Self-adaptive control method for humanoid robot and related equipment

Abstract

The invention discloses a humanoid robot adaptive control method and related equipment, and relates to the technical field of robots, and the method comprises the steps: obtaining parameter data related to an industrial scene; the parameter data comprises environment illumination intensity, task types, movement speed and workpiece precision requirements; determining a target industrial scene type based on the parameter data; the target industrial scene type comprises a high-illumination high-precision assembling scene, a low-illumination inspection scene, a high-speed moving scene or a heavy load carrying scene; generating a linkage control instruction according to the target industrial scene type; in response to the linkage control instruction, the sensor is synchronously controlled to be switched to a corresponding working mode, and the actuator is adjusted to a matched operation parameter; the adjustment priority of the working mode and the adjustment amplitude of the operation parameter have a scene association mapping relationship. The working mode of the sensor and the operation parameters of the actuator are adjusted in real time according to the industrial scene type, higher efficiency is achieved, and dynamic balance between energy consumption optimization and operation precision can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robots, and in particular to a humanoid robot adaptive control method and related equipment. BACKGROUND

[0002] In the current industrial humanoid robot working process, fixed sensor parameters and actuator running modes are usually adopted. For example, in the mechanical and electrical component assembly scene, the robot needs to maintain high-precision visual recognition and humanoid robot arm control, but still maintains high-resolution imaging when moving, resulting in a sharp increase in energy consumption. In the low-illumination warehouse inspection scene, if the default optical sensor parameters are used, the image noise will be too high to affect the target detection accuracy, and additional light supplementing equipment needs to be configured, increasing the system complexity.

[0003] The prior art has the following defects: 1. The sensor and actuator parameters are fixed and cannot be dynamically adjusted according to the scene, resulting in a mismatch between energy consumption and performance; 2. The contradiction between the diversity of industrial scenes (such as illumination, precision requirements, and motion states) and the insufficient adaptability of the robot is prominent; 3. Manual parameter recalibration is required when switching between multiple tasks, affecting production efficiency. SUMMARY

[0004] The main purpose of the embodiments of the present application is to propose a humanoid robot adaptive control method and related equipment to achieve the dynamic balance between energy consumption optimization and work accuracy of the humanoid robot in the industrial scene.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application proposes a humanoid robot adaptive control method, which comprises the following steps: Obtaining parameter data related to an industrial scene; wherein the parameter data includes environmental illumination intensity, task type, motion speed, and workpiece precision requirement; Determining a target industrial scene type based on the parameter data; wherein the target industrial scene type includes at least one of a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene, or a heavy load carrying scene; Generating a linkage control instruction according to the target industrial scene type; wherein the linkage control instruction is used to dynamically match the working mode of the sensor and the running parameter of the actuator based on the scene, and there is a scene correlation mapping relationship between the adjustment priority of the working mode and the adjustment amplitude of the running parameter; In response to the linkage control instruction, the sensor is switched to the corresponding working mode and the actuator is adjusted to the matching running parameter.

[0006] In some embodiments, the linkage control instruction is generated according to the target industrial scene type, which comprises the following steps: dynamically match the working mode of the sensor and the running parameter of the actuator based on a scene; wherein the working mode comprises a high-resolution imaging mode, a low-power consumption scanning mode and a depth enhancement mode; the running parameter comprises a motor output torque, a humanoid robot arm motion accuracy, a leg motion accuracy, a waist motion accuracy and a joint response speed; adjust the priority of the working mode and the amplitude of the running parameter obtained by matching according to the scene correlation mapping relationship; wherein the scene correlation mapping relationship comprises: in a high-precision scene, preferentially guaranteeing the resolution of the sensor and synchronously reducing the output torque of the actuator; in a high-speed scene, preferentially improving the response speed of the actuator and synchronously reducing the sampling frequency of the sensor.

