Robot motion control system and robot thereof

Abstract

The invention relates to the field of robots, and discloses a robot motion control system to solve the problems proposed in the background technology. In order to achieve the purpose, the invention provides the following technical scheme that the robot motion control system is characterized by comprising a multi-sensor module used for collecting environment information, and the multi-sensor module comprises a laser radar, a binocular camera and two USB cameras; from the view of the mapping effect, the three-dimensional map constructed by using the FAST-LIO algorithm can capture environment information with high precision, the availability is high, and a precise reference frame is provided for the realization of positioning and navigation functions; from the perspective of functionality, the robot has the capability of processing various tasks, including autonomous navigation, inspection and the like, so that the robot can play a role in different application scenes, and the use value and the application range of the robot are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, in particular to a robot motion control system and a robot thereof. Background Art

[0002] Existing robots on the market have exposed numerous performance shortcomings when faced with complex environments. In terms of positioning accuracy, existing robot positioning systems struggle to accurately obtain their own position due to numerous interference factors in complex environments, such as dynamic obstacles and multipath effects. This leads to large positioning errors, making it impossible for robots to accurately perform subsequent tasks.

[0003] In terms of path planning capabilities, the path planning algorithms used by existing robots are often overly simplistic and fail to fully consider the various constraints and dynamic changes in complex environments. When encountering narrow passages, irregular obstacles, or unexpected obstacles, they are unable to promptly plan an optimal and feasible path, which can easily lead to path planning failures or even unfeasible planned paths.

[0004] Obstacle avoidance is also unsatisfactory. Existing robot sensor systems have limited detection range, low accuracy, and difficulty recognizing obstacles made of unusual materials. This results in the robot only reacting when it approaches an obstacle, and its avoidance movements are not flexible or timely, leading to collisions. This can damage the robot itself and surrounding objects, and can also affect the normal execution of tasks.

[0005] Furthermore, existing robots lack comprehensive autonomous navigation capabilities. Most require human intervention or rely on pre-set, fixed routes for movement, and are unable to autonomously adjust their action strategies based on environmental changes and mission requirements. During inspection missions, they fail to conduct comprehensive and detailed inspections of target areas according to pre-set inspection routes and standards, leading to missed inspections and false positives. Regarding material delivery, they lack intelligent cargo identification, grasping, and handling capabilities, and are unable to automatically adjust delivery methods based on material type, size, and weight. This results in low delivery efficiency and is prone to errors.

[0006] Therefore, we propose a robot motion control system and its robot to solve the above problems. Summary of the Invention

[0007] The object of the present invention is to provide a robot motion control system to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a robot motion control system, comprising:

[0009] A multi-sensor module for collecting environmental information, the multi-sensor module comprising a Livox Mid-360 laser radar produced by DJI, an Intel D435 binocular camera, and two USB cameras;

[0010] A main controller as a core control of the entire control system, the main controller being built-in with a navigation module;

[0011] An LCD display screen connected with the main controller, for receiving image information from the main controller;

[0012] A USB-to-CAN module;

[0013] A chassis motor driving control unit for driving the steering motor and the traveling motor of the wheels on the chassis of the robot;

[0014] A host computer;

[0015] When the robot is in a manual control mode, the main controller receives ROS messages from the host computer and directly outputs motion control instructions to the chassis motor driving control unit; when the robot is in an autonomous navigation mode, the image data of the Intel D435 binocular camera and the USB cameras, the point cloud data of the laser radar, and the inertial navigation data are transmitted to the navigation module of the main controller, the current position of the robot and the information of the obstacles around the vehicle are obtained by analyzing the information of the multi-sensor modules, the global motion path of the robot is calculated according to the current position and the global map information using the A Star algorithm, the local movement path of the robot is obtained using the DWA path planning algorithm in combination with the obstacle information, and finally the wheel motion control information suitable for the robot, including the motion speed and the steering angle, is output through kinematics solving, the corresponding control commands are sent to the chassis motor driving control unit through the USB-to-CAN module to drive the robot to move.

