Robot control method, electronic equipment and readable storage medium

Abstract

The invention is suitable for the technical field of robots, and provides a robot control method, electronic equipment and a readable storage medium. The robot is provided with multiple sensors, and the control method of the robot comprises the steps that sensor data collected by the multiple sensors respectively are obtained; determining current performance information of each sensor in the plurality of sensors; according to the current performance information, the fusion weight of the multiple sensors is determined; according to the fusion weight, fusing the sensor data of the plurality of sensors to obtain fusion data; and controlling the robot to execute a task according to the fused data. According to the embodiment of the invention, the decision-making quality of the robot can be improved.

Description

Technical Field

[0001] The present application belongs to the field of robotics technology, and in particular, relates to a robot control method, electronic equipment, and readable storage medium. Background Art

[0002] A robot is an intelligent machine that can work semi-autonomously or fully autonomously. Traditional robots mostly rely on a single type of sensor, such as a camera, infrared sensor, or radar, to detect changes in the environment. Such robots have poor environmental perception capabilities in complex environments, resulting in reduced decision-making quality in some scenarios. Summary of the invention

[0003] The embodiments of the present application provide a robot control method, device, electronic device and readable storage medium, which can solve the problem of low decision-making quality of robots in related technologies.

[0004] A first aspect of an embodiment of the present application provides a control method for a robot, wherein the robot is configured with multiple sensors, and the control method for the robot includes: acquiring sensor data collected by each of the multiple sensors; determining current performance information of each of the multiple sensors; determining fusion weights of the multiple sensors based on the current performance information; fusing the sensor data of the multiple sensors based on the fusion weights to obtain fusion data; and controlling the robot to perform a task based on the fusion data.

[0005] In some embodiments of the first aspect, determining the current performance information of each sensor among the multiple sensors includes: determining an effective acquisition range of a corresponding sensor based on the sensor data of each sensor to obtain the current performance information; determining the fusion weights of the multiple sensors based on the current performance information includes: determining the fusion weight based on the effective acquisition range of each sensor among the multiple sensors, the fusion weight of each sensor being positively correlated with its effective acquisition range.

[0006] In some embodiments of the first aspect, the multiple sensors include an image sensor, and the sensor data collected by the image sensor is an environmental image; determining the effective collection range of the corresponding sensor based on the sensor data of each sensor includes: determining the degree of occlusion of the image sensor based on the environmental image; and determining the effective collection range of the image sensor based on the degree of occlusion.

[0007] In some embodiments of the first aspect, the multiple sensors include a laser radar, and the sensor data collected by the laser radar is point cloud data; determining the effective collection range of the corresponding sensor based on the sensor data of each sensor includes: determining the laser beam that collects the effective point cloud data by the laser radar based on the point cloud data; and determining the effective collection range of the laser radar based on the number of laser beams.

[0008] In some embodiments of the first aspect, determining the current performance information of each sensor among the multiple sensors includes: obtaining benchmark data of the multiple sensors under standard working conditions; comparing the sensor data of each sensor with the corresponding benchmark data to obtain the current performance information of each sensor among the multiple sensors.

[0009] In some embodiments of the first aspect, controlling the robot to perform a task based on the fusion data includes: determining contour data of a target object based on the fusion data; determining motion state information of the target object based on the contour data; and controlling the robot to perform a task based on the motion state information.

[0010] In some embodiments of the first aspect, controlling the robot to perform a task based on the motion state information includes: estimating a visit destination of the target object based on the motion state information; and controlling the robot to perform a guidance task based on the visit destination.

[0011] In some embodiments of the first aspect, controlling the robot to perform a task based on the motion state information includes: if the target object enters a target area, approaches the robot, or the target object continues to move within a preset distance of the robot or stops facing the robot, then controlling the robot to perform a welcoming task.

[0012] A second aspect of an embodiment of the present application provides a control device for a robot, wherein the robot is configured with multiple sensors, and the control device of the robot includes: a data acquisition unit, used to acquire sensor data collected by each of the multiple sensors; a performance determination unit, used to determine the current performance information of each of the multiple sensors; a weight adjustment unit, used to determine the fusion weight of the multiple sensors according to the current performance information; a data fusion unit, used to fuse the sensor data of the multiple sensors according to the fusion weight to obtain fused data; and a task execution unit, used to control the robot to perform a task according to the fused data.

