A kind of inspection robot control system and method based on inspection information

Abstract

The application relates to the technical field of robot control, and discloses a kind of inspection robot control system and method based on inspection information, through the cooperative work of movement module, data acquisition module, data analysis module and control module, the accurate path planning and dynamic motion control of inspection robot are effectively realized, so that inspection robot can realize the dynamic optimization of path planning according to real-time environmental state and self-motion state, effectively improve the autonomous decision-making ability and task execution efficiency of robot in complex environment.Especially through the deep linkage of data analysis module and control module, the inspection robot has the identification ability and emergency handling ability to abnormal environmental state, can quickly generate emergency control information when abnormal situation occurs, and automatically switch to suitable motion mode, to ensure the safety and reliability of the inspection task.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to a control system and method for an inspection robot based on inspection information. Background Technology

[0002] With the rapid development of intelligent inspection robots, their applications in fields such as power, petrochemicals, and transportation are becoming increasingly widespread. These fields have high requirements for the accuracy, efficiency, and safety of inspection tasks. Inspection robots need to perform tasks in complex and ever-changing environments, while possessing real-time path planning and dynamic adjustment capabilities. However, existing inspection robot control systems mainly rely on fixed paths or preset rules, exhibiting weak adaptability and dynamic adjustment capabilities to environmental changes. Especially in complex terrain or unexpected situations, they cannot quickly react and adjust their movement paths, resulting in insufficient stability and reliability of inspection tasks. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a control system and method for an inspection robot based on inspection information, so as to solve the problem that the inspection robot in the prior art does not have deep linkage with the inspection environment and has low adaptability and dynamic adjustment ability to environmental changes.

[0004] The first aspect of this invention discloses a patrol robot control system based on patrol information, the system comprising a motion module, a data acquisition module, a data analysis module, and a control module;

[0005] The motion module is used to control the inspection robot to perform motion actions according to the control information, and to send the motion information generated during the motion process to the data analysis module and the control module.

[0006] The data acquisition module is equipped with several environmental sensors to collect environmental data within the inspection area and send the environmental data to the data analysis module.

[0007] The data analysis module is used to receive the motion information and the environmental data, and to perform feature extraction operations based on the received motion information and environmental data, and generate environmental state information based on the extracted features;

[0008] The control module is used to generate control information based on the environmental state information and motion information, and to adjust the motion action of the motion module through the control information.

[0009] Furthermore, the motion information includes position, direction of travel, speed of travel, and posture information.

[0010] Furthermore, the motion module also includes a motion control unit and a motion feedback unit;

[0011] The motion control unit is used to control the inspection robot to perform motion actions according to the control information from the control module; the motion actions include adjusting the direction of travel, adjusting the speed of travel, and adjusting the posture.

[0012] The motion feedback unit is used to collect motion information of the inspection robot during its movement and send the motion information to the data analysis module and the control module.

[0013] Furthermore, the motion control unit controls the inspection robot to perform motion actions based on control information, including:

[0014] The optimal motion path of the inspection robot under different terrains and postures is calculated based on the kinematic model, control information, and motion information collected by the motion feedback unit, and path execution instructions are generated.

[0015] The control information includes environmental constraint information, inspection route information, and attitude information.

[0016] Furthermore, the process of calculating the optimal motion path of the inspection robot under different terrains and postures based on the kinematic model using control information and motion feedback unit includes:

[0017] A kinematic model is constructed based on the structural parameters of the inspection robot; the structural parameters include wheel track, wheelbase, turning radius, and center of gravity height.

[0018] Based on environmental constraint information and historical motion information, identify the terrain type and attitude change trend of the inspection area, and determine the equilibrium constraint conditions;

[0019] The kinematic model is used to calculate the balance path and turning path of the inspection robot under different postures based on balance constraints, travel route information and posture adjustment information, and to generate preliminary path execution instructions.

[0020] Based on real-time motion information, optimize the path smoothness and turning radius of the initial path execution command to generate the optimal path execution command.

