A method and device for intelligent planning of mobile robot's motion operation

Abstract

The application relates to the field of artificial intelligence, and discloses a marching operation intelligent planning method for a mobile robot, which comprises the following steps: acquiring a marching operation environment of the mobile robot, constructing a marching environment map of the mobile robot according to the marching operation environment; receiving a marching scheduling task of the mobile robot, dividing marching nodes of the mobile robot in the marching environment map according to the marching scheduling task, detecting an initial walking path of the mobile robot in the process of executing the marching scheduling task based on the marching nodes; calculating a node heat value of the marching nodes of the mobile robot in the process of executing the marching scheduling task in real time; weighting the marching nodes according to the node heat value to obtain weighted marching nodes, updating the initial walking path of the mobile robot based on the weighted marching nodes to obtain a final walking path of the mobile robot. The marching path of the mobile robot can be dynamically and timely adjusted, and the mobile robot can be ensured to be in an optimal operation path in real time.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a method, device, electronic device, and computer-readable storage medium for intelligently planning the movement of a mobile robot. Background Art

[0002] With the continuous development of information technology, the development speed of robots has continued to increase, and the scope of application has continued to expand. Many types of robots have emerged, such as operating robots, information robots, mobile robots, etc. For mobile robots, walking path planning has been the core algorithm in the field of mobile robots in recent years. By intelligently planning an optimal path for the mobile robot from the current position to the mission target point, the work efficiency of the mobile robot can be significantly improved.

[0003] Currently, mobile robots usually perform tasks according to pre-set navigation programs. However, in actual business scenarios, there will be many external interferences, which will cause the environment to change when the mobile robot is performing tasks. As a result, the pre-set navigation program cannot be adjusted in time, and thus it is impossible to accurately ensure that the mobile robot is in the optimal operation path. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method, device, electronic device and computer-readable storage medium for intelligent planning of the movement operation of a mobile robot, which can dynamically and in real time adjust the movement path of the mobile robot to ensure that the mobile robot is in the optimal operation path in real time.

[0005] In a first aspect, the present invention provides an intelligent planning method for a mobile robot's movement operation, comprising:

[0006] Acquiring a movement operating environment of the mobile robot, and constructing a movement environment map of the mobile robot based on the movement operating environment;

[0007] receiving a movement scheduling task of the mobile robot, dividing movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detecting an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes;

[0008] Calculating in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task;

[0009] According to the node heat value, the behavior nodes are weighted to obtain weighted behavior nodes, and based on the weighted behavior nodes, the preset behavior planning algorithm is used to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot.

[0010] In a possible implementation of the first aspect, constructing a movement environment map of the mobile robot according to the movement operation environment includes:

[0011] Determining the operation starting position, operation ending position, and operation area information of the mobile robot during the operation according to the operation environment;

[0012] Constructing a plane rectangular coordinate system of the mobile robot according to the operation starting position and the operation ending position;

[0013] According to the operation area information, an operation grid of the mobile robot is created in the plane rectangular coordinate system, and a movement environment map of the mobile robot is generated.

[0014] In a possible implementation of the first aspect, creating an operation grid of the mobile robot in the plane rectangular coordinate system based on the operation area information and generating a movement environment map of the mobile robot includes:

[0015] Determining the number of operating grids of the mobile robot in the plane rectangular coordinate system according to the operating area information to obtain an initial operating grid;

[0016] The data in the initial operation grid is encoded to obtain an encoded operation grid, and obstacles are marked on the encoded operation grid to generate a movement environment map of the mobile robot.

[0017] In a possible implementation of the first aspect, dividing the movement nodes of the mobile robot in the movement environment map according to the movement scheduling task includes:

[0018] determining a movement direction and a movement length of the mobile robot according to the movement scheduling task;

[0019] According to the travel direction and the travel length, the travel area passed by the mobile robot is marked in the travel environment map, and the travel area is used as the travel node.