[0013] A third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned robot control method when executing the computer program.

[0014] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned robot control method are implemented.

[0015] A fifth aspect of an embodiment of the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute the steps of the above-mentioned robot control method.

[0016] In an implementation manner of the present application, by acquiring sensor data collected by each of the multiple sensors of the robot, the current performance information of each of the multiple sensors is determined, and based on the current performance information, the fusion weights of the multiple sensors are determined. Based on the fusion weights, the sensor data of the multiple sensors are fused to obtain fused data. When the robot is controlled to perform a task based on the fused data, the fused data used as a reference for robot decision-making can be mainly based on the sensor data of sensors with good current performance. In scenarios where some sensors fail, decisions can still be made based on valid sensors, which helps to improve the decision-making quality of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 It is a schematic diagram of the implementation flow of a robot control method provided in an embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of a specific implementation process of determining the fusion weight provided in an embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of a specific implementation process of determining current performance information provided in an embodiment of the present application;

[0021] Figure 4 This is a schematic diagram of a specific implementation process of controlling a robot to perform a task provided by an embodiment of the present application;

[0022] Figure 5is a schematic structural diagram of a robot control device provided in an embodiment of the present application;

[0023] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are protected by the present application.

[0025] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0026] In the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] A robot is an intelligent machine that can work semi-autonomously or fully autonomously. Traditional robots mostly rely on a single type of sensor, such as a camera, infrared sensor, or radar, to detect changes in the environment. Such robots have poor environmental perception in complex environments. Some new technologies use multi-sensor data fusion to improve the robot's environmental perception ability, but its fusion method is difficult to adapt to different application scenarios, resulting in a decrease in the robot's decision-making quality in some scenarios.

[0029] In view of this, the present application proposes a robot control method, which can make the fusion data referred to by the robot decision-making mainly based on the sensor data of sensors with good current performance. In the scenario where some sensors fail, decisions can still be made based on valid sensors, which helps to improve the decision-making quality of the robot.

[0030] In order to illustrate the technical solution of the present application, a specific embodiment is provided below for illustration.

[0031] Figure 1 The present invention provides a schematic diagram of a robot control method according to an embodiment of the present invention, which can be applied to an electronic device. The electronic device can be a computer, a mobile phone or other intelligent device for controlling or communicating with the robot, or the robot itself.

[0032] Specifically, the above robot control method may include the following steps S101 to S105.

[0033] Step S101, acquiring sensor data collected by various sensors.

[0034] Specifically, the above-mentioned robot can be equipped with a variety of sensors, including but not limited to: image sensors, infrared sensors, ultrasonic sensors, and lidar sensors. Each sensor can collect data in real time during the operation of the robot to obtain sensor data. Among them, the image sensor can be an industrial camera or a depth camera, and the depth camera can collect a three-dimensional image of the scene and obtain the distance information between the object and the depth camera. The distance information can be used to determine the size, shape and precise position of the object. Infrared sensors can provide additional object detection capabilities under low light conditions and can be used to collect thermal information, such as detecting human body heat and other heat sources in a completely dark environment, to make up for the limitations of optical equipment under dark light conditions. Ultrasonic sensors can be used to collect depth information, and measure the distance to the object by emitting sound waves and receiving their echoes. It is particularly important in the blind spot of the depth camera or in close-range measurement scenarios.

[0035] Step S102: determining current performance information of each sensor among the multiple sensors.

[0036] In the implementation manner of the present application, the current performance information is used to characterize the performance of the sensor when collecting sensor data, and can be measured by dimensions such as accuracy, error rate, and stability.

[0037] Step S103: determining the fusion weights of the multiple sensors according to the current performance information.

[0038] The fusion weight refers to the weight when performing data fusion. The higher the fusion weight, the higher the influence of the data on the fusion result. In the implementation of the present application, the fusion weights of multiple sensors can be determined according to the current performance information. The better the current performance, the higher the fusion weight, and the worse the current performance, the lower the fusion weight.

[0039] Step S104: fusing the sensor data of the multiple sensors according to the fusion weights to obtain fused data.