[0021] Furthermore, the calculation process of the balanced path includes:

[0022] The centroid position (x) of the inspection robot on the path is obtained based on the motion information. c ,y c ,z c ), pitch angle θ p and roll angle θ r .

[0023] Furthermore, the data analysis module performs probability updates on environmental data collected by different sensors using a multi-sensor data fusion algorithm based on Bayesian inference.

[0024] Furthermore, before generating control information based on environmental state information and motion information, the control module performs an anomaly judgment operation on environmental state information. When it is determined that there is an anomaly in the environmental state information, an emergency mechanism is triggered.

[0025] After the emergency mechanism is triggered, emergency control information is generated based on environmental status information and real-time motion information, and then sent to the motion module.

[0026] Furthermore, the motion control unit in the motion module automatically switches motion modes based on emergency control information and a preset motion mode library, and performs motion actions according to the switched motion mode.

[0027] The second aspect of this invention discloses a control method for an inspection robot based on inspection information, which is applied to the system disclosed in the first aspect. The method includes:

[0028] The inspection robot is controlled to perform motion actions based on the control information, and motion information generated during the inspection robot's movement is collected and recorded.

[0029] Several environmental sensors are installed to collect environmental data within the inspection area.

[0030] Based on the motion information and the environmental data, a feature extraction operation is performed, and environmental state information is generated based on the extracted features.

[0031] New control information is generated based on the environmental state information and motion information, and the movement of the inspection robot is adjusted using the new control information.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] This invention effectively achieves precise path planning and dynamic motion control for inspection robots through the collaborative work of motion, data acquisition, data analysis, and control modules. This enables the robot to dynamically optimize its path planning based on real-time environmental conditions and its own motion status, significantly improving its autonomous decision-making ability and task execution efficiency in complex environments. In particular, the deep integration of the data analysis and control modules equips the inspection robot with the ability to identify and handle abnormal environmental conditions. It can quickly generate emergency control information and automatically switch to a suitable motion mode when abnormal situations occur, ensuring the safety and reliability of inspection tasks. Attached Figure Description

[0034] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this application, do not limit the scope of the invention. In the drawings:

[0035] Figure 1 This is a schematic diagram of the structure of an inspection robot control system based on inspection information disclosed in Embodiment 1 of the present invention;

[0036] Figure 2 This is a flowchart illustrating a method for controlling an inspection robot based on inspection information, as disclosed in another embodiment of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0038] Example 1

[0039] The first aspect of this invention discloses a patrol robot control system based on patrol information. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of the structure of an inspection robot control system based on inspection information disclosed in an embodiment of the present invention. The system includes a motion module, a data acquisition module, a data analysis module, and a control module.

[0040] The motion module is used to control the inspection robot to perform motion actions according to the control information, and to send the motion information generated during the motion process to the data analysis module and the control module.

[0041] Several environmental sensors are set up in the data acquisition module to collect environmental data in the inspection area and send the environmental data to the data analysis module.

[0042] The data analysis module is used to receive motion information and environmental data, and to perform feature extraction operations based on the received motion information and environmental data, and generate environmental status information based on the extracted features;

[0043] The control module is used to generate control information based on environmental status information and motion information, and to adjust the motion actions of the motion module through the control information.

[0044] Specifically, in this embodiment of the invention, the motion information includes the position, direction of travel, speed, and posture information of the inspection robot.

[0045] In this embodiment of the invention, environmental state information refers to the comprehensive perception results of the inspection robot during the inspection process. It is used to determine the environmental changes in the inspection area and to provide environmental constraint information to the control decision module, ensuring that the inspection robot can safely and smoothly complete its inspection tasks in different environments. This includes, but is not limited to, gas concentration information, temperature information, flame detection information, noise information, obstacle location and type, and terrain feature information.

