Target point determination method and device, robot and storage medium

Abstract

The invention relates to the technical field of indoor robots, and discloses a target point determination method and device, a robot and a storage medium, and the method comprises the steps: determining environment state data and robot state data; determining at least one target comment value of at least one candidate target point according to the environment state data and the robot state data; wherein the target comment value comprises an environment feedback value, an information gain value and a path cost value; and selecting a mapping target point from the candidate target points according to the at least one target comment value of the at least one candidate target point. According to the method, the autonomous mapping efficiency and the dynamic adaptability can be improved.

Description

Technical Field

[0001] The present invention relates to the field of indoor robot technology, and in particular to a method and device for determining a target point, a robot and a storage medium. Background Art

[0002] An indoor robot is an intelligent robot that can operate autonomously or semi-autonomously in an indoor environment and perform various tasks. It has a wide range of applications in household cleaning, transportation services, and security monitoring.

[0003] In the process of autonomous mapping, indoor robots use their own sensors to perceive and detect the surrounding environment, and then use algorithms to process and analyze the acquired data to build an environmental map. In autonomous mapping technology, algorithms are then used to process and analyze the acquired data to build an environmental map. In autonomous mapping technology, target selection and path planning are the core links to ensure that the robot can effectively complete the task. Autonomous mapping methods in the prior art usually select target points based on grid maps or environmental features, and then navigate based on the selected target points combined with path planning strategies. This method of selecting target points depends on the robot's current positioning information and existing environmental maps. It can work well in static or known environments, but in dynamic and complex environments, it lacks flexible adaptability and is prone to inefficient or redundant exploration in the autonomous mapping process. Summary of the invention

[0004] The present invention provides a method, device, robot and storage medium for determining a target point, so as to improve the efficiency and dynamic adaptability of autonomous mapping.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining a target point, the method comprising:

[0006] Determine environment state data and robot state data;

[0007] Determining at least one target evaluation value of at least one candidate target point according to the environmental state data and the robot state data;

[0008] The target review value includes environmental feedback value, information gain value and path cost value;

[0009] A mapping target point is selected from the candidate target points according to at least one target comment value of at least one candidate target point.

[0010] In a second aspect, an embodiment of the present invention further provides a device for determining a target point, the device comprising:

[0011] A data determination module, used for determining environment state data and robot state data;

[0012] A target review value determination module, used to determine at least one target review value of at least one candidate target point according to the environmental state data and the robot state data;

[0013] The target review value includes environmental feedback value, information gain value and path cost value;

[0014] The mapping target point determination module is used to select a mapping target point from the candidate target points according to at least one target comment value of at least one candidate target point.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for determining a target point as described in any one of the embodiments of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a method for determining a target point as described in any one of the embodiments of the present invention.

[0017] The technical solution of the embodiment of the present invention obtains environmental status data and robot status data in real time, and determines at least one target evaluation value of the candidate target point based on the environmental status data and the robot status data, including: environmental feedback value, information gain value and path cost value, and then selects the mapping target point from each candidate target point based on at least one target evaluation value of each candidate target point. This solves the problem that the method of selecting target points in the prior art relies on the current positioning information of the robot and the existing environmental map, cannot flexibly adapt to dynamic and complex environments, and easily leads to inefficient or redundant exploration in the autonomous mapping process. The technical solution of the embodiment of the present invention can improve the efficiency and dynamic adaptability of autonomous mapping.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0020] Figure 1is a flow chart of a method for determining a target point provided in Embodiment 1 of the present invention;

[0021] Figure 2 is a flow chart of a method for determining a target point provided in Embodiment 2 of the present invention;

[0022] Figure 3 is a schematic diagram of the structure of a device for determining a target point provided in Embodiment 3 of the present invention;

[0023] Figure 4 It is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the embodiments of the present application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned, and they should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or must use the solution.

[0026] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0027] Embodiment 1

[0028] Figure 1A flowchart of a method for determining a target point is provided for the first embodiment of the present invention. This embodiment is applicable to the case of selecting a target point during autonomous mapping by an indoor robot. The method can be executed by a target point determination device, which can be implemented in the form of hardware and / or software and can be configured in the robot.

[0029] like Figure 1 As shown, the method includes:

[0030] S110: Determine environment status data and robot status data.

