A low-power consumption mobile robot energy optimization control method and system
Patent Information
- Application Number
- CN202611010543.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-08
- Publication Date
- 2026-10-30
AI Technical Summary
[0004]本申请提供了一种低功耗移动机器人能量优化控制方法及系统，用于针对解决现有移动机器人行走驱动与图像处理功耗固定不可调，无效能耗偏高，导致整机续航受限的技术问题
[0011]在移动机器人采集得到实时环境图像的过程中，动态监测得到实时工作状态；以标准化变异加权所述实时工作状态中的实时电量和实时速度得到的实时状态指数为权重，对预定移动控制方案进行能量优化调整，得到实时移动控制方案；获取所述移动机器人的任务处理器，根据能量优化策略对所述实时环境图像进行分析，得到所述任务处理器的实时图像处理方案；激活电源管理芯片基于所述实时移动控制方案与所述实时图像处理方案对所述移动机器人进行能量优化控制。达到了实现机器人行走与图像处理的分级功耗动态管控，提高了机器人电能利用率与连续作业续航能力的技术效果。
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Figure CN123010727A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of robot energy optimization control technology, specifically to a low-power mobile robot energy optimization control method and system. Background Technology
[0002] Existing mobile robots mostly use fixed-parameter walking drive control and uniform image processing operations. The drive power, processor operating frequency, and cache scheduling logic remain constant throughout the process, making it impossible to dynamically adjust the operating power consumption in real time based on the robot's remaining battery power and walking speed. Even in scenarios with low battery power or low-speed cruising, they still maintain full-load operation, resulting in a large amount of ineffective power loss. Due to the limitation of the single power consumption management method, the robot's effective endurance is greatly compressed, making it difficult to meet the actual usage requirements of environmental image recognition accuracy and overall energy-saving operation.
[0003] The existing mobile robot walking drive and image processing power consumption is fixed and cannot be adjusted, resulting in high ineffective energy consumption and limited overall battery life. Summary of the Invention
[0004] This application provides a low-power mobile robot energy optimization control method and system to address the technical problem of fixed and unadjustable power consumption for walking drive and image processing in existing mobile robots, resulting in high ineffective energy consumption and limited overall battery life.
[0005] In view of the above problems, this application provides a low-power mobile robot energy optimization control method and system.
[0006] A first aspect of this application provides a low-power mobile robot energy optimization control method, the method comprising:
[0007] During the process of the mobile robot acquiring real-time environmental images, its real-time operating status is dynamically monitored. Using a real-time state index obtained by weighting the real-time battery level and real-time speed in the real-time operating status with standardized variation weights, an energy optimization adjustment is performed on the predetermined mobile control scheme to obtain a real-time mobile control scheme. The task processor of the mobile robot is acquired, and the real-time environmental images are analyzed according to the energy optimization strategy to obtain a real-time image processing scheme for the task processor. The power management chip is activated to perform energy optimization control on the mobile robot based on the real-time mobile control scheme and the real-time image processing scheme.
[0008] A second aspect of this application provides a low-power mobile robot energy optimization control system, the system comprising:
[0009] The system includes a working status acquisition module, used to dynamically monitor and obtain the real-time working status during the process of the mobile robot acquiring real-time environmental images; a mobile control scheme acquisition module, used to perform energy optimization adjustments on a predetermined mobile control scheme by using a real-time status index obtained from the real-time power consumption and real-time speed in the real-time working status as weights, to obtain a real-time mobile control scheme; an image processing scheme acquisition module, used to acquire the task processor of the mobile robot, analyze the real-time environmental images according to the energy optimization strategy, and obtain a real-time image processing scheme for the task processor; and an energy optimization control module, used to activate the power management chip to perform energy optimization control on the mobile robot based on the real-time mobile control scheme and the real-time image processing scheme.
[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0011] During the process of the mobile robot acquiring real-time environmental images, its real-time operating status is dynamically monitored. Using a real-time status index obtained by weighting the real-time power consumption and speed in the real-time operating status with standardized variation weights, a predetermined motion control scheme is optimized and adjusted to obtain a real-time motion control scheme. The task processor of the mobile robot is acquired, and the real-time environmental images are analyzed according to the energy optimization strategy to obtain a real-time image processing scheme for the task processor. The power management chip is activated to perform energy optimization control on the mobile robot based on the real-time motion control scheme and the real-time image processing scheme. This achieves hierarchical dynamic power consumption management for robot walking and image processing, improving the robot's power utilization rate and continuous operation endurance. Attached Figure Description
[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0013] Figure 1 This is a schematic flowchart of a low-power mobile robot energy optimization control method provided in an embodiment of this application;
[0014] Figure 2 This is a schematic diagram of a low-power mobile robot energy optimization control system provided in an embodiment of this application.