[0046] Furthermore, the reason this invention considers motion information when generating environmental state information is that the position, direction of travel, speed, and posture of the inspection robot constantly change during its inspection tasks. These dynamic changes directly affect the environmental data collected by the sensors. For example, different robot postures may cause shifts in the sensor's measurement angle, thus affecting the accuracy of the measurement results; and changes in the robot's speed during travel also affect the sensor's sampling frequency and data timeliness. Therefore, by combining motion information, sensor data can be dynamically calibrated to correct measurement errors caused by changes in motion state. Simultaneously, motion information can also help identify the spatial distribution characteristics of environmental data, such as the relative positions of obstacles and changes in path slope, thereby more comprehensively constructing environmental state information for the inspection area and improving the accuracy of control decisions.

[0047] Furthermore, the data analysis module uses a multi-sensor data fusion algorithm based on Bayesian inference to perform probability updates on environmental data collected by different sensors.

[0048] Preferably, it is assumed that there are n environmental sensors, and the observation data of the sensors are:

[0049] Z = {z1, z2, ..., z} i ,…,z n}

[0050] Among them, z i Let be the observation value of the i-th sensor.

[0051] A prior probability distribution P(X) is established for the data features of each sensor, where X represents the possible value of the environmental state. For example, X represents the presence of a flame (flame present / no flame), and the prior probability P(X) represents the initial judgment of the environmental state when no data is observed.

[0052] The observation z for each sensor i Calculate the likelihood function P(z) i |X), representing the value z observed by the sensor when the environmental state is X. i The probability. For example, in the presence of a flame, the flame sensor has a higher probability of detecting the flame.

[0053] The posterior probability P(X|Z) is calculated using Bayes' theorem, which represents the probability that the environmental state is X given the observed sensor data Z.

[0054]

[0055] Where P(Z|X) is the joint likelihood function of all sensor observations; P(X) is the prior probability; and P(Z) is the marginal probability distribution of the observation data.

[0056] During the inspection process, as new sensor data is continuously collected, the posterior probability of the environmental state is updated in real time.

[0057]

[0058] Among them, Z t+1 For the new sensor observation, P(X|Z) t The probability distribution of the environmental state at the previous time step; P(X|Z) t+1 P(Z) represents the updated probability distribution of the environment state; t+1 |X) is the joint likelihood function of all new sensor observations; P(Z) t+1 ) represents the marginal probability distribution of the new observation data.

[0059] In this embodiment of the invention, by utilizing a Bayesian inference algorithm, the probability distribution of the environmental state can be continuously updated based on real-time changes in sensor data, thereby achieving dynamic perception of environmental changes. Furthermore, the probability-updated environmental state information provides more precise environmental constraints for the control decision module, enabling real-time adjustment of movement paths and attitudes according to environmental changes, thus improving the safety and efficiency of inspection tasks.

[0060] Furthermore, the motion module also includes a motion control unit and a motion feedback unit.

[0061] The motion control unit is used to control the inspection robot to perform motion actions based on the control information from the control module. These motion actions include adjusting the direction of travel, adjusting the speed of travel, and adjusting the posture.

[0062] The motion feedback unit is used to collect motion information of the inspection robot during its movement and send the motion information to the data analysis module and the control module.

[0063] Furthermore, the motion control unit controls the inspection robot to perform motion actions based on the control information, including:

[0064] The kinematic model calculates the optimal motion path of the inspection robot under different terrains and postures based on the control information and motion information collected by the motion feedback unit, and generates path execution instructions; among which, the control information includes environmental constraint information, inspection route information and posture information.

[0065] Specifically, in this embodiment of the invention, the control module first extracts environmental constraint information from the environmental state information, such as obstacle location, terrain type, ground slope, and temperature changes. For example, when an obstacle is detected, the environmental constraint information records the specific location and size of the obstacle. Then, based on the requirements of the inspection task and the preset inspection route, inspection route information is generated. This inspection route information includes not only the target location and path but also path segments that are adjusted in real time according to environmental changes. Finally, based on the posture information in the motion information, it is determined whether the inspection robot needs to adjust its posture (such as turning, accelerating, or decelerating) to ensure that the inspection robot maintains balance and stability in different terrains.

