An autonomous navigation and real-time monitoring integrated inspection robot system

Abstract

The application provides an autonomous navigation and real-time monitoring integrated inspection robot system. The autonomous navigation and real-time monitoring integrated inspection robot system comprises a mobile platform for providing the motion capability of the robot in a two-dimensional plane, a multi-sensor fusion module comprising a laser radar, a visual camera, an inertial measurement unit and an ultrasonic sensor for collecting environmental data, generating a high-precision two-dimensional map and realizing the perception of a dynamic environment. The autonomous navigation and real-time monitoring integrated inspection robot system significantly improves the adaptability and task execution efficiency of the inspection robot in a complex dynamic environment by integrating the autonomous navigation and real-time monitoring functions, adopts the multi-sensor fusion module, combines the laser radar, the visual camera, the inertial measurement unit and the ultrasonic sensor, realizes the accurate collection of environmental data and the generation of a high-precision two-dimensional map, and enhances the positioning and navigation capability of the robot under various environmental conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of inspection robot systems, in particular to an inspection robot system integrating autonomous navigation and real-time monitoring. BACKGROUND

[0002] An inspection robot system integrating autonomous navigation and real-time monitoring mainly consists of a mobile platform, a sensor module, an autonomous navigation system, a real-time monitoring system, and a control and communication module. The mobile platform provides basic walking ability, usually adopting omni-directional wheel design to achieve flexible movement performance. The sensor module includes laser radar, camera, and infrared sensor for environment perception and data acquisition. The autonomous navigation system based on SLAM (Simultaneous Localization and Mapping) algorithm constructs an environment map in real time through sensor data and plans an optimal path, enabling the robot to autonomously avoid obstacles and conduct inspection. The real-time monitoring system is responsible for collecting temperature, humidity, and gas concentration environmental data and uploading information to a central control unit or cloud platform through a wireless communication module. The modules work collaboratively through an embedded control system to achieve functional integration of precise inspection and data monitoring.

[0003] In two-dimensional position or channel control, when environmental factors such as wind, terrain complexity, or water flow disturbance change dramatically, the dynamic response speed of existing systems may be insufficient, leading to increased positioning and channel control errors. For complex scenarios, the data acquisition capability of sensors and the real-time performance of algorithms have certain deficiencies, making it difficult to ensure the stability and reliability of the system under extreme conditions. Algorithm optimization of current control systems is mostly based on specific application scenarios, lacking general adaptability to multiple types of transportation tools or multiple scenarios, limiting the expandability of the technology. SUMMARY

[0004] To address the deficiencies of the prior art, the present application provides an inspection robot system integrating autonomous navigation and real-time monitoring, solving the problems of insufficient accuracy and dynamic response, limited adaptability in complex environments, and generalization of control algorithms.

[0005] To achieve the above objectives, the present application is implemented through the following technical solution: an inspection robot system integrating autonomous navigation and real-time monitoring, comprising:

[0006] A mobile platform for providing the robot's movement capability in a two-dimensional plane; a multi-sensor fusion module including a laser radar, a visual camera, an inertial measurement unit, and an ultrasonic sensor for collecting environmental data, generating a high-precision two-dimensional map, and realizing the perception of a dynamic environment; a dynamically adaptive autonomous navigation system, the dynamically adaptive autonomous navigation system including a path planning and optimization module, the dynamically adaptive autonomous navigation system including a SLAM positioning module, based on multi-sensor data, constructing a two-dimensional environmental map in real time and synchronously and performing accurate positioning, the dynamically adaptive autonomous navigation system adaptively adjusting the sensor weight distribution in the SLAM positioning module through real-time calculation of environmental interference parameters, the calculation formula being:

[0007] ;

[0008] wherein, is the weight of the sensor, is the error variance of the sensor data, ensuring accurate fusion of sensor data in a complex environment;

[0009] The path planning and optimization module generates an optimal navigation path adaptive to environmental interference based on a dynamic Bayesian network and a reinforcement learning algorithm, and the path planning optimization uses the following formula:

[0010] ;