[0040] In the implementation of the present application, fused data is the result of data fusion. According to the fusion weight, the sensor data of multiple sensors can be fused to obtain fused data. Specifically, the sensor data of different sensors can include detection data of the same object. In this case, the detection data of the same type of the same object can be fused according to the fusion weight.

[0041] For example, an image sensor and an ultrasonic sensor can detect the distance between the same object and the robot. According to the fusion weight w1 of the image sensor and the distance detection result d1, and the fusion weight w2 of the ultrasonic sensor and the distance detection result d2, the distance d = w1 × d1 + w2 × d2 can be fused as fusion data. Here, a relatively simple fusion algorithm is used as an example for illustration. Other existing fusion algorithms are also applicable to this application. For example, a Kalman filter or a neural network can be used to integrate different sensor data and optimize the fusion data. Among them, the Kalman filter is suitable for processing sensor data with noise and uncertainty.

[0042] In order to improve the reliability of fusion, the sensor data can be preprocessed before fusion. For example, the adaptive histogram equalization technology can be used to process the images collected by the depth camera to optimize the image quality, enhance the local contrast of the image, and make the details clearer.

[0043] Step S105, controlling the robot to perform the task according to the fused data.

[0044] In the implementation of the present application, the fused data can affect the robot's decision-making, and the robot can be controlled to perform tasks based on the fused data. The above tasks may include but are not limited to welcoming tasks, guiding tasks, delivery tasks, and obstacle avoidance tasks. The specific task type can be selected according to the robot's functional settings.

[0045] In an implementation manner of the present application, by acquiring sensor data collected by each of the multiple sensors of the robot, the current performance information of each of the multiple sensors is determined, and based on the current performance information, the fusion weights of the multiple sensors are determined. Based on the fusion weights, the sensor data of the multiple sensors are fused to obtain fused data. When the robot is controlled to perform a task based on the fused data, the fused data used as a reference for robot decision-making can be mainly based on the sensor data of sensors with good current performance. In scenarios where some sensors fail, decisions can still be made based on valid sensors, which helps to improve the decision-making quality of the robot.

[0046] In some embodiments of the present application, Figure 2 As shown, determining the current performance information of each sensor among the multiple sensors may include: step S201. Determining the fusion weights of the multiple sensors according to the current performance information may include: step S202.

[0047] Step S201, according to the sensor data of each sensor, determine the effective collection range of the corresponding sensor to obtain current performance information.

[0048] The effective collection range is the range in which the sensor can collect valid data. The larger the effective collection range, the better the current performance. Based on the sensor data, the amount of valid data of the current sensor or the environmental conditions of the environment can be determined, thereby determining the effective collection range of the corresponding sensor and obtaining the current performance information.

[0049] In some embodiments of the present application, the above-mentioned multiple sensors may include an image sensor, and the sensor data collected by the image sensor is an environmental image. At this time, determining the effective collection range of the corresponding sensor based on the sensor data of each sensor may include: determining the degree of occlusion of the image sensor based on the environmental image, and determining the effective collection range of the image sensor based on the degree of occlusion.

[0050] Specifically, the electronic device can use image processing technology to identify obstructions that appear in the environmental image, and determine the degree of obstruction based on the image area where the obstruction is located. The larger the area of ​​the image area where the obstruction is located, the higher the degree of obstruction, and the smaller the effective acquisition range of the image sensor. In other implementations, the area of ​​the area with continuous color or texture changes can be determined by edge detection, image segmentation and other algorithms to obtain the degree of obstruction, or by determining the brightness of the image, the darker the image, the higher the degree of obstruction.

[0051] In other embodiments of the present application, the above-mentioned multiple sensors may include a laser radar, and the sensor data collected by the laser radar is point cloud data. In this case, determining the effective collection range of the corresponding sensor according to the sensor data of each sensor may include: determining the laser beams that can collect effective point cloud data by the laser radar according to the point cloud data, and determining the effective collection range of the laser radar according to the number of laser beams. It can be understood that the more laser beams that can collect effective point cloud data, the larger the effective collection range of the laser radar. Conversely, the fewer laser beams that can collect effective point cloud data, the smaller the effective collection range of the laser radar.

[0052] Step S202: determining a fusion weight according to an effective acquisition range of each sensor among the multiple sensors.

[0053] Among them, the fusion weight of each sensor is positively correlated with its effective acquisition range.