[0066] Furthermore, the process of calculating the optimal motion path of the inspection robot under different terrains and postures using a kinematic model based on control information and motion feedback unit includes:

[0067] A kinematic model is constructed based on the structural parameters of the inspection robot; the structural parameters include, but are not limited to, wheelbase, turning radius, and center of gravity height.

[0068] Based on environmental constraint information and historical motion information, identify the terrain type and attitude change trend of the inspection area, and determine the equilibrium constraint conditions;

[0069] The kinematic model is used to calculate the balance path and turning path of the inspection robot under different postures based on balance constraints, travel route information and posture adjustment information, and to generate preliminary path execution instructions.

[0070] Based on real-time motion information, optimize the path smoothness and turning radius of the initial path execution command to generate the optimal path execution command.

[0071] Specifically, when constructing the kinematic model, the differential drive model or the Ackermann steering model is often used to construct the model based on the structural parameters of the inspection robot. The embodiments of the present invention do not limit the specific formula of the kinematic model.

[0072] In this embodiment of the invention, the inspection robot needs to maintain balance under different terrain conditions to avoid tipping over, slipping, or losing control. Therefore, in determining the balance constraints, the terrain type of the current inspection area is first identified based on environmental constraint information, such as flat ground, slopes, steps, slippery surfaces, etc. After identifying the terrain type of the current inspection area, the position of the center of gravity is calculated based on the robot's center of gravity height and current posture information, and it is determined whether it is within a stable support surface. The stable support surface refers to the polygon formed by the robot's wheels in contact with the ground. If the center of gravity exceeds the stable support surface, the robot is at risk of tipping over. After determining that it is within the stable support surface, different tipping angle thresholds are set according to the slope variation trend of different terrains to ensure that the robot maintains balance throughout its movement.

[0073] Specifically, balance constraints may include terrain type constraints, rollover angle constraints, steering angle constraints, speed constraints, center of gravity position adjustment constraints, tire contact pressure constraints, etc.

[0074] Furthermore, in this embodiment of the invention, historical motion information includes actual motion data of the inspection robot on the same or similar terrain. This data can help the system predict attitude change trends, predict the robot's attitude change patterns on similar terrain based on historical data, and adjust attitude control parameters in advance. Simultaneously, by analyzing historical data, it can identify areas prone to slippage, tipping, etc., and set corresponding constraints.

[0075] Preferably, the calculation process for the equilibrium path includes:

[0076] The centroid position (x) of the inspection robot on the path is obtained based on the motion information. c ,y c ,z c ), pitch angle θ p and roll angle θ r ;

[0077] The equilibrium path C is calculated based on the following formula. balance :

[0078]

[0079] Among them, h g The height of the inspection robot's center of gravity; w g L is the width of the inspection robot's center of gravity; W is the front-to-rear wheelbase; α is the left-to-right wheelbase; F is the terrain complexity weighting factor. t This is a factor influencing terrain complexity.

[0080] Furthermore, the formula for calculating the turning path is:

[0081]

[0082] Where R is the turning path, and also the actual turning radius of the inspection robot when turning; L w denoted as the front and rear wheel track; v is the travel speed of the inspection robot; w is the angular velocity of the inspection robot.

[0083] Path smoothness represents the continuity and smoothness of a robot's movement path. Too many sharp turns or abrupt changes in the path can lead to robot loss of control or tipping over. Therefore, path smoothness needs to be optimized during path planning. In this embodiment of the invention, a Bézier curve is used to smooth the initial path, and interpolation calculations are performed on key points in the path to generate a more continuous path curve, resulting in a more natural path transition.

[0084] When optimizing the turning radius, consider the following factors:

[0085] Current terrain type: In narrow sections or areas with many obstacles, reduce the turning radius; in wide areas, the turning radius can be appropriately increased.