[0007] In some embodiments, the parameter data related to the industrial scene is obtained, comprising the following steps: collecting the ambient light intensity by an optical sensor; obtaining the task type and the workpiece accuracy requirement by a task scheduling system; collecting the joint motion parameter and the motion speed of the robot by a motion sensor.

[0008] In some embodiments, the method further comprises the following steps: in a high-illumination high-precision assembly scene, generating and executing a first instruction; wherein the first instruction is used to control the sensor to enable a high-resolution imaging mode and the actuator to enable a high-precision low-torque mode; in a low-illumination inspection scene, generating and executing a second instruction; wherein the second instruction is used to control the sensor to enable a depth enhancement mode and reduce the optical resolution, and the actuator to enable a low-power consumption cruise mode.

[0009] In some embodiments, the generation of the linkage control instruction according to the target industrial scene type comprises the following steps: selecting a target linkage parameter template from at least three pre-stored linkage parameter templates of the sensor and the actuator corresponding to the industrial scene; generating the linkage control instruction based on the target linkage parameter template.

[0010] In some embodiments, the determination of the target industrial scene type based on the parameter data comprises the following steps: when the ambient light intensity is greater than a first light intensity threshold and the workpiece accuracy requirement is less than or equal to a preset accuracy threshold, determining that the target industrial scene type is the high-illumination high-precision assembly scene; determining that the target industrial scene type is the low-illumination inspection scene when the ambient light intensity is less than or equal to a second light intensity threshold and the task type is path inspection; wherein the first light intensity threshold is greater than the second light intensity threshold.

[0011] In some embodiments, if the target industrial scene type is the high-speed movement scene, when the linkage control instruction is responded to, the following steps are performed: controlling the visual sensor to switch to a low-resolution frame rate priority mode; reducing the joint damping coefficient of the humanoid robot arm to a target percentage of a preset threshold; executing an energy consumption optimization algorithm of the leg motor of the humanoid robot to optimize energy consumption; wherein the expression of the energy consumption optimization algorithm is: ; wherein, is real-time power consumption, is a speed coefficient, is a movement speed, is a load coefficient, is the mass of the humanoid robot, is acceleration.

[0012] To achieve the above-mentioned purposes, another aspect of the embodiments of the present application proposes a humanoid robot adaptive control device, which comprises: a data acquisition unit configured to acquire parameter data related to an industrial scene; wherein the parameter data comprises ambient light intensity, task type, movement speed, and workpiece precision requirement; a data processing unit configured to determine a target industrial scene type based on the parameter data; wherein the target industrial scene type comprises at least one of a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene, or a heavy-load carrying scene; an instruction generation unit configured to generate a linkage control instruction according to the target industrial scene type; wherein the linkage control instruction is used to dynamically match the working mode of a sensor and the operating parameter of an actuator based on a scene, and there is a scene correlation mapping relationship between the adjustment priority of the working mode and the adjustment amplitude of the operating parameter; an execution control unit configured to respond to the linkage control instruction to synchronously control the sensor to switch to the corresponding working mode and the actuator to adjust to the matched operating parameter.

[0013] To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0014] To achieve the above object, another aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the above method when executed by a processor.

[0015] To achieve the above object, another aspect of the embodiments of the present application provides a computer program product, which comprises a computer program, and the computer program implements the above method when executed by a processor.