[0011] wherein, is the path deviation, is the obstacle distance cost, is the speed control cost, is a weight factor, ensuring optimal adaptability of the path planning in a dynamic environment; a control system including a real-time monitoring module for coordinating the operation of the multi-sensor fusion module, the navigation system, and the real-time monitoring module, and simultaneously transmitting data to the cloud or a local terminal, the real-time monitoring module based on a multi-modal data fusion algorithm uses a Kalman filter model to improve the stability of monitoring data in harsh environments, and its state update formula is:

[0012] ;

[0013] wherein, is the current estimated state, is the Kalman gain, is the observation value, is the measurement matrix; a communication module for realizing remote control and data transmission through wireless communication technology; a power management module for optimizing the energy consumption of the system and providing a backup power supply to ensure long-term autonomous operation of the robot.

[0014] ​Preferably, the path planning and optimization module adopts a multi-scenario adaptive learning mechanism, based on the following update formula:

[0015] ;

[0016] wherein, is the state value function of the selected action , is the learning rate, is the discount factor, and the path is dynamically optimized through a reinforcement learning algorithm.

[0017] Preferably, the power management module integrates a dynamic energy consumption prediction model, which evaluates the remaining power of the robot task in real time based on the following formula:

[0018] ;

[0019] wherein, is the remaining power, is the initial power, is the instantaneous power consumption.

[0020] Preferably, the path planning and optimization module supports multi-objective task scheduling and can dynamically adjust the weight according to the importance of the task to optimize the inspection efficiency. The inspection robot can generate a visual report based on cloud computing after completing the inspection, and realize multi-platform synchronization and sharing.

[0021] Preferably, the dynamic adaptive autonomous navigation system further comprises:

[0022] a real-time monitoring module for acquiring temperature and humidity, gas concentration and vibration signals in the inspection environment, and generating a comprehensive monitoring report through multi-dimensional data fusion technology; a dynamic response enhancement module including a feedback controller based on model predictive control for real-time adjustment of the robot's position and attitude, whose dynamic response algorithm adopts the following formula:

[0023] ;

[0024] wherein, is the state vector, is the control input, is the environmental disturbance, is the system matrix, to improve the robustness and real-time performance under complex environmental disturbances.

[0025] ​Preferably, the dynamic response enhancement module combines extended Kalman filtering and predictive control algorithm to dynamically adjust the priority of robot navigation and monitoring tasks, the communication module supports 5G network and edge computing collaboration to ensure real-time processing of high-capacity data and low-latency remote control, the robot has real-time feedback and self-learning ability to optimize navigation and monitoring algorithm through inspection data, the multi-sensor fusion module enhances the effectiveness of low signal-to-noise ratio sensor data in the environment through adaptive signal noise reduction algorithm, and the control system adopts distributed fault-tolerant design to ensure high reliability of key modules.

[0026] The application provides an autonomous navigation and real-time monitoring integrated inspection robot system.

[0027] The autonomous navigation and real-time monitoring integrated inspection robot system significantly improves the adaptability and task execution efficiency of the inspection robot in complex dynamic environments by integrating autonomous navigation and real-time monitoring functions, adopts a multi-sensor fusion module combining laser radar, visual camera, inertial measurement unit and ultrasonic sensor to realize accurate collection of environmental data and high-precision two-dimensional map generation, and enhances the positioning and navigation ability of the robot in various environmental conditions. Through the dynamic adaptive autonomous navigation system, the robot can adjust the sensor weight in real time and optimize the path planning through reinforcement learning to ensure accurate positioning and path optimization in complex environments. At the same time, the system can adaptively adjust according to the priority of the task and the change of the environment, optimize the inspection efficiency, and provide more robust navigation and task scheduling functions through the SLAM positioning module and multi-scenario adaptive learning mechanism to ensure effective operation in various complex task scenarios.