[0054] Taking the image sensor as an example, if the degree of occlusion is slight, the effective acquisition range is slightly lower than the maximum acquisition range of the image sensor. At this time, since the obstruction is light and does not affect the main function, the fusion weight of other sensors (such as infrared sensors and ultrasonic sensors) can be moderately increased, and the fusion weight of the image sensor can be slightly reduced. If the degree of occlusion is severe, the effective acquisition range is severely reduced compared to the maximum acquisition range of the image sensor, and the image sensor can hardly provide effective data. The fusion weight of other sensors can be significantly increased, and the fusion weight of the image sensor can be significantly reduced.

[0055] In this way, the weight of each sensor data can be dynamically adjusted based on the effective collection range of the sensor to improve the reliability of the fused data.

[0056] In other embodiments of the present application, Figure 3 As shown, determining the current performance information of each sensor among the multiple sensors may include: step S301 to step S302.

[0057] Step S301, obtaining benchmark data of various sensors under standard working conditions.

[0058] Standard working conditions refer to the normal working conditions of the sensor, and benchmark data refers to the data that the sensor can collect under normal working conditions.

[0059] Step S302 : comparing the sensor data of each sensor with the corresponding benchmark data to obtain current performance information of each sensor among the multiple sensors.

[0060] By comparing the sensor data of each sensor with the corresponding benchmark data, we can analyze whether there is a significant deviation between the currently collected sensor data and the benchmark data, so as to determine whether the current working conditions have an impact on the performance of the sensor, and obtain the current performance information of each sensor among the multiple sensors. The smaller the deviation between the sensor data of a sensor and the corresponding benchmark data, the better the current performance. For sensors with obvious performance degradation, their fusion weight in data fusion can be reduced to prevent inaccurate data from having a negative impact on the overall decision. For sensors with stable performance, their fusion weight in data fusion can be increased, and their high-reliability data can be used to improve the overall decision quality of the system.

[0061] In some other embodiments of the present application, determining the current performance information of each sensor among the multiple sensors may also include: acquiring test data of the sensor to obtain the current performance information.

[0062] For example, the accuracy, response time, stability, false alarm rate, and missed alarm rate can be tested separately to obtain test data. Specifically, the accuracy can be obtained by measuring the deviation between the sensor output and the actual value, for example, using a depth camera to measure an object at a known distance to detect whether its accuracy is within an acceptable range. The response time can be obtained by evaluating the speed at which the sensor responds to environmental changes. Stability can be obtained by checking the consistency of the sensor output and whether fluctuations or abnormal data occur frequently. The false alarm rate can be obtained by calculating the frequency with which the sensor incorrectly identifies an event, such as incorrectly detecting an obstacle that does not exist. The missed alarm rate can be obtained by calculating the frequency with which the sensor fails to identify an actual event, for example, by determining that an actual obstacle is not detected.

[0063] For sensors with significantly reduced accuracy, stability or response time or increased false alarm rate and missed alarm rate, their fusion weight in data fusion can be reduced, which can prevent inaccurate data from having a negative impact on the overall decision. For sensors with stable accuracy, stability or response time and low false alarm rate and missed alarm rate, their fusion weight in data fusion can be increased, and their high reliability data can be used to improve the overall decision quality of the system.

[0064] In some embodiments of the present application, Figure 4 As shown, controlling the robot to perform a task according to the fused data may include steps S401 to S403.

[0065] Step S401, determining the contour data of the target object according to the fused data.

[0066] Specifically, the target object may refer to a person, an animal or other object. Based on the fused data, an improved Canny edge detection algorithm may be used to extract contours in the image, and then a support vector machine (SVM) classifier may be used to classify these contours and distinguish the objects to which the contours belong, so as to determine whether the contour data is the target object.

[0067] Step S402: determining the motion state information of the target object according to the contour data.

[0068] The motion state information can characterize the motion parameters of the target object. The motion parameters may include but are not limited to position and motion speed. Specifically, based on multi-frame contour data, a dynamic time warping (DTW) algorithm can be used to analyze the contour changes of the target object in consecutive frames to track the dynamic contour of the target object. The Kalman filter can be used to estimate the position and speed of the dynamic contour in real time, thereby improving the prediction accuracy of the target motion state.

