Inspection robot control system and method based on inspection information

Abstract

The invention relates to the technical field of robot control, and discloses an inspection robot control system and method based on inspection information, and the system effectively achieves the precise path planning and dynamic motion control of an inspection robot through the cooperative work of a motion module, a data collection module, a data analysis module and a control module. Therefore, the inspection robot can realize dynamic optimization of path planning according to the real-time environment state and the motion state of the inspection robot, and the autonomous decision-making capability and task execution efficiency of the robot in a complex environment are effectively improved. Particularly, through deep linkage of the data analysis module and the control module, the inspection robot has the recognition capability and the emergency processing capability for abnormal environment states, emergency control information can be quickly generated when abnormal conditions occur, the inspection robot is automatically switched to a proper motion mode, and safety and reliability of inspection tasks are ensured.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a patrol robot control system and method based on patrol information. Background Art

[0002] With the rapid development of intelligent inspection robots, their application is becoming increasingly widespread in fields such as power generation, petrochemicals, and transportation. These sectors place high demands on the accuracy, efficiency, and safety of inspection tasks. Inspection robots must perform tasks in complex and changing environments while possessing real-time path planning and dynamic adjustment capabilities. However, existing inspection robot control systems primarily rely on fixed paths or preset rules, with limited adaptability to environmental changes and dynamic adjustment capabilities. In particular, they are unable to quickly react and adjust their motion paths in complex terrain or unexpected situations, resulting in insufficient stability and reliability in inspection tasks. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a patrol robot control system and method based on patrol information to solve the problems in the prior art that the patrol robot is not deeply linked with the patrol environment and has low adaptability and dynamic adjustment capabilities to environmental changes.

[0004] The first aspect of the present 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 send the motion information generated during the motion process to the data analysis module and the control module;

[0006] The data acquisition module is provided with a plurality of environmental sensors, which 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, perform a feature extraction operation based on the received motion information and environmental data, and generate environmental state information according to the extracted features;

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

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

[0010] Furthermore, the motion module further 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 of the control module; the motion actions include adjustment of the direction of travel, adjustment of the travel speed and adjustment of the posture;

[0012] The motion feedback unit is used to collect motion information of the inspection robot during its motion process 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 according to the control information, including:

[0014] 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;

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

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

[0017] Constructing a kinematic model based on the structural parameters of the inspection robot; the structural parameters include wheelbase, turning radius, and center of gravity height;

[0018] Identify the terrain type and posture change trend of the inspection area based on environmental constraint information and historical motion information, and determine the balance constraint conditions;

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

[0020] The path smoothness and turning radius of the preliminary path execution instructions are optimized according to the real-time motion information to generate the optimal path execution instructions.

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

[0022] According to the motion information, the center of mass position (x c ,y c ,z c ), pitch angle θ p and roll angle θ r .

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

[0024] Furthermore, the control module performs an abnormality determination operation of the environmental status information before generating the control information according to the environmental status information and the motion information, and triggers an emergency mechanism when it is determined that the environmental status information has an abnormality;

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

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

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

[0028] Control the inspection robot to perform motion according to the control information, and collect and record the motion information generated during the movement of the inspection robot;

[0029] Set up several environmental sensors to collect environmental data within the inspection area;

[0030] performing a feature extraction operation based on the motion information and the environmental data, and generating environmental state information according to the extracted features;

[0031] New control information is generated according to the environmental state information and the motion information, and the motion of the inspection robot is adjusted by the new control information.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] Through the collaborative work of the motion module, data acquisition module, data analysis module, and control module, the present invention effectively achieves precise path planning and dynamic motion control for the inspection robot. This enables the inspection robot to dynamically optimize its path planning based on the real-time environmental state and its own motion state, effectively improving the robot's autonomous decision-making ability and task execution efficiency in complex environments. In particular, through the deep linkage between the data analysis module and the control module, the inspection robot is equipped with the ability to identify abnormal environmental conditions and handle emergencies. When an abnormal situation occurs, it can quickly generate emergency control information and automatically switch to the appropriate motion mode, ensuring the safety and reliability of the inspection task. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute part of the economic application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

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

[0036] Figure 2 The present invention is a flowchart of a method for controlling an inspection robot based on inspection information disclosed in another embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0038] Example 1

[0039] The first aspect of the present invention discloses a patrol robot control system based on patrol information, see 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 send the motion information generated during the motion process to the data analysis module and the control module;

[0041] Several environmental sensors are set 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, perform feature extraction based on the received motion information and environmental data, and generate environmental state information based on the extracted features;

[0043] The control module is used to generate control information according to the environmental state information and the motion information, and adjust the motion action of the motion module through the control information.