[0028] The technical solution also introduces advanced dynamic response enhancement module and power management module in robot control and energy management. The dynamic response enhancement module combines model predictive control and extended Kalman filtering algorithm to adjust the position and attitude of the robot in real time under complex environmental interference, improve its robustness and real-time reaction ability in dynamic changes. The power management module integrates a dynamic energy consumption prediction model to ensure the continuous and stable operation of the robot in long-term tasks, optimizes energy use by real-time assessment of remaining power, and reduces energy waste. The scheme also supports 5G network and edge computing collaboration to realize real-time transmission of high-capacity data and low-latency remote control, so that the robot has self-learning and real-time feedback ability and can continuously optimize navigation and monitoring algorithm in actual inspection tasks. In summary, the technical solution effectively improves the stability, reliability and efficiency of the inspection robot in complex and variable environments by comprehensively using advanced sensor fusion, path planning, dynamic response and energy management technologies. BRIEF DESCRIPTION OF DRAWINGS

[0029] Fig. 1 This is a schematic diagram comparing the effects of multi-sensor data fusion and optimization in this invention;

[0030] Fig. 2 This is a schematic diagram of the path planning and optimization process of the present invention;

[0031] Fig. 3 This is a schematic diagram illustrating the control effect of the dynamic response enhancement module of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1

[0034] like Figs. 1-3 As shown, this embodiment of the invention provides an inspection robot system integrating autonomous navigation and real-time monitoring, including a mobile platform for providing the robot with the ability to move in a two-dimensional plane.

[0035] The multi-sensor fusion module, including LiDAR, visual camera, inertial measurement unit and ultrasonic sensor, is used to collect environmental data, generate high-precision two-dimensional maps, and realize dynamic environment perception.

[0036] The dynamically adaptive autonomous navigation system includes a path planning and optimization module. Through real-time calculation of environmental interference parameters, the system adaptively adjusts the sensor weight allocation in the SLAM positioning module. The calculation formula is as follows:

[0037] ;

[0038] in, For sensors The weight, To mitigate the error variance of sensor data and ensure accurate fusion of sensor data in complex environments, the path planning and optimization module employs a multi-scenario adaptive learning mechanism based on the following update formula:

[0039] ;

[0040] in, For state Select action The value function, For learning rate, is the discount factor, the path is dynamically optimized by reinforcement learning algorithm, the path planning and optimization module supports multi-objective task scheduling, can dynamically adjust the weight according to the importance of the task, optimize the inspection efficiency, the inspection robot can generate a visual report based on cloud computing after the inspection is completed, and realize multi-platform synchronization sharing, the dynamic adaptive autonomous navigation system includes a SLAM positioning module, based on multi-sensor data, real-time synchronization constructs a two-dimensional environment map and performs accurate positioning.

[0041] The path planning and optimization module generates an optimal navigation path adaptive to environmental interference based on dynamic Bayesian network and reinforcement learning algorithm, wherein the path planning optimization adopts the following formula:

[0042] ;

[0043] wherein, is the path deviation, is the obstacle distance cost, is the speed control cost, is the weight factor, to ensure the optimal adaptability of path planning in dynamic environment, the dynamic adaptive autonomous navigation system further includes:

[0044] The real-time monitoring module is used to obtain temperature and humidity, gas concentration and vibration signals in the inspection environment, and generate a comprehensive monitoring report through multi-dimensional data fusion technology. The dynamic response enhancement module includes a model predictive control-based feed-back controller for real-time adjustment of the robot position and attitude, and its dynamic response algorithm adopts the following formula:

[0045] ;

[0046] wherein, is the state vector, is the control input, is the environmental interference, is the system matrix, to improve the robustness and real-time performance under complex environmental interference, the dynamic response enhancement module combines extended Kalman filter and predictive control algorithm to dynamically adjust the priority of robot navigation and monitoring tasks, the communication module supports 5G network and edge computing collaboration to ensure real-time processing of high-capacity data and low-latency remote control, the robot has real-time feedback and self-learning ability, and the navigation and monitoring algorithm is optimized through inspection data, the multi-sensor fusion module enhances the effectiveness of low signal-to-noise ratio sensor data in the environment through adaptive signal noise reduction algorithm, and the control system adopts distributed fault-tolerant design to ensure high reliability of key modules.