[0016] Preferably, an auxiliary function module control panel is further included, the auxiliary function module control panel is connected with the main controller, the auxiliary function module control panel includes a GD32 main control chip, a telephone short message module, a voice interaction module and an RGB dot matrix display screen, the GD32 main control chip is connected with the telephone short message module, the voice interaction module and the RGB dot matrix display screen; when the robot performs a task, the external environment data are collected by the multi-sensor module and transmitted to the main controller, then data analysis is performed, position information and task progress are extracted, the analyzed data are converted by logic to generate corresponding operation instructions, which are transmitted to the GD32 main control chip, after receiving the instructions from the main controller, the GD32 main control chip controls the telephone short message module to send telephone and short message notifications, at the same time, voice broadcast information is generated by the voice interaction module and transmitted to the user, in addition, the GD32 main control chip encodes text information and displays interactive information through the RGB dot matrix screen to provide visual feedback and information display, the user interacts with the system through the WeChat applet, and the interaction data are transmitted to the main controller through the upper computer.

[0017] Preferably, the laser radar and the inertial measurement unit are used to collect point cloud and inertial data in the environment, after accumulating the point cloud, state estimation is performed by forward propagation, and it is judged whether it is stable, if it is stable, the state is updated and the odometry data is output at a frequency of 10-100 Hz; if it is not stable, reverse propagation and residual calculation are performed to correct the state, and k nearest neighbor (kNN) search is further optimized. New point cloud is inserted into the ikd tree, and it is detected whether it affects the existing map structure, if it has an effect, it is judged whether the map area is offset, and the corresponding area is deleted, the ikd tree is reconstructed, the whole process is executed in a high frequency cycle, through fusion of the inertial navigation and the laser radar data, efficient and accurate real-time map construction is realized, the system provides real-time map visualization function, and the user can intuitively observe the change of the environment around the robot and the construction process of the map through the interface.

[0018] Preferably, the chassis is installed at the bottom of the frame body, the Livox Mid-360 laser radar is installed at the top center of the frame body, the USB camera is symmetrically installed at the top of the frame body, the Intel D435 binocular camera is installed at the front position of the top of the frame body, the RGB dot matrix display screen is installed at the front of the frame body, and the LCD display screen is installed at the rear of the frame body.

[0019] Preferably, a partition plate is fixed in the inside of the frame body, a storage area is arranged on the upper side of the partition plate, and a hardware integration area is arranged on the lower side of the partition plate.

[0020] Preferably, the USB-to-CAN module, the main controller and the chassis motor driving unit are integrated on the same circuit board, and the circuit board and the auxiliary function module control panel are located in the hardware integration area.

[0021] Preferably, the Intel D435 binocular camera and the two USB cameras are mounted on the frame body through adjustable supports.

[0022] Preferably, the robot chassis adopts an Ackerman steering chassis.

[0023] Preferably, the Livox Mid-360 laser radar is provided with a lifting rod between the top of the frame body.

[0024] Compared with the prior art, the present application has the following beneficial effects:

[0025] From the effect of mapping, the three-dimensional map constructed using the FAST-LIO algorithm can capture environmental information with high precision and high availability, providing an accurate reference frame for the realization of positioning and navigation functions; from the functionality, the robot has the ability to handle multiple tasks, including autonomous navigation, inspection, etc., enabling the robot to play a role in different application scenarios and significantly improving its use value and scope of application; from the user experience, whether it is a multi-purpose robot management software or a WeChat applet, the operation interface is efficient and easy to use, and the user experience is good, and during the operation of the robot, thanks to the addition of the auxiliary function module, the human-computer interaction experience is further improved in the visual and auditory dimensions. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 It is a schematic diagram of the overall structure of the mobile robot;

[0027] Figure 2 It is a vehicle motion control flowchart;