[0069] Step S403, controlling the robot to execute the task according to the motion state information.

[0070] In some embodiments of the present application, the motion trend of the target object can be analyzed based on the motion state information to control the robot to perform tasks based on the motion trend. Specifically, based on the dynamic profile and the motion state information, the behavior pattern (such as walking, stopping) of the target object can be analyzed to determine its intention.

[0071] Specifically, in some embodiments of the present application, controlling the robot to perform a task based on motion state information may include: estimating a visit destination of the target object based on the motion state information, and controlling the robot to perform a guidance task based on the visit destination.

[0072] Specifically, based on the motion state information, the visit destinations that can be reached in the target object's forward direction can be analyzed, and then a visitor route is generated based on the visit destination. The robot is controlled according to the visitor route to greet and provide services, and guide the visitor to the visit destination.

[0073] In other embodiments of the present application, based on the motion state information, controlling the robot to perform a task may include: if the target object enters the target area, when the target object approaches the robot, or when the target object continues to move within a preset distance of the robot or stops facing the robot, then controlling the robot to perform a welcoming task.

[0074] Specifically, if the target object enters a specific target area (such as an entrance or a landmark location), the robot should trigger a welcome. If the distance between the target object and the robot gradually decreases, it means that the target object is approaching the robot, indicating that the target object may be approaching in order to interact with the robot, and a welcome should be triggered at this time. If the target object stops near the robot and faces the robot, or moves back and forth near the robot, it may be looking for help or waiting to interact with the robot, and a welcome should be triggered at this time. The robot can automatically start a voice broadcast to welcome visitors, or it can display a welcome message and navigation instructions on a touch screen or other display device to further interact with visitors.

[0075] In this way, you can interact with visitors at the right time and enhance their reception experience.

[0076] It should be noted that, for the sake of simplicity of description, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders.

[0077] like Figure 5 Shown is a schematic structural diagram of a robot control device 500 provided in an embodiment of the present application, wherein the robot control device 500 is configured on an electronic device.

[0078] Specifically, the robot control device 500 may include:

[0079] A data acquisition unit 501 is used to acquire sensor data collected by each of the multiple sensors;

[0080] a performance determination unit 502, configured to determine current performance information of each sensor among the plurality of sensors;

[0081] A weight adjustment unit 503, configured to determine the fusion weights of the multiple sensors according to the current performance information;

[0082] A data fusion unit 504, configured to fuse the sensor data of the plurality of sensors according to the fusion weight to obtain fused data;

[0083] The task execution unit 505 is used to control the robot to execute a task according to the fusion data.

[0084] In some embodiments of the present application, the performance determination unit 502 is specifically used to: determine the effective acquisition range of the corresponding sensor according to the sensor data of each sensor, and obtain the current performance information; the weight adjustment unit 503 is specifically used to: determine the fusion weight according to the effective acquisition range of each sensor among the multiple sensors, and the fusion weight of each sensor is positively correlated with its effective acquisition range.

[0085] In some embodiments of the present application, the multiple sensors include an image sensor, and the sensor data collected by the image sensor is an environmental image; the performance determination unit 502 is specifically used to: determine the degree of occlusion of the image sensor based on the environmental image; and determine the effective collection range of the image sensor based on the degree of occlusion.

[0086] In some embodiments of the present application, the performance determination unit 502 is specifically used to: obtain benchmark data of the multiple sensors under standard working conditions; compare the sensor data of each sensor with the corresponding benchmark data to obtain current performance information of each sensor among the multiple sensors.

[0087] In some embodiments of the present application, the task execution unit 505 is specifically used to: determine contour data of the target object based on the fusion data; determine motion state information of the target object based on the contour data; and control the robot to perform the task based on the motion state information.

[0088] In some embodiments of the present application, the task execution unit 505 is specifically used to: estimate the visit destination of the target object according to the motion state information; and control the robot to perform the guidance task according to the visit destination.

[0089] In some embodiments of the present application, the task execution unit 505 is specifically used to control the robot to perform the welcoming task if the target object enters the target area, approaches the robot, or continues to move within a preset distance of the robot or stops facing the robot.

[0090] It should be noted that, for the convenience and simplicity of description, the specific working process of the control device 500 of the robot can be referred to Figures 1 to 4 The corresponding process of the method will not be repeated here.