[0086] Real-time motion information: Dynamically adjust the turning radius based on the current speed and angular velocity to avoid overly sharp turns.

[0087] It is understood that the above factors are only the preferred examples listed in the embodiments of the present invention, and the embodiments of the present invention do not limit the range of factors to be considered when optimizing the turning radius.

[0088] In this embodiment of the invention, the optimal motion path of the inspection robot under different terrains and postures is calculated using a kinematic model, which effectively improves the path planning accuracy and motion control stability of the inspection robot in complex environments. By combining environmental constraint information and historical motion information to identify the terrain type and posture change trend of the inspection area, balance constraints can be dynamically determined, enabling the robot to maintain balance in complex scenarios such as slopes, steps, and slippery surfaces, reducing the risk of tipping over and slipping. In addition, during the path planning process, it can also ensure that the inspection robot executes the inspection task according to the optimal path, making the path execution process smoother, avoiding sharp turns or frequent adjustments, improving the continuity and safety of motion, and also improving the environmental adaptability, path planning accuracy, and real-time motion control of the inspection robot, thereby achieving a more efficient, stable, and safe inspection task execution effect.

[0089] Furthermore, before generating control information based on environmental status information and motion information, the control module performs an anomaly judgment operation on environmental status information. When an anomaly is detected in the environmental status information, an emergency mechanism is triggered.

[0090] After the emergency mechanism is triggered, emergency control information is generated based on environmental status information and real-time motion information, and then sent to the motion module.

[0091] Furthermore, the motion control unit in the motion module automatically switches motion modes based on emergency control information and a preset motion mode library, and performs motion actions according to the switched motion mode.

[0092] Specifically, during the inspection process, the inspection robot may encounter various abnormal situations, such as detecting sudden fires, smoke, gas leaks, obstacles, or other environmental anomalies, or experiencing abnormal states such as posture instability, path deviation, or motion malfunctions. Therefore, in the control decision-making process, this invention ensures that the inspection robot responds promptly when an anomaly is detected by setting an anomaly judgment operation based on environmental state information, triggering an emergency mechanism and switching to a suitable motion mode to achieve safe task execution and fault avoidance.

[0093] Anomaly detection of environmental status information is a crucial step in the control module before generating control information. Its purpose is to identify abnormal environmental conditions within the inspection area. Specifically, this includes determining whether environmental sensor data is abnormal, such as excessive hydrogen concentration, excessive smoke concentration, abnormal flame detection signals, or excessively high temperatures; and whether motion information is abnormal, such as the robot tilting at an angle exceeding a safety threshold on a slope, deviating from the preset route, or undergoing drastic changes in posture.

[0094] When the control module detects an anomaly in the environmental status information, it immediately triggers an emergency mechanism. Upon triggering the emergency mechanism, the system generates emergency control information and sends it to the motion module. This emergency control information is a control command generated by the inspection robot to adjust its movement mode and ensure safe obstacle avoidance after detecting an anomaly. The generation of emergency control information primarily considers current environmental status information, such as the type, location, and severity of the abnormal event, as well as real-time motion information, such as the robot's current position, direction of travel, speed, and attitude. The emergency control information can include obstacle avoidance commands, stop commands, and evacuation commands.

[0095] Upon receiving emergency control information, the motion module automatically switches to a suitable motion mode based on a preset motion mode library and executes corresponding motion actions. The motion mode library is a set of pre-defined motion control strategies for different scenarios, including but not limited to normal inspection mode, obstacle avoidance mode, emergency evacuation mode, and stop mode. After switching to the matching motion mode, the module adjusts its route, speed, and posture, and executes motion actions according to the switched motion mode to complete emergency operations such as obstacle avoidance, evacuation, or stopping.

