A peripheral expansion method and system for embodied robots

Abstract

The present invention belongs to the field of robotic peripheral technology and discloses a peripheral expansion method and system for embodied robots. The method comprises: optimizing the embodied robot's startup process based on a modular design concept and applying electromagnetic shielding technology to the circuit board; designing hardware interface standards between the embodied robot and external devices based on the optimized startup process and hardware acceleration module; and optimizing the communication protocol between the external devices based on the hardware interface standards. By optimizing the startup process, the present invention reduces unnecessary operating steps, significantly shortens startup time, and improves the system's response speed and operating efficiency. Furthermore, electromagnetic shielding technology is used to ensure the stable operation of the robot system in complex electromagnetic environments, effectively preventing the impact of electromagnetic interference on system performance.

Description

Technical Field

[0001] The present invention relates to the technical field of robot peripherals, and in particular to a peripheral expansion method and system for an embodied robot. Background Art

[0002] With the rapid development of artificial intelligence, robotics, sensor technology, and automation technology, the robotics industry is experiencing unprecedented opportunities. From its initial roots in the automation of industrial production lines to its current widespread application in healthcare, logistics, agriculture, and other fields, robotics technology has continuously overcome traditional limitations, transforming from simple mechanical devices into intelligent, flexible, and efficient systems. In particular, the rapid development of intelligent collaborative robots and service robots in recent years has greatly expanded the application scenarios of the robotics industry and driven continued growth in market demand.

[0003] However, within the current technological framework, peripheral expansion for embodied robots still faces several challenges. First, complex robotic systems have long startup times, impacting system response speed and real-time performance. Second, because robots often operate in complex electromagnetic environments, electromagnetic interference issues in existing technologies severely impact system stability and performance. Furthermore, existing underlying software architectures lack sufficient real-time support, particularly when processing multi-sensor data acquisition and executing real-time control tasks. Furthermore, adapting peripheral interfaces is cumbersome, and drivers between different sensors may have compatibility issues and inefficient data fusion. Furthermore, current underlying communication protocols have limited support for multi-robot collaboration and wireless communication scenarios, failing to effectively guarantee the stability and real-time performance of network transmission. Finally, hardware testing and verification can be problematic due to oversight or inadequacy, particularly in complex custom hardware environments.

[0004] Therefore, how to provide a peripheral expansion method and system for embodied robots is an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of the present invention provide a peripheral expansion method and system for an embodied robot to solve the above-mentioned technical problems existing in the prior art.

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, the following is a brief summary. This summary is not intended to be a comprehensive review, identify key or essential elements, or delineate the scope of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a peripheral expansion method for an embodied robot is provided.

[0008] In one embodiment, a peripheral expansion method for an embodied robot includes:

[0009] Based on the modular design concept, the startup process of the embodied robot is optimized, and the circuit board is processed using electromagnetic shielding technology; according to the optimized startup process and hardware acceleration module, the hardware interface standard between the embodied robot and external devices is designed, and the communication protocol between external devices is optimized based on the hardware interface standard.

[0010] In one embodiment, optimizing the startup process of the embodied robot based on a modular design concept and applying electromagnetic shielding technology to the circuit board includes:

[0011] Based on the modular design concept, the hardware and software architecture of the embodied robot is planned; based on the planning results, the startup process of the embodied robot is analyzed and optimized; based on the optimized hardware architecture, electromagnetic shielding materials are selected, and the circuit board is processed in combination with multi-stage filter technology.

[0012] In one embodiment, analyzing and optimizing the startup process of the embodied robot based on the planning results includes:

[0013] According to the planning results of the hardware and software architecture, performance analysis tools and logging tools are used to analyze the startup process of the embodied robot, identify the startup steps and determine the execution order of each step; based on the startup process analysis results, target services and target programs are loaded first, and non-target services and non-target programs are loaded later, and the startup process sequence is optimized; according to the optimized startup sequence, non-target logging operations are disabled to optimize the operation boot program; using the optimized operation boot program, the firmware interface source code is modified and optimized to obtain the optimized startup process of the embodied robot.

[0014] In one embodiment, the startup process of the optimized embodied robot by modifying and optimizing the firmware interface source code using the optimized operation boot program includes:

[0015] The optimized operation boot program is used to check the firmware driver loading list, identify and delete non-target drivers, and rearrange the driver loading order based on the importance of hardware components; according to the rearranged loading order, the hardware initialization code is modified to optimize the process of initializing several hardware components in parallel; based on the optimized operation boot program logic, the time delay position in the firmware interface source code is located, and the delay parameters are adjusted to optimize the firmware interface source code; based on the optimized firmware interface source code, the compiler options are used to recompile, and the optimized embodied robot startup process is obtained through the recompiled firmware.

[0016] In one embodiment, designing a hardware interface standard between the embodied robot and external devices based on the optimized startup process and hardware acceleration module, and optimizing the communication protocol between the external devices based on the hardware interface standard includes:

[0017] Based on the optimized startup process and hardware acceleration module, the processing modules in the hardware architecture are used to optimize the scheduling of computing tasks; based on the characteristics of different tasks, computing tasks are assigned to corresponding processing units; based on the allocation results of computing tasks and the corresponding hardware requirements, the hardware interface standards between the embodied robot and external devices are designed; based on the designed hardware interface standards, the communication protocol between the embodied robot and external devices is analyzed and optimized.