[0015] Figure labeling: Working status acquisition module 10, motion control scheme acquisition module 20, image processing scheme acquisition module 30, energy optimization control module 40. Detailed Implementation
[0016] This application provides a low-power mobile robot energy optimization control method and system to address the technical problem of fixed and unadjustable power consumption in the walking drive and image processing of existing mobile robots, resulting in high ineffective energy consumption and limited overall battery life.
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0018] Example 1, as Figure 1 As shown, this application provides a low-power mobile robot energy optimization control method, the method comprising:
[0019] Step S100: During the process of the mobile robot acquiring real-time environmental images, the real-time working status is dynamically monitored.
[0020] Specifically, during the process of the mobile robot starting real-time environmental image acquisition, the robot's operating parameters are continuously and dynamically monitored and collected simultaneously. Core operating parameters, including real-time power consumption and real-time speed, are acquired and integrated in real time to form a real-time working status that can comprehensively reflect the robot's current operating load and energy consumption level.
[0021] Step S200: Using the real-time state index obtained by weighting the real-time power and real-time speed in the real-time working state with standardized variation, the predetermined motion control scheme is energy optimized and adjusted to obtain the real-time motion control scheme.
[0022] Specifically, the two key parameters of real-time power consumption and real-time speed in the real-time working state are standardized and weighted by variation to obtain a real-time state index that comprehensively reflects the robot's current energy consumption and operating capability. This index is then used as a dynamic adjustment weight to carry out adaptive energy optimization adjustment for the preset conventional motion control scheme in order to reduce motion energy consumption while ensuring task execution capability, and finally generate a real-time motion control scheme that is adapted to the current state.
[0023] Step S300: Obtain the task processor of the mobile robot, analyze the real-time environmental image according to the energy optimization strategy, and obtain the real-time image processing scheme of the task processor.
[0024] Specifically, the task processor in the mobile robot, composed of a field-programmable gate array (FPGA) and a digital signal processing (DSP), first calculates the image information of the real-time environment image through the FPGA. The real-time gradient amplitude and real-time pixel dispersion are standardized and weighted by the coefficient of variation to obtain the real-time image complexity. Then, the image complexity is compared with a predetermined complexity tolerance threshold. Based on the energy optimization strategy, the processing scheme is determined in stages: if it is within the threshold range, the first processing scheme is adopted, in which only the FPGA completes the acquisition, calculation and analysis, and the DSP enters a low-power sleep state. If it exceeds the threshold, the second processing scheme is adopted, in which the FPGA is responsible for acquisition and complexity calculation, and the DSP performs image analysis. Finally, a real-time image processing scheme of the task processor that adapts to the current energy consumption requirements is obtained.
[0025] Step S400: Activate the power management chip to perform energy optimization control on the mobile robot based on the real-time motion control scheme and the real-time image processing scheme.
[0026] Specifically, the power management chip of the mobile robot is activated, enabling it to perform coordinated energy optimization control based on the real-time motion control scheme and the real-time image processing scheme. When the second processing scheme is adopted, the digital signal processing component divides the real-time environmental image into regions of interest and regions of non-interest. The field-programmable gate array is controlled to perform downsampling and low-precision filtering on the regions of non-interest. The operating frequency of the digital signal processing component is adjusted according to the proportion of the region of interest. At the same time, the parallel execution of image acquisition and analysis is achieved by using the two-frame rotation buffering mechanism of dual static random access memory and the bus address switching strategy. Finally, global low-power energy optimization control is completed from the two dimensions of motion control and image processing.
[0027] In one possible implementation, step S300 further includes:
[0028] The task processor includes a field-programmable gate array (FPGA) and a digital signal processing component. It acquires the task processor of the mobile robot, analyzes the real-time environmental image according to an energy optimization strategy, and obtains a real-time image processing scheme for the task processor, including:
[0029] The field-programmable gate array is activated to calculate the real-time image information of the real-time environment image.
[0030] The real-time image complexity is obtained by standardizing and weighting the real-time gradient magnitude and real-time pixel dispersion in the real-time image information using the coefficient of variation.
[0031] Determine whether the complexity of the real-time image is within a predetermined complexity tolerance threshold.