[0020] In a possible implementation of the first aspect, detecting the initial walking path of the mobile robot in the process of executing the movement scheduling task based on the movement node and using a preset movement planning model includes:

[0021] Obtaining the initial weight of the path node and calculating the time window for the mobile robot to pass through the path node;

[0022] Calculating the shortest travel time of the mobile robot in the process of executing the travel scheduling task using the time optimal function in the preset travel planning model according to the initial weight and the time window;

[0023] According to the shortest travel time, an activation function in the preset travel planning model is used to output an initial walking path of the mobile robot.

[0024] In a possible implementation of the first aspect, calculating the time window for the mobile robot to pass through the movement node includes:

[0025] The time window for the mobile robot to pass through the movement node is calculated using the following formula:

[0026]

[0027] in, represents the time window of the mobile robot passing through the travel node i and the travel node j, l(i,j) represents the path length between the travel node i and the travel node j, l i and l j Respectively represent the node lengths of path node i and path node j, v s Represents the movement speed of the mobile robot passing through movement node i and movement node j.

[0028] In a possible implementation of the first aspect, the time optimal function includes:

[0029]

[0030] Among them, min t represents the shortest travel time, i represents the i-th travel node, k represents the number of travel nodes, represents the time window of action node i and action node j, The time window between action node j and action node k, w ij represents the node weights of path node i and path node j, w jk represents the node weights of path node j and path node k, represents the waiting time of the mobile robot, The turning time of the mobile robot is the waiting time.

[0031] In a possible implementation of the first aspect, the real-time calculation of the node heat value of the behavior node when the mobile robot performs the behavior scheduling task includes:

[0032] The node heat value of the movement node when the mobile robot performs the movement scheduling task is calculated in real time using the following formula:

[0033]

[0034] Among them, F(g) represents the node heat value, m g represents the g-th mobile robot, M represents the number of mobile robots, N represents the number of walking paths corresponding to the action nodes, and h k Represents the frequency of the mobile robot passing through the kth behavior node.

[0035] In a possible implementation of the first aspect, weighting the behavior nodes according to the node heat values ​​to obtain weighted behavior nodes includes:

[0036] The initial weight of the behavior node is obtained, the initial weight is weighted according to the node heat value to obtain a weighted weight, and a weighted behavior node is generated according to the weighted weight.

[0037] In a second aspect, the present invention provides an intelligent planning device for a mobile robot's movement operation, the device comprising:

[0038] A movement environment map construction module, configured to obtain a movement operating environment of the mobile robot and construct a movement environment map of the mobile robot based on the movement operating environment;

[0039] an initial walking path detection module, configured to receive a movement scheduling task of the mobile robot, divide movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detect an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes;

[0040] A node heat value calculation module, used for calculating in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task;

[0041] The final walking path generation module is used to weight the behavior nodes according to the node heat value to obtain weighted behavior nodes, and based on the weighted behavior nodes, use the preset behavior planning algorithm to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot.

[0042] Compared with the existing technology, the technical principle and beneficial effects of this solution are:

[0043] The scheme can determine the work area of the mobile robot when performing autonomous work, guarantee the path planning prerequisite of the subsequent mobile robot, and divide the mobile robot path nodes in the mobile robot path environment map, so as to more accurately calculate the time required by the mobile robot to perform the mobile robot scheduling task, further guarantee the accuracy of the path planning of the subsequent mobile robot, and based on the mobile robot path nodes, use a preset mobile robot path planning model to detect the initial walking path of the mobile robot in the process of performing the mobile robot scheduling task, to obtain the initial walking path of the mobile robot in the static state when performing the mobile robot scheduling task. Secondly, the embodiment of the application calculates the node heat value of the mobile robot path node in real time when performing the mobile robot scheduling task, so as to realize the mobile robot path environment in real time, so as to realize the path dynamic adjustment in time, guarantee the mobile robot to be in the optimal walking path in real time, and improve the work efficiency of the mobile robot. Further, the embodiment of the application obtains the weighted mobile robot path node by weighting the mobile robot path node according to the node heat value, to update the initial walking path of the mobile robot, and obtain the final walking path of the mobile robot, guarantee the mobile robot to be in the optimal work path in real time. Therefore, the mobile robot path operation intelligent planning method, device, electronic equipment and computer readable storage medium provided by the embodiment of the application can dynamically and real-timely adjust the mobile robot path, guarantee the mobile robot to be in the optimal work path in real time. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor.