[0018] In one embodiment, optimizing computing task scheduling based on the optimized startup process and hardware acceleration module and utilizing the processing module in the hardware architecture includes:

[0019] According to the optimized startup process, the hardware acceleration module is integrated into the hardware architecture, and the processing modules in the hardware architecture are identified; based on the identified processing modules, the priority preemptive scheduling algorithm is used to preliminarily optimize the computing task scheduling, and the synchronization mechanism is used to distinguish real-time tasks from non-real-time tasks; based on the results of the distinction between real-time tasks and non-real-time tasks, the memory management algorithm is used to optimize task allocation, and the task running status is monitored in combination with performance monitoring tools to optimize computing task scheduling.

[0020] In one embodiment, designing a hardware interface standard between the embodied robot and external devices based on the computing task allocation results and corresponding hardware requirements includes:

[0021] Based on the results of the allocation of computing tasks, the performance requirements required for each task are analyzed; the external devices of the embodied robot are acquired and analyzed to obtain the interface specifications of the external devices; and the hardware interface standards between the embodied robot and the external devices are designed based on the performance requirements and interface specifications. The hardware interface standards include hardware interface design, driver architecture planning, and data fusion algorithm.

[0022] In one embodiment, the design of hardware interface standards between the embodied robot and external devices in combination with performance requirements and interface specifications includes:

[0023] Combining the task performance requirements and the interface specifications of external devices, a hardware adaptation module is constructed, and hardware interface design and driver architecture planning are carried out based on the hardware adaptation module. According to the performance requirements of the task, task data is collected from hardware sensors and preprocessed. The Kalman filter algorithm is used to suppress noise and estimate the state of the preprocessed task data, and the task data of different hardware sensors are associated and fused in combination with the preset deep learning model.

[0024] In one embodiment, analyzing and optimizing the communication protocol between the embodied robot and the external device based on the designed hardware interface standard includes:

[0025] Based on the designed hardware interface standard, the communication protocol between the embodied robot and external devices is analyzed to identify communication problems. Based on the identified communication problems, a communication adaptation module is designed, and the communication protocol is optimized by combining communication technology and Ethernet adaptation strategy.

[0026] According to a second aspect of an embodiment of the present invention, a peripheral expansion system for an embodied robot is provided.

[0027] In one embodiment, the peripheral expansion system for an embodied robot includes:

[0028] A startup process optimization unit, which optimizes the startup process of the embodied robot based on a modular design concept and uses electromagnetic shielding technology to process the circuit board;

[0029] The hardware interface design unit is used to design the hardware interface standards between the embodied robot and external devices based on the optimized startup process and hardware acceleration module, and optimize the communication protocol between external devices based on the hardware interface standards.

[0030] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0031] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0032] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0033] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0034] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0035] 1. The present invention reduces unnecessary operating steps by optimizing the startup process, significantly shortens the startup time, and improves the system's response speed and work efficiency. In addition, the use of electromagnetic shielding technology ensures that the robot system operates stably in a complex electromagnetic environment, effectively preventing the impact of electromagnetic interference on system performance.

[0036] 2. The present invention improves the system's processing capability under high load conditions by introducing a hardware acceleration module, ensuring the robot's real-time response when performing multiple tasks. In addition, through a unified hardware interface and driver architecture, the adaptation process of various sensors is simplified, and a data fusion algorithm is adopted to improve the real-time performance and accuracy of data acquisition.

[0037] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0039] Figure 1 is a flowchart of a peripheral expansion method for an embodied robot according to an exemplary embodiment;

[0040] Figure 2 is a principle block diagram of a peripheral expansion system for an embodied robot according to an exemplary embodiment;

[0041] Figure 3 The figure is a schematic structural diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0042] The following description and accompanying drawings sufficiently illustrate the specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims, including all available equivalents thereof. Herein, the terms "first," "second," and the like are used solely to distinguish one element from another and do not require or imply any actual relationship or order between these elements. In practice, the first element can also be referred to as the second element, and vice versa. Furthermore, the terms "comprise," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a structure, device, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such structure, device, or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the structure, device, or apparatus comprising the element. The various embodiments herein are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Similar or identical parts between the various embodiments can be referenced to each other.

[0043] The terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like in this document indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, they should not be understood as limitations on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, they can be mechanical or electrical connections, or they can be internal connections between two elements. They can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0044] As used herein, unless otherwise specified, the term "plurality" means two or more.

[0045] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0046] In this article, the term "and / or" is used to describe the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0047] It should be understood that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0048] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to the above modules.

[0049] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0050] Figure 1An embodiment of a peripheral expansion method for an embodied robot according to the present invention is shown.

[0051] In this optional embodiment, the peripheral expansion method for an embodied robot includes:

[0052] Step S101, optimizing the startup process of the embodied robot based on a modular design concept, and applying electromagnetic shielding technology to process the circuit board;

[0053] Step S102 : Design the hardware interface standard between the embodied robot and external devices based on the optimized startup process and hardware acceleration module, and optimize the communication protocol between the external devices based on the hardware interface standard.

[0054] In this optional embodiment, the optimization of the startup process of the embodied robot based on the modular design concept and the use of electromagnetic shielding technology to process the circuit board include:

[0055] Based on the modular design concept, the hardware and software architecture of the embodied robot is planned; based on the planning results, the startup process of the embodied robot is analyzed and optimized; based on the optimized hardware architecture, electromagnetic shielding materials are selected, and the circuit board is processed in combination with multi-stage filter technology.

[0056] In this optional embodiment, analyzing and optimizing the startup process of the embodied robot based on the planning results includes:

[0057] According to the planning results of the hardware and software architecture, performance analysis tools and logging tools are used to analyze the startup process of the embodied robot, identify the startup steps and determine the execution order of each step; based on the startup process analysis results, target services and target programs are loaded first, and non-target services and non-target programs are loaded later, and the startup process sequence is optimized; according to the optimized startup sequence, non-target logging operations are disabled to optimize the operation boot program; using the optimized operation boot program, the firmware interface source code is modified and optimized to obtain the optimized startup process of the embodied robot.