[0096] In this embodiment of the invention, by setting up anomaly detection operations and emergency response triggering mechanisms for environmental state information in the control module, the safety and emergency handling capabilities of the inspection robot can be greatly improved. When an anomaly occurs, the system can identify changes in the environmental state in real time, generate emergency control information, and automatically switch to an appropriate motion mode, enabling the inspection robot to quickly avoid dangerous areas or retreat to safe areas, preventing tipping, collisions, or damage. This real-time anomaly handling capability and dynamic motion mode switching mechanism give the inspection robot higher environmental adaptability, task execution safety, and autonomous decision-making capabilities, thereby significantly improving the reliability and intelligence level of inspection tasks.

[0097] Example 2

[0098] The second aspect of this invention discloses a method for controlling an inspection robot based on inspection information. Please refer to [link / reference]. Figure 2 , Figure 2 This is a flowchart illustrating a method for controlling an inspection robot based on inspection information, as disclosed in another embodiment of the present invention. The method includes:

[0099] The inspection robot is controlled to perform motion actions based on the control information, and motion information generated during the inspection robot's movement is collected and recorded.

[0100] Several environmental sensors are installed to collect environmental data within the inspection area.

[0101] Perform feature extraction based on motion information and environmental data, and generate environmental state information based on the extracted features;

[0102] New control information is generated based on environmental status information and motion information, and the movement of the inspection robot is adjusted based on the new control information.

[0103] Furthermore, motion information includes position, direction of travel, speed of travel, and posture information.

[0104] Furthermore, the movement includes adjusting the direction of travel, adjusting the speed of travel, and adjusting the posture.

[0105] Furthermore, controlling the inspection robot to perform motion actions based on control information includes:

[0106] The optimal motion path of the inspection robot under different terrains and postures is calculated based on control and motion information using a kinematic model, and path execution instructions are generated.

[0107] The control information includes environmental constraint information, inspection route information, and attitude information.

[0108] Furthermore, the process of calculating the optimal motion path of the inspection robot under different terrains and postures based on control and motion information using a kinematic model includes:

[0109] A kinematic model is constructed based on the structural parameters of the inspection robot; the structural parameters include wheel track, wheelbase, turning radius, and center of gravity height.

[0110] Based on environmental constraint information and historical motion information, identify the terrain type and attitude change trend of the inspection area, and determine the equilibrium constraint conditions;

[0111] The kinematic model is used to calculate the balance path and turning path of the inspection robot under different postures based on balance constraints, travel route information and posture adjustment information, and to generate preliminary path execution instructions.

[0112] Based on real-time motion information, optimize the path smoothness and turning radius of the initial path execution command to generate the optimal path execution command.

[0113] Furthermore, the calculation process for the equilibrium path includes:

[0114] The centroid position (x) of the inspection robot on the path is obtained based on the motion information. c ,y c ,z c ), pitch angle θ p and roll angle θ r ;

[0115] The equilibrium path C is calculated based on the following formula. balance :

[0116]

[0117] Among them, h g The height of the inspection robot's center of gravity; w g L is the width of the inspection robot's center of gravity; W is the front-to-rear wheelbase; α is the left-to-right wheelbase; F is the terrain complexity weighting factor. t This is a factor influencing terrain complexity.

[0118] Furthermore, the method also includes a multi-sensor data fusion algorithm based on Bayesian inference to perform probability updates on environmental data collected by different sensors.

[0119] Furthermore, before generating control information based on environmental state information and motion information, the method also includes performing an anomaly judgment operation on environmental state information, and triggering an emergency mechanism when it is determined that there is an anomaly in the environmental state information.

[0120] After the emergency mechanism is triggered, emergency control information is generated based on environmental status information and real-time motion information;

[0121] The system automatically switches between exercise modes based on emergency control information and a pre-set exercise mode library, and performs exercise actions according to the switched exercise mode.

[0122] It should be noted that the specific implementation process of Embodiment 2 is similar to that of Embodiment 1, and will not be repeated in this embodiment.