[0028] Figure 3 It is an auxiliary function module flowchart;

[0029] Figure 4 It is a software framework for map construction using the FAST-LIO algorithm;

[0030] Figure 5 It is a hardware architecture diagram of the mobile robot;

[0031] Figure 6 It is a schematic diagram of the rear structure of the mobile robot;

[0032] Figure 7 It is a schematic diagram of the relative position of the circuit board. DETAILED DESCRIPTION

[0033] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0034] Please refer to Figures 1-7 The robot motion control system mainly comprises a multi-sensor module, a main controller, an LCD display screen 13, a USB-to-CAN module, a chassis motor driving control unit, an upper computer, and an auxiliary function module control board.

[0035] The multi-sensor module is responsible for collecting environmental information, specifically including a Livox Mid-360 laser radar 2 produced by DJI, an Intel D435 binocular camera 4, and two USB cameras 3. In order to achieve all-around environmental perception and high-precision spatial positioning, the laser radar needs to have a wide field of view, high resolution, and stable performance, so the Livox Mid-360 laser radar 2 is selected as the environmental perception device of the mobile robot. It is installed at the top center of the robot frame body 1, and a lifting rod is provided between the top of the frame body 1 and the laser radar to expand its detection range. The Intel D435 binocular camera 4 is installed at the front top position of the frame body 1. Its function is to realize accurate depth perception and enhanced three-dimensional environment mapping capability, so it is required to have high resolution, wide field of view, and stable depth acquisition characteristics. This camera has a resolution of up to 1920x1080 and a maximum field of view of 90 degrees, which can capture wide and detailed visual information, greatly enhancing the robot's environmental understanding and decision-making ability. Two USB cameras 3 are symmetrically installed on the top of the frame body 1, which are used to better monitor the environment around the robot. The requirements for the cameras are small size, small data transmission volume, and independent stable work. Based on this, a USB industrial camera module is used, which has a size of 32mmx24mmx16mm, a transmission frame rate of 30fps, and a resolution of 480P. It can run stably on Jetson ORIN NX without driver, and the data is returned through USB, which meets the expected requirements. The three cameras are installed on the frame body 1 through adjustable supports, which can be adjusted in angle according to actual needs.

[0036] The main controller 9 is the core of the whole control system and is built-in with a navigation module. In order to provide higher data processing capacity and realize complex navigation and environmental interaction functions, the core controller needs to have high-performance processing, multi-interface support and stable system running characteristics. Therefore, Jetson Orin NX is selected as the core controller of the mobile robot. The main controller 9 is connected with the LCD display screen 13, which is used to receive image information from the main controller 9 and display. The USB-to-CAN module 8, the main controller 9 and the chassis motor driving control unit 10 are integrated on the same circuit board 11, which is located in the hardware integration area 7 on the lower side of the inner partition 5 of the frame body 1, and is fixed by unified wiring and screws to ensure the stability of the components and facilitate maintenance. The chassis motor driving control unit 10 is used to drive the steering motor and the wheel driving motor on the robot chassis.

[0037] When the robot is in manual control mode, the main controller 9 receives the ROS message from the host computer and directly outputs the motion control instruction to the chassis motor driving control unit 10, so as to control the robot motion. When the robot is in autonomous navigation mode, the image data of the Intel D435 binocular camera 4 and the USB camera 3, the point cloud data of the laser radar and the inertial navigation data are transmitted to the navigation module of the main controller 9. The main controller 9 obtains the current position of the robot and the information of the obstacles around the vehicle by analyzing the information of these multi-sensor modules. According to the current position and the global map information, the global motion path of the robot is calculated using the A Star algorithm; combined with the obstacle information, the local movement path of the robot is obtained using the DWA path planning algorithm. Finally, through kinematics solving, the wheel motion control information suitable for the robot is output, including the motion speed and the steering angle, and the corresponding control command is sent to the chassis motor driving control unit 10 through the USB-to-CAN module 8 to drive the robot motion.