[0031] The environmental status data may include map information and obstacle information, among which map information refers to the currently known environmental map information, and obstacle information refers to the static or dynamic obstacle information detected in the environment. The environmental status data may be collected in real time by the robot through sensors (such as lidar sensors, depth cameras, and IMU (Inertial Measurement Unit)).

[0032] The robot status data refers to the current position and posture of the robot, and the robot status data can also be collected by the robot's sensors.

[0033] In this embodiment, by collecting environmental status data and robot status data in real time, the environmental changes around the robot can be perceived in real time, so that subsequent target point selection can be performed according to the dynamic changes of the environment.

[0034] S120. Determine at least one target evaluation value of at least one candidate target point according to the environment state data and the robot state data.

[0035] The candidate target points can be determined by known environment map information. For example, the boundary points of the known environment map or the location points with obvious features can be used as candidate target points. In this embodiment, the candidate target points can be represented in the form of a point set, for example, T={T1, T2, ...T n}.

[0036] Among them, the target review value is used to indicate the priority of the candidate target point or the adaptability of the selection. The target review value may include an environmental feedback value, an information gain value, and a path cost value.

[0037] The environmental feedback value is used to indicate the degree of feedback or influence of the dynamic changes in the environment on the candidate target point. The larger the environmental feedback value of the candidate target point, the lower the priority of the candidate target point, and the less suitable it is to be selected for the next step of path planning; conversely, the smaller the environmental feedback value of the candidate target point, the higher the priority of the candidate target point, and the more suitable it is to be selected for the next step of path planning.

[0038] By determining the environmental feedback value of the candidate target point, the selection of subsequent mapping target points can respond to changes in environmental conditions in real time, thereby improving the dynamic adaptability of autonomous mapping.

[0039] The information gain value is used to measure the contribution of the robot's exploration of the area where the candidate target point is located to the accuracy of the map. Information gain refers to the amount of new information that can be obtained by exploring the area where the candidate target point is located, thereby improving the accuracy of the map or updating the known map. The larger the information gain value of the candidate target point, the higher the priority of the candidate target point, and the more suitable it is to be selected for the next step of path planning; conversely, the smaller the information gain value of the candidate target point, the lower the priority of the candidate target point, and the less suitable it is to be selected for the next step of path planning.

[0040] By determining the information gain value of the candidate target point, the contribution of each candidate target point to the accuracy and completeness of the map can be quantitatively evaluated. When selecting subsequent mapping target points, candidate target points that are more valuable to the map can be selected for exploration, thereby improving mapping efficiency and effectively improving the accuracy and completeness of the map.

[0041] The path cost is used to measure the path cost corresponding to the planned path from the robot's current position to the candidate target point after the robot selects the candidate target point. It can be determined by factors such as the distance of the planned path, the time required for the robot to travel, and the energy consumption required for the robot to travel. The larger the path cost value of the candidate target point, the lower the priority of the candidate target point, and the less suitable it is to be selected for the next step of path planning; conversely, the smaller the path cost value of the candidate target point, the higher the priority of the candidate target point, and the more suitable it is to be selected for the next step of path planning.

[0042] By determining the path cost of the candidate target points, the coordinated work of target point selection and path planning is achieved, which saves unnecessary path exploration, saves resource consumption of robot movement and calculation, and improves the efficiency of the mapping process.

[0043] In this embodiment, by evaluating candidate target points in different dimensions such as environmental feedback, information gain, and path cost, a multi-dimensional and quantitative evaluation of candidate target points is achieved, which can comprehensively evaluate the priority of each candidate target point, avoid the limitations of a single target point selection method, and improve the efficiency and accuracy of target point selection, thereby improving the efficiency and accuracy of robot mapping.

[0044] S130: Select a mapping target point from the candidate target points according to at least one target comment value of at least one candidate target point.

[0045] Among them, the mapping target point refers to the target point selected from the candidate target points and is most suitable for subsequent exploration and mapping.

[0046] In this embodiment, the mapping target point is selected from the candidate target points based on one of the environmental feedback value, the information gain value, and the path cost value, or a combination of two or more of them.

[0047] In an optional embodiment, when the target review value is one of the environmental feedback value, the information gain value, and the path cost value, the candidate target points can be sorted according to their target review values, and the candidate target point corresponding to the maximum or minimum value is selected as the mapping target point.