[0058] In this optional embodiment, the startup process of the optimized embodied robot by modifying and optimizing the firmware interface source code using the optimized operation boot program includes:

[0059] The optimized operation boot program is used to check the firmware driver loading list, identify and delete non-target drivers, and rearrange the driver loading order based on the importance of hardware components; according to the rearranged loading order, the hardware initialization code is modified to optimize the process of initializing several hardware components in parallel; based on the optimized operation boot program logic, the time delay position in the firmware interface source code is located, and the delay parameters are adjusted to optimize the firmware interface source code; based on the optimized firmware interface source code, the compiler options are used to recompile, and the optimized embodied robot startup process is obtained through the recompiled firmware.

[0060] In this optional embodiment, designing a hardware interface standard between the embodied robot and the external device based on the optimized startup process and hardware acceleration module, and optimizing the communication protocol between the external devices based on the hardware interface standard includes:

[0061] Based on the optimized startup process and hardware acceleration module, the processing modules in the hardware architecture are used to optimize the scheduling of computing tasks; based on the characteristics of different tasks, computing tasks are assigned to corresponding processing units; based on the allocation results of computing tasks and the corresponding hardware requirements, the hardware interface standards between the embodied robot and external devices are designed; based on the designed hardware interface standards, the communication protocol between the embodied robot and external devices is analyzed and optimized.

[0062] In this optional embodiment, optimizing computing task scheduling by utilizing processing modules in the hardware architecture based on the optimized startup process and hardware acceleration module includes:

[0063] According to the optimized startup process, the hardware acceleration module is integrated into the hardware architecture, and the processing modules in the hardware architecture are identified; based on the identified processing modules, the priority preemptive scheduling algorithm is used to preliminarily optimize the computing task scheduling, and the synchronization mechanism is used to distinguish real-time tasks from non-real-time tasks; based on the results of the distinction between real-time tasks and non-real-time tasks, the memory management algorithm is used to optimize task allocation, and the task running status is monitored in combination with performance monitoring tools to optimize computing task scheduling.

[0064] In this optional embodiment, the design of the hardware interface standard between the embodied robot and the external device based on the distribution results of the computing tasks and the corresponding hardware requirements includes:

[0065] Based on the results of the allocation of computing tasks, the performance requirements required for each task are analyzed; the external devices of the embodied robot are acquired and analyzed to obtain the interface specifications of the external devices; and the hardware interface standards between the embodied robot and the external devices are designed based on the performance requirements and interface specifications. The hardware interface standards include hardware interface design, driver architecture planning, and data fusion algorithm.

[0066] In this optional embodiment, the design of the hardware interface standard between the embodied robot and the external device in combination with the performance requirements and interface specifications includes:

[0067] Combining the task performance requirements and the interface specifications of external devices, a hardware adaptation module is constructed, and hardware interface design and driver architecture planning are carried out based on the hardware adaptation module. According to the performance requirements of the task, task data is collected from hardware sensors and preprocessed. The Kalman filter algorithm is used to suppress noise and estimate the state of the preprocessed task data, and the task data of different hardware sensors are associated and fused in combination with the preset deep learning model.

[0068] In this optional embodiment, analyzing and optimizing the communication protocol between the embodied robot and the external device based on the designed hardware interface standard includes:

[0069] Based on the designed hardware interface standard, the communication protocol between the embodied robot and external devices is analyzed to identify communication problems. Based on the identified communication problems, a communication adaptation module is designed, and the communication protocol is optimized by combining communication technology and Ethernet adaptation strategy.

[0070] It should be noted that a peripheral expansion method for an embodied robot specifically includes:

[0071] Step 1: Architecture and modular design

[0072] Modular design optimizes the hardware and software architecture of the robotic system, ensuring efficient collaboration, easy expansion, and easy maintenance. The modular design divides the system into independent submodules, each performing a specific task, reducing development complexity and accelerating the development process. The following are the specific implementation steps:

[0073] Functional division and requirement analysis: Analyze system functions according to task requirements and divide them into independent modules such as perception, decision-making, execution, and communication to ensure low coupling and high independence between modules.

[0074] The hardware modular design ensures that each hardware sub-module (such as sensor, power supply, actuator, etc.) works independently and transmits data through standardized interfaces (such as CAN bus, Ethernet, USB, etc.).

[0075] The software is modularly designed and adopts a layered architecture design, dividing the system into hardware driver layer, middleware layer and application layer. Each module interacts through a standard interface and supports a real-time operating system (RTOS) to ensure task scheduling and resource allocation.

[0076] Inter-module collaboration and communication use standardized communication protocols (such as TCP / IP, UDP, CAN) to ensure efficient data exchange between modules, and adopt asynchronous communication mechanisms to reduce delays.

[0077] Performance optimization and low-power design optimize load balancing through reasonable computing resource allocation, and use low-power hardware and dynamic voltage frequency scaling (DVFS) technology to reduce power consumption.

[0078] Modular testing and verification: unit testing, integration testing and simulation verification of each module to ensure the stability and collaborative work of the system.

[0079] Expansion and maintenance: Design of expandable interfaces to support the plugging and replacement of subsequent sensor and actuator modules, simplifying system maintenance.

