A multimodal perception adaptive modular home robot
By fusing data from multimodal sensing components and central processing components, and combining standardized quick-release connections and safe interruption mechanisms, the problems of perception blind spots and insufficient functional expansion in existing home robots have been solved, enabling efficient and safe multifunctional home robot applications.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI POLYTECHNIC UNIV MECHANICAL & ELECTRICAL COLLEGE
- Filing Date
- 2026-05-27
- Publication Date
- 2026-07-24
AI Technical Summary
Existing home robots rely on a single sensor, are susceptible to environmental interference, have blind spots in perception, have limited functional expansion capabilities, and lack sufficient safety, especially for special groups of people, where there is a lack of real-time monitoring and risk avoidance.
It adopts a multimodal sensing component to integrate laser ranging, visual acquisition, ultrasonic detection and infrared detection, combined with a central processing component for data fusion and preprocessing, configured with standardized quick-release connection components to achieve modular expansion, and has a built-in time-series scene recognition model and priority scheduling mechanism, and has a system-level safety interruption mechanism.
It improves the accuracy and robustness of environmental perception, reduces jamming and misoperation rates, enables flexible expansion of functional modules, enhances safety protection for special groups, and strengthens the robot's operational safety and adaptability in the home environment.
Smart Images
Figure CN122442702A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of robotics, specifically to a multimodal perception adaptive modular home robot. Background Technology
[0002] The existing home robot market has reached a certain scale, mainly including categories such as robotic vacuum cleaners, companion robots for the elderly, and security monitoring robots. Among the existing technologies, robotic vacuum cleaners mostly rely on a single LiDAR or visual sensor to build an environmental map, and their working modes are fixed; companion robots mainly rely on voice interaction and have a high degree of functional integration; security robots focus only on video monitoring, and the functional boundaries of each category are clear.
[0003] The existing CN219720573U, "A Modular Separable Sweeping Robot," only focuses on a simple structural split design for sweeping robots, achieving only physical module separation. It does not adopt multimodal and multi-sensor fusion perception and still relies on a single laser or vision sensor. This makes it susceptible to interference from strong light and low light environments, resulting in perception blind spots. The robot is prone to getting stuck, missing areas, and colliding with others. At the same time, it lacks a universal standardized quick-release interface, which can only be adapted to cleaning components and cannot be expanded to cross-category functional modules such as companionship, security, and pet care, thus limiting its expansion capabilities.
[0004] Secondly, safety needs to be improved. There is a lack of real-time monitoring and risk avoidance mechanisms for special groups (the elderly and children). For example, the robot vacuum cleaner may collide with elderly people who move slowly, and the high-temperature function of the care module may be touched by children. Summary of the Invention
[0005] The purpose of this invention is to provide a multimodal perception adaptive modular home robot to solve the problems in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution: A multimodal sensing adaptive modular home robot includes a multimodal sensing component, a central processing component, a standardized quick-release connection component, a motion execution component, a power supply component, and a wireless communication component; The multimodal sensing component is integrated into the top front end of the robot body and is used to collect environmental geometry, texture, ground material, heat source and dynamic target information; the multimodal sensing component is equipped with a signal conditioning circuit for preliminary processing such as filtering and amplification of the raw sensor signals; The central processing unit is connected to the multimodal sensing unit, the standardized quick-release connection unit, the mobile execution unit, the power supply unit, and the wireless communication unit, respectively. The central processing unit is equipped with a data preprocessing module for noise reduction, time synchronization, spatial alignment, and format unification of multiple raw environmental data. The standardized quick-release connection assembly is located on the middle of the side of the robot body, and is used to realize detachable electrical connection and data communication between the robot body and the replaceable functional module; The mobile execution component is located at the bottom of the robot and is used to realize the robot's movement, turning and obstacle avoidance under the control of the central processing component; The power supply component is located in the center of the chassis and is used to provide adaptive power supply for the robot as a whole and its various functional modules. The power supply component has a built-in programmable power management chip, which can automatically adjust the output voltage and current limit according to the functional module type identified by the central processing component, while monitoring the power consumption of the whole machine and dynamically adjusting the power output under high load. The wireless communication component is used to enable data interaction, remote control, and status uploading between the robot and external terminals and smart home devices. The central processing component is configured to: perform fusion mapping and scene recognition based on preprocessed multimodal perception data; combine user data and remote commands obtained by the wireless communication component; generate an adaptive task queue according to a preset priority; and schedule functional modules to execute.