[0046] Figure 1 A flowchart of a mobile robot path operation intelligent planning method provided by an embodiment of the present application is shown in the figure.

[0047] Figure 2 A flowchart of one step of a mobile robot path operation intelligent planning method provided by an embodiment of the present application is shown in the figure. Figure 1

[0048] A flowchart of one step of a mobile robot path operation intelligent planning method provided by an embodiment of the present application is shown in the figure. Figure 3 In one embodiment of the present invention Figure 1 A flowchart of another step of a method for intelligently planning a mobile robot's trajectory operation is provided;

[0049] Figure 4 A schematic diagram of a module of an intelligent motion planning device for a mobile robot according to an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of the internal structure of an electronic device for implementing an intelligent planning method for a mobile robot's movement operation provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] An embodiment of the present invention provides a method for intelligently planning the movement of a mobile robot. The execution subject of the method includes but is not limited to at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present invention. In other words, the method for intelligently planning the movement of a mobile robot can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, 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 (CDNs), and big data and artificial intelligence platforms.

[0053] Example 1:

[0054] See Figure 1 FIG. 1 is a flow chart of an intelligent planning method for a mobile robot's trajectory operation according to an embodiment of the present invention. Figure 1 The intelligent planning method for mobile robot's motion operation described in the paper includes:

[0055] S1. Acquire a movement operating environment of a mobile robot, and construct a movement environment map of the mobile robot according to the movement operating environment.

[0056] In an embodiment of the present invention, the mobile robot refers to a machine device that can work autonomously, such as a transport robot, a sweeping robot, and a food serving robot. The behavioral operating environment refers to the working environment in which the mobile robot is located when performing autonomous work, which is generated based on different business scenarios. For example, in a logistics picking scenario, the behavioral operating environment may be a logistics transport warehouse, and in a sweeping scenario, the behavioral operating environment may be the area to be cleaned.

[0057] Furthermore, the embodiment of the present invention constructs a movement environment map of the mobile robot according to the movement operation environment to determine the operation area of ​​the mobile robot when performing autonomous operation, thereby ensuring the prerequisite for subsequent path planning of the mobile robot.

[0058] As an embodiment of the present invention, see Figure 2 As shown, constructing the movement environment map of the mobile robot according to the movement operation environment includes:

[0059] S201, determining the operation start position, operation end position, and operation area information of the mobile robot during the operation according to the operation environment;

[0060] S202: Constructing a plane rectangular coordinate system of the mobile robot according to the operation starting position and the operation ending position;

[0061] S203: Create an operation grid of the mobile robot in the plane rectangular coordinate system according to the operation area information, and generate a movement environment map of the mobile robot.

[0062] Among them, the operation starting position and the operation ending position can be understood as the starting position sequence information and the target position sequence information of the mobile robot in the process of performing autonomous operation. The operation area information refers to the environmental information of the position area passed by the mobile robot in the process of performing autonomous operation, such as obstacle information, ground parameter information, etc. The plane rectangular coordinate system refers to the operation plane diagram used to characterize the mobile robot in the process of performing autonomous operation. The operation grid is used to divide the operation area passed by the mobile robot in the process of performing autonomous operation according to the grid, so as to facilitate the subsequent calculation of the area value of the mobile robot during the movement operation, and ensure the efficiency of the subsequent movement path planning of the mobile robot.

[0063] Furthermore, in an optional embodiment of the present invention, the plane rectangular coordinate system can be constructed through a two-dimensional coordinate system, that is, the position sequence of the starting position of the operation can be used as the origin of the two-dimensional coordinate system, and the position sequence of the ending position of the operation can be used as the end point of the two-dimensional coordinate system to generate the plane rectangular coordinate system.