[0048] Exemplarily, the target review value is taken as the environmental feedback value for explanation. Specifically, the smaller the environmental feedback value of the candidate target point, the higher the priority of the candidate target point, and the more suitable it is to be selected for the next step of path planning. Therefore, the candidate target points can be sorted in order from small to large according to the environmental feedback value, and the candidate target point with the smallest environmental feedback value is selected as the mapping target point. Correspondingly, if the target review value is the information gain value, the candidate target points are sorted in order from large to small according to the information gain value, and the candidate target point with the largest information gain value is selected as the mapping target point. And, if the target review value is the path cost value, the candidate target points can be sorted in order from small to large according to the path cost value, and the candidate target point with the smallest path cost value is selected as the mapping target point.

[0049] In another optional embodiment, when the target review value is two of the environmental feedback value, the information gain value, and the path cost value, the product / quotient / sum or difference of the two different target review values ​​can be calculated, and then the product / quotient / sum or difference can be sorted, and the candidate target point corresponding to the maximum value or minimum value is selected as the mapping target point.

[0050] Exemplarily, taking the target review value as the environmental feedback value and the information gain value as an example, selecting a mapping target point from the candidate target points according to at least one target review value of at least one candidate target point may include: determining the ratio of the information gain value to the environmental feedback value of each candidate target point, and determining the mapping target point according to the ratio of each candidate target point. Since the information gain value is proportional to the priority of the candidate target point, and the environmental feedback value is inversely proportional to the priority of the candidate target point, the ratio (i.e., the quotient) of the information gain value to the environmental feedback value is taken as the basis for selecting the candidate target point in this embodiment. The ratios are sorted in order from large to small, and the candidate target point with the largest ratio is selected as the mapping target point.

[0051] Correspondingly, if the target review value is the information gain value and the path cost value, the ratio of the information gain value to the path cost value of each candidate target point can be determined, the ratios are sorted in order from large to small, and the candidate target point with the largest ratio is selected as the mapping target point. Also, if the target review value is the environmental feedback value and the path cost value, since the environmental feedback value and the path cost value are both inversely proportional to the priority of the candidate target point, the product of the environmental feedback value and the path cost value of each candidate target point can be determined, the products are sorted in order from small to large, and the candidate target point with the smallest product is selected as the mapping target point.

[0052] For example, when the target review value is the environmental feedback value and the information gain value, the difference between the product of the information gain value and the information gain weight of each candidate target point and the environmental feedback value can also be determined, and the difference values ​​are sorted in descending order, and the candidate target point with the largest difference is selected as the mapping target point. Among them, the information gain weight is used to balance the environmental feedback value and the information gain value.

[0053] Correspondingly, if the target review value is an information gain value and a path cost value, selecting a mapping target point from the candidate target points according to at least one target review value of at least one candidate target point may further include: determining the difference between the product of the information gain value and the information gain weight of each candidate target point and the path cost value, and determining the mapping target point according to the difference of each candidate target point. Specifically, the difference values ​​are sorted in order from large to small, and the candidate target point with the largest difference value is selected as the mapping target point. At this time, the goal of target point selection is to minimize the path cost and maximize the information gain, thereby optimizing the target point selection and path planning at the same time.

[0054] If the target review value is the environmental feedback value and the path cost value, the sum of the environmental feedback value and the path cost value of each candidate target point can be determined, and the sum values ​​can be sorted in order from small to large, and the candidate target point with the smallest sum value can be selected as the mapping target point.

[0055] In another optional embodiment, when the target review value is a combination of an environmental feedback value, an information gain value, and a path cost value, selecting a mapping target point from the candidate target points according to at least one target review value of at least one candidate target point may also include: performing weighted summation of the environmental feedback value, information gain value, and path cost value of each candidate target point to obtain a sum value, and determining the mapping target point according to the sum value of each candidate target point; wherein the environmental feedback weight and the path cost weight are negative values, and the sum of the absolute values ​​of the environmental feedback weight, the information gain weight, and the absolute values ​​of the path cost weight is 1.

[0056] Since the information gain value is proportional to the priority of the candidate target point, and the environmental feedback value and path cost value are inversely proportional to the priority of the candidate target point, when the three are weighted and summed, the weights of the environmental feedback value and path cost value are both negative. When determining the mapping target point based on the summed values ​​of each candidate target point, the summed values ​​are sorted in descending order, and the candidate target point with the largest summed value is selected as the mapping target point.