[0047] The control system includes a real-time monitoring module, which coordinates the operation of the multi-sensor fusion module, the navigation system, and the real-time monitoring module. It also transmits data to the cloud or a local terminal. The real-time monitoring module uses a multi-modal data fusion algorithm and a Kalman filter model to improve the stability of monitoring data in harsh environments. Its state update formula is:

[0048] ;

[0049] in, For the current estimated state, For Kalman gain, For the observed values, This is the measurement matrix.

[0050] The communication module enables remote control and data transmission through wireless communication technology.

[0051] The power management module optimizes system energy consumption and provides backup power to ensure the robot can operate autonomously for extended periods. It integrates a dynamic energy consumption prediction model, which uses the following formula to assess the robot's remaining battery power in real time:

[0052] ;

[0053] in, Remaining battery power This is the initial charge level. This represents instantaneous power consumption.

[0054] Example 2

[0055] like Figs. 1-3 As shown, this embodiment of the invention provides an inspection robot system integrating autonomous navigation and real-time monitoring, including a mobile platform for providing the robot with the ability to move in a two-dimensional plane.

[0056] The multi-sensor fusion module, including LiDAR, visual camera, inertial measurement unit and ultrasonic sensor, is used to collect environmental data, generate high-precision two-dimensional maps, and realize dynamic environment perception.

[0057] The dynamically adaptive autonomous navigation system includes a path planning and optimization module. Through real-time calculation of environmental interference parameters, the system adaptively adjusts the sensor weight allocation in the SLAM positioning module. The calculation formula is as follows:

[0058] ;

[0059] in, For sensors The weight, For the error variance of sensor data, to ensure the accurate fusion of sensor data in complex environments, the path planning and optimization module adopts a multi-scene adaptive learning mechanism based on the following update formula:

[0060] ;

[0061] wherein, is the value function of the state and the action selected below, is the learning rate, is the discount factor, the path is dynamically optimized by reinforcement learning algorithm, the path planning and optimization module supports multi-objective task scheduling, can dynamically adjust the weight according to the importance of the task, optimize the inspection efficiency, the inspection robot can generate a visual report based on cloud computing after completing the inspection, and realize multi-platform synchronization sharing, the dynamic adaptive autonomous navigation system includes a SLAM positioning module, which constructs a two-dimensional environment map in real time based on multi-sensor data and performs accurate positioning.

[0062] The path planning and optimization module generates an optimal navigation path that is adaptive to environmental disturbances based on dynamic Bayesian networks and reinforcement learning algorithms, wherein the path planning optimization uses the following formula:

[0063] ;

[0064] wherein, is the path deviation, is the obstacle distance cost, is the speed control cost, is the weight factor, to ensure the optimal adaptability of path planning in dynamic environment, the dynamic adaptive autonomous navigation system also includes:

[0065] The real-time monitoring module is used to obtain the temperature and humidity, gas concentration and vibration signal in the inspection environment, and generate a comprehensive monitoring report through multi-dimensional data fusion technology. The dynamic response enhancement module includes a model predictive control-based feedback controller for real-time adjustment of the robot's position and attitude, and its dynamic response algorithm uses the following formula:

[0066] ;

[0067] wherein, is the state vector, is the control input, is the environmental disturbance, For system matrix, to improve the robustness and real-time performance under complex environmental interference, the dynamic response enhancement module combines extended Kalman filter and predictive control algorithm, which is used to dynamically adjust the priority of robot navigation and monitoring tasks, the communication module supports 5G network and edge computing cooperation, ensures real-time processing of high-capacity data and low-delay remote control, the robot has real-time feedback and self-learning ability, optimizes navigation and monitoring algorithm through inspection data, the multi-sensor fusion module enhances the effectiveness of low signal-to-noise ratio sensor data in the environment through adaptive signal noise reduction algorithm, the control system adopts distributed fault-tolerant design to ensure high reliability of key modules.