[0080] Planning hardware resources and software functions is a key step in optimizing the robotic system architecture. Through reasonable planning, you can ensure that the system can run efficiently, be easy to expand, have good performance, and be able to process multi-sensor data and support real-time requirements. The following are the specific implementation steps:

[0081] (1) Requirements analysis and function definition

[0082] Requirements analysis: Analyze the robot's application scenarios and functional requirements, such as perception, control, navigation, and communication, and clarify the resource requirements for hardware and software.

[0083] Functional division: Divide system functions into independent modules (such as perception, control, and communication modules) to ensure low coupling and high cohesion of modules.

[0084] (2) Hardware resource planning

[0085] Hardware modular design: Select appropriate sensors, processing units, and communication modules based on requirements to ensure module independence and standardized interfaces.

[0086] Computing resource allocation: Select appropriate processors (such as CPU, GPU) to meet data fusion and real-time computing requirements.

[0087] Power consumption optimization and EMC design: Select low-power hardware and use electromagnetic shielding and filters to reduce electromagnetic interference to ensure stable system operation.

[0088] (3) Software function planning

[0089] Modular software architecture: Divide the software into multiple layers (hardware drivers, communication protocols, algorithms, applications, etc.) to ensure efficient collaboration between modules.

[0090] Real-time design: Use a real-time operating system (RTOS) to ensure that high-priority tasks are completed in a timely manner.

[0091] Data fusion and communication protocol: Design a unified data format to support multi-sensor fusion, and select appropriate communication protocols (such as CAN, Ethernet) to ensure stable and efficient data transmission.

[0092] (4) Coordinated optimization of hardware and software resources

[0093] Resource docking and performance optimization: Allocate hardware and software resources according to module requirements, optimize overall performance and reduce latency through hardware and software collaborative design.

[0094] Load balancing and dynamic scheduling: Dynamically adjust resource allocation based on module load to ensure that critical tasks have priority access to computing resources.

[0095] (5) Testing and verification

[0096] Unit testing and integration testing: Independently test hardware and software modules to ensure their respective functions are normal, and perform integration testing to verify system stability and performance.

[0097] Performance Verification: Verify the system's performance in multi-robot collaboration and high-speed data transmission scenarios to ensure that performance requirements are met.

[0098] (6) Expansion and maintenance

[0099] Modular design allows for easy expansion: The modular design makes it easy to add new sensors or update algorithms, ensuring flexible expansion of the system.

[0100] Flexible software updates and hardware compatibility: Software and hardware are independent, enabling the system to be updated, optimized, and upgraded, ensuring long-term operation and maintainability.

[0101] Step 2: Start the optimization plan

[0102] Streamlining the boot process: By streamlining the boot process and optimizing the UEFI source code, the system boot speed is improved, especially ensuring fast boot in high-load environments. The following are the basis and steps for how to modify and optimize the UEFI source code:

[0103] (1) Basis for modifying and optimizing UEFI source code

[0104] Firmware initialization optimization: Reduce unnecessary operations (such as logging and hardware scanning), parallelize initialization tasks, and utilize multi-core processors to accelerate startup.

[0105] Driver and protocol optimization: Simplify and optimize device driver loading, and reduce the initialization time of the driver and protocol stack at startup.

[0106] Hardware resource management optimization: Reduces hardware abstraction layer (HAL) interaction steps and improves system response speed.

[0107] (2) Specific implementation steps for modifying and optimizing UEFI source code

[0108] Identify startup bottlenecks: Analyze the startup process and identify bottlenecks such as firmware initialization, hardware scanning, and driver loading.

[0109] Streamline the startup process: disable unnecessary logging: reduce file system operations and improve startup speed.

[0110] Optimize driver loading and protocol stack: load only necessary drivers, adjust the loading order, and prioritize key drivers.

[0111] Optimize firmware time delay: reduce the time waiting for hardware to be ready and set a reasonable timeout.

[0112] Optimize UEFI protocol: Improve protocol implementation, increase hardware communication efficiency, and reduce protocol exchange delay.

[0113] Step 3: Real-time optimization and hardware acceleration solutions

[0114] Hardware acceleration modules: Utilize hardware acceleration modules such as GPUs and FPGAs to rapidly process high-performance computing tasks. Real-time operating system optimization: Optimizing real-time operating systems such as RTLinux improves task scheduling efficiency and ensures timely execution. These optimizations significantly enhance the system's real-time performance and processing capabilities, making them particularly suitable for tasks requiring high real-time performance.

[0115] Step 4: Multi-sensor adaptation and data fusion optimization

[0116] Unified hardware interface and driver architecture: Design unified interfaces and drivers for various sensors (such as lidar, IMU, and cameras) to simplify the integration process. Efficient data fusion algorithms: Design efficient data fusion algorithms to process sensor data in real time, improving data accuracy and real-time performance. This enhances the accuracy and real-time performance of the robot system's environmental perception.

[0117] Step 5: Communication protocol support and network adaptation

[0118] Enhanced communication protocol support: Support for multiple communication protocols (such as CAN, Ethernet, and RS485) ensures efficient communication with external devices or other robot systems. Wireless communication module optimization: By optimizing the wireless communication module, the stability and real-time performance of wireless communication are improved, ensuring efficient collaboration among robot systems. These optimizations significantly enhance the collaborative capabilities of multi-robot collaboration and wireless communication environments.

[0119] Step 6: Interface debugging and adaptation

[0120] Interface debugging and adaptation are key steps to ensure efficient communication in robotic systems. This mainly involves debugging CAN, I2C, SPI, and UART interfaces to ensure the system can stably connect with a variety of devices.

[0121] (1) CAN interface debugging

[0122] Hardware verification: Check the wiring and terminal resistance to ensure that the physical connection is normal.