[0057] Exemplarily, when the target review value is a combination of the environmental feedback value, the information gain value, and the path cost value, it is also possible to calculate the product of the environmental feedback value and the path cost value of each candidate target point, and then calculate the quotient of the information gain value and the product, and sort them in order from large to small, and select the candidate target point with the largest quotient as the mapping target point.

[0058] It should be noted that when there are at least two target review values, when selecting the target points for mapping in combination with the different target review values, it is necessary to first normalize the different target review values ​​to improve the accuracy of target point selection.

[0059] The technical solution of the embodiment of the present invention obtains environmental status data and robot status data in real time, and determines at least one target evaluation value of the candidate target point based on the environmental status data and the robot status data, including: environmental feedback value, information gain value and path cost value, and then selects the mapping target point from each candidate target point based on at least one target evaluation value of each candidate target point. This solves the problem that the method of selecting target points in the prior art relies on the current positioning information of the robot and the existing environmental map, cannot flexibly adapt to dynamic and complex environments, and easily leads to inefficient or redundant exploration in the autonomous mapping process. The technical solution of the embodiment of the present invention can improve the efficiency and dynamic adaptability of autonomous mapping.

[0060] Embodiment 2

[0061] Figure 2This is a flow chart of a method for determining a target point provided in the second embodiment of the present invention. Based on the above embodiments, the embodiment of the present invention further specifies the process of determining the environmental feedback value, the information gain value and the path cost value.

[0062] like Figure 2 As shown, the method includes:

[0063] S210: Determine environment status data and robot status data.

[0064] S220. Determine, based on the environmental state data and the robot state data, an environmental feedback change value within a time interval between a current moment and an initial moment.

[0065] Among them, the environmental feedback change value is determined based on the change in the distance from the candidate target point to the robot at the current moment and the initial moment, the change in the obstacle density matching the candidate target point at the current moment and the initial moment, and the environmental dynamic factor of the candidate target point at the current moment compared to the initial moment.

[0066] Among them, the environmental feedback change value is the feedback change caused by the change of the environmental state (obstacle movement, target point change, etc.) between the current moment and the initial moment. The obstacle density matching the candidate target point can be expressed according to the number of obstacles in the area with the candidate target point as the center and the preset distance. Specifically, taking the acquisition of point cloud data by laser radar as an example, it is possible to determine whether there are obstacles around the candidate target point by the number of point clouds, and calculate the number of obstacles in the area with the candidate target point as the center and the preset distance. The environmental dynamic factor of the candidate target point at the current moment compared to the initial moment is used to indicate the degree of environmental dynamic change of the candidate target point at the current moment compared to the initial moment. The environmental dynamic factor of the candidate target point at the current moment compared to the initial moment can be expressed by the change of obstacles at the current moment compared to the initial moment in the area with the candidate target point as the center and the preset distance (for example, the sum of the displacements of each obstacle or the average displacement), or it can also be expressed by the change of point clouds at the current moment compared to the initial moment in the area with the candidate target point as the center and the preset distance.

[0067] Specifically, the environmental feedback change value can be expressed by the following formula: , It is used to indicate the change in the distance from the candidate target point to the robot between the current moment and the initial moment. It is used to indicate the change in the density of obstacles matching the candidate target point between the current time and the initial time. It is used to represent the environmental dynamic factor of the candidate target point at the current moment compared with the initial moment. , as well as Represents the weight coefficient, which is used to balance the impact of various factors on the change value of environmental feedback.

[0068] S230: Determine the environment feedback value of the candidate target point according to the initial environment feedback value of the candidate target point at the initial moment and the environment feedback change value.

[0069] The initial environmental feedback value is determined based on the distance from the candidate target point to the robot at the initial moment, the obstacle density matching the candidate target point at the initial moment, and the environmental dynamic factor matching the candidate target point at the initial moment.

[0070] Specifically, the initial environmental feedback value can be expressed by the following formula: ,in, represents the distance from the candidate target point to the robot at the initial moment, represents the obstacle density matching the candidate target point at the initial moment, Indicates the environmental dynamic factor that matches the candidate target point at the initial moment.

[0071] The environmental feedback value of the candidate target point can be represented by the sum of the initial environmental feedback value of the candidate target point at the initial moment and the environmental feedback change value.