[0007] Preferably, the multimodal sensing component includes at least three of the following: laser ranging, visual acquisition, ultrasonic detection, and infrared detection, and includes at least an infrared detection unit; the optical axis of the visual acquisition unit is at a 15°-30° depression angle to the ground.
[0008] Preferably, the central processing component constructs a three-dimensional semantic map containing geometric information, material information, heat source information, and dynamic target markers based on multimodal data fusion; wherein, the ground material information is identified by inputting the RGB image acquired by the visual acquisition unit into a pre-trained convolutional neural network (CNN) classification model (such as ResNet) for identification, and distinguishing it into categories such as carpet, wood flooring, and tile; at the same time, the echo attenuation characteristics of the ultrasonic sensor are combined to help determine the hardness of the material.
[0009] Preferably, the central processing component has a built-in time-series scene recognition model and a priority scheduling mechanism, with the scheduling priorities being: security protection > remote command > preset task > autonomous decision-making.
[0010] Preferably, the standardized quick-release connection assembly includes a female snap-fit connector, a spring pin power contact, and a flexible data interface on the robot body side, and a male snap-fit connector, a power contact, and a data interface on the functional module side, which can automatically identify the type of replaceable functional module and complete power supply adaptation and communication handshake.
[0011] Preferably, the central processing component has a system-level safety interruption mechanism. When an elderly person or a child is detected entering a risk area, the control function module performs power reduction, pause, or steering avoidance actions, and uploads alarm information through the wireless communication component.
[0012] Preferably, the central processing component adjusts the working parameters and movement path in real time in a closed loop; when the power supply component's battery level is lower than a set threshold, it automatically records the task breakpoint and returns to charging; after charging is completed, it continues to execute the task from the breakpoint; when recording the task breakpoint, the central processing component stores the current robot's coordinates in the 3D semantic map, the executed path points, and the working progress of functional modules (such as the cleaning area coverage rate) into non-volatile memory; after returning to charging, it reloads the state and continues execution from the recorded coordinate position.
[0013] A robot control method, applicable to the aforementioned multimodal perception adaptive modular home robot, includes the following steps: Step 1: Collect multiple channels of raw environmental data through the multimodal sensing component, and complete noise reduction, time synchronization, spatial alignment and format unification through the data preprocessing module in the central processing component; Step 2: The central processing component constructs a 3D semantic map based on the fusion of the processed multimodal data, and obtains user habit data and remote control commands through the wireless communication component; Step 3: The central processing unit generates an adaptive task queue based on the real-time scene recognition results and according to the priority rule of safety first; when the priority of a new task is higher than that of the currently executing task, the central processing unit pauses the current task and saves its state, and puts the new task at the head of the queue for immediate execution; the original task is automatically resumed after the new task is completed. Step 4: The central processing component calls and controls the corresponding functional modules to execute tasks through standardized quick-release connection components, and performs real-time closed-loop control and proactive safety avoidance during the execution process.
[0014] Preferably, the scene recognition is based on time-series continuous sensing data, and security protection is the highest priority, which can interrupt all current non-security tasks.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention integrates multimodal sensing components into the top front end of the robot body and sets at least three of them, namely laser ranging, visual acquisition, ultrasonic detection, and infrared detection, to form a multi-sensor collaborative sensing structure. This structure can collect environmental geometry, texture, ground material, heat source and dynamic target information. After the data preprocessing module completes noise reduction, time synchronization, spatial alignment and format unification processing, compared with the single sensor solution, it improves the accuracy and robustness of environmental perception and reduces the occurrence rate of jamming or misoperation caused by perception blind spots.
[0016] 2. This invention uses a standardized quick-release connection component located in the middle of the side of the robot body. It employs a snap-fit female connector, spring-loaded power contacts, and a flexible data interface to cooperate with the snap-fit male connector, power contacts, and data interface on the functional module side. This enables detachable electrical connection and data communication between the robot body and replaceable functional modules. This structure can automatically identify module types and complete power supply adaptation and communication handshake, allowing users to replace functional modules as needed without modifying the robot body, thus improving the robot's functional expandability and usage flexibility.