[0068] The control system includes a real-time monitoring module for coordinating the operation of the multi-sensor fusion module, navigation system and real-time monitoring module, and transmitting data to the cloud or local terminal. The real-time monitoring module uses a Kalman filter model based on multi-modal data fusion algorithm to improve the stability of monitoring data in harsh environments. Its state update formula is:

[0069] ;

[0070] Where, is the current estimated state, is the Kalman gain, is the observation value, is the measurement matrix.

[0071] The communication module realizes remote control and data transmission through wireless communication technology.

[0072] The power management module is used to optimize the energy consumption of the system and provide backup power to ensure long-term autonomous operation of the robot. The power management module integrates a dynamic energy consumption prediction model to evaluate the remaining power of the robot task in real time based on the following formula:

[0073] ;

[0074] Where, is the remaining power, is the initial power, is the instantaneous power consumption.

[0075] The intelligent environment perception and adaptive decision-making module combines deep learning and reinforcement learning methods to enhance the robot's perception of environmental changes and make autonomous adaptive decisions based on real-time changes in the environment. Environment perception not only relies on the fusion of multi-sensor data, but also can adjust the inspection strategy in advance through prediction of dynamic changes in the environment to ensure that the robot can flexibly perform tasks in complex and uncertain environments.

[0076] Deep environment perception: using convolutional neural networks to recognize the environment around the robot through depth images, feature extraction from image data collected by visual cameras through convolutional neural network models to identify objects, obstacles, structural changes in environmental features, and depth information provided by laser radar and ultrasonic sensors to further enhance the robot's three-dimensional environmental perception capabilities. Environment prediction and adaptive adjustment: combining long short-term memory network models to predict time series changes in dynamic environments, anticipating possible changes in the environment, based on prediction information, the robot can adjust the inspection path, task priority or working mode in advance, when encountering obstacles, the robot predicts the moving trajectory of the obstacle through the long short-term memory network, and plans an avoidance path in advance to avoid collision. Adaptive decision making: combining reinforcement learning with environmental prediction, enabling the robot to make autonomous decisions based on real-time perception and prediction information, selecting the optimal task execution order and path. Through real-time environmental information updates and reward mechanisms, the robot can optimize its behavior, reduce resource waste, and improve inspection efficiency.

[0077] Value update in reinforcement learning:

[0078] ;

[0079] Environment prediction:

[0080] ;

[0081] where, is the predicted value of the environment state at the current time, is the depth feature of the environmental image data, is the time series prediction result based on historical environmental data.

[0082] Example three

[0083] As Figs. 1-3 shown, the present application provides an autonomous navigation and real-time monitoring integrated inspection robot system, including a mobile platform for providing the robot's motion capability in a two-dimensional plane.

[0084] A multi-sensor fusion module including a laser radar, a visual camera, an inertial measurement unit and an ultrasonic sensor for collecting environmental data, generating a high-precision two-dimensional map and realizing dynamic environment perception.

[0085] A dynamically adaptive autonomous navigation system, the dynamically adaptive autonomous navigation system includes a path planning and optimization module, the dynamically adaptive autonomous navigation system adjusts the sensor weight distribution in the SLAM positioning module through real-time calculation of environmental interference parameters, the calculation formula is:

[0086] ;

[0087] in, For sensors The weight, To mitigate the error variance of sensor data and ensure accurate fusion of sensor data in complex environments, the path planning and optimization module employs a multi-scenario adaptive learning mechanism based on the following update formula:

[0088] ;

[0089] in, For state Select action The value function, For learning rate, As a discount factor, the path is dynamically optimized through reinforcement learning algorithm. The path planning and optimization module supports multi-objective task scheduling and can dynamically adjust the weight according to the importance of the task to optimize inspection efficiency. After the inspection is completed, the inspection robot can generate a visual report based on cloud computing and achieve synchronous sharing on multiple platforms. The dynamic adaptive autonomous navigation system includes a SLAM positioning module, which constructs a two-dimensional environmental map in real time and performs accurate positioning based on multi-sensor data.