[0064] Furthermore, in an optional embodiment of the present invention, the operation grid of the mobile robot is created in the plane rectangular coordinate system according to the operation area information, and the behavior environment map of the mobile robot is generated, including: determining the number of operation grids of the mobile robot in the plane rectangular coordinate system according to the operation area information to obtain an initial operation grid, encoding the data in the initial operation grid to obtain an encoded operation grid, and marking the encoded operation grid with obstacles to generate the behavior environment map of the mobile robot.

[0065] Furthermore, in another optional embodiment of the present invention, the number of working grids can be obtained by dividing the X-axis and Y-axis in the plane rectangular coordinate system by the preset side length of the grid and then rounding up, the encoding of the data in the initial working grid is obtained by calculating the coordinates of each grid in the initial working grid in the plane rectangular coordinate system, and the obstacle marking of the encoded working grid is realized by judging whether there are obstacles in the working area information.

[0066] S2. Receive the movement scheduling task of the mobile robot, divide the movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and use a preset movement planning model based on the movement nodes to detect the initial walking path of the mobile robot in the process of executing the movement scheduling task.

[0067] In an embodiment of the present invention, the path scheduling task refers to the work task that needs to be performed by the mobile robot, such as cargo handling tasks, restaurant serving tasks, and sanitation cleaning tasks. It should be understood that when the mobile robot performs the path scheduling task, due to the complexity of its working environment, in order to better subdivide the working path of the mobile robot, the embodiment of the present invention divides the path nodes of the mobile robot in the path environment map according to the path scheduling task, and fine-grainedly splits the working area that the mobile robot is to pass through, so as to more accurately calculate the time required for the mobile robot to perform the path scheduling task, and further ensure the accuracy of the subsequent path planning of the mobile robot. Among them, the path node can be understood as the path area passed by the mobile robot in the path environment map.

[0068] As an embodiment of the present invention, dividing the movement nodes of the mobile robot in the movement environment map according to the movement scheduling task includes: determining the movement direction and movement length of the mobile robot according to the movement scheduling task, marking the movement area passed by the mobile robot in the movement environment map according to the movement direction and the movement length, and using the movement area as the movement node.

[0069] Among them, the travel direction refers to the direction in which the mobile robot travels when performing the travel scheduling task, such as due south, due north, due east and due west, and the travel length refers to the travel distance of the mobile robot when performing the travel scheduling task, which can be obtained by setting the travel speed of the mobile robot in the travel scheduling task.

[0070] Furthermore, embodiments of the present invention utilize a preset path planning model based on the path nodes to detect the initial path of the mobile robot during execution of the path scheduling task, thereby obtaining the initial path of the mobile robot when executing the path scheduling task in a static state (i.e., a state not subject to external interference). The preset path planning model can be constructed using a Dijkstra algorithm, which iteratively finds the shortest path time for the mobile robot to travel from its starting position to its ending position, thereby obtaining the optimal path for the mobile robot.

[0071] As an embodiment of the present invention, see Figure 3 As shown, based on the behavior node, a preset behavior planning model is used to detect the initial walking path of the mobile robot in the process of performing the behavior scheduling task, including:

[0072] S301, obtaining the initial weight of the movement node, and calculating the time window of the mobile robot passing through the movement node;

[0073] S302, calculating the shortest travel time of the mobile robot in the process of executing the travel scheduling task using the time optimal function in the preset travel planning model according to the initial weight and the time window;

[0074] S303: Outputting an initial walking path of the mobile robot according to the shortest walking time using an activation function in the preset walking planning model.

[0075] Among them, the initial weight refers to the difficulty of the mobile robot performing the behavior scheduling task when the behavior node is in the behavior environment map, which can be set based on the working environment of the behavior node. The time window refers to the execution time required for the mobile robot to execute any two node sections in the behavior node.

[0076] Furthermore, in an optional embodiment of the present invention, the time window for the mobile robot to pass through the movement node is calculated using the following formula:

[0077]

[0078] in, represents the time window of the mobile robot passing through the travel node i and the travel node j, l(i,j) represents the path length between the travel node i and the travel node j, l i and l j Respectively represent the node lengths of path node i and path node j, v s Represents the movement speed of the mobile robot passing through movement node i and movement node j.