[0017] 3. This invention utilizes a built-in time-series scene recognition model and priority scheduling mechanism in the central processing unit to prioritize safety protection and configure a system-level safety interruption mechanism. When elderly people or children are detected entering a risk area, the central processing unit's control module performs power reduction, pause, or reversal avoidance actions, and uploads alarm information via wireless communication. This mechanism achieves a shift from passive protection to active avoidance, reducing the likelihood of safety threats to vulnerable groups during robot operation and improving the robot's operational safety in home environments. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the main body of the robot of the present invention.
[0019] Figure 2 This is a schematic diagram showing the modular functional unit breakdown of the present invention.
[0020] Figure 3 This is a schematic diagram illustrating the working principle of the home robot of the present invention.
[0021] Figure label annotations: 1. Multimodal sensing component; 2. Central processing component; 3. Standardized quick-release connection component; 4. Motion execution component; 5. Power supply component; 6. Wireless communication component. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.
[0023] In one embodiment, such as Figures 1-3 As shown, a multimodal sensing adaptive modular home robot includes a multimodal sensing component 1, a central processing component 2, a standardized quick-release connection component 3, a mobile execution component 4, a power supply component 5, and a wireless communication component 6. The multimodal sensing component 1 is integrated into the top front end of the robot body and is used to collect environmental geometry, texture, ground material, heat source and dynamic target information; the multimodal sensing component 1 is equipped with a signal conditioning circuit for filtering, amplification and other preliminary processing of the raw sensor signals; The central processing unit 2 is equipped with a data preprocessing module for noise reduction, time synchronization, spatial alignment, and format unification of multiple raw environmental data streams. The time synchronization adopts a hardware and software combination based on timestamps: using the system clock of the central processing unit 2 as a reference, each frame of data from the LiDAR, vision camera, and ultrasonic sensor is timestamped with high precision, and then interpolation or alignment algorithms are used to achieve precise time synchronization of the data frames. The spatial alignment transforms all sensor data into a unified coordinate system with the robot's center of mass as the origin by pre-calibrating the intrinsic and extrinsic parameters of each sensor. The central processing unit 2 is connected to the multimodal sensing unit 1, the standardized quick-release connection unit 3, the mobile execution unit 4, the power supply unit 5, and the wireless communication unit 6, respectively. The standardized quick-release connection component 3 is located on the middle of the side of the robot body, and is used to realize the detachable electrical connection and data communication between the robot body and the replaceable functional module; The mobile execution component 4 is located at the bottom of the robot and is used to enable the robot to move, turn and avoid obstacles under the control of the central processing component 2; The power supply component 5 is located in the center of the chassis and is used to provide adaptive power supply for the robot as a whole and its various functional modules. The power supply component 5 has a built-in programmable power management chip, which can automatically adjust the output voltage and current limit according to the functional module type identified by the central processing component 2, while monitoring the power consumption of the whole machine and dynamically adjusting the power output under high load. Wireless communication component 6 is used to enable data interaction, remote control, and status uploading between the robot and external terminals and smart home devices; The central processing component 2 is configured to perform fusion mapping and scene recognition based on preprocessed multimodal perception data, and combine user data and remote commands obtained by the wireless communication component 6 to generate an adaptive task queue according to preset priorities and schedule the functional modules to execute.
[0024] In this embodiment, the robot collects multi-dimensional information about the home environment in real time through the multimodal perception component 1. The data preprocessing module in the central processing component 2 completes the synchronization and normalization of multiple raw data. The processed perception data is then transmitted to the central processing component 2, which performs environmental modeling and scene recognition accordingly. At the same time, the robot obtains user-defined data and external control commands through the wireless communication component 6, generates a task queue adapted to the current scene and user needs according to preset priority rules, and then schedules the corresponding functional modules to start running through the standardized quick-release connection component 3. The mobile execution component 4 completes the robot's displacement and obstacle avoidance actions under the control of the central processing component 2. The power supply component 5 provides stable adaptive power supply for the entire system, enabling the robot to autonomously complete the complete workflow of perception, judgment, scheduling and execution in the home environment.