[0016] The embodiments of the present application at least have the following beneficial effects: The present application provides a humanoid robot adaptive control method and related equipment, the present application scheme obtains the parameter data related to the industrial scene;Among them, the parameter data includes the environment illumination intensity, the task type, the motion speed and the workpiece precision requirement;Determine the target industrial scene type based on the parameter data;Among them, the target industrial scene type includes at least one of high-illumination high-precision assembly scene, low-illumination inspection scene, high-speed movement scene or heavy load carrying scene;According to the target industrial scene type, the linkage control instruction is generated;Among them, the linkage control instruction is used to dynamically match the working mode of the sensor and the running parameter of the actuator based on the scene, and the adjustment priority of the working mode and the adjustment amplitude of the running parameter exist scene correlation mapping relationship;In response to the linkage control instruction, the sensor is switched to the corresponding working mode and the actuator is adjusted to the matching running parameter synchronously.The present application adjusts the working mode of the sensor and the running parameter of the actuator according to the type of the industrial scene, which has higher efficiency than manual adjustment, and can realize the dynamic balance of energy consumption optimization and work precision. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flowchart of a humanoid robot adaptive control method provided by the embodiments of the present application is shown in the figure. Figure 2 A structural diagram of a humanoid robot adaptive control device provided by the embodiments of the present application is shown in the figure. Figure 3An example structure diagram of a humanoid robot adaptive control device provided by an embodiment of the present application is provided. Figure 4 An example flowchart of scene determination provided by an embodiment of the present application is provided. Figure 5 A mode switching timing diagram provided by an embodiment of the present application is provided. Figure 6 A power consumption comparison diagram in different industrial scenes provided by an embodiment of the present application is provided. Figure 7 A hardware structure schematic diagram of an electronic device provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementation described in the following exemplary embodiments does not represent all implementations consistent with embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as described in the appended claims.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0021] Referring to Figure 1 The embodiments of the present application provide a humanoid robot adaptive control method, which can include but is not limited to S100 to S130, specifically as follows: S100: Obtain parameter data related to an industrial scene; wherein the parameter data includes ambient light intensity, task type, motion speed and workpiece precision requirement; S110: Determine a target industrial scene type based on the parameter data; wherein the target industrial scene type includes at least one of a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene or a heavy load carrying scene; S120: Generate a linkage control instruction according to the target industrial scene type; wherein the linkage control instruction is used to dynamically match the working mode of the sensor and the running parameter of the actuator based on the scene, and the adjustment priority of the working mode and the adjustment amplitude of the running parameter have a scene correlation mapping relationship; S130: in response to the linkage control instruction, synchronously control the sensor to switch to the corresponding working mode and the actuator to adjust to the matched operating parameter.

[0022] Optionally, the generating linkage control instruction according to the target industrial scene type comprises the following steps: matching the working mode of the sensor and the operating parameter of the actuator based on the scene dynamics; wherein, the working mode comprises a high-resolution imaging mode, a low-power consumption scanning mode and a depth enhancement mode; the operating parameter comprises motor output torque, humanoid robot arm motion accuracy, leg motion accuracy, waist motion accuracy and joint response speed; adjusting the priority of the working mode and the amplitude of the operating parameter obtained by matching according to the scene correlation mapping relationship; wherein, the scene correlation mapping relationship comprises preferentially guaranteeing the resolution of the sensor and synchronously reducing the output torque of the actuator in a high-precision scene; preferentially improving the response speed of the actuator and synchronously reducing the sampling frequency of the sensor in a high-speed scene.

[0023] Optionally, the obtaining parameter data related to the industrial scene comprises the following steps: collecting the ambient light intensity through an optical sensor; obtaining the task type and the workpiece accuracy requirement through a task scheduling system; collecting the joint motion parameter and the motion speed of the robot through a motion sensor.

[0024] Optionally, the method further comprises the following steps: in a high-illumination high-precision assembly scene, generating and executing a first instruction; wherein, the first instruction is used to control the sensor to enable a high-resolution imaging mode and the actuator to enable a high-precision low-torque mode; in a low-illumination inspection scene, generating and executing a second instruction; wherein, the second instruction is used to control the sensor to enable a depth enhancement mode and reduce optical resolution, and the actuator to enable a low-power consumption cruise mode.

[0025] Optionally, the generating linkage control instruction according to the target industrial scene type comprises the following steps: selecting a target linkage parameter template from at least three pre-stored linkage parameter templates of the sensor and the actuator corresponding to the industrial scene; generating the linkage control instruction based on the target linkage parameter template.