[0038] In terms of navigation framework design process, the robot first receives ICP positioning data, laser radar sensor data and odometer data. The odometer data is converted into position information (odom) and combined with sensor data to construct a map (maps). In the navigation module, the global planner (global_planner) plans a global path according to the target position, while creating a global cost map (global_costmap); the local planner (local_planner) adjusts the path according to the current environment and obstacles, and generates a local cost map (local_costmap). When the path planning fails, the system triggers the recovery behavior (recovery_behaviors). Finally, the planned motion path is converted into a speed command (cmd_vel) to drive the robot to move along the planned path. Throughout the process, the system continuously updates the sensor data and adjusts the path planning in real time to ensure the accuracy and reliability of navigation.

[0039] In terms of positioning of mobile robots, the works adopts Fast-LIO (Fast LiDAR Inertial Odometry) positioning method, which is significantly better than the common ACML (Adaptive Monte Carlo Localization) positioning. Fast-LIO fuses laser radar point cloud data and inertial measurement unit (IMU) data, and uses tight coupling filtering algorithm to realize high-precision, low-delay real-time positioning. Specifically, laser radar and inertial measurement unit are used to collect point cloud and inertial data in the environment, and after accumulating the point cloud, state estimation is performed through forward propagation, and whether it is stable is judged. If it is stable, update the state and output the odometer data at a frequency of 10-100Hz; if it is not stable, perform backward propagation and residual calculation to correct the state, and further optimize through k nearest neighbor (kNN) search. New point cloud is inserted into ikd tree, and whether it affects the existing map structure is detected, if it affects, whether the map area is shifted is judged, and the corresponding area is deleted, the ikd tree is reconstructed, and the whole process is executed in high frequency cycle. Compared with ACML, Fast-LIO has centimeter-level positioning accuracy and higher robustness, and is not easily affected by environmental light changes and occlusions, and is suitable for complex dynamic environments. At the same time, Fast-LIO is adaptable and can work stably in indoor and outdoor and large-scale scenes, greatly improving the performance and reliability of mobile robots in application scenarios. The system also provides real-time map visualization function, users can intuitively observe the changes of the environment around the robot and the construction process of the map through the interface.

[0040] In addition, the system also includes an auxiliary function module control board 12 connected with the main controller 9. The auxiliary function module control board 12 uses GD32F407VET6 of Meiya Innovation Technology Co., Ltd. as the main control chip, and at the same time provides multiple 12V and 5V power outputs, which can provide power for Jetson Orin, laser radar, camera and other devices. The auxiliary function module control board 12 includes a GD32 main control chip, a telephone message module, a voice interaction module and an RGB dot matrix display screen 14, and the GD32 main control chip is connected with the telephone message module, the voice interaction module and the RGB dot matrix display screen 14. The RGB dot matrix display screen is installed at the front of the frame body 1 and is used to display interactive information. When the robot performs a task, the external environment data is collected through the multi-sensor module and transmitted to the main controller 9, and then the main controller 9 analyzes the data and extracts the position information and task progress. The analyzed data is converted by logic to generate corresponding operation instructions and transmitted to the GD32 main control chip. After receiving the instructions from the main controller 9, the GD32 main control chip controls the telephone message module to send telephone and message notifications, and at the same time generates voice broadcast information through the voice interaction module and transmits it to the user. In addition, the GD32 main control chip encodes the text information and displays the interactive information through the RGB dot matrix screen to provide visual feedback and information display. The user interacts with the system through the WeChat applet, and the interaction data is transmitted to the main controller 9 through the upper computer. Since the 40PIN expansion IO provided by the main controller 9 (Jetson Orin NX) is not enough to drive the related peripherals of these functions at the same time, the auxiliary function module control board 12 is designed to drive the RGB dot matrix screen, the telephone message module and the voice interaction module and other interactive peripherals.