[0072] In this embodiment, by real-time perception of environmental feedback changes and real-time update of environmental feedback values, the selection of target points can respond to dynamic changes in the environment in real time, thereby ensuring the dynamic adaptability of autonomous mapping and avoiding inefficient redundant exploration.

[0073] S240. Determine the current map entropy according to the environment state data and the robot state data.

[0074] Among them, the current map entropy is used to represent the uncertainty of the current map or the degree of information confusion and ambiguity. When the map is represented in different forms, there are different ways to calculate the current map entropy. Taking the representation of the map in the form of a grid map as an example, the map is divided into small grid units. For each grid, the probability of being occupied, idle or unknown is determined based on the environmental state data and the robot state data. For example, through laser radar measurement, if an obstacle is detected at a certain grid position, the probability of the grid being occupied will increase. Assume P i represents the probability that the i-th grid is occupied, 1-P i This is the probability that the grid is idle. For unknown states, an initial probability can be assigned according to certain rules. Finally, the current map entropy can be calculated using the formula: This embodiment does not limit the specific method of calculating the current map entropy.

[0075] S250: Determine the map condition entropy matching the candidate target point.

[0076] Map conditional entropy refers to the conditional entropy of the map after the robot selects a candidate target point.

[0077] Specifically, taking the representation of the map in the form of a grid map as an example, the joint probability P(M) of grid i being in different states (occupied / idle / unknown) under the condition of selecting a candidate target point is calculated for each grid i. i ,T), which can be estimated based on the environmental state data and prior knowledge of the environment; the conditional probability distribution is calculated based on the joint probability distribution ,in, is the probability of the candidate target point appearing; finally, the conditional entropy of the map matching the candidate target point is calculated by the conditional entropy formula: .

[0078] S260: Taking the difference between the current map entropy and the map condition entropy as the information gain value of the candidate target point.

[0079] The difference between the current map entropy and the map conditional entropy when the robot selects a candidate target point is the information gain value of the candidate target point. The larger the difference, the greater the improvement in map accuracy and integrity when selecting the candidate target point. Incorporating information gain into the selection of target points can ensure that the target area that improves the accuracy and integrity of the map construction the most is selected first, effectively improving the accuracy and integrity of the map and significantly improving the efficiency of mapping.

[0080] S270. Determine a candidate path that matches the candidate target point based on the environment state data and the robot state data.

[0081] In this embodiment, a planned path for the robot to reach the candidate target point can be determined by a conventional path planning method as a candidate path matching the candidate target point.

[0082] In this embodiment, the path planning and target selection are coordinated and optimized to ensure that when the robot selects the target point, the path planning is optimized at the same time, thereby avoiding path redundancy and unnecessary detours.

[0083] S280: Determine a path cost according to at least one path parameter of the candidate path.

[0084] The path parameters include path length, time consumption and robot energy consumption. Path length refers to the length of the candidate path, time consumption refers to the time required for the robot to travel from the current position along the candidate path to the candidate target point, which can be calculated based on the path length and the average speed of the robot, and the robot energy consumption can be calculated based on the time consumption and the average energy consumption of the robot during movement.

[0085] The path cost value is determined according to the path parameters. Specifically, the path length time and the robot energy consumption may be weightedly summed, which is not limited in this embodiment.

[0086] In this embodiment, by calculating the path cost value, the coordinated optimization of target point selection and path planning is achieved, unnecessary exploration paths are reduced, the resource consumption of robot movement and calculation is saved, and the overall efficiency of robot autonomous mapping is improved.

[0087] S290 , performing weighted summation on the environmental feedback value, information gain value, and path cost value of each candidate target point to obtain a sum value, and determining a mapping target point according to the sum value of each candidate target point.

[0088] Among them, the environmental feedback weight and the path cost weight are negative values, and the sum of the absolute value of the environmental feedback weight, the information gain weight, and the absolute value of the path cost weight is 1.

[0089] The specific process of performing weighted summation according to the environmental feedback value, the information gain value and the path cost value, and determining the mapping target point based on the summation value has been described in the above embodiment, and will not be repeated in this embodiment.