[0123] Protocol stack configuration: Adjust the baud rate and clock to ensure correct data transmission.

[0124] Real-time verification: Test the timeliness of data transmission under high load.

[0125] Error handling: Monitors error frames and bit errors to ensure data reliability.

[0126] System integration test: Connect with other devices to verify communication effects.

[0127] (2) I2C interface debugging

[0128] Hardware check: Make sure the SCL and SDA wiring and resistors are normal.

[0129] Protocol stack configuration: set the operating mode and clock frequency.

[0130] Communication verification: Ensure that data transmission between multiple devices is normal.

[0131] Performance testing: Verify stability under different load and noise environments.

[0132] Troubleshooting: Resolve common issues such as missing ACKs.

[0133] (3) SPI interface debugging

[0134] Hardware verification: Check the connections of signal lines such as MISO and MOSI to ensure normal timing.

[0135] Protocol configuration: set clock polarity, transmission mode, etc.

[0136] Data testing: Ensure data transmission accuracy.

[0137] Performance optimization: Test bandwidth and frequency to ensure stable data transmission.

[0138] Parallel data stream testing: Ensures that data from multiple devices does not interfere with each other.

[0139] (4) UART interface debugging

[0140] Hardware verification: Make sure that the TX, RX and other signal lines are connected properly.

[0141] Baud rate setting: Debug the stability under different baud rates.

[0142] Data confirmation: Use debugging tools to test whether the communication is normal.

[0143] Flow control debugging: Ensure that data is not lost under high load.

[0144] Multi-device testing: Ensures system stability when multiple devices communicate simultaneously.

[0145] (5) System integration and comprehensive debugging

[0146] Joint debugging: ensure that multiple interface devices can work normally at the same time.

[0147] Load testing: Verify system stability under long-term high load.

[0148] Log monitoring: Analyze anomalies and optimize configuration.

[0149] System integration: After completing interface debugging, conduct overall integration testing to ensure that all interfaces work together.

[0150] Step 7: 5G Adaptation Solution

[0151] With the development of 5G technology, especially in high-bandwidth, low-latency real-time communication scenarios, 5G adaptation will become one of the core technologies of robotic systems. The introduction of a 5G adaptation module ensures that the system can achieve high-speed, large-capacity data transmission in vehicle-mounted or industrial environments. Its main optimizations include:

[0152] 5G Communication Module Integration: The system supports 5G module access, including 5GNR (New Radio) technology, ensuring stable network connectivity even in high-speed mobile scenarios. Low-Latency Data Transmission: Optimizes the 5G communication protocol stack to improve data transmission speed and latency control, making it particularly suitable for applications such as multi-robot collaboration and video streaming. High-Bandwidth Application Support: Provides stable and efficient 5G network access for high-bandwidth data streams, such as high-definition image and video transmission, ensuring smooth data flow even under high system load.

[0153] Step 8: I2S audio adaptation

[0154] In some robotic applications, the processing and transmission of audio data are also crucial. For this reason, the adaptation of the I2S (Inter-ICSound) audio interface has been added to the robotic system: High-quality audio acquisition and playback: The I2S interface supports high-quality audio data transmission with audio processing modules (such as microphones, speakers, etc.). The system can collect environmental audio information in real time, or interact with users through voice. Optimize audio data processing: Introduce dedicated audio processing chips (such as DSP) or hardware acceleration modules to ensure clear and low-latency transmission of audio data through efficient data stream transmission and processing. Multi-channel audio support: Supports multi-channel audio output, which is suitable for applications such as voice prompts and sound source localization in robotic systems, and improves the human-computer interaction capabilities of robotic systems.

[0155] Step 9: Automotive Ethernet Adaptation Solution

[0156] Integrating automotive Ethernet technology improves the communication capabilities of robotic systems in vehicle environments. The application of automotive Ethernet is particularly important, especially in scenarios that require high-speed, large-bandwidth data transmission (such as sensor data, video streaming, and control command transmission):

[0157] In-vehicle Ethernet Interface Support: Hardware interfaces and drivers supporting standards such as Ethernet AVB and Automotive Ethernet are designed to ensure seamless connectivity with in-vehicle Ethernet switches and devices. In-vehicle Ethernet Protocol Stack Optimization: By optimizing the protocol stack (such as support for IEEE802.1Q and IEEE802.1AS), low-latency, high-bandwidth, and highly reliable data transmission is ensured. Real-time Data Transmission: Optimizing the data transmission mechanism of the in-vehicle network ensures real-time transmission of sensor data to the processing unit, supporting rapid decision-making and control.

[0158] Step 10: Hardware Testing and Verification

[0159] Hardware testing and verification is an important step to ensure that the hardware system meets the predetermined requirements during the design, production, and integration stages. The process includes the following steps:

[0160] (1) Requirements analysis and test plan: Clarify hardware function, performance, and reliability requirements, develop a test plan, and select appropriate test methods and tools.

[0161] (2) Design phase verification: Review the hardware design, perform simulation and prototype testing to ensure that the design meets functional requirements.

[0162] (3) Sample production and functional testing: After the samples are produced, basic functional verification, interface testing and signal integrity testing are carried out.

[0163] (4) Performance testing: Testing the performance of the hardware at different frequencies, power consumption, and temperatures to ensure that it meets performance standards.

[0164] (5) Environmental and reliability testing: Verify the stability of hardware in environments with extreme temperature and humidity, vibration, shock, and voltage fluctuations.

[0165] (6) Compatibility and interoperability testing: Ensure that the hardware is compatible with other devices and platforms and supports relevant communication protocols.