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

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

[0046] In addition, the reason why the present invention considers motion information when generating environmental status information is that when the inspection robot performs an inspection task, its position, direction of travel, speed of travel and posture information will continue to change with the movement process, and these dynamic changes will directly affect the environmental data collected by the sensor. For example, the robot's different postures may cause the sensor's measurement angle to shift, thereby affecting the accuracy of the measurement results; and the changes in the robot's speed during movement will also affect the sensor's sampling frequency and data timeliness. Therefore, by combining motion information, the sensor data can be dynamically calibrated to correct measurement errors caused by changes in motion state. At the same time, motion information can also help identify the spatial distribution characteristics of environmental data, such as the relative position of obstacles, changes in the slope of the path, etc., so as to more comprehensively construct the environmental status information of the inspection area and improve the accuracy of control decisions.

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

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

[0049] Z={z1,z2,…,z i ,…,z n}

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

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

[0052] The observation value z for each sensor i Calculate the likelihood function P(z i |X), which means that when the environment state is X, the sensor observes the value z i For example, in the presence of a flame, the probability that the flame sensor observes the flame is high.

[0053] The posterior probability P(X|Z) is calculated according to the Bayesian formula, that is, the probability that the environment state is X when the sensor data Z is observed:

[0054]

[0055] Among them, 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 is the new sensor observation, P(X|Z t ) probability distribution of the environment state at the previous moment; P(X|Z t+1 ) is the probability distribution of the updated environment state; P(Z t+1 |X) is the joint likelihood function of all new sensor observations; P(Z t+1 ) is the marginal probability distribution of the new observation data.

[0059] In this embodiment of the present invention, by utilizing a Bayesian inference algorithm, the probability distribution of environmental states is continuously updated based on real-time changes in sensor data, thereby achieving dynamic perception of environmental changes. This probabilistically updated environmental state information provides the control decision module with more precise environmental constraints, enabling real-time adjustments to motion paths and postures based on environmental changes, thereby 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] Among them, the motion control unit is used to control the inspection robot to perform motion actions according to the control information of the control module. The motion actions include moving direction adjustment, moving speed adjustment and posture adjustment.

[0062] The motion feedback unit is used to collect the motion information of the inspection robot during the movement process 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 according to 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 an embodiment of the present invention, the control module first extracts environmental constraint information from the environmental status information, such as obstacle location, terrain type, ground slope, temperature changes, etc. For example, when an obstacle is detected, the environmental constraint information will record 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. The inspection route information includes not only the target location and path, but also path segments that are adjusted in real time based on environmental changes. 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, decelerating, etc.) 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 based on the control information and the motion information collected by the motion feedback unit through the kinematic model includes:

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

[0068] Identify the terrain type and posture change trend of the inspection area based on environmental constraint information and historical motion information, and determine the balance constraint conditions;

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

[0070] The path smoothness and turning radius of the preliminary path execution instructions are optimized according to the real-time motion information to generate the optimal path execution instructions.

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

[0072] In an embodiment of the present invention, the inspection robot needs to maintain balance under different terrain conditions to avoid tipping over, slipping or losing control. Therefore, when determining the balance constraint conditions, the terrain type of the current inspection area is first identified based on the environmental constraint information, such as flat ground, slopes, steps, slippery roads, etc. After identifying the terrain type of the current inspection area, the center of gravity position is calculated based on the center of gravity height and current posture information of the inspection robot, and it is determined whether it is located within a stable support surface. Among them, the stable support surface refers to the polygon formed by the wheels of the robot contacting 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 located within the stable support surface, different tipping angle thresholds are set according to the slope change trend of different terrains to ensure that the robot always maintains balance during movement.

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

[0074] Furthermore, in this embodiment of the present 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 posture change trends. Based on this historical data, the robot's posture change patterns on similar terrain can be predicted and posture control parameters adjusted in advance. Furthermore, by analyzing this historical data, areas prone to slipping, tipping, and other situations can be identified and corresponding constraints can be set.

[0075] Preferably, the calculation process of the balance path includes:

[0076] According to the motion information, the center of mass position (x 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 is the center of gravity height of the inspection robot; w g is the width of the inspection robot’s center of gravity; L is the front and rear wheelbase; W is the left and right wheelbase; α is the terrain complexity weight factor; F t is the influencing factor of terrain complexity.