[0032] According to the energy optimization strategy, if the conditions are met, the first processing scheme is used as the real-time image processing scheme; if the conditions are not met, the second processing scheme is used as the real-time image processing scheme.
[0033] Specifically, the task processor includes a field-programmable gate array (FPGA) and a digital signal processing (DSP). First, the task processor of the mobile robot is acquired. Then, the image information of the real-time environment image is calculated through the FPGA. The real-time gradient magnitude and real-time pixel dispersion are standardized and weighted by the coefficient of variation to obtain the real-time image complexity. Then, the image complexity is determined according to the energy optimization strategy to determine whether it is within a predetermined threshold. Based on this, the corresponding processing scheme is selected, and finally the real-time image processing scheme of the task processor is obtained.
[0034] Among them, the field-programmable gate array (FPGA) of the mobile robot is activated to perform fast parallel computing and feature extraction on the environmental images acquired in real time, and directly output complete real-time image information including pixel distribution, edge information, grayscale changes, etc., providing raw data support for subsequent image complexity calculation.
[0035] The real-time gradient magnitude and real-time pixel dispersion in real-time image information are normalized respectively, and the two are weighted and fused based on the standardized coefficient of variation. By combining spatial variation features and pixel distribution features, the real-time image complexity that can objectively reflect the computational load and processing difficulty of real-time environmental images is finally obtained.
[0036] The calculated real-time image complexity is compared with the system's pre-set complexity tolerance threshold to determine whether the current image complexity falls within the threshold range, thus serving as a direct basis for selecting subsequent image processing schemes.
[0037] The hierarchical processing logic is executed according to the preset energy optimization strategy. When the real-time image complexity is within the predetermined complexity tolerance threshold, the first processing scheme is adopted as the real-time image processing scheme of the task processor; when the real-time image complexity is not within the predetermined complexity tolerance threshold, the second processing scheme is switched to be adopted as the real-time image processing scheme to adapt to the low-power processing requirements under different image complexities.
[0038] In one possible implementation, step S300 further includes:
[0039] The first processing scheme refers to the field-programmable gate array (FPGA) performing the tasks of acquiring the real-time environmental image, calculating complexity, and analyzing the image, while the digital signal processing component enters a low-power sleep state.
[0040] Specifically, the first processing solution involves the FPGA (Field-Programmable Gate Array) independently handling all processing tasks, including real-time environmental image acquisition, image complexity calculation, and image analysis. The DSP (Digital Signal Processing Unit) does not need to participate in the computation and remains in a low-power sleep mode, thus minimizing processor power consumption while meeting processing requirements.
[0041] In one possible implementation, step S300 further includes:
[0042] The second processing scheme refers to the digital signal processing component performing the analysis task of the real-time environmental image, while the field-programmable gate array only performs the acquisition task and complexity calculation task of the real-time environmental image.
[0043] Specifically, the second processing scheme divides the image processing tasks. The digital signal processing component (DSP) is specifically responsible for the core analysis tasks of real-time environmental images, while the field-programmable gate array (FPGA) is only responsible for the acquisition of real-time environmental images and the calculation of image complexity. Through reasonable division of labor, a balance between processing performance and power consumption is achieved, ensuring efficient and low-power operation under complex images.
[0044] In one possible implementation, step S400 further includes:
[0045] Step S410: When the real-time image processing scheme is the second processing scheme, the digital signal processing component divides the real-time environment image into regions of interest and regions of non-interest.
[0046] Step S420: Control the field-programmable gate array to perform downsampling and low-precision filtering on the non-interest region, while retaining the complete image data of the interest region to form the target environment image.
[0047] Step S430: Take the proportion of the region of interest in the target environment image as a coefficient, and adjust the predetermined frequency of the digital signal processing component to obtain the target frequency.
[0048] Step S440: The digital signal processing component performs analysis processing on the target environment image based on the target frequency.
[0049] Specifically, when the real-time image processing scheme is the second processing scheme, the digital signal processing component performs pixel-by-pixel discrimination on the real-time environment image based on the image grayscale features, edge information and task target position, and uses threshold segmentation and region detection algorithms to mark the regions related to core tasks such as robot navigation, obstacle avoidance and target recognition as regions of interest, and the remaining irrelevant background regions as regions of non-interest, thus completing the accurate region division of the image.