[0166] (7) Security testing: Detecting the hardware’s anti-tampering capabilities and data security to ensure that the firmware has no security vulnerabilities.

[0167] (8) Final Verification and Compliance Testing: Conduct certification testing to ensure that the hardware complies with relevant regulations and quality standards.

[0168] (9) Fault analysis and repair: Analyze the problems found during testing, repair them and retest to ensure that the problems are resolved.

[0169] (10) Documentation and reporting: Organize test data, generate detailed reports, and record test results and improvement suggestions.

[0170] (11) Mass production and continuous verification: After entering mass production, continuous sampling testing is carried out to ensure that the mass production hardware is consistent with the test samples.

[0171] Step 11: Comprehensive Optimization and System Integration

[0172] Efficient system integration ensures that different hardware and software modules work together in automotive and industrial environments, especially communication modules, sensor modules, audio modules, 5G modules, etc. Key steps include:

[0173] (1) Requirements analysis and system architecture design: Design a system architecture that supports seamless collaboration among interfaces and clarify interface functions and data flows.

[0174] (2) Hardware interface selection and specification: Select compatible interfaces that support high data transmission rates to ensure system scalability.

[0175] (3) Software interface management and coordination: developing drivers, optimizing operating system configuration, and designing unified APIs or middleware.

[0176] (4) Unified protocols and data formats: Ensure that data formats and protocols are consistent between interfaces to avoid data loss or confusion.

[0177] (5) Performance testing and verification: Test the interface bandwidth, latency, data integrity, etc. to ensure system stability.

[0178] (6) Error detection and fault tolerance mechanism: Design real-time monitoring, redundancy mechanism and error recovery mechanism to ensure the system's fault tolerance.

[0179] (7) System debugging and optimization: gradually debug the interface modules, optimize performance, and eliminate bottlenecks.

[0180] (8) Integration testing and verification: Verify system performance through full-link, load, and long-term operation tests.

[0181] (9) Documentation and training: Organize technical documentation to ensure that team members master the use and maintenance of the system.

[0182] (10) Continuous monitoring and maintenance: Ensure long-term stable operation of the system through real-time monitoring and regular optimization.

[0183] (11) Through these steps, the system can be integrated efficiently, ensuring that different modules work together in a complex environment.

[0184] Optimize resource scheduling and data flow control to improve system responsiveness by dynamically adjusting bandwidth and priority. Specific implementation steps include:

[0185] (1) Demand analysis and load modeling: Establish a load model and analyze characteristics such as interface traffic and bandwidth requirements.

[0186] (2) System resource monitoring and data collection: Real-time monitoring of hardware resources and interface loads to obtain performance data.

[0187] (3) Intelligent scheduling algorithm design: Design bandwidth allocation and priority scheduling algorithms to dynamically adjust resources based on load.

[0188] (4) Dynamic load balancing and resource adjustment: Perform resource scheduling and bandwidth adjustment based on load conditions to ensure optimal system performance.

[0189] (5) Real-time monitoring and feedback mechanism: Continuously monitor system performance and adjust scheduling strategies.

[0190] (6) Fault-tolerance and high availability design: Design redundant resources and fault recovery mechanisms to ensure system stability.

[0191] (7) Performance testing and optimization: perform load, throughput, and latency testing, and optimize scheduling algorithms.

[0192] (8) Documentation and training: Record optimization strategies and train technical personnel to understand and apply scheduling algorithms.

[0193] Through intelligent scheduling and optimization, the system can ensure smooth and efficient operation when the load changes, improving resource utilization.

[0194] A specific implementation of a peripheral expansion method for embodied robots mainly describes the hardware and software optimization solutions used in vehicle-mounted systems, including boot time optimization, electromagnetic interference protection, hardware acceleration processing, sensor data fusion, and communication protocol optimization. The purpose is to improve the stability, real-time performance, adaptability, and intelligence of the system. The specific implementation is as follows:

[0195] Step 1: Startup time optimization method

[0196] (1) Boot program optimization, prioritize loading core services and necessary drivers, delay loading non-core modules, and reduce waiting time during startup.

[0197] (2) Streamline the startup process, adjust system parameters, disable unnecessary background processes and logging, reduce resource usage, and optimize data transmission.

[0198] (3) Delay service startup and delay loading of non-core services to ensure that core services are started first.

[0199] (4) Kernel parameter optimization, kernel configuration adjustment, memory management and CPU scheduling optimization, and improved startup efficiency.

[0200] (5) Compress data transmission and use compression algorithms to reduce the amount of data transmitted and resource usage during startup.

[0201] Step 2: Anti-electromagnetic interference design

[0202] (1) Electromagnetic shielding design

[0203] Special electromagnetic shielding materials are used in the hardware design. To ensure the system can operate normally in the high electromagnetic noise environment on board, a metal casing with high electromagnetic shielding effect is designed. Shielding is used to isolate the internal circuit from the external environment, reducing the impact of electromagnetic interference on the signal.

[0204] (2) Power line filtering

[0205] The power management module utilizes a more sophisticated power filter circuit design. By adding a multi-stage filter, the system's resistance to electromagnetic interference is significantly improved, particularly for effectively isolating high-frequency noise. Specifically, a multi-stage capacitor and inductor filter is designed at the power input to effectively block high-frequency noise generated by the engine and other wireless devices. The technical effect: Improved interference resistance by approximately 20%-30%.

[0206] Step 3: Hardware acceleration and processing performance improvement methods

[0207] (1) GPU acceleration module integration

[0208] Hardware integration: Select a suitable GPU (such as NVIDIA Jetson or ARM MaliGPU), combine it with the ARM processor, share memory through a high-speed interface, and optimize memory management.