[0079] Furthermore, in an optional embodiment of the present invention, the time optimal function includes:

[0080]

[0081] Among them, min t represents the shortest travel time, i represents the i-th travel node, k represents the number of travel nodes, represents the time window of action node i and action node j, The time window between action node j and action node k, w ij represents the node weights of path node i and path node j, w jk represents the node weights of path node j and path node k, represents the waiting time of the mobile robot, The turning time of the mobile robot is the waiting time.

[0082] S3. Calculate in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task.

[0083] It should be understood that the initial walking path in the above S2 is the optimal path detected when the mobile robot is not subject to external interference, that is, it is obtained under the premise that the weight of the behavior node does not change. However, it can be understood that in actual business scenarios, the behavior node will be subject to external interference, making the behavior environment of the mobile robot complicated. For example, the appearance of an unknown object at the behavior node causes the behavior environment of the mobile robot to become complicated. Therefore, the embodiment of the present invention calculates the node heat value of the behavior node when the mobile robot performs the behavior scheduling task in real time to understand the behavior environment of the mobile robot in real time, so that dynamic adjustment of the path can be made in time to ensure that the mobile robot is in the optimal walking path in real time, thereby improving the work efficiency of the mobile robot.

[0084] As an embodiment of the present invention, the node heat value of the path node when the mobile robot performs the path scheduling task is calculated in real time using the following formula:

[0085]

[0086] Among them, F(g) represents the node heat value, mg represents the g-th mobile robot, M represents the number of mobile robots, N represents the number of walking paths corresponding to the action nodes, and h k Represents the frequency of the mobile robot passing through the kth behavior node.

[0087] S4. According to the node heat value, the behavior nodes are weighted to obtain weighted behavior nodes, and based on the weighted behavior nodes, the preset behavior planning algorithm is used to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot.

[0088] The embodiment of the present invention weights the path nodes by the node heat value to update the weight of the path nodes, thereby ensuring the premise for determining the optimal walking path of the mobile robot subsequently.

[0089] As an embodiment of the present invention, weighting the path nodes according to the node heat value to obtain weighted path nodes includes: obtaining the initial weight of the path node, weighting the initial weight according to the node heat value to obtain a weighted weight, and generating a weighted path node according to the weighted weight.

[0090] Furthermore, in an optional embodiment of the present invention, the initial weight is weighted using the following formula:

[0091] W=w ij +F(g)

[0092] Among them, W represents the weighted weight, w ij represents the initial weight, and F(g) represents the node heat value.

[0093] Furthermore, the embodiment of the present invention updates the initial walking path of the mobile robot based on the weighted behavior node and adopts the preset behavior planning algorithm to obtain the final walking path of the mobile robot, thereby ensuring that the mobile robot is in the optimal walking path in real time. It should be noted that the initial walking path update of the mobile robot is the same as the principle implemented by the above-mentioned initial walking path detection, which will not be further elaborated here.

[0094] It can be seen that this solution obtains the mobile robot's behavioral operating environment to construct a behavioral environment map of the mobile robot, which can determine the working area of ​​the mobile robot when performing autonomous operations, and ensure the premise of subsequent path planning of the mobile robot. By receiving the mobile robot's behavioral scheduling task, the mobile robot's behavioral nodes are divided in the behavioral environment map, and the working area to be passed by the mobile robot can be finely divided, so that the time required for the mobile robot to perform the behavioral scheduling task can be calculated more accurately, further ensuring the accuracy of the subsequent path planning of the mobile robot, and based on the behavioral nodes, a preset behavioral planning model is used to detect the mobile robot's performance in performing the behavioral scheduling task. The initial walking path of the service process is calculated to obtain the initial walking path of the mobile robot when performing the action scheduling task in a static state; secondly, the embodiment of the present invention calculates the node heat value of the action node of the mobile robot when performing the action scheduling task in real time to understand the action environment of the mobile robot in real time, so that the path can be adjusted dynamically in time to ensure that the mobile robot is in the optimal walking path in real time, thereby improving the work efficiency of the mobile robot; further, the embodiment of the present invention weights the action nodes according to the node heat value to obtain the weighted action nodes to update the initial walking path of the mobile robot, obtain the final walking path of the mobile robot, and ensure that the mobile robot is in the optimal operation path in real time. Therefore, the intelligent planning method for the action operation of a mobile robot proposed in the embodiment of the present invention can dynamically adjust the action path of the mobile robot in real time to ensure that the mobile robot is in the optimal operation path in real time.

