Intelligent four-wheel-drive reservoir area inspection robot system and method based on multi-sensor fusion

Abstract

The invention relates to the technical field of storage Internet of Things, and discloses an intelligent four-wheel-drive warehouse area inspection robot system and method based on multi-sensor fusion, the system is internally provided with eight modules, and a robot inspection starting module is responsible for starting the system and performing inspection; the multi-sensor monitoring module collects reservoir area data in real time; the multi-sensor fusion processing module carries out fusion processing and state evaluation on the data; the intelligent obstacle avoidance decision-making module realizes automatic obstacle avoidance through a steering angle alpha needing to be adjusted by the robot; the environment risk early warning module triggers an early warning mechanism through the environment risk index; the solar endurance management module dynamically adjusts the power consumption distribution strategy of each module through the remaining disposable power of the robot; the remote data transmission module calculates the data compression efficiency and sends the compressed data to a remote control center; the robot comprehensive control module integrates input of all the modules, arbitration and decision making are carried out according to preset logic and remote instructions, robot behaviors are controlled, and therefore intelligent inspection is achieved.

Description

Technical Field

[0001] This invention relates to the field of warehouse Internet of Things (IoT) technology, specifically to an intelligent four-wheel drive warehouse inspection robot system and method based on multi-sensor fusion. Background Technology

[0002] With the rapid development of the modern warehousing and logistics industry, the demand for intelligent and automated warehouse management is becoming increasingly prominent. However, the traditional warehouse inspection management model still faces many challenges: First, efficiency and cost issues, mainly relying on manual inspection, which has drawbacks such as low efficiency, high labor costs, high workload, and frequent human error; Second, prominent safety risks, warehouses often have hidden dangers such as dangerous gas leaks and abnormal temperature and humidity, and manual inspection is directly exposed to dangerous environments, making it difficult to guarantee personal health and safety; Third, insufficient monitoring capabilities, fixed monitoring cameras have blind spots, and cannot achieve comprehensive and real-time perception and data linkage of environmental parameters (such as the concentration of various harmful gases, local temperature and humidity), resulting in delayed safety assessment; Fourth, limitations of existing automation solutions, some inspection robots on the market have problems such as high procurement and maintenance costs, single function (such as only image monitoring), poor battery life, closed algorithms that are difficult to customize and optimize, and generally lack multi-sensor fusion and intelligent decision-making capabilities for the complex environment of warehouses, making it difficult to achieve sustainable deployment and application with limited investment. Therefore, there is an urgent need for a reservoir inspection technology solution that is cost-effective, feature-rich, highly intelligent, and has good growth potential. Summary of the Invention

[0003] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an intelligent four-wheel drive warehouse inspection robot system and method based on multi-sensor fusion. It has the advantages of improving efficiency and reducing costs, ensuring safety, making scientific judgments, optimizing algorithms, having good growth potential, and anchoring key technology reserves. It solves the problems of low efficiency, high cost, and high safety risks in traditional manual inspection mode, as well as the problems of incomplete monitoring, single function, high cost, and poor continuity and adaptability of existing automation solutions.

[0004] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: an intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion, comprising a robot inspection start-up module, a multi-sensor monitoring module, a multi-sensor fusion processing module, an intelligent obstacle avoidance decision-making module, an environmental risk early warning module, a solar power management module, a remote data transmission module, and a robot integrated control module; The robot system is based on a four-wheel drive chassis, an open-source main control board, multi-source sensors and structural components to construct the physical entity of the robot system. It adopts a modular design to complete the mechanical structure assembly and signal and power line layout, providing a stable hardware foundation for the system. The robot inspection start module is responsible for the system power-on self-test and initialization parameter loading, and issues a start command to drive the robot system into the preset inspection standby state. The multi-sensor monitoring module is responsible for collecting multi-dimensional environmental data of the reservoir area in real time, providing a data source for fusion processing; The multi-sensor fusion processing module is responsible for fusing the raw data collected by the multi-sensor monitoring module to obtain fused data. The intelligent obstacle avoidance decision-making module calculates the required steering angle for the robot based on the fused data. Dynamic programming for collision-free paths; The environmental risk early warning module calculates the environmental risk index based on the fused data. Quantify the safety risks in the storage area and automatically trigger early warning signals of different levels; The solar power management module calculates the robot's remaining available power based on the fused data. It dynamically optimizes the energy distribution between solar power and components such as motors to maximize range; The remote data transmission module calculates the data compression efficiency based on the collected raw data. It performs efficient compression and encryption of the fused data; The robot integrated control module summarizes, arbitrates, and makes decisions on the output information of all upstream modules, and finally generates coordinated control commands to realize the robot's intelligent inspection.

[0005] Preferably, the multi-sensor monitoring module includes a temperature and humidity dynamic acquisition unit, a harmful gas concentration detection unit, an obstacle distance recognition unit, and an inspection location positioning unit.