[0123] Finally, it should be noted that the inspection robot control system and method based on inspection information disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A patrol robot control system based on patrol information, characterized in that, The system includes a motion module, a data acquisition module, a data analysis module, and a control module; The motion module is used to control the inspection robot to perform motion actions according to the control information, and to send the motion information generated during the motion process to the data analysis module and the control module. The data acquisition module is equipped with several environmental sensors to collect environmental data within the inspection area and send the environmental data to the data analysis module. The data analysis module is used to receive the motion information and the environmental data, and to perform feature extraction operations based on the received motion information and environmental data, and to generate environmental state information based on the extracted features. The control module is used to generate control information based on the environmental state information and motion information, and to adjust the motion action of the motion module through the control information. The control information includes environmental constraint information, inspection route information, and attitude information; the process of generating control information based on the environmental state information and motion information includes: A kinematic model is constructed based on the structural parameters of the inspection robot; the structural parameters include wheel track, wheelbase, turning radius, and center of gravity height. Based on environmental constraint information and historical motion information, identify the terrain type and attitude change trend of the inspection area, and determine the equilibrium constraint conditions; The kinematic model is used to calculate the balance path and turning path of the inspection robot under different postures based on balance constraints, travel route information and posture adjustment information, and to generate preliminary path execution instructions. Based on real-time motion information, optimize the path smoothness and turning radius of the initial path execution command to generate the optimal path execution command.

2. The inspection robot control system based on inspection information according to claim 1, characterized in that, The motion information includes position, direction of travel, speed of travel, and posture information.

3. The inspection robot control system based on inspection information according to claim 2, characterized in that, The motion module also includes a motion control unit and a motion feedback unit; The motion control unit is used to control the inspection robot to perform motion actions according to the control information from the control module; the motion actions include adjusting the direction of travel, adjusting the speed of travel, and adjusting the posture. The motion feedback unit is used to collect motion information of the inspection robot during its movement and send the motion information to the data analysis module and the control module.

4. The inspection robot control system based on inspection information according to claim 3, characterized in that, The calculation process for the balance path includes: The centroid position of the inspection robot on the path is obtained based on motion information. Pitch angle and roll angle .

5. The inspection robot control system based on inspection information according to claim 1, characterized in that, The data analysis module uses a multi-sensor data fusion algorithm based on Bayesian inference to perform probability updates on environmental data collected by different sensors.

6. The inspection robot control system based on inspection information according to claim 1, characterized in that, Before generating control information based on environmental status information and motion information, the control module performs an anomaly judgment operation on environmental status information. When it is determined that there is an anomaly in the environmental status information, an emergency mechanism is triggered. After the emergency mechanism is triggered, emergency control information is generated based on environmental status information and real-time motion information, and then sent to the motion module.

7. The inspection robot control system based on inspection information according to claim 6, characterized in that, The motion control unit in the motion module automatically switches motion modes based on emergency control information and a preset motion mode library, and performs motion actions according to the switched motion mode.

8. A method for controlling an inspection robot based on inspection information, wherein the method is applied to the system described in any one of claims 1-7, characterized in that, The method includes: The inspection robot is controlled to perform motion actions based on the control information, and motion information generated during the inspection robot's movement is collected and recorded. Several environmental sensors are installed to collect environmental data within the inspection area. Based on the motion information and the environmental data, a feature extraction operation is performed, and environmental state information is generated based on the extracted features. New control information is generated based on the environmental state information and motion information, and the motion of the inspection robot is adjusted through the new control information. The control information includes environmental constraint information, inspection route information, and attitude information; the process of generating control information based on the environmental state information and motion information includes: A kinematic model is constructed based on the structural parameters of the inspection robot; the structural parameters include wheel track, wheelbase, turning radius, and center of gravity height. Based on environmental constraint information and historical motion information, identify the terrain type and attitude change trend of the inspection area, and determine the equilibrium constraint conditions; The kinematic model is used to calculate the balance path and turning path of the inspection robot under different postures based on balance constraints, travel route information and posture adjustment information, and to generate preliminary path execution instructions. Based on real-time motion information, optimize the path smoothness and turning radius of the initial path execution command to generate the optimal path execution command.

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