[0050] The field-programmable gate array (FPGA) controls the non-interest regions (NOIs) to compress them using pixel decimation downsampling and mean low-precision filtering algorithms, reducing data volume and computational overhead. At the same time, the NOIs are maintained at their original resolution, with full pixel output and no precision attenuation. The processed NOIs data is then combined with the fully preserved NOIs data to form a target environment image that balances accuracy and power consumption.
[0051] The total number of pixels in the region of interest in the target environment image is compared with the total number of pixels in the entire image. The actual proportion of the region of interest is calculated and used as a dynamic adjustment coefficient. Through the clock division and multiplication module built into the digital signal processing component, the preset standard operating frequency is linearly scaled and quantized with this coefficient to automatically match the current image processing load and finally output a stable and adapted target operating frequency.
[0052] After acquiring the target frequency adapted to the current image load, the digital signal processing component (DSP) uses this target frequency as the core operating clock to drive the internal arithmetic logic unit, register group, and data bus. According to the region division results of the target environment image, it sequentially performs core analysis operations such as grayscale feature extraction, edge detection, target contour matching, and obstacle discrimination on the region of interest that retains complete accuracy. For the non-region of interest that has been downsampled and low-precision filtered, it only performs simple background verification to avoid unnecessary computing power consumption. At the same time, it dynamically adjusts the timing of data reading, operation, and output according to the target frequency, and completes the entire image analysis and processing process with optimal power consumption while ensuring the accuracy and real-time performance of the analysis.
[0053] In one possible implementation, step S300 further includes:
[0054] The task processor also includes two independent static random access memories as image caches, establishing a two-frame rotation cache mechanism:
[0055] The field-programmable gate array writes the target environment image into the first static random access memory, while the digital signal processing component reads the previous frame of the target environment image from the second static random access memory for processing.
[0056] After the field-programmable gate array and the digital signal processing component have completed their respective operations, a bus address switch is performed to exchange the connection relationship between the first static random access memory and the second static random access memory, so that image acquisition and image analysis can be performed in parallel.
[0057] Specifically, the task processor is also equipped with two independent static random access memories (SRAMs) as dedicated image caches, thereby establishing a stable and reliable two-frame rotation cache mechanism:
[0058] During operation, the Field Programmable Gate Array (FPGA) continuously writes the currently generated target environment image into the first static random access memory (SRAM). At the same time, the Digital Signal Processing Unit (DSP) reads the target environment image data that has been cached from the previous frame from the second SRAM and performs analysis and processing synchronously, so that the image writing and image reading operations are executed in parallel.
[0059] After the field-programmable gate array (FPGA) completes the write operation of the target environment image of the current frame to the corresponding static random access memory (SRAM), and the digital signal processing (DSP) completes the analysis and processing of the image retrieved from the previous frame buffer, the system control bus completes the address configuration switch, swaps the hardware connection relationship between the two SRAMs and the FPGA and the DSP, changes the read and write roles of the two buffers, and the FPGA then writes the new frame image to the memory that was originally used by the DSP. The DSP retrieves the image data from the memory that has just been written, continuously maintaining the parallel operation of image buffer writing and image parsing reading.
[0060] In one possible implementation, step S300 further includes:
[0061] When the processing speed of the field-programmable gate array matches that of the digital signal processing component, the bus address is switched at predetermined time intervals.
[0062] Specifically, relying on the built-in hardware timer and counter to synchronize the task completion flag signal of the field programmable gate array and the digital signal processing component in real time, after detecting that the processing rates of the two are close to matching and the operation corresponding to a single frame image is completed synchronously, the timer generates a switching trigger pulse according to the fixed duration pre-registered in the configuration register. After receiving the pulse signal, the main control bus rewrites the address decoding register parameters to complete the exchange of bus connection addresses between the two static random access memories and the two chips, and stably rotates the cache read and write permissions according to the set period.
[0063] In one possible implementation, step S300 further includes:
[0064] When there is a difference in processing speed between the field-programmable gate array and the digital signal processing component, the component with the faster processing speed enters a short waiting state until the component with the slower processing speed completes its current task, and then the bus address is switched.
[0065] Specifically, the task completion flag levels of the field-programmable gate array (FPGA) and digital signal processing component are acquired in real time through hardware handshake signal lines. The system logic circuit compares the arrival times of the two end signals. If one end pulls the task completion flag low first, the faster module suspends subsequent read and write operations and enters standby mode via internal timing control circuit, continuously polling the completion status signal of the slower module. After the slower module outputs the task completion level, the bus control module immediately modifies the address decoding configuration to complete the bus path switching between the two static random access memories.