[0209] Software optimization: Assign highly parallel tasks (such as image processing and path planning) to the GPU and serial tasks to the CPU. Use CUDA or OpenCL for programming to optimize data transfer and computing task scheduling.

[0210] (2) Hardware acceleration task allocation

[0211] Task analysis: CPU is suitable for control logic and low-parallel tasks, while GPU is suitable for high-parallel tasks (such as image processing and deep learning).

[0212] Task allocation: Dynamically allocate tasks based on task parallelism and real-time requirements to ensure efficient use of CPU and GPU resources.

[0213] Scheduling and load balancing: Monitor system load in real time, adjust task allocation, and ensure load balancing and real-time response.

[0214] Step 4: Sensor data fusion optimization method

[0215] (1) Unify the hardware interface design and design the adaptation module so that devices such as lidar, camera, ultrasonic sensor, etc. can be connected through a unified interface to simplify sensor adaptation.

[0216] (2) Efficient data fusion algorithm, combining Kalman filtering and deep learning, improves data processing accuracy and real-time performance, and increases efficiency by 30%-50%. The steps are as follows:

[0217] Data acquisition and preprocessing: Collect and process sensor data, remove noise, remove anomalies, and normalize.

[0218] Deep learning fusion: using CNN, RNN, and LSTM for data fusion:

[0219] Early Fusion: Stitching Sensor Data

[0220] Mid-term fusion: merge after branch processing

[0221] Late fusion: weighted merging of predictions

[0222] Training and optimization: Optimize deep learning models through data training, and use loss functions (such as MSE and cross entropy) and transfer learning to improve efficiency.

[0223] Post-processing and decision-making: Smooth fusion results for path planning, collision detection, etc.

[0224] Real-time optimization: Use GPU / TPU to accelerate inference and optimize algorithms to improve real-time performance and processing capabilities.

[0225] Step 5: Communication protocol optimization and network adaptation methods

[0226] (1) Optimize the underlying communication protocols, optimize the in-vehicle Ethernet and 5G protocols, improve compatibility and efficiency, reduce data transmission delays and improve stability.

[0227] Automotive Ethernet optimization: Improve bandwidth utilization, optimize flow control (such as the TSN protocol), priority scheduling, and low-latency transmission. Strengthen error detection and recovery, and introduce multipath communication.

[0228] 5G protocol optimization: Utilizing network slicing technology to allocate priority and bandwidth, dynamic spectrum management, and millimeter wave optimization to reduce latency and increase throughput. Low-latency communication protocols and MEC technology are introduced to optimize the responsiveness of autonomous driving.

[0229] (2) Multi-network collaboration and compatibility optimization: ensure seamless switching between in-vehicle networks (such as in-vehicle Ethernet, 5G, Wi-Fi, etc.), optimize network switching protocols and QoS strategies, and use gateway technology to improve compatibility. Develop an adaptive protocol stack to dynamically select the most appropriate communication protocol based on the network environment.

[0230] Protocol optimization implementation steps:

[0231] Demand analysis: Set optimization goals (such as latency, bandwidth, etc.) based on the needs of in-vehicle applications.

[0232] Protocol design and modification: Optimize the protocol layer, focusing on aspects such as flow control and priority scheduling.

[0233] Simulation and verification: Use simulation tools to verify the optimization effect and ensure stability.

[0234] Hardware and software implementation: Implement optimized protocols on the vehicle platform to ensure compatibility.

[0235] System integration and testing: Integrate optimized protocols and conduct real-world scenario testing.

[0236] (3) Enhance wireless communication adaptability, design adaptation modules to support seamless switching of networks such as Wi-Fi, 5G, and in-vehicle Ethernet, and improve communication stability and real-time performance by approximately 40%-60%.

[0237] By optimizing the in-vehicle Ethernet and 5G protocols, network efficiency and stability are improved, and the system's adaptability and performance in complex in-vehicle environments are enhanced.

[0238] By optimizing the bootloader and streamlining the startup process, unnecessary operations (such as logging) are reduced, and startup time is shortened by approximately 30%-50%, significantly improving system response speed and work efficiency. Especially in an in-vehicle environment, fast startup reduces idle time and enhances the driving experience. Anti-interference technologies (such as electromagnetic shielding and power supply filtering) are incorporated into the circuit design to improve system stability in high electromagnetic noise environments, increasing anti-interference capabilities by 20%-30%. The introduction of a GPU acceleration module significantly enhances processing power under high load conditions, ensuring real-time system response when multitasking. The processing power has increased by 40%-60%, meeting the high real-time and multitasking requirements of in-vehicle systems.

[0239] By unifying the hardware interface and driver architecture, the adaptation process for different sensors (such as lidar, cameras, and ultrasonic sensors) is simplified. Efficient data fusion algorithms improve the real-time and accuracy of data acquisition, increasing fusion efficiency by 30%-50%, enhancing road perception and driving safety. Optimization of the underlying communication protocol and improvements to the driver and adaptation of wireless communication modules ensure efficient and stable communication in dynamic network environments. This is particularly true in vehicle-mounted environments, with support for wireless communication methods such as in-vehicle Ethernet, 5G, and Wi-Fi, improving communication stability and real-time performance by 40%-60%.

[0240] A rigorous testing and verification process was incorporated into the hardware design to ensure stable module operation in harsh environments such as high temperature and vibration. This reduced hardware failure rates by 20%-30%, improving system reliability and ensuring the long-term stability of the vehicle system. The in-vehicle audio system utilizes advanced signal processing technology to enhance voice recognition, noise suppression, and audio output quality. This significantly improves the recognition accuracy of the voice assistant and navigation system, particularly in noisy environments, enhancing the driver's experience.

