object in the rule group are generated.

6. The robot road network planning method based on must-pass rules according to claim 1 is characterized in that: The generating of the road network sub-path of the rule group according to the precise rules on the global map includes: Map data is obtained to generate multiple paths with optional connections according to the positions of each rule element in the precise rule, and the multiple paths are allocated to the rule group to generate road network sub-paths.

7. The robot road network planning method based on must-pass rules according to claim 1 is characterized in that: The obtaining of map path constraints and path traffic status to optimize the selection of road network sub-paths and outputting the target road network includes: Obtain the map data and traffic data of the hospital, generate the access constraints, access control constraints and action constraints of the optional paths according to the map data, and filter the optional road network sub-paths of different rule groups according to the constraints of the optional paths; The congestion threshold of each road network sub-path is generated in real time according to the traffic data, the optional road network sub-path is secondary screened according to the congestion threshold, and a target road network that meets the distribution requirements of all rule groups is output.

8. A robot road network planning system based on mandatory rules, characterized in that: The system comprises: The rule configuration module is used to pre-configure the rule elements of the robot, determine the optional rule groups according to the delivery tasks, and generate atomic rules according to the rule elements; A rule assembly module, used for assembling the atomic rules into precise rules matching the rule group according to priority sorting; The road network planning module is used to generate road network sub-paths of rule groups based on precise rules on the global map, obtain map path constraints and path traffic conditions to optimize the selection of road network sub-paths, and output the target road network.

9. A robot road network planning device based on mandatory rules, characterized in that: include: at least one processor, and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the robot road network planning method based on mandatory rules as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the robot road network planning method based on mandatory rules as described in any one of claims 1 to 7.

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