[0090] The technical solution of this embodiment, when there are multiple candidate target points, reflects the response of the candidate target points to the dynamic changes of the environment through the calculation of the environmental feedback value, so that the selection of the target point has dynamic adaptability. Through the calculation of the information gain value, the contribution of each candidate target point to the improvement of the mapping accuracy and integrity is evaluated, so that the selection of the target point can be combined with the information gain, ensuring that the target area with the greatest improvement in mapping accuracy is preferentially selected, and the accuracy and integrity of the map are effectively improved. Through the calculation of the path cost value, the coordinated work of the target point selection and path planning is realized, unnecessary exploration paths are reduced, the resource consumption of robot movement and calculation is saved, and the overall efficiency of the mapping process is improved. Through the continuous priority evaluation of multiple candidate target points, the selection of mapping target points, path planning and real-time feedback, the robot can intelligently select between multiple target points, avoid inefficient redundant exploration, and make adjustments according to real-time feedback. At the same time, after exploring the mapping target points, the map data can be continuously updated, so as to continue the above process, and finally complete the autonomous mapping task, which improves the mapping efficiency while also improving the accuracy and integrity of the map.

[0091] Embodiment 3

[0092] Figure 3 This is a schematic diagram of the structure of a device for determining a target point provided in Embodiment 3 of the present invention. Figure 3 As shown, the device comprises:

[0093] A data determination module 310, for determining environment state data and robot state data;

[0094] A target review value determination module 320, for determining at least one target review value of at least one candidate target point according to the environment state data and the robot state data;

[0095] The target review value includes environmental feedback value, information gain value and path cost value;

[0096] The mapping target point determination module 330 is used to select a mapping target point from the candidate target points according to at least one target comment value of at least one candidate target point.

[0097] The technical solution of the embodiment of the present invention obtains environmental status data and robot status data in real time, and determines at least one target evaluation value of the candidate target point based on the environmental status data and the robot status data, including: environmental feedback value, information gain value and path cost value, and then selects the mapping target point from each candidate target point based on at least one target evaluation value of each candidate target point. This solves the problem that the method of selecting target points in the prior art relies on the current positioning information of the robot and the existing environmental map, cannot flexibly adapt to dynamic and complex environments, and easily leads to inefficient or redundant exploration in the autonomous mapping process. The technical solution of the embodiment of the present invention can improve the efficiency and dynamic adaptability of autonomous mapping.

[0098] Based on the above embodiment, optionally, the target review value determination module 320 includes:

[0099] An environment feedback change value determination unit, used to determine the environment feedback change value within the time interval between the current moment and the initial moment according to the environment state data and the robot state data;

[0100] The environmental feedback change value is determined according to the change of the distance from the candidate target point to the robot at the current moment and the initial moment, the change of the obstacle density matching the candidate target point at the current moment and the initial moment, and the environmental dynamic factor of the candidate target point at the current moment compared with the initial moment;

[0101] An environment feedback value determination unit, used to determine the environment feedback value of the candidate target point according to the initial environment feedback value of the candidate target point at the initial moment and the environment feedback change value;

[0102] The initial environmental feedback value is determined based on the distance from the candidate target point to the robot at the initial moment, the obstacle density matching the candidate target point at the initial moment, and the environmental dynamic factor matching the candidate target point at the initial moment.

[0103] Based on the above embodiment, optionally, the target review value determination module 320 includes:

[0104] a current map entropy determination unit, configured to determine a current map entropy according to the environment state data and the robot state data;

[0105] A map condition entropy determination unit, used to determine the map condition entropy matching the candidate target point;

[0106] The information gain value determining unit is used to use the difference between the current map entropy and the map condition entropy as the information gain value of the candidate target point.

[0107] Based on the above embodiment, optionally, the target review value determination module 320 includes:

[0108] A candidate path determination unit, configured to determine a candidate path matching a candidate target point according to the environment state data and the robot state data;

[0109] a path cost value determining unit, configured to determine a path cost value according to at least one path parameter of the candidate path;

[0110] The path parameters include path length, time consumption and robot energy consumption.

[0111] Based on the above embodiment, optionally, the mapping target point determination module 330 includes:

[0112] The first mapping target point determination unit is used to determine the ratio of the information gain value to the environmental feedback value of each candidate target point, and determine the mapping target point according to the ratio of each candidate target point.

[0113] Based on the above embodiment, optionally, the mapping target point determination module 330 includes:

[0114] The second mapping target point determination unit is used to determine the difference between the product of the information gain value and the information gain weight of each candidate target point and the path cost value, and determine the mapping target point according to the difference between each candidate target point.