[0026] Optionally, the determining a target industrial scene type based on the parameter data comprises the following steps: determining that the target industrial scene type is the high-illumination high-precision assembly scene when the ambient light intensity is greater than a first light intensity threshold and the workpiece precision requirement is less than or equal to a preset precision threshold; determining that the target industrial scene type is the low-illumination inspection scene when the ambient light intensity is less than or equal to a second light intensity threshold and the task type is path inspection; wherein the first light intensity threshold is greater than the second light intensity threshold.

[0027] Optionally, if the target industrial scene type is the high-speed movement scene, then in response to the linkage control instruction, the following steps are performed: controlling the visual sensor to switch to a low-resolution frame rate priority mode; reducing the joint damping coefficient of the humanoid robot arm to a target percentage of a preset threshold; executing an energy consumption optimization algorithm of the leg motor of the humanoid robot to optimize energy consumption; wherein the expression of the energy consumption optimization algorithm is: ; wherein, is real-time power consumption, is a speed coefficient, is a movement speed, is a load coefficient, is the mass of the humanoid robot, is an acceleration.

[0028] Referring to Figure 2 , the embodiments of the present application also provide a humanoid robot adaptive control device, which can implement the above-mentioned humanoid robot adaptive control method. The device comprises: a data acquisition unit configured to acquire parameter data related to an industrial scene; wherein the parameter data comprises ambient light intensity, task type, movement speed, and workpiece precision requirement; a data processing unit configured to determine a target industrial scene type based on the parameter data; wherein the target industrial scene type comprises at least one of a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene, or a heavy load carrying scene; an instruction generation unit configured to generate a linkage control instruction according to the target industrial scene type; wherein the linkage control instruction is used to dynamically match the working mode of a sensor and the operating parameter of an actuator based on a scene, and there is a scene correlation mapping relationship between the adjustment priority of the working mode and the adjustment amplitude of the operating parameter; an execution control unit configured to synchronously control the sensor to switch to the corresponding working mode and the actuator to adjust to the matched operating parameter in response to the linkage control instruction.

[0029] It can be understood that the contents in the above method embodiments are applicable to the device embodiments, the device embodiments specifically implement the functions same as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0030] Next, some optional embodiments of the present application will be described in detail with reference to specific application examples.

[0031] The embodiment of the present application provides a humanoid robot adaptive control device, system and related equipment, and belongs to the technical field of humanoid robots. The device comprises: a data acquisition unit configured to acquire parameter data related to an industrial scene, the parameter data comprising ambient light intensity, task type, motion speed and workpiece precision requirement; a data processing unit configured to determine a specific industrial scene type based on the parameter data; an instruction generation unit configured to generate a control instruction according to the specific industrial scene type to adjust the sensor working mode and the actuator operating parameter of the humanoid robot; and an execution control unit configured to respond to the instruction execution mode switching. The embodiment of the present application dynamically adjusts the working mode of the robot in different industrial scenes, meets the scene requirements of high-precision operation or high-efficiency movement, significantly reduces energy consumption and improves system adaptability, and is especially suitable for industrial environments with frequent task switching.

[0032] Specifically, the humanoid robot adaptive control device of the embodiment of the present application comprises: a data acquisition unit configured to acquire parameter data related to an industrial scene, the parameter data comprising ambient light intensity, task type, motion speed and workpiece precision requirement; a data processing unit configured to determine a target industrial scene type based on the parameter data, the target industrial scene type comprising a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene and a heavy load carrying scene; an instruction generation unit configured to generate a linkage control instruction according to the target industrial scene type, the linkage control instruction being used to dynamically match the sensor working mode and the actuator operating parameter based on the scene, wherein there is a scene correlation mapping relationship between the adjustment priority of the sensor working mode and the adjustment amplitude of the actuator operating parameter; an execution control unit configured to respond to the linkage control instruction, and synchronously control the sensor to switch to the corresponding working mode and the actuator to adjust to the matching operating parameter; The sensor working mode includes a high-resolution imaging mode, a low-power consumption scanning mode and a depth enhancement mode; the actuator operating parameter includes a motor output torque, a humanoid robot arm motion precision, a leg motion precision, a waist motion precision and a joint response speed; and the scene correlation mapping relationship is that the sensor resolution is preferentially guaranteed and the actuator output torque is simultaneously reduced in a high-precision scene, and the actuator response speed is preferentially improved and the sensor sampling frequency is simultaneously reduced in a high-speed scene.