[0025] In this embodiment, specifically, the multimodal perception component 1 includes at least three of the following: laser ranging, visual acquisition, ultrasonic detection, and infrared detection, and at least includes an infrared detection unit. This includes, but is not limited to, lidar, a high-definition visual camera, an ultrasonic sensor, and an infrared sensor; the lidar, visual camera, ultrasonic sensor, and infrared sensor are all uniformly encapsulated within a spherical cover on the top of the robot body, and the optical axis of the visual camera forms a 15°-30° downward angle with the ground. Figure 1 As shown, this depression angle is achieved through the mounting bracket inside the spherical cover.
[0026] Multiple types of detection units work together to acquire environmental information from different dimensions, making up for the perception blind spots of a single detection unit. After the data preprocessing module processes the data from multiple sources in a unified manner, it can provide more accurate and reliable data support for subsequent environmental modeling and scene recognition, thereby improving the robot's adaptability to complex home environments.
[0027] In this embodiment, specifically, the central processing component 2 constructs a three-dimensional semantic map containing geometric information, material information, heat source information, and dynamic target markers based on multimodal data fusion. When performing multimodal fusion, the central processing component 2 first synchronizes the multimodal perception data (laser point cloud, visual features, infrared thermal image) with timestamps and aligns its spatial coordinates. It then constructs a geometric skeleton based on the lidar data and uses visual features for loop closure detection and relocation, thereby generating a three-dimensional map containing semantic labels.
[0028] This 3D semantic map includes environmental geometry information, ground material information, heat source information, and dynamic target markers, enabling the robot to fully understand its home environment, distinguish the functional attributes and environmental states of different areas, and provide environmental basis for scene recognition, path planning, and task execution, avoiding operational deviations or execution errors. Specifically, the ground material information is obtained by inputting RGB images acquired by the visual acquisition unit into a pre-trained convolutional neural network (CNN) classification model (such as ResNet) for identification, classifying materials into categories such as carpet, wood flooring, and tile; simultaneously, the echo attenuation characteristics of ultrasonic sensors are used to assist in determining the hardness or softness of the material.
[0029] In this embodiment, specifically, the central processing component 2 incorporates a time-series scene recognition model and a priority scheduling mechanism. The scheduling priority is as follows: safety protection > remote command > preset task > autonomous decision-making. The time-series scene recognition model can determine the current home scene based on continuous environmental perception data. The priority scheduling mechanism can reasonably allocate the execution order according to the importance of different tasks, prioritizing the execution of safety-related tasks, avoiding conflicts between the robot and the home environment or people during operation, and improving the rationality and safety of robot operation.
[0030] The temporal scene recognition model is specifically a deep learning model based on temporal convolutional network (TCN) or recurrent neural network (RNN). Its input is a continuous multimodal perception data stream, and its output is the current scene category (such as cleaning scene, nursing scene, security scene).
[0031] In this embodiment, specifically, the standardized quick-release connection component 3 includes a female buckle, a spring pin power contact, and a flexible data interface on the robot body side, and a male buckle, a power contact, and a data interface on the functional module side, which can automatically identify the type of replaceable functional module and complete power supply adaptation and communication handshake.
[0032] This structure can automatically identify the types of replaceable functional modules connected, quickly complete power supply adaptation and communication handshake, and enable a stable and reliable detachable connection between the functional modules and the robot body. Users can replace different functional modules according to their needs without modifying the robot body, thus improving the robot's functional expandability and usage flexibility. Each functional module connected by the standardized quick-release connection component 3 adopts a lightweight design, and the robot's bottom center of gravity has been optimized to ensure overall stability even after replacing different modules.
[0033] In this embodiment, specifically, the central processing component 2 has a system-level safety interruption mechanism. When it detects that an elderly person or a child has entered a risk area, the control function module performs power reduction, pause, or steering avoidance actions, and uploads alarm information through the wireless communication component 6.
[0034] When the multimodal perception component 1 detects that elderly people, children or other special groups have entered the risk area, the central processing component 2 immediately controls the currently operating functional modules to perform power reduction, pause or turn to avoid the risk. At the same time, it uploads alarm information to the outside through the wireless communication component 6, changing from passive protection to active avoidance, reducing the possibility of special groups being threatened during the operation of the robot.