[0090] The path planning and optimization module, based on dynamic Bayesian networks and reinforcement learning algorithms, generates optimal navigation paths that adapt to environmental disturbances. The path planning optimization uses the following formula:

[0091] ;

[0092] in, This is due to path deviation. The cost of obstacle distance, For the sake of speed control, As weighting factors, to ensure optimal adaptability of path planning in dynamic environments, the dynamically adaptive autonomous navigation system also includes:

[0093] The real-time monitoring module acquires temperature, humidity, gas concentration, and vibration signals in the inspection environment and generates a comprehensive monitoring report through multi-dimensional data fusion technology. The dynamic response enhancement module includes a model predictive control-based feedback controller for real-time adjustment of the robot's position and attitude. Its dynamic response algorithm uses the following formula:

[0094] ;

[0095] in, For state vectors, To control the input, Due to environmental interference, For system matrix, to improve the robustness and real-time performance under complex environmental interference, dynamic response enhancement module combines extended Kalman filter and predictive control algorithm, which is used to dynamically adjust the priority of robot navigation and monitoring task, communication module supports 5G network and edge computing cooperation, ensures real-time processing of high-capacity data and low-delay remote control, robot has real-time feedback and self-learning ability, optimizes navigation and monitoring algorithm through inspection data, multi-sensor fusion module enhances the effectiveness of low signal-to-noise ratio sensor data in the environment through adaptive signal noise reduction algorithm, control system adopts distributed fault-tolerant design to ensure high reliability of key modules.

[0096] Control system, including real-time monitoring module, for coordinating multi-sensor fusion module, navigation system and real-time monitoring module, while transmitting data to cloud or local terminal, real-time monitoring module based on multi-modal data fusion algorithm uses Kalman filter model to improve the stability of monitoring data in harsh environment, its state update formula is:

[0097] ;

[0098] Wherein, is the current estimated state, is the Kalman gain, is the observation value, is the measurement matrix.

[0099] Communication module, through wireless communication technology to realize remote control and data transmission.

[0100] Power management module, for optimizing the energy consumption of the system, and providing backup power to ensure long-term autonomous operation of the robot, power management module integrates dynamic energy consumption prediction model, based on the following formula to evaluate the remaining power of the robot task in real time:

[0101] ;

[0102] Wherein, is the remaining power, is the initial power, is the instantaneous power consumption.

[0103] Collaborative task execution and resource sharing module, collaborative task execution and resource sharing module allows multiple inspection robots to allocate tasks and work together, improving the inspection efficiency and flexibility of the entire system, through resource sharing and task cooperation, multiple robots can complete one or more complex tasks together, avoiding the heavy workload of a single robot, and can adjust the work distribution between robots in real time according to task requirements and environmental conditions.

[0104] Based on the task priority, workload, and environmental changes of the robotic system, a distributed algorithm is used for task allocation. Through cooperative game theory, the system can automatically calculate the dependencies and priorities between tasks and allocate tasks to different robots for execution. Through wireless communication between robots, the task progress and resource requirements are synchronized in real time to ensure that tasks are not repeated and redundant work is avoided.

[0105] When multiple robots perform tasks together, path planning needs to consider not only the individual paths of each robot, but also the relative positions and cooperative work between robots. For example, when multiple robots perform inspection tasks in the same area, they need to avoid path intersection or collision while ensuring coverage of the entire area. In this case, robots use distributed cooperative path planning algorithms, such as multi-agent reinforcement learning, to enable multiple robots to collaborate in shared areas and adjust paths in real time.

[0106] When a robot has too heavy a task burden or insufficient power, the system can intelligently schedule other robots to share tasks or replace execution. The system dynamically allocates resources based on the remaining power of each robot, task progress, and current state information to ensure load balancing of the entire team. The battery management system is also combined with the task scheduling system to optimize energy distribution and avoid robots stopping work due to insufficient power.

[0107] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, and it is intended that the scope of the application be limited only by the appended claims and their equivalents.