[0033] Further, the data acquisition unit acquires the parameter data by at least one of the following ways: The optical sensor collects the ambient light intensity; The task scheduling system acquires the task type and the workpiece precision requirement; The motion sensor collects the robot motion speed and the joint motion parameter.

[0034] Further, the instruction generation unit is further configured to: in a high-illumination high-precision assembly scene, generate an instruction for controlling the sensor to enable the high-resolution imaging mode and the actuator to enable the high-precision low-torque mode; in a low-illumination inspection scene, generate an instruction for controlling the sensor to enable the depth enhancement mode and reduce the optical resolution, and the actuator to enable the low-power consumption cruise mode.

[0035] Further, the device further comprises a mode storage unit configured to pre-store at least three sensor-actuator linkage parameter templates corresponding to industrial scenes, and the instruction generation unit generates the control instruction based on the template.

[0036] Further, in the data acquisition unit, when performing data processing, the specific industrial scene type is determined by the following rules: when the ambient light intensity is greater than 500 lux and the workpiece precision requirement is less than or equal to 0.1 mm, the high-illumination high-precision assembly scene is determined; when the ambient light intensity is less than or equal to 200 lux and the task type is path inspection, the low-illumination inspection scene is determined.

[0037] Further, in the instruction generation step of the instruction generation unit, the control instruction generated for the high-speed moving scene includes: controlling the visual sensor to switch to a low-resolution frame rate priority mode; reducing the humanoid robot arm joint damping coefficient to 60-80% of a preset threshold value; starting an energy consumption optimization algorithm of the leg motor, wherein the energy consumption optimization algorithm satisfies the formula: ; In the formula, is the real-time power consumption (unit: W), is a speed coefficient (value range 0.02-0.05), is a motion speed (unit: m / s), is a load coefficient (value range 0.1-0.3), is a mass of the humanoid robot (unit: kg), is an acceleration (unit: m / s 2 ).

[0038] The embodiment of the application further provides a humanoid robot system, the system comprising the humanoid robot adaptive control device, and the system further comprising: at least two visual sensors, respectively arranged at a head of the robot and an end of an arm of the humanoid robot; a 7-axis humanoid robot arm actuator and a wheeled / biped mobile mechanism; an environment perception module comprising a dust sensor and a temperature and humidity sensor.

[0039] Further, the humanoid robot of the system is applicable to warehouse inspection, automobile polishing workshop, electromechanical component assembly line and scene, and can automatically switch working modes in three or more industrial scenes.

[0040] More specifically, the embodiment of the application provides the following device implementation scheme: As shown in Figure 3 , the humanoid robot adaptive control device 100 comprises: a data acquisition unit 110: composed of a visual sensor (resolution 12 million pixels), a multi-axis IMU motion sensor and a task interface module, a sampling frequency of 10 Hz, used for synchronously collecting environment and task parameters; a data processing unit 120: adopting an FPGA+ARM architecture, built-in scene determination algorithm, capable of completing scene type identification within 50 ms; an instruction generation unit 130: pre-storing 4 types of scene mode templates, supporting output of control instructions through an industrial bus (EtherCAT); an execution control unit 140: connected to a humanoid robot arm servo driver and a sensor control circuit, with a response delay ≤20 ms.