[0041] A robot includes a frame body 1, a chassis installed at the bottom of the frame body 1, a Livox Mid-360 laser radar 2 installed at the top center of the frame body 1, a USB camera 3 symmetrically installed at the top of the frame body 1, an Intel D435 binocular camera 4 installed at the front position of the top of the frame body 1, and an RGB dot matrix display screen installed at the front of the frame body 1, and an LCD display screen installed at the back of the frame body 1.

[0042] A partition 5 is fixed inside the frame body 1, a storage area 6 is arranged on the upper side of the partition 5, and a hardware integration area 7 is arranged on the lower side of the partition 5.

[0043] The USB to CAN module 8, the main controller 9 and the chassis motor drive control unit 10 are integrated on the same circuit board 11, and the circuit board 11 and the auxiliary function module control board 12 are located in the hardware integration area 7. The hardware integration area 7 is reinforced by unified wiring and screws to ensure the stability of the components and facilitate maintenance

[0044] The Intel D435 binocular camera 4 and the two USB cameras 3 are both mounted on the frame body 1 through adjustable supports.

[0045] The robot chassis adopts an Ackermann steering chassis, which has similar characteristics to a car and has obvious advantages on ordinary cement and asphalt roads. Compared with a four-wheel differential chassis, the Ackermann steering chassis has higher load capacity and can achieve higher motion speed and can be applied to the fields of unmanned inspection, security, scientific research, logistics, etc.

[0046] The Livox Mid-360 laser radar 2 is provided between the frame body 1 and the top of the frame body 1.

[0047] The navigation framework design process is as follows: first, the robot receives ICP positioning data, laser radar sensor data and odometer data. The odometer data is converted into position information (odom), and combined with the sensor data to construct a map (maps). In the navigation module, the global planner (global_planner) plans a global path according to the target position, and creates a global cost map (global_costmap) at the same time. The local planner (local_planner) adjusts the path according to the current environment and obstacles, and generates a local cost map (local_costmap). When the path planning fails, the system triggers the recovery behavior (recovery_behaviors). Finally, the planned motion path is converted into a speed command (cmd_vel) to drive the robot to move along the planned path. Throughout the process, the system continuously updates the sensor data and adjusts the path planning in real time to ensure the accuracy and reliability of navigation.

[0048] In terms of positioning of the mobile robot, the Fast-LIO (Fast LiDAR Inertial Odometry) positioning method is adopted, which is significantly better than the common ACML (Adaptive Monte Carlo Localization) positioning. Fast-LIO fuses laser radar point cloud data and inertial measurement unit (IMU) data, and uses a tightly coupled filtering algorithm to achieve high-precision, low-delay real-time positioning. Compared with ACML, Fast-LIO has centimeter-level positioning accuracy and higher robustness, and is not easily affected by changes in environmental light and occlusion, and is suitable for complex dynamic environments. At the same time, Fast-LIO is highly adaptable and can work stably in indoor and outdoor and large-scale scenes, greatly improving the performance and reliability of the mobile robot in application scenarios.

[0049] Theoretical basis of SLAM

[0050] Mathematical description of SLAM problem

[0051] SLAM is a technique that builds a model of the environment and estimates the motion of the entity (e.g. robot) carrying the sensors without prior knowledge of the environment. Since the sensors on the entity collect data at certain fixed intervals, the SLAM problem is described as a discrete system. Let x k be the position data of the sensor at time k, z k,j be the observation data generated by the sensor at x k position, the SLAM problem can be described by two things: "motion" and "observation". The "motion" examines the change of the sensor (entity) from x k-1 to x k , while the "observation" means that the sensor sees a building label y k at x j at time k, thus generating an observation data z k,j . Therefore, the SLAM process can be composed of a set of motion equations and a set of observation equations in any case:

[0052]

[0053] where f is called the motion equation, h is called the observation equation, u k is the reading or input of the moving sensor. Ω is a set that records which label is observed at which time. These two sets of equations describe the most basic SLAM problem. k is the motion noise, which is usually caused by uncertainty in the system, external interference, and modeling error, etc.; v k,j is the observation noise, which is usually caused by errors in the sensor measurement process, environmental interference, and limitations of the measurement system itself. The introduction of noise makes the model a stochastic model. When the reading u k of the motion measurement and the reading z k,j of the sensor are known, how to solve the positioning problem (estimate x k ) and the mapping problem (estimate y j ) is modeled as a state estimation problem: how to estimate the internal and hidden state variables through noisy measurement data?