[0095] Example 2:

[0096] like Figure 4 , which is a functional module diagram of the intelligent planning device for the movement operation of the mobile robot of the present invention.

[0097] The mobile robot's intelligent movement planning device 400 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the mobile robot's intelligent movement planning device can include a movement environment map construction module 401, an initial movement path detection module 402, a node heat value calculation module 403, and a final movement path generation module 404. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0098] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0099] The movement environment map construction module 401 is used to obtain the movement operation environment of the mobile robot and construct the movement environment map of the mobile robot according to the movement operation environment;

[0100] The initial walking path detection module 402 is configured to receive a movement scheduling task of the mobile robot, divide movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detect an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes;

[0101] The node heat value calculation module 403 is used to calculate the node heat value of the movement node when the mobile robot performs the movement scheduling task in real time;

[0102] The final walking path generation module 404 is used to weight the behavior nodes according to the node heat values ​​to obtain weighted behavior nodes, and based on the weighted behavior nodes, use the preset behavior planning algorithm to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot.

[0103] In detail, the modules in the mobile robot's intelligent planning device 400 for trajectory operation according to the embodiment of the present invention are used in the same manner as above. Figures 1 to 3 The same technical means are used as the intelligent planning method for the mobile robot's movement operation described in the specification and can produce the same technical effects, so they will not be repeated here.

[0104] Example 3:

[0105] like Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device for realizing the intelligent planning method of the mobile robot's movement operation according to the present invention.

[0106] The electronic device may include a processor 50, a memory 51, a communication bus 52 and a communication interface 53, and may also include a computer program stored in the memory 51 and executable on the processor 50, such as an intelligent motion operation planning program for a mobile robot.

[0107] In some embodiments, the processor 50 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 50 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits, and executing programs or modules stored in the memory 51 (such as a program for intelligently planning the movement of a mobile robot) and calling data stored in the memory 51 to perform various functions of the electronic device and process data.

[0108] The memory 51 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 51 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 51 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. equipped on the electronic device. Furthermore, the memory 51 can also include both an internal storage unit of the electronic device and an external storage device. The memory 51 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the mobile robot's intelligent planning program for movement operations, but can also be used to temporarily store data that has been output or is to be output.

[0109] The communication bus 52 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 51 and at least one processor 50, etc.

[0110] The communication interface 53 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, for displaying information processed in the electronic device and for displaying a visual user interface.

[0111] Figure 5 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 5 The structure shown does not limit the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0112] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to the at least one processor 50 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0113] It should be understood that the embodiment is for illustrative purposes only and the scope of the patent invention is not limited to this structure.

[0114] The mobile robot's intelligent movement operation planning program stored in the memory 51 of the electronic device is a combination of multiple computer programs. When running in the processor 50, it can achieve the following:

[0115] Acquiring a movement operating environment of the mobile robot, and constructing a movement environment map of the mobile robot based on the movement operating environment;

[0116] receiving a movement scheduling task of the mobile robot, dividing movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detecting an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes;

[0117] Calculating in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task;

[0118] According to the node heat value, the behavior nodes are weighted to obtain weighted behavior nodes, and based on the weighted behavior nodes, the preset behavior planning algorithm is used to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot.