[0041] Referring to Figure 4 , the scene determination rule is: The data processing unit 120 determines the scene type through the following threshold values: 1. High-illumination high-precision assembly scene: environmental illumination intensity > 500 lux, workpiece precision requirement ≤0.1 mm, motion speed <0.5 m / s; 2. Low-illumination inspection scene: environmental illumination intensity ≤200 lux, task type is "path inspection", load mass <5 kg; 3. High-speed moving scenario: moving speed ≥ 1.0 m / s, task type is "scene switching"; 4. Heavy load carrying scenario: load mass ≥ 50 kg, moving speed < 0.8 m / s.

[0042] Mode control strategy: 1. High-illumination high-precision assembly scenario: Sensor: the visual sensor is switched to 12 million pixels @ 30 fps, and sub-pixel level edge detection is enabled; Actuator: the joint precision of the humanoid robot arm is ≤ 0.02 mm, and the motor output torque is limited to 50% of the rated value (to prevent overshoot).

[0043] 2. Low-illumination inspection scenario: Sensor: the visual sensor is switched to 2 million pixels @ 15 fps, infrared imaging mode is enabled, and the laser radar scanning frequency is reduced to 5 Hz; Actuator: the moving mechanism speed is limited to 0.6 m / s, and the humanoid robot arm is kept in a downward vertical folding state to reduce wind resistance.

[0044] 3. High-speed moving scenario: Sensor: the visual sensor is switched to 4 million pixels @ 60 fps (frame rate priority), and the depth camera is turned off; Actuator: the energy consumption optimization algorithm of claim 7 is applied, and the leg motor power consumption is reduced by about 40%.

[0045] 4. Heavy load carrying scenario: Sensor: 3D visual positioning is enabled, and the sampling frequency is reduced to 5 Hz; Actuator: the motor output torque is increased to 120% of the rated value (short-time overload), and the joint damping coefficient is increased by 50%.

[0046] Figure 5 The mode switching timing diagram includes the time sequence of parameter monitoring, instruction issuing, and execution feedback.

[0047] Mode switching logic: When the scene parameters meet the threshold of the new scene for 3 sampling periods, the mode switching is started by the execution control unit 140, the robot posture is kept stable during the switching process, and the switching time is ≤ 300 ms.

[0048] Reference Figure 6 The power consumption comparison chart in different scenarios provided by the embodiments of the present application shows that Figure 6 The power consumption in the high-speed moving scenario is significantly reduced compared with the default mode.

[0049] The specific implementation of the specific industrial scenario is as follows: Embodiment 1: Application of electromechanical component assembly line.

[0050] In the robot controller mainboard assembly scene, the humanoid robot needs to complete the "material taking - moving - assembly" cycle operation. When it is at the assembly station (high illumination high precision scene), the control device triggers the following actions: 1. The visual sensor switches to 12 million pixel mode, and the positioning accuracy of the 0402 package element is ±0.01mm; 2. The linear motion speed of the humanoid robot arm is reduced to 50mm / s, and the moveJ fine adjustment step is 0.005mm; 3. Energy consumption monitoring is displayed, and the average power consumption is 280W at this time.

[0051] When the robot moves to the next station (high speed moving scene): 1. The visual sensor switches to 4 million pixels @ 60fps, and only the contour detection algorithm is retained; 2. The leg motor enables the energy consumption optimization mode, and the power consumption is reduced to 160W when the motion speed is 0.6m / s; 3. The humanoid robot arm is vertically folded downward, and the wind resistance coefficient is reduced by 35%.

[0052] Embodiment 2: Warehouse inspection scene.

[0053] In a warehouse with light intensity of 150lux, the humanoid robot performs a shelf inspection task: 1. The data acquisition unit detects that the light intensity is ≤200lux, the task type is "inspection", and it is determined as a low illumination scene; 2. The instruction generation unit controls the infrared camera to start, and the scanning frequency of the laser radar is reduced from 10Hz to 5Hz; 3. The moving mechanism switches to the silent mode, the speed is 0.3m / s, and the endurance time is prolonged by 2.3 hours compared with the default mode.