[0006] Preferably, the temperature and humidity dynamic acquisition unit collects temperature and humidity data of different areas of the storage area in real time through an integrated digital temperature and humidity sensor and performs anomaly monitoring; the hazardous gas concentration detection unit detects the concentration of hazardous gases such as methane, carbon monoxide, hydrogen sulfide, volatile organic compounds and oxygen through a semiconductor or electrochemical gas sensor.

[0007] Preferably, the obstacle distance recognition unit measures the distance between the obstacle and the robot using a laser / infrared sensor; the inspection position positioning unit, combined with a tracking sensor and an encoder, determines the robot's real-time position, fills in blind spots in warehouse area monitoring, and records the inspection trajectory.

[0008] Preferably, the multi-sensor fusion processing module includes a fused data preprocessing unit and a feature extraction unit; The fused data preprocessing unit filters out abnormal data and unifies the data format; The feature extraction unit extracts feature parameters from the fused data.

[0009] Preferably, the intelligent obstacle avoidance decision-making module avoids collisions by adjusting the robot's steering angle α, and its calculation formula is as follows: In the formula, This indicates the steering angle that the robot needs to adjust. Indicates the safe obstacle avoidance distance. Indicates the actual distance between the obstacle and the robot. This indicates the positional deviation of the robot from the centerline of the path. This represents the obstacle distance correction factor. This represents the path deviation correction factor.

[0010] Preferably, the environmental risk early warning module uses an environmental risk index. Different levels of alerts are triggered, and the calculation formula is as follows: In the formula, This indicates the environmental risk index. Indicates the actual concentration of harmful gases. Indicates the safety threshold for harmful gases. , These represent the actual temperature and humidity, respectively. , λ, β, and γ represent the standard temperature and humidity of the reservoir area, respectively, and represent the risk weights of gas, temperature, and humidity, respectively.

[0011] Preferably, the solar power management module utilizes the robot's remaining available power. The formula for optimizing the allocation of solar energy supplementation and motor energy consumption is as follows: In the formula, This indicates the robot's remaining available power. This indicates the real-time output power of the solar panel. Indicates solar energy conversion efficiency. This indicates the real-time energy consumption of the motor. This indicates energy transmission loss.

[0012] Preferably, the remote data transmission module improves data compression efficiency. To ensure that the compressed data can be efficiently transmitted to the control center, the calculation formula is as follows: And satisfy ≤ In the formula, Indicates data compression efficiency. Indicates the original fused data volume. Indicates the amount of data after compression. Indicates data transmission delay. This indicates network bandwidth.

[0013] The operation method of an intelligent four-wheel drive warehouse inspection robot based on multi-sensor fusion includes the following operation steps: Step 1: Build a robot inspection system. The system includes a robot inspection start-up module, a multi-sensor monitoring module, a multi-sensor fusion processing module, an intelligent obstacle avoidance decision-making module, an environmental risk early warning module, a solar power management module, a remote data transmission module, and a robot integrated control module. Step 2: The robot inspection start-up module is responsible for starting the system and inspecting the warehouse area path by moving in four-wheel drive tracking mode; Step 3: The multi-sensor monitoring module collects real-time data on temperature, humidity, gas, obstacles, and location. Step 4: The multi-sensor fusion processing module performs data preprocessing, feature extraction, fusion, and state assessment. Step 5: The intelligent obstacle avoidance decision module calculates the required steering angle α for the robot based on the fused data, thereby achieving automatic obstacle avoidance; Step Six: The environmental risk early warning module calculates the environmental risk index. This triggers the early warning mechanism; Step 7: The solar power management module calculates the robot's remaining available power. Dynamically adjust the power consumption allocation strategy of each module to optimize energy use and extend the robot's battery life; Step 8: The remote data transmission module calculates the data compression efficiency. Under the condition of meeting the transmission delay requirements, the compressed data is sent to the remote control center to receive control commands from the robot integrated control module; Step 9: The robot integrated control module integrates all module inputs, arbitrates and makes decisions based on preset logic and remote commands, controls the robot's behavior, and realizes intelligent inspection.

[0014] Compared with existing technologies, this invention provides an intelligent four-wheel drive warehouse inspection robot system and method based on multi-sensor fusion, which has the following beneficial effects: 1. This invention avoids collisions during robot movement by calculating the required steering angle α for the robot. When path deviation occurs, the real-time calculated α value can correct the robot's direction of travel to return it to the preset trajectory. When an obstacle appears ahead, the real-time calculated α value can guide the robot to actively plan an alternative path, thereby avoiding collision risks and achieving a balance between safety and accuracy. Ultimately, it achieves the beneficial effect of greatly improving the safety of the equipment itself and the warehouse facilities while ensuring the continuity of inspection.