Claims

1. An autonomous navigation and real-time monitoring integrated inspection robot system, characterized in that, Comprise: A mobile platform for providing the robot's movement ability in a two-dimensional plane; A multi-sensor fusion module including a laser radar, a visual camera, an inertial measurement unit and an ultrasonic sensor for collecting environmental data, generating a high-precision two-dimensional map and realizing the perception of dynamic environment; A dynamic adaptive autonomous navigation system, the dynamic adaptive autonomous navigation system comprises a path planning and optimization module, the dynamic adaptive autonomous navigation system comprises a SLAM positioning module, based on multi-sensor data, a two-dimensional environment map is constructed in real time and synchronization and accurate positioning is carried out, the dynamic adaptive autonomous navigation system adjusts the sensor weight distribution in the SLAM positioning module through real-time calculation of environmental interference parameters, and the calculation formula is: ； wherein, is a weight of the sensor , is an error variance of the sensor data, ensuring accurate fusion of sensor data in complex environments; The path planning and optimization module generates an optimal navigation path adaptive to environmental interference based on dynamic Bayesian network and reinforcement learning algorithm, wherein the path planning optimization adopts the following formula: ； wherein, is a path deviation, is an obstacle distance cost, is a speed control cost, is a weight factor, ensuring the optimal adaptability of path planning in dynamic environment; a control system, including a real-time monitoring module, for coordinating a multi-sensor fusion module, a navigation system and the operation of the real-time monitoring module, while transmitting data to the cloud or a local terminal, the real-time monitoring module based on a multi-modal data fusion algorithm uses a Kalman filter model to improve the stability of monitoring data in harsh environments, and its state update formula is: ； wherein, is the current estimated state, is the Kalman gain, is the observation, is the measurement matrix; a communication module that enables remote control and data transmission through wireless communication technology; a power management module for optimizing the energy consumption of the system and providing a backup power supply to ensure long-term autonomous operation of the robot.

2. The autonomous navigation and real-time monitoring integrated inspection robot system according to claim 1, characterized in that: The path planning and optimization module adopts a multi-scene adaptive learning mechanism based on the following update formula: ； wherein, is the state the next action the value function, is the learning rate, is the discount factor, the path is dynamically optimized by a reinforcement learning algorithm.

3. The autonomous navigation and real-time monitoring integrated inspection robot system according to claim 1, wherein: The power management module integrates a dynamic energy consumption prediction model, which evaluates the remaining power of the robot in real time based on the following formula: ； wherein, is the remaining power, is the initial power, is the instantaneous power consumption.

4. The autonomous navigation and real-time monitoring integrated inspection robot system of claim 1, wherein: The path planning and optimization module supports multi-target task scheduling, can dynamically adjust the weight according to the importance of the task, optimizes the inspection efficiency, and the inspection robot can generate a visual report based on cloud computing after completing the inspection, and realize multi-platform synchronization sharing.

5. The autonomous navigation and real-time monitoring integrated inspection robot system according to claim 1, wherein: The dynamic adaptive autonomous navigation system further comprises: The real-time monitoring module is used for acquiring temperature and humidity, gas concentration and vibration signal in the inspection environment, and generating a comprehensive monitoring report through multi-dimensional data fusion technology; A dynamic response enhancement module including a feedback controller based on model predictive control for adjusting the position and attitude of the robot in real time, and its dynamic response algorithm adopts the following formula: ； wherein, is a state vector, is a control input, is an environmental disturbance, is a system matrix to improve robustness and real-time performance under complex environmental disturbances.

6. The autonomous navigation and real-time monitoring integrated inspection robot system according to claim 5, wherein: The dynamic response enhancement module combines extended Kalman filtering and predictive control algorithm to dynamically adjust the priority of the robot navigation and monitoring task, the communication module supports 5G network and edge computing cooperation to ensure real-time processing of high-capacity data and low-delay remote control, the robot has real-time feedback and self-learning ability, and the navigation and monitoring algorithm is optimized through inspection data, the multi-sensor fusion module enhances the effectiveness of low signal-to-noise ratio sensor data in the environment through adaptive signal noise reduction algorithm, and the control system adopts distributed fault-tolerant design to ensure high reliability of the key modules.

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