[0054] The embodiment of the application realizes the dynamic balance of the industrial humanoid robot among precision, efficiency and energy consumption through scene adaptive control, and is especially suitable for complex scenes such as flexible manufacturing production line and intelligent warehouse.

[0055] Compared with the prior art, the embodiment of the application has at least the following beneficial effects: 1. Energy consumption optimization: reduce sensor resolution and actuator power in non-high precision scene, energy consumption can be reduced by 30%-50%; 2. Scene adaptability: determine the scene type through multi-dimensional parameters to ensure stable performance when the light changes suddenly or the task switches; 3. Cost reduction: no need to configure additional hardware (such as fill light) for special scenes, scene adaptation is realized through software algorithm optimization; 4. Efficiency improvement: automatic mode switching, reducing manual intervention, multi-scenario operation switching time shortened to within 100ms.

[0056] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor. The memory stores a computer program, and the processor implements the method of the embodiment of the present application when executing the computer program. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0057] It can be understood that the contents in the method embodiments are applicable to the device embodiments, the device embodiments specifically implement the functions of the method of the present application, and achieve the same beneficial effects as the method of the present application.

[0058] Please refer to Figure 7 , Figure 7 The hardware structure of the electronic device of another embodiment is illustrated, and the electronic device comprises: The processor 701 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application. The memory 702 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 702 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 702 and are called and executed by the processor 701 to implement the method of the present application. The input / output interface 703 is used to realize information input and output. The communication interface 704 is used to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.). The bus 705 transmits information between various components (for example, the processor 701, the memory 702, the input / output interface 703, and the communication interface 704) of the device. The processor 701, the memory 702, the input / output interface 703, and the communication interface 704 are connected to each other through the bus 705 for internal communication in the device.

[0059] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method of the present application.

[0060] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiment, the present storage medium embodiment specifically realizes the functions of the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0061] The embodiment of the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to realize the method.

[0062] The memory, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0063] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0064] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps, or different steps.

[0065] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0066] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functions of the modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0067] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, based on stated preference or the like, and that "default" or other orderings are thus permissible. Further, the terms "comprise", "comprising", "include", "including", and the like, are specifically intended to be open-ended. That is, references to individual steps and the like do not suhstantially exclude the presence of two or more of a given step or its integral presence in the process, method, system, article, or apparatus having been made with a wider scope. The use of notation such as "first", "second", "third", etc. does not generally limit the areas, but is used to connect like elements or to distinguish one claim from another. These terms can be used interchangeably when appropriate. Terms concerning the relative position of elements can be interpreted such that their use adheres to their normal meaning, but they can also be interpreted to mean the opposite according to specific claims.

[0068] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0069] In several embodiments provided by the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above-mentioned units is only a logical functional division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0070] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0071] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0072] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0073] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and the scope of the rights of the embodiments of the present application is not limited thereto. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A humanoid robot adaptive control method, characterized by, The method comprises the following steps: Obtaining parameter data related to an industrial scene; wherein the parameter data comprises ambient light intensity, task type, motion speed and workpiece precision requirement; Determining a target industrial scene type based on the parameter data; wherein the target industrial scene type comprises at least one of a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene or a heavy load carrying scene; Generating a linkage control instruction according to the target industrial scene type; wherein the linkage control instruction is used to dynamically match the working mode of a sensor and the operating parameter of an actuator based on the scene, and there is a scene correlation mapping relationship between the adjustment priority of the working mode and the adjustment amplitude of the operating parameter; In response to the linkage control instruction, synchronously control the sensor to switch to the corresponding working mode and the actuator to adjust to the matched operating parameter.