[0080] Furthermore, the calculation formula of the steering path is:

[0081]

[0082] Among them, R is the turning path, which is also the actual turning radius of the inspection robot when turning; L w is the front and rear wheelbase; v is the driving speed of the inspection robot; w is the angular velocity of the inspection robot.

[0083] Path smoothness indicates the continuity and smoothness of a robot's path. Excessive sharp turns or sudden changes in the path can cause the robot to lose control or even tip over. Therefore, path smoothness needs to be optimized during path planning. In this embodiment of the present invention, a preliminary path is smoothed using a Bézier curve, and key points in the path are interpolated to generate a more continuous path curve, resulting in a more natural path transition.

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

[0085] Current terrain type: Reduce the turning radius in narrow sections or areas with many obstacles; increase the turning radius appropriately in wide areas.

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

[0087] It is understandable that the above factors are merely factors preferably listed in the embodiment of the present invention, and the embodiment of the present invention does not limit the scope of factors considered when optimizing the turning radius.

[0088] In an embodiment of the present invention, the optimal motion path of the inspection robot under different terrains and postures is calculated through a kinematic model, which can effectively improve the path planning accuracy and motion control stability of the inspection robot in complex environments. The terrain type and posture change trend of the inspection area are identified in combination with environmental constraint information and historical motion information, and the balance constraint conditions can be dynamically determined, so that the robot can maintain balance in complex scenes such as slopes, steps, and slippery surfaces, reducing the risks of tipping over and slipping. In addition, during the path planning process, it can also ensure that the inspection robot performs 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 the movement, and at the same time improving the environmental adaptability of the inspection robot, the accuracy of path planning, and the real-time performance of motion control, thereby achieving a more efficient, stable, and safe inspection task execution effect.

[0089] Furthermore, before generating the control information according to the environmental state information and the motion information, the control module performs an abnormality determination operation of the environmental state information, and triggers an emergency mechanism when it is determined that the environmental state information has an abnormality;

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

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

[0092] Specifically, inspection robots may encounter various abnormal situations while performing their inspection tasks, such as detecting environmental anomalies such as sudden fires, smoke, gas leaks, and obstacles, or experiencing abnormal conditions such as posture instability, path deviation, and motion failures. Therefore, in the control decision-making process, the present invention implements abnormality judgment operations based on environmental status information to ensure that the inspection robot responds promptly when an abnormal situation is detected, triggering emergency mechanisms and switching to an appropriate motion mode to ensure safe mission execution and fault avoidance.

[0093] Abnormal environmental status information detection is a crucial step before the control module generates control information. Its purpose is to identify abnormal environmental conditions within the inspection area. This includes determining whether environmental sensor data is abnormal, such as excessive hydrogen concentration, excessive smoke concentration, abnormal flame detection signals, or excessive temperature; and whether motion information is abnormal, such as the robot's tilt angle on a slope exceeding a safety threshold, its path deviating from the preset route, or a drastic change in posture.

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

[0095] When the motion module receives emergency control information, the motion control unit automatically switches to a motion mode appropriate for the situation based on a preset motion mode library and executes the corresponding motion action. The motion mode library is a collection 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 a matching motion mode, the system adjusts its route, speed, and posture, and executes motion actions based on the switched motion mode to complete emergency operations such as obstacle avoidance, evacuation, or stopping.

[0096] In an embodiment of the present invention, by setting up an abnormality judgment operation and an emergency mechanism triggering mechanism for environmental status information in the control module, the safety and emergency handling capabilities of the inspection robot can be greatly improved. When an abnormal situation occurs, the system can identify changes in environmental status in real time, generate emergency control information, and automatically switch to the appropriate motion mode, allowing the inspection robot to quickly avoid dangerous areas or evacuate to safe areas to avoid tipping, collisions, or damage. This real-time abnormality 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 the present invention discloses a control method for an inspection robot based on inspection information. Figure 2 , Figure 2 FIG. 1 is a flow chart of a method for controlling an inspection robot based on inspection information disclosed in another embodiment of the present invention, the method comprising:

[0099] Control the inspection robot to perform motion according to the control information, and collect and record the motion information generated during the movement of the inspection robot;

[0100] Set up several environmental sensors to collect environmental data within the inspection area;

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

[0102] New control information is generated according to the environmental state information and motion information, and the motion of the inspection robot is adjusted by the new control information.

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

[0104] Furthermore, the movement action includes moving direction adjustment, moving speed adjustment and posture adjustment.