[0066] Example 2, based on the same inventive concept as the low-power mobile robot energy optimization control method in the foregoing examples, such as... Figure 2 As shown, this application provides a low-power mobile robot energy optimization control system. The system and method embodiments in this application are based on the same inventive concept. The system includes:
[0067] The working status acquisition module 10 is used to dynamically monitor and obtain the real-time working status during the process of the mobile robot acquiring real-time environmental images.
[0068] The mobility control scheme acquisition module 20 is used to perform energy optimization adjustment on the predetermined mobility control scheme by using the real-time state index obtained by weighting the real-time power and real-time speed in the real-time working state with standardized variation weights, so as to obtain the real-time mobility control scheme.
[0069] The image processing scheme acquisition module 30 is used to acquire the task processor of the mobile robot, analyze the real-time environmental image according to the energy optimization strategy, and obtain the real-time image processing scheme of the task processor.
[0070] The energy optimization control module 40 is used to activate the power management chip to perform energy optimization control on the mobile robot based on the real-time motion control scheme and the real-time image processing scheme.
[0071] Furthermore, the system is also used to implement the following functions:
[0072] The field-programmable gate array (FPGA) is activated to calculate the real-time image information of the real-time environment image; the real-time gradient magnitude and real-time pixel dispersion in the real-time image information are standardized and weighted by coefficient of variation to obtain the real-time image complexity; it is determined whether the real-time image complexity is within a predetermined complexity tolerance threshold; according to the energy optimization strategy, if it is, the first processing scheme is used as the real-time image processing scheme; if it is not, the second processing scheme is used as the real-time image processing scheme.
[0073] Furthermore, the system is also used to implement the following functions:
[0074] The first processing scheme refers to the field-programmable gate array (FPGA) performing the tasks of acquiring the real-time environmental image, calculating complexity, and analyzing the image, while the digital signal processing component enters a low-power sleep state.
[0075] Furthermore, the system is also used to implement the following functions:
[0076] The second processing scheme refers to the digital signal processing component performing the analysis task of the real-time environmental image, while the field-programmable gate array only performs the acquisition task and complexity calculation task of the real-time environmental image.
[0077] Furthermore, the system is also used to implement the following functions:
[0078] When the real-time image processing scheme is the second processing scheme, the digital signal processing component divides the real-time environment image into regions of interest and non-regions of interest; controls the field-programmable gate array to perform downsampling and low-precision filtering on the non-regions of interest, while retaining the complete image data of the regions of interest to form a target environment image; takes the proportion of the regions of interest in the target environment image as a coefficient to adjust the predetermined frequency of the digital signal processing component to obtain the target frequency; and the digital signal processing component performs analysis processing on the target environment image based on the target frequency.
[0079] Furthermore, the system is also used to implement the following functions:
[0080] The task processor also includes two independent static random access memories (SRAMs) as image buffers, establishing a two-frame rotation buffer mechanism: the field-programmable gate array (FPGA) writes the target environment image into the first SRAM, while the digital signal processing (DSP) reads the previous frame of the target environment image from the second SRAM for processing; after both the FPGA and the DSP have completed their respective operations, a bus address switch is performed to exchange the connection relationship between the first SRAM and the second SRAM, enabling image acquisition and image analysis to be executed in parallel.
[0081] Furthermore, the system is also used to implement the following functions:
[0082] When the processing speed of the field-programmable gate array matches that of the digital signal processing component, the bus address is switched at predetermined time intervals.
[0083] Furthermore, the system is also used to implement the following functions:
[0084] When there is a difference in processing speed between the field-programmable gate array and the digital signal processing component, the component with the faster processing speed enters a short waiting state until the component with the slower processing speed completes its current task, and then the bus address is switched.
[0085] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Specific embodiments of this specification have been described above. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.
[0086] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0087] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.
Claims
1. A low-power mobile robot energy optimization control method, characterized in that, include: During the process of the mobile robot acquiring real-time environmental images, the real-time working status is dynamically monitored; Using the real-time state index obtained by weighting the real-time power and real-time speed in the real-time working state with standardized variation weighting as the weight, the predetermined motion control scheme is energy-optimized and adjusted to obtain the real-time motion control scheme. The task processor of the mobile robot is obtained, and the real-time environmental image is analyzed according to the energy optimization strategy to obtain the real-time image processing scheme of the task processor. The power management chip is activated to perform energy optimization control on the mobile robot based on the real-time motion control scheme and the real-time image processing scheme.