[0015] 2. This invention calculates an environmental risk index. Assess the current environmental safety status of the reservoir area; when the environmental risk index... When the environmental risk index is in the range of 0-1, the current risk is judged to be at a safe level; only data is recorded without triggering an early warning to avoid excessive intervention. When the risk level is within the range of 1-2, the current risk is determined to be mild, triggering an audible and visual alarm and sending early warning data to the remote center to alert personnel; when the environmental risk index... When the risk level is between 2 and 3, the current risk is determined to be severe, triggering an emergency shutdown (the robot returns to the safe zone) and sending a personnel-restricted alarm to quickly prevent dangerous contact. By quantifying the risk, subjective judgment errors are avoided, achieving the beneficial effects of accurate safety risk classification, timely and effective early warning response, protection of personnel life safety, and reduction of accident risk in the storage area.

[0016] 3. This invention calculates the robot's remaining available power. Assessing the current energy supply and demand balance serves as the core standard for energy consumption adjustment and replenishment strategies; when the robot has remaining available power When the power consumption is >5W, the surplus energy is stored in the backup battery to reserve energy for low-light environments; when the robot has remaining available power... Within the 0-5W range, maintain the current motor speed and sensor acquisition frequency to ensure inspection efficiency; when the robot has remaining available power... When the power consumption is less than 0W, the motor speed is reduced (e.g., from 1m / s to 0.5m / s), the frequency of non-core sensor acquisition is reduced, and energy consumption is reduced. By dynamically optimizing energy allocation, the problems of high energy consumption, short battery life and reliance on external power supply of traditional inspection equipment are solved, achieving the beneficial effects of efficient use of solar energy, extended inspection battery life, reduced operation and maintenance energy costs, and adaptability to scenarios without external power supply.

[0017] 4. This invention calculates data compression efficiency. This is used as a key indicator for evaluating data transmission load and real-time performance, to assess the data throughput pressure of the current network channel, and when data compression efficiency... When the data compression efficiency is ≥80%, the complete data packet is transmitted; when the data compression efficiency is ≥80%, the complete data packet is transmitted. When the data compression efficiency is between 60% and 80%, the selected key data frames are transmitted; when the data compression efficiency is... When the sampling rate is less than 60%, the system can ensure the accessibility of core information by reducing the data sampling rate or deleting invalid data in the storage location. This allows the system to prioritize the low-latency and high-reliability transmission of critical data for early warning and control commands within limited bandwidth resources, ultimately achieving the beneficial effects of remote, accurate judgment and rapid response. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the operation of the method of the present invention. Detailed Implementation

[0019] 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.

[0020] Please see Figure 1 The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion includes a robot inspection start-up module (start-up execution), a multi-sensor monitoring module (data acquisition), a multi-sensor fusion processing module (data processing), an intelligent obstacle avoidance decision-making module (path decision-making), an environmental risk early warning module (safety assurance), a solar power management module (energy support), a remote data transmission module (interactive collaboration), and a robot integrated control module (central control execution). The robot system is based on a four-wheel drive chassis, an open-source main control board (such as the STM32 series), multi-source sensors and structural components to build the physical entity of the robot system. It adopts a modular design to complete the mechanical structure assembly and signal and power line layout, providing a stable hardware foundation for the system. The robot inspection start module is responsible for the system's power-on self-test and initialization parameter loading (such as the tracking path and safety threshold), and issues a start command to drive the robot system into the preset inspection standby state. The multi-sensor monitoring module is responsible for real-time and synchronous collection of multi-dimensional environmental data in the reservoir area, providing a data source for fusion processing; The multi-sensor fusion processing module is responsible for cleaning, standardizing, and feature mining the raw data collected by the multi-sensor monitoring module, fusing multi-source heterogeneous data into unified and reliable environmental status information, and obtaining fused data. The intelligent obstacle avoidance decision-making module calculates the steering angle that the robot needs to adjust based on the fused data. Dynamic programming for collision-free paths; The environmental risk early warning module calculates the environmental risk index based on the fused data. It quantifies the safety risks in the storage area and automatically triggers early warning signals of different levels (such as audible and visual alarms and remote notifications). The solar-powered endurance management module calculates the robot's remaining available power based on the fused data. It dynamically optimizes the energy distribution between solar power and components such as motors to maximize range; The remote data transmission module calculates data compression efficiency based on the collected raw data. The system performs efficient compression and encryption of the fused data to ensure that critical information can be transmitted to the remote control center with low latency and confidentiality. The robot's integrated control module aggregates, arbitrates, and makes decisions on the output information of all upstream modules, and finally generates coordinated control commands (instructions include adjusting movement speed, triggering alarms, and entering energy-saving mode) to realize the robot's intelligent and adaptive inspection.

[0021] The advantages are: by reducing maintenance costs through the modular hardware architecture, improving data reliability through multi-sensor fusion, optimizing inspection efficiency through dynamic decision-making modules, and ensuring data security through remote encrypted transmission, it solves the problems of low efficiency, high labor costs, high security risks