[0115] Based on the above embodiment, optionally, the mapping target point determination module 330 includes:

[0116] The third mapping target point determination unit is used to perform weighted summation on the environmental feedback value, information gain value and path cost value of each candidate target point to obtain a sum value, and determine the mapping target point according to the sum value of each candidate target point;

[0117] Among them, the environmental feedback weight and the path cost weight are negative values, and the sum of the absolute value of the environmental feedback weight, the information gain weight, and the absolute value of the path cost weight is 1.

[0118] The target point determination device provided in the embodiment of the present invention can execute the target point determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0119] Embodiment 4

[0120] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0121] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0122] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0123] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a target point.

[0124] In some embodiments, the method for determining a target point may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining a target point described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method for determining a target point in any other appropriate manner (e.g., by means of firmware).

[0125] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0127] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0130] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0131] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0132] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a target point, characterized in that: include: Determine environment state data and robot state data; Determine at least one target evaluation value of at least one candidate target point according to the environmental state data and the robot state data; The target review value includes environmental feedback value, information gain value and path cost value; A mapping target point is selected from the candidate target points according to at least one target comment value of at least one candidate target point.

2. The method for determining a target point according to claim 1, characterized in that: Determining an environmental feedback value of at least one candidate target point according to the environmental state data and the robot state data includes: Determine, according to the environmental state data and the robot state data, an environmental feedback change value within a time interval between a current moment and an initial moment; The environmental feedback change value is determined according to the change of the distance from the candidate target point to the robot at the current moment and the initial moment, the change of the obstacle density matching the candidate target point at the current moment and the initial moment, and the environmental dynamic factor of the candidate target point at the current moment compared with the initial moment; Determining the environmental feedback value of the candidate target point according to the initial environmental feedback value of the candidate target point at the initial moment and the environmental feedback change value; The initial environmental feedback value is determined based on the distance from the candidate target point to the robot at the initial moment, the obstacle density matching the candidate target point at the initial moment, and the environmental dynamic factor matching the candidate target point at the initial moment.

3. The method for determining a target point according to claim 1, characterized in that: Determining an information gain value of at least one candidate target point according to the environment state data and the robot state data, comprising: Determining a current map entropy according to the environment state data and the robot state data; Determine the map condition entropy matching the candidate target point; The difference between the current map entropy and the map conditional entropy is used as the information gain value of the candidate target point.

4. The method for determining a target point according to claim 1, characterized in that: Determining a path cost value of at least one candidate target point according to the environment state data and the robot state data includes: Determining a candidate path matching a candidate target point according to the environment state data and the robot state data; Determining a path cost value according to at least one path parameter of the candidate path; The path parameters include path length, time consumption and robot energy consumption.

5. The method for determining a target point according to any one of claims 1 to 4, characterized in that: According to at least one target comment value of at least one candidate target point, selecting a mapping target point from the candidate target points includes: Determine the ratio of the information gain value to the environmental feedback value of each candidate target point, and determine the mapping target point based on the ratio of each candidate target point.

6. The method for determining a target point according to any one of claims 1 to 4, characterized in that: According to at least one target comment value of at least one candidate target point, selecting a mapping target point from the candidate target points includes: The difference between the product of the information gain value and the information gain weight of each candidate target point and the path cost value is determined, and the mapping target point is determined according to the difference between each candidate target point.

7. The method for determining a target point according to any one of claims 1 to 4, characterized in that: According to at least one target comment value of at least one candidate target point, selecting a mapping target point from the candidate target points includes: Perform weighted summation on the environmental feedback value, information gain value, and path cost value of each candidate target point to obtain a sum value, and determine the mapping target point based on the sum value of each candidate target point; Among them, the environmental feedback weight and the path cost weight are negative values, and the sum of the absolute value of the environmental feedback weight, the information gain weight, and the absolute value of the path cost weight is 1.

8. A device for determining a target point, characterized in that: include: A data determination module, used for determining environment state data and robot state data; A target review value determination module, used to determine at least one target review value of at least one candidate target point according to the environmental state data and the robot state data; The target review value includes environmental feedback value, information gain value and path cost value; The mapping target point determination module is used to select a mapping target point from the candidate target points according to at least one target comment value of at least one candidate target point.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the target point determination method as described in any one of claims 1-7 is implemented.

10. A storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the target point determination method according to any one of claims 1 to 7.

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