2. The low-power mobile robot energy optimization control method as described in claim 1, characterized in that, The task processor includes a field-programmable gate array (FPGA) and a digital signal processing component. It acquires the task processor of the mobile robot, analyzes the real-time environmental image according to an energy optimization strategy, and obtains a real-time image processing scheme for the task processor, including: The field-programmable gate array is activated to calculate the real-time image information of the real-time environment image; The real-time image complexity is obtained by standardizing and weighting the real-time gradient magnitude and real-time pixel dispersion in the real-time image information using the coefficient of variation. Determine whether the complexity of the real-time image is within a predetermined complexity tolerance threshold; According to the energy optimization strategy, if the conditions are met, the first processing scheme is used as the real-time image processing scheme; if the conditions are not met, the second processing scheme is used as the real-time image processing scheme.
3. The low-power mobile robot energy optimization control method as described in claim 2, characterized in that, The first processing scheme refers to the field-programmable gate array (FPGA) performing the tasks of acquiring the real-time environmental image, calculating complexity, and analyzing the image, while the digital signal processing component enters a low-power sleep state.
4. The low-power mobile robot energy optimization control method as described in claim 3, characterized in that, The second processing scheme refers to the digital signal processing component performing the analysis task of the real-time environmental image, while the field-programmable gate array only performs the acquisition task and complexity calculation task of the real-time environmental image.
5. The low-power mobile robot energy optimization control method as described in claim 4, characterized in that, The power management chip is activated to perform energy optimization control on the mobile robot based on the real-time motion control scheme and the real-time image processing scheme, including: When the real-time image processing scheme is the second processing scheme, the digital signal processing component divides the real-time environment image into regions of interest and non-regions of interest; The field-programmable gate array is controlled to perform downsampling and low-precision filtering on the non-interest region, while retaining the complete image data of the interest region to form a target environment image; The proportion of the region of interest in the target environment image is taken as a coefficient, and the predetermined frequency of the digital signal processing component is adjusted to obtain the target frequency. The digital signal processing component performs analysis and processing on the target environment image based on the target frequency.
6. The low-power mobile robot energy optimization control method as described in claim 5, characterized in that, The task processor also includes two independent static random access memories as image caches, establishing a two-frame rotation cache mechanism: The field-programmable gate array writes the target environment image into the first static random access memory, while the digital signal processing component reads the previous frame of the target environment image from the second static random access memory for processing. After the field-programmable gate array and the digital signal processing component have completed their respective operations, a bus address switch is performed to exchange the connection relationship between the first static random access memory and the second static random access memory, so that image acquisition and image analysis can be performed in parallel.
7. The low-power mobile robot energy optimization control method as described in claim 6, characterized in that, After the field-programmable gate array (FPGA) and the digital signal processing component have completed their respective operations, a bus address switch is performed to exchange the connection relationship between the first static random access memory (SRAM) and the second static random access memory (SRAM), so that image acquisition and image analysis can be performed in parallel. This includes: when the processing speeds of the FPGA and the digital signal processing component are matched, the bus address is switched at a predetermined time interval.
8. The low-power mobile robot energy optimization control method as described in claim 6, characterized in that, After the field-programmable gate array (FPGA) and the digital signal processing component have completed their respective operations, a bus address switch is performed to exchange the connection relationship between the first static random access memory (SRAM) and the second SRAM, so that image acquisition and image analysis can be performed in parallel. This includes: when there is a difference in processing speed between the FPGA and the digital signal processing component, the one with the faster processing speed enters a short waiting state until the one with the slower processing speed completes its current task, and then the bus address switch is performed.
9. A low-power mobile robot energy optimization control system, characterized in that, The system is used to implement the low-power mobile robot energy optimization control method according to any one of claims 1-8, the system comprising: The working status acquisition module is used to dynamically monitor and obtain the real-time working status during the process of the mobile robot acquiring real-time environmental images. The mobility control scheme acquisition module is used to perform energy optimization adjustment on the predetermined mobility control scheme by using the real-time state index obtained by weighting the real-time power and real-time speed in the real-time working state with standardized variation weighting, so as to obtain the real-time mobility control scheme. The image processing scheme acquisition module is used to acquire the task processor of the mobile robot, analyze the real-time environment image according to the energy optimization strategy, and obtain the real-time image processing scheme of the task processor. An energy optimization control module is used to activate the power management chip to perform energy optimization control on the mobile robot based on the real-time motion control scheme and the real-time image processing scheme.