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

[0106] The kinematic model calculates the optimal motion path of the inspection robot under different terrains and postures based on control information and motion information, and generates path execution instructions;

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

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

[0109] Constructing a kinematic model based on the structural parameters of the inspection robot; the structural parameters include wheelbase, turning radius, and center of gravity height;

[0110] Identify the terrain type and posture change trend of the inspection area based on environmental constraint information and historical motion information, and determine the balance constraint conditions;

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

[0112] The path smoothness and turning radius of the preliminary path execution instructions are optimized according to the real-time motion information to generate the optimal path execution instructions.

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

[0114] According to the motion information, the center of mass position (x 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 is the center of gravity height of the inspection robot; w g is the width of the inspection robot’s center of gravity; L is the front and rear wheelbase; W is the left and right wheelbase; α is the terrain complexity weight factor; F t is the influencing factor of terrain complexity.

[0118] Furthermore, the method also includes probabilistically updating the environmental data collected by different sensors using a multi-sensor data fusion algorithm based on Bayesian inference.

[0119] Furthermore, before generating the control information based on the environmental state information and the motion information, the method further includes executing an abnormality determination operation of the environmental state information, and triggering an emergency mechanism when it is determined that the environmental state information has an abnormality;

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

[0121] The motion mode is automatically switched based on the emergency control information and the preset motion mode library, and the motion action is performed according to the switched motion mode.

[0122] It should be noted that the specific implementation process of Example 2 is similar to that of Example 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 embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various 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 send the motion information generated during the motion process to the data analysis module and the control module; The data acquisition module is provided with a plurality of environmental sensors, which collect environmental data in the inspection area through the environmental sensors, 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 perform a feature extraction operation based on the received motion information and environmental data, and generate environmental state information according to the extracted features; The control module is used to generate control information according to the environmental state information and the motion information, and adjust the motion action of the motion module through the control information.

2. The inspection robot control system based on inspection information according to claim 1, characterized in that: The motion information includes position, moving direction, moving speed 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 of the control module; the motion actions include moving direction adjustment, moving speed adjustment and posture adjustment; The motion feedback unit is used to collect motion information of the inspection robot during its motion process, 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 is characterized in that: The motion control unit controls the inspection robot to perform motion actions according to the control information, including: 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; The control information includes environmental constraint information, inspection route information and posture information.

5. The inspection robot control system based on inspection information according to claim 4, characterized in that: The process of calculating the optimal motion path of the inspection robot under different terrains and postures based on the control information and the motion information collected by the motion feedback unit through the kinematic model includes: A kinematic model is constructed based on the structural parameters of the inspection robot; the structural parameters include wheelbase, wheelbase, turning radius, and center of gravity height; Identify the terrain type and posture change trend of the inspection area based on environmental constraint information and historical motion information, and determine the balance constraint conditions; The kinematic model is used to calculate the balance path and steering path of the inspection robot in different postures based on the balance constraints, route information, and posture adjustment information, and generate preliminary path execution instructions; The path smoothness and turning radius of the preliminary path execution instructions are optimized according to the real-time motion information to generate the best path execution instructions.

6. The inspection robot control system based on inspection information according to claim 5, characterized in that: The calculation process of the equilibrium path includes: According to the motion information, the center of mass position (x c ,y c ,z c ), pitch angle θ p and roll angle θ r .

7. The inspection robot control system based on inspection information according to claim 1, characterized in that: The data analysis module probabilistically updates the environmental data collected by different sensors through a multi-sensor data fusion algorithm based on Bayesian inference.

8. The inspection robot control system based on inspection information according to claim 1, characterized in that: The control module performs an abnormality judgment operation on the environmental state information before generating control information according to the environmental state information and the motion information, and triggers an emergency mechanism when it is judged that the environmental state information has an abnormality; After the emergency mechanism is triggered, emergency control information is generated based on the environmental status information and the real-time motion information, and the emergency control information is sent to the motion module.

9. The inspection robot control system based on inspection information according to claim 8, characterized in that: The motion control unit in the motion module automatically switches the motion mode based on the emergency control information and the preset motion mode library, and performs the motion action according to the switched motion mode.

10. A method for controlling an inspection robot based on inspection information, the method being applied to the system according to any one of claims 1 to 9, characterized in that: The method comprises: Control the inspection robot to perform motion according to the control information, and collect and record the motion information generated during the movement of the inspection robot; Set up a number of environmental sensors to collect environmental data within the inspection area; Performing a feature extraction operation based on the motion information and the environmental data, and generating environmental state information according to the extracted features; New control information is generated according to the environmental state information and the motion information, and the motion of the inspection robot is adjusted by the new control information.

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