[0054] The estimation variables (x k and y j ) of the above formula are written as x k , and the state estimation problem described by the SLAM problem is simplified as:

[0055]

[0056] where w k N(0, R k), v k N(0, Q k ) is random noise of high-dimensional Gaussian distribution.

[0057] SLAM framework

[0058] The classic SLAM framework generally includes the following steps: sensor data reading, front-end odometry, loop detection, back-end optimization, mapping. For some point cloud registration frameworks using Scan to Scan method, the loop detection part is not included.

[0059] Sensor data reading: In three-dimensional laser radar SLAM, the sensor data is mainly the point cloud data of the laser radar. In the laser inertial odometry system, IMU data also needs to be obtained. In addition, preliminary processing of the data is required, such as voxel filtering of the point cloud to obtain a point cloud with fewer points.

[0060] Front-end odometry: For two-dimensional laser radar SLAM, the work is to use the traditional ICP method to realize the scan registration of laser radar point cloud, or to realize the image method of Gaussian likelihood field according to the characteristics of two dimensions, and then to obtain the optimal pose estimation according to the registration. For three-dimensional laser radar SLAM, ICP method is generally used to realize point cloud registration, and NDT method can also be used to realize point cloud registration according to statistical method.

[0061] Loop detection: The work of loop detection is to judge whether the subject has arrived at the previous position at the current time. If it is detected that it has arrived, that is, a loop is detected, the information will be transmitted to the back end for processing.

[0062] Back-end optimization: The back end receives the subject pose measured by the front-end odometry at different times and the loop detection information, and optimizes them to obtain a globally consistent trajectory and map.

[0063] Mapping: According to the estimated trajectory and point cloud registration result, the corresponding point cloud map can be established after back-end optimization.

[0064] Kinematic equation model

[0065] Since the three-dimensional laser radar SLAM system is used to describe the motion of three-dimensional space, and the motion of three-dimensional space is composed of 3 axes, the motion of the subject is described by translation on 3 axes and rotation on 3 axes, so there are 6 degrees of freedom in total. As can be known from the above analysis, the rotation and translation of the object can be described by a rotation matrix and a translation vector. When they change continuously with time, they become functions R(t) and t(t) that change with time. From the properties of R, it can be known that:

[0066] RR T =R T R=I,

[0067] R T R = I, take the derivative of both sides with respect to t, we get:

[0068]

[0069] Therefore, we have:

[0070]

[0071] It can be seen that the above matrix is an anti-symmetric matrix, so it can be written in the form of a differential equation:

[0072]

[0073] where The rotation matrix at the initial time t0 is R(t0), and according to this initial value, the above first-order differential equation can be solved, and the solution is:

[0074] R(t) = R(t0) exp(w^(t-t0)).

[0075] This formula is used to handle the rotational motion of an object, i.e. to handle the changes in physical quantities such as angular velocity. On this basis, considering the linear velocity, the motion equation of the system is:

[0076]

[0077] This description method is simple and intuitive, and is widely used.