[0035] The wireless communication component 6 supports Wi-Fi 6, Bluetooth 5.2, and ZigBee protocols and is integrated into the rear of the robot. The safety interruption mechanism specifically works as follows: when the infrared sensor or visual sensor detects a human heat source or silhouette entering a preset risk area (e.g., within 1 meter in front of the robot), the central processing component 2 immediately sends a hardware-level interrupt signal to the currently executing functional module, forcing it to stop moving or reduce power. This safety interruption mechanism has the highest and inviolable authority; even if a remote command conflicts with safety logic is received, the robot will still prioritize power reduction, pausing, or avoidance actions, and will send a safety warning and command conflict notification to the remote terminal via the wireless communication component 6.
[0036] In this embodiment, specifically, the central processing component 2 adjusts the working parameters and movement path in real time in a closed loop; when the power supply component 5 is lower than the set threshold, it automatically records the task breakpoint and returns to charging, and resumes the task from the breakpoint after charging is completed.
[0037] When the power supply component 5's charge level falls below a set threshold, the central processing unit 2 automatically records the current task breakpoint and controls the robot to return to the charging position. After charging is complete, the robot resumes execution from the recorded breakpoint, avoiding the need to restart execution after task interruption and improving the robot's work efficiency and battery life adaptability. When recording the task breakpoint, the central processing unit 2 stores the robot's current coordinates in the 3D semantic map, executed path points, and the work progress of functional modules (such as cleaning area coverage) into non-volatile memory. After returning to charging, it reloads this state and resumes execution from the recorded coordinate position.
[0038] A robot control method, applicable to the aforementioned multimodal perception adaptive modular home robot, includes the following steps: Step 1: Collect multiple channels of raw environmental data through the multimodal sensing component 1, and complete noise reduction, time synchronization, spatial alignment and format unification through the data preprocessing module in the central processing component 2; Step 2: The central processing component 2 constructs a three-dimensional semantic map based on the fusion of the processed multimodal data, and obtains user habit data and remote control commands through the wireless communication component 6; Step 3: Based on the real-time scene recognition results, the central processing unit 2 generates an adaptive task queue according to the priority rule of safety first; when the priority of a new task is higher than that of the currently executing task, the central processing unit 2 pauses the current task and saves its state, and puts the new task at the head of the queue for immediate execution; after the new task is completed, the original task is automatically resumed. Step 4: The central processing component 2 calls and controls the corresponding functional modules to execute tasks through the standardized quick-release connection component 3, and performs real-time closed-loop control and proactive safety avoidance during the execution process.
[0039] In this embodiment, scene recognition is specifically implemented based on continuous temporal sensing data, with safety protection as the highest priority, capable of interrupting all current non-safe tasks. Scene recognition, based on continuous temporal sensing data, can continuously track environmental changes and personnel activity. With safety protection set to the highest priority, when a safety risk occurs, all current non-safe tasks can be directly interrupted, prioritizing safety avoidance operations to ensure the robot's safety and reliability in the home environment.
[0040] Any aspects of this invention not described in detail are well-known to those skilled in the art.
[0041] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A multimodal sensing adaptive modular home robot, characterized in that, It includes a multimodal sensing component (1), a central processing component (2), a standardized quick-release connection component (3), a mobile execution component (4), a power supply component (5), and a wireless communication component (6). The multimodal sensing component (1) is integrated into the top front end of the robot body and is used to collect environmental geometry, texture, ground material, heat source and dynamic target information; the multimodal sensing component (1) is provided with a signal conditioning circuit for filtering and amplifying the original sensor signal; The central processing unit (2) is connected to the multimodal sensing unit (1), the standardized quick-release connection unit (3), the mobile execution unit (4), the power supply unit (5), and the wireless communication unit (6), respectively; the central processing unit (2) is equipped with a data preprocessing module for noise reduction, time synchronization, spatial alignment, and format unification of multiple raw environmental data. The standardized quick-release connection component (3) is located on the middle side of the robot body. The standardized quick-release connection component (3) is used to realize the detachable electrical connection and data communication between the robot body and the replaceable functional module. The mobile execution component (4) is located at the bottom of the robot and is used to realize the robot's movement, turning and obstacle avoidance under the control of the central processing component (2); The power supply component (5) is located in the center of the robot chassis. The power supply component (5) is used to provide adaptive power supply for the robot as a whole and each functional module. The power supply component (5) has a built-in programmable power management chip, which can automatically adjust the output voltage and current limit according to the functional module type identified by the central processing component (2). The wireless communication component (6) is used to realize data interaction, remote control and status upload between the robot and external terminals and smart home devices; The central processing component (2) is configured to: perform fusion mapping and scene recognition based on preprocessed multimodal perception data, and generate an adaptive task queue and schedule the functional modules to execute according to preset priorities, in conjunction with user data and remote instructions obtained by the wireless communication component (6).