2. The adaptive control method for a humanoid robot according to claim 1, wherein The linkage control instruction according to the target industrial scene type comprises the following steps: Dynamically matching the working mode of the sensor and the operating parameter of the actuator based on the scene; wherein the working mode comprises a high-resolution imaging mode, a low-power scanning mode and a depth enhancement mode; the operating parameter comprises motor output torque, humanoid robot arm motion precision, leg motion precision, waist motion precision and joint response speed; Adjusting the priority of the matched working mode and the amplitude of the matched operating parameter according to the scene correlation mapping relationship; wherein the scene correlation mapping relationship comprises preferentially guaranteeing the resolution of the sensor and synchronously reducing the output torque of the actuator in a high-precision scene; preferentially improving the response speed of the actuator and synchronously reducing the sampling frequency of the sensor in a high-speed scene.

3. The adaptive control method for a humanoid robot according to claim 1, wherein The parameter data related to the industrial scene comprises the following steps: Collecting the ambient light intensity through an optical sensor; Obtaining the task type and the workpiece precision requirement through a task scheduling system; Collecting the joint motion parameters of a robot and the motion speed through a motion sensor.

4. The adaptive control method for humanoid robots according to claim 1, wherein, The method further comprises the following steps: In a high-illumination high-precision assembly scene, generating and executing a first instruction; wherein the first instruction is used to control the sensor to enable a high-resolution imaging mode and the actuator to enable a high-precision low-torque mode; In a low-illumination inspection scene, generating and executing a second instruction; wherein the second instruction is used to control the sensor to enable a depth enhancement mode and reduce optical resolution, and the actuator to enable a low-power cruise mode.

5. The adaptive control method for humanoid robots according to claim 1, wherein, The linkage control instruction according to the target industrial scene type comprises the following steps: Selecting a target linkage parameter template from at least three pre-stored linkage parameter templates of sensors and actuators corresponding to the industrial scenes; Generating the linkage control instruction based on the target linkage parameter template.

6. The adaptive control method for a humanoid robot according to claim 1, wherein The determination of the target industrial scene type based on the parameter data comprises the following steps: When the ambient light intensity is greater than a first light intensity threshold and the workpiece precision requirement is less than or equal to a preset precision threshold, determining that the target industrial scene type is the high-illumination high-precision assembly scene; determining that the target industrial scene type is the low-illumination inspection scene when the ambient light intensity is less than or equal to a second light intensity threshold and the task type is path inspection; wherein the first light intensity threshold is greater than the second light intensity threshold.

7. The adaptive control method for a humanoid robot according to any one of claims 1 to 6, characterized in that, If the target industrial scene type is the high-speed movement scene, when responding to the linkage control instruction, the following steps are performed: controlling the visual sensor to switch to a low-resolution frame rate priority mode; reducing the joint damping coefficient of the humanoid robot arm to a target percentage of a preset threshold; performing an energy consumption optimization algorithm of the leg motor of the humanoid robot to optimize energy consumption; wherein the expression of the energy consumption optimization algorithm is: ； wherein, is a real-time power consumption, is a speed coefficient, is a motion speed, is a load coefficient, is a mass of the humanoid robot, is an acceleration.

8. A humanoid robot adaptive control device characterized by comprising: The device comprises: a data acquisition unit configured to acquire parameter data related to an industrial scene; wherein the parameter data comprises ambient light intensity, task type, movement speed, and workpiece precision requirement; a data processing unit configured to determine a target industrial scene type based on the parameter data; wherein the target industrial scene type comprises at least one of a high-illumination high-precision assembly scene, a low-illumination inspection scene, a high-speed movement scene, or a heavy load carrying scene; an instruction generation unit configured to generate a linkage control instruction according to the target industrial scene type; wherein the linkage control instruction is used to dynamically match the working mode of the sensor and the running parameter of the actuator based on the scene, the adjustment priority of the working mode and the adjustment amplitude of the running parameter have a scene correlation mapping relationship; an execution control unit configured to respond to the linkage control instruction and synchronously control the sensor to switch to the corresponding working mode and the actuator to adjust to the matched running parameter.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the method of any one of claims 1 to 7.

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