[0119] Specifically, the specific implementation method of the processor 50 for the above computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0120] Furthermore, if the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0121] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0122] Acquiring a movement operating environment of the mobile robot, and constructing a movement environment map of the mobile robot based on the movement operating environment;

[0123] receiving a movement scheduling task of the mobile robot, dividing movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detecting an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes;

[0124] Calculating in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task;

[0125] According to the node heat value, the behavior nodes are weighted to obtain weighted behavior nodes, and based on the weighted behavior nodes, the preset behavior planning algorithm is used to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot.

[0126] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0127] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0128] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0129] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0130] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0131] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0132] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent planning of mobile robot movement operations, characterized in that: The method comprises: Acquiring a movement operating environment of the mobile robot, and constructing a movement environment map of the mobile robot based on the movement operating environment; receiving a movement scheduling task of the mobile robot, dividing movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detecting an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes; Calculating in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task; The action nodes are weighted according to the node heat values ​​to obtain weighted action nodes, and based on the weighted action nodes, the preset action planning algorithm is used to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot; Based on the behavior node, a preset behavior planning model is used to detect the initial walking path of the mobile robot in the process of executing the behavior scheduling task, including: obtaining the initial weight of the behavior node and calculating the time window for the mobile robot to pass through the behavior node; according to the initial weight and the time window, using the time optimal function in the preset behavior planning model to calculate the shortest behavior time of the mobile robot in the process of executing the behavior scheduling task; according to the shortest behavior time, using the activation function in the preset behavior planning model to output the initial walking path of the mobile robot.

2. The method according to claim 1, characterized in that The step of constructing a movement environment map of the mobile robot according to the movement operation environment includes: Determining the operation starting position, operation ending position, and operation area information of the mobile robot during the operation according to the operation environment; Constructing a plane rectangular coordinate system of the mobile robot according to the operation starting position and the operation ending position; According to the operation area information, an operation grid of the mobile robot is created in the plane rectangular coordinate system, and a movement environment map of the mobile robot is generated.

3. The method according to claim 2, characterized in that The step of creating an operation grid of the mobile robot in the plane rectangular coordinate system according to the operation area information and generating a movement environment map of the mobile robot includes: Determining the number of operating grids of the mobile robot in the plane rectangular coordinate system according to the operating area information to obtain an initial operating grid; The data in the initial operation grid is encoded to obtain an encoded operation grid, and obstacles are marked on the encoded operation grid to generate a movement environment map of the mobile robot.

4. The method according to claim 1, wherein The dividing the movement nodes of the mobile robot in the movement environment map according to the movement scheduling task includes: determining a movement direction and a movement length of the mobile robot according to the movement scheduling task; According to the travel direction and the travel length, the travel area passed by the mobile robot is marked in the travel environment map, and the travel area is used as the travel node.

5. An intelligent planning device for mobile robot movement operation, characterized in that: The device comprises: A movement environment map construction module, configured to obtain a movement operating environment of the mobile robot and construct a movement environment map of the mobile robot based on the movement operating environment; an initial walking path detection module, configured to receive a movement scheduling task of the mobile robot, divide movement nodes of the mobile robot in the movement environment map according to the movement scheduling task, and detect an initial walking path of the mobile robot in the process of executing the movement scheduling task using a preset movement planning model based on the movement nodes; A node heat value calculation module, used for calculating in real time the node heat value of the movement node when the mobile robot performs the movement scheduling task; a final walking path generation module, configured to weight the path nodes according to the node heat values ​​to obtain weighted path nodes, and based on the weighted path nodes, use the preset path planning algorithm to update the initial walking path of the mobile robot to obtain the final walking path of the mobile robot; Based on the behavior node, a preset behavior planning model is used to detect the initial walking path of the mobile robot in the process of executing the behavior scheduling task, including: obtaining the initial weight of the behavior node and calculating the time window for the mobile robot to pass through the behavior node; according to the initial weight and the time window, using the time optimal function in the preset behavior planning model to calculate the shortest behavior time of the mobile robot in the process of executing the behavior scheduling task; according to the shortest behavior time, using the activation function in the preset behavior planning model to output the initial walking path of the mobile robot.

Images