2. The multimodal perception adaptive modular home robot according to claim 1, characterized in that, The multimodal sensing component (1) includes a laser ranging unit, a visual acquisition unit, an ultrasonic detection unit, and an infrared detection unit, and at least includes an infrared detection unit; the optical axis of the visual acquisition unit is at a 15° angle to the ground. 30° depression angle.
3. The multimodal perception adaptive modular home robot according to claim 1, characterized in that, The central processing component (2) constructs a three-dimensional semantic map containing geometric information, material information, heat source information and dynamic target markers based on multimodal data fusion; wherein, the ground material information is input into the pre-trained convolutional neural network classification model through the RGB image obtained by the visual acquisition unit for identification, and the echo attenuation characteristics of the ultrasonic sensor are combined to assist in judging the softness and hardness of the material.
4. The multimodal perception adaptive modular home robot according to claim 1, characterized in that, The central processing component (2) has a built-in time-series scene recognition model and priority scheduling mechanism. The scheduling priority is as follows: security protection > remote command > preset task > autonomous decision.
5. A multimodal perception adaptive modular home robot according to claim 1, characterized in that, The standardized quick-release connection component (3) includes a female buckle, spring pin power contact and elastic data interface on the robot body side, and a male buckle, power contact and data interface on the functional module side. It can automatically identify the type of replaceable functional module and complete power supply adaptation and communication handshake. Each functional module connected by the standardized quick-release connection component (3) adopts a lightweight design, and the bottom center of gravity design of the robot has been optimized to ensure that the overall stability can still be maintained after replacing different modules.
6. A multimodal perception adaptive modular home robot according to claim 1, characterized in that, The central processing component (2) has a system-level safety interruption mechanism. When the elderly or children are detected to enter the risk area, the control function module performs power reduction, pause or turn to avoid the risk, and uploads alarm information through the wireless communication component (6).
7. A multimodal perception adaptive modular home robot according to claim 1, characterized in that, The central processing component (2) adjusts the working parameters and movement path in real time in a closed loop; when the power of the power component (5) is lower than the set threshold, it automatically records the task breakpoint and returns to charging. After charging is completed, it continues to execute the task from the breakpoint; when the central processing component (2) records the task breakpoint, it stores the current robot's coordinates in the three-dimensional semantic map, the executed path points, and the working progress status information of the functional modules into non-volatile memory. After returning to charging, the state is reloaded and execution continues from the recorded coordinate position.
8. A robot control method, characterized in that, A multimodal sensing adaptive modular home robot according to any one of claims 1-7 includes the following steps: Step 1: Collect multiple channels of raw environmental data through the multimodal sensing component (1), and complete noise reduction, time synchronization, spatial alignment and format unification through the data preprocessing module in the central processing component (2); Step 2: The central processing component (2) constructs a three-dimensional semantic map based on the fusion of the processed multimodal data, and obtains user habit data and remote control commands through the wireless communication component (6); Step 3: The central processing component (2) generates an adaptive task queue based on the real-time scene recognition results and according to the priority rule of safety first; when the priority of a new task is higher than that of the currently executing task, the central processing component (2) pauses the current task and saves its state, and puts the new task at the head of the queue for immediate execution; after the new task is completed, the original task is automatically restored. Step 4: The central processing component (2) calls and controls the corresponding functional modules to perform tasks through the standardized quick-release connection component (3), and performs real-time closed-loop control and active safety avoidance during the execution process.
9. A robot control method according to claim 8, characterized in that, The scene recognition is based on continuous temporal sensing data, and security protection is the highest priority, which can interrupt all current non-security tasks.