[0091] like Figure 6, which is a schematic diagram of an electronic device provided in an embodiment of the present application. Specifically, the electronic device 6 may include: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as a robot control program. When the processor 60 executes the computer program 62, the steps in the above-mentioned robot control method embodiments are implemented, such as Figure 1 Alternatively, when the processor 60 executes the computer program 62, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 5 The functions of the data acquisition unit 501, the performance determination unit 502, the weight adjustment unit 503, the data fusion unit 504 and the task execution unit 505 are shown.

[0092] The computer program may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0093] For example, the computer program can be divided into: a data acquisition unit, a performance determination unit, a weight adjustment unit, a data fusion unit and a task execution unit. The specific functions of each unit are as follows: a data acquisition unit, used to acquire the sensor data collected by each of the multiple sensors; a performance determination unit, used to determine the current performance information of each of the multiple sensors; a weight adjustment unit, used to determine the fusion weights of the multiple sensors according to the current performance information; a data fusion unit, used to fuse the sensor data of the multiple sensors according to the fusion weights to obtain fusion data; a task execution unit, used to control the robot to perform tasks according to the fusion data.

[0094] The electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 These are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0095] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0096] The memory 61 may be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. The memory 61 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 61 may also include both an internal storage unit of the electronic device and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the electronic device. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0097] It should be noted that, for the convenience and brevity of description, the structure of the above electronic device can also refer to the specific description of the structure in the method embodiment, which will not be repeated here.

[0098] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0104] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0105] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A robot control method, characterized in that: The robot is equipped with a variety of sensors, and the control method of the robot includes: Acquire sensor data collected by each of the multiple sensors; determining current performance information for each of the plurality of sensors; Determining fusion weights of the multiple sensors according to the current performance information; fusing the sensor data of the plurality of sensors according to the fusion weights to obtain fused data; The robot is controlled to perform a task according to the fused data.

2. The robot control method according to claim 1, characterized in that: The determining of current performance information of each sensor among the plurality of sensors comprises: According to the sensor data of each sensor, determining the effective acquisition range of the corresponding sensor, and obtaining the current performance information; The determining, according to the current performance information, the fusion weights of the multiple sensors includes: The fusion weight is determined according to the effective acquisition range of each sensor among the multiple sensors, and the fusion weight of each sensor is positively correlated with its effective acquisition range.

3. The robot control method according to claim 2, characterized in that: The multiple sensors include an image sensor, and the sensor data collected by the image sensor is an environment image; The determining, according to the sensor data of each sensor, an effective acquisition range of the corresponding sensor includes: Determining, according to the environmental image, the degree to which the image sensor is blocked; An effective acquisition range of the image sensor is determined according to the degree of occlusion.

4. The robot control method according to claim 2, characterized in that: The multiple sensors include a laser radar, and the sensor data collected by the laser radar is point cloud data; The determining, according to the sensor data of each sensor, an effective acquisition range of the corresponding sensor includes: According to the point cloud data, determine the laser beam of the laser radar to collect valid point cloud data; The effective collection range of the lidar is determined based on the number of laser beams.

5. The robot control method according to claim 1, characterized in that: The determining of current performance information of each sensor among the plurality of sensors comprises: Obtaining benchmark data of the plurality of sensors under standard operating conditions; The sensor data of each sensor is compared with the corresponding benchmark data to obtain current performance information of each sensor in the multiple sensors.

6. The robot control method according to any one of claims 1 to 5, characterized in that: The step of controlling the robot to perform a task according to the fused data comprises: Determining contour data of the target object according to the fused data; Determining motion state information of the target object according to the contour data; The robot is controlled to perform a task according to the motion state information.

7. The robot control method according to claim 6, characterized in that: The step of controlling the robot to perform a task according to the motion state information comprises: estimating a visit destination of the target object according to the motion state information; According to the access destination, the robot is controlled to perform a guidance task.

8. The robot control method according to claim 6, characterized in that: The step of controlling the robot to perform a task according to the motion state information comprises: If the target object enters the target area, approaches the robot, or continues to move within a preset distance from the robot or stops facing the robot, the robot is controlled to perform the welcoming task.

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 computer program, the steps of the robot control method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the robot control method according to any one of claims 1 to 8 are implemented.

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