[0078] It should be noted that in this article, relational terms such as first and second are used only to distinguish one entity or action from another entity or action, and do not necessarily require or imply that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0079] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A robot motion control system, characterized in that: include: A multi-sensor module, used to collect environmental information, includes a lidar, a binocular camera, and two USB cameras; The main controller is the core control of the entire control system, and the main controller has a built-in navigation module; An LCD display screen is connected to the main controller and is used to receive image information from the main controller; USB to CAN module; Chassis motor drive control unit, used to drive the wheel steering motor and wheel driving motor on the chassis of the robot; Host computer; When the robot is in manual control mode, the main controller receives ROS messages from the host computer and directly outputs motion control instructions to the chassis motor drive control unit. When the robot is in autonomous navigation mode, the image data of the binocular camera and USB camera, the point cloud data of the lidar, and the inertial navigation data are transmitted to the navigation module of the main controller. By parsing the information of these multi-sensor modules, the current position of the robot and the information of obstacles around the vehicle are obtained. Based on the current position and global map information, the global motion path of the robot is calculated using the A Star algorithm. Combined with the obstacle information, the local movement path of the robot is obtained using the DWA path planning algorithm. Finally, through kinematic solution, the wheel motion control information suitable for the robot is output, including the motion speed and steering angle. The corresponding control commands are sent to the chassis motor drive control unit through the USB to CAN module to drive the robot movement.

2. The robot motion control system according to claim 1, characterized in that: It also includes an auxiliary function module control board, which is connected to the main controller. The auxiliary function module control board includes a main control chip, a phone and text message module, a voice interaction module and an RGB dot matrix display screen. The main control chip is connected to the phone and text message module, the voice interaction module and the RGB dot matrix display screen. When the robot performs a task, it collects data from the external environment through the multi-sensor module and transmits this data to the main controller. Then, the data is parsed to extract the location information and task progress. The parsed data is converted into corresponding operation instructions through logic conversion and passed to the main control chip. After receiving the instructions from the main controller, the main control chip controls the phone and text message module to send phone and text message notifications, and generates voice broadcast information through the voice interaction module to transmit to the user. In addition, after the main control chip encodes the text information, it displays the interactive information through the RGB dot matrix screen to provide visual feedback and information display. The user interacts with the system through the WeChat applet, and the interactive data is transmitted to the main controller through the host computer.

3. The robot motion control system according to claim 2, characterized in that: LiDAR and inertial measurement units are used to collect point clouds and inertial data in the environment. After accumulating point clouds, the state is estimated through forward propagation to determine whether it is stable. If it is stable, the state is updated and the odometer data is output at a frequency of 10-100Hz; if it is unstable, backpropagation and residual calculation are performed to correct the state, and further optimization is performed through k-nearest neighbor (kNN) search; the new point cloud is inserted into the IKD tree and tested to see if it affects the existing map structure. If so, it is determined whether the map area is offset, the corresponding area is deleted, and the IKD tree is reconstructed. The entire process is executed in a high-frequency loop. By integrating inertial navigation and LiDAR data, efficient and accurate real-time map construction is achieved. The system provides real-time map visualization function, and users can intuitively observe changes in the robot's surrounding environment and the map construction process through the interface.

4. A robot adapted to the robot motion control system according to any one of claims 1 to 3, characterized in that: It includes a frame, the chassis is installed at the bottom of the frame, the laser radar is installed at the top center of the frame, the USB cameras are symmetrically installed on both sides of the top of the frame, the binocular camera is installed at the front position of the top of the frame, the RGB dot matrix display is installed in the front of the frame, and the LCD display is installed at the back of the frame.

5. The robot according to claim 4, characterized in that A partition is fixed on the inner side of the frame, a storage area is provided on the upper side of the partition, and a hardware integration area is provided on the lower side of the partition.

6. The robot according to claim 4, characterized in that The USB to CAN module, the main controller and the chassis motor drive control unit are integrated on the same circuit board, and the circuit board and the auxiliary function module control board are located in the hardware integration area.

7. The robot according to claim 4, characterized in that The binocular camera and two USB cameras are all mounted on the frame through adjustable brackets.

8. The robot according to claim 4, characterized in that The robot chassis adopts Ackerman steering chassis.

9. The robot according to claim 4, characterized in that A lifting rod is provided between the laser radar and the top of the frame.

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