[0241] Figure 2 An embodiment of a peripheral expansion system for an embodied robot according to the present invention is shown.

[0242] In this optional embodiment, the peripheral expansion system for an embodied robot includes:

[0243] A startup process optimization unit 201 is used to optimize the startup process of the embodied robot based on a modular design concept and to process the circuit board using electromagnetic shielding technology;

[0244] The hardware interface design unit 202 is used to design the hardware interface standard between the embodied robot and the external device according to the optimized startup process and hardware acceleration module, and optimize the communication protocol between the external devices based on the hardware interface standard.

[0245] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment are implemented.

[0246] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0247] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0248] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0249] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0250] The present invention is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A peripheral expansion method for an embodied robot, characterized in that: The method includes: Based on the modular design concept, plan the hardware and software architecture of the embodied robot; Based on the planning results of the hardware and software architecture, use performance analysis tools and logging tools to analyze the startup process of the embodied robot, identify the startup steps and determine the execution order of each step; Based on the results of the startup process analysis, target services and target programs are loaded first, and non-target services and non-target programs are loaded later, thus optimizing the startup process sequence. Disable non-target logging operations according to the optimized startup sequence to optimize the operation boot process; Leveraging an optimized bootloader, it checks the firmware driver load list, identifies and removes non-target drivers, and rearranges the driver load order based on the importance of hardware components. Modify the hardware initialization code according to the rearranged loading order to optimize the process of initializing several hardware in parallel; Based on the optimized operation boot program logic, locate the time delay position in the firmware interface source code and adjust the delay parameters to optimize the firmware interface source code; Based on the optimized firmware interface source code, recompile using compiler options, and obtain the optimized embodied robot startup process through the recompiled firmware; Based on the optimized hardware architecture, electromagnetic shielding materials are selected and the circuit board is processed in combination with multi-stage filter technology; According to the optimized startup process and hardware acceleration module, the hardware interface standard between the embodied robot and external devices is designed, and the communication protocol between external devices is optimized based on the hardware interface standard.

2. The peripheral expansion method for an embodied robot according to claim 1, characterized in that: The process of designing the hardware interface standard between the embodied robot and external devices based on the optimized startup process and hardware acceleration module, and optimizing the communication protocol between external devices based on the hardware interface standard, includes: Based on the optimized startup process and hardware acceleration module, the processing module in the hardware architecture is used to optimize the scheduling of computing tasks; Assign computing tasks to corresponding processing units based on the characteristics of different tasks; Design hardware interface standards between embodied robots and external devices based on the computational task allocation results and corresponding hardware requirements; Based on the designed hardware interface standards, the communication protocol between the embodied robot and external devices is analyzed and optimized.

3. The peripheral expansion method for an embodied robot according to claim 2, characterized in that: The optimization of computing task scheduling based on the optimized startup process and hardware acceleration module and utilizing the processing module in the hardware architecture includes: According to the optimized startup process, the hardware acceleration module is integrated into the hardware architecture, and the processing modules in the hardware architecture are identified; Based on the identified processing modules, the priority preemptive scheduling algorithm is used to preliminarily optimize the scheduling of computing tasks, and the synchronization mechanism is used to distinguish between real-time tasks and non-real-time tasks; Based on the results of distinguishing real-time tasks from non-real-time tasks, the memory management algorithm is used to optimize task allocation, and the performance monitoring tool is used to monitor the task running status and optimize the computing task scheduling.

4. The peripheral expansion method for an embodied robot according to claim 3, characterized in that: The hardware interface standards between the embodied robot and external devices are designed based on the distribution results of the computing tasks and the corresponding hardware requirements, including: Based on the results of computing task allocation, analyze the performance requirements of each task; Acquire and analyze the external devices of the embodied robot and obtain the interface specifications of the external devices; Combining performance requirements and interface specifications, a hardware interface standard between the embodied robot and external devices is designed. The hardware interface standard includes hardware interface design, drive architecture planning, and data fusion algorithm.

5. The peripheral expansion method for an embodied robot according to claim 4, characterized in that: The hardware interface standards between the embodied robot and external devices are designed based on the performance requirements and interface specifications, including: Combine the task performance requirements and the interface specifications of external devices to build a hardware adaptation module, and then design the hardware interface and plan the driver architecture based on the hardware adaptation module; Collect mission data from hardware sensors and pre-process the mission data according to the performance requirements of the mission; The Kalman filter algorithm is used to suppress noise and estimate the state of the preprocessed task data, and the preset deep learning model is combined to associate and fuse the task data of different hardware sensors.

6. The peripheral expansion method for an embodied robot according to claim 5, characterized in that: The analysis and optimization of the communication protocol between the embodied robot and external devices based on the designed hardware interface standard include: Based on the designed hardware interface standards, analyze the communication protocol between the embodied robot and external devices to identify communication problems; Based on the identified communication problems, a communication adaptation module is designed, and the communication protocol is optimized by combining communication technology and Ethernet adaptation strategy.

7. A peripheral expansion system for embodied robots, characterized in that: The system includes: A startup process optimization unit, which optimizes the startup process of the embodied robot based on a modular design concept and uses electromagnetic shielding technology to process the circuit board; The hardware interface design unit is used to design the hardware interface standards between the embodied robot and external devices based on the optimized startup process and hardware acceleration module, and optimize the communication protocol between external devices based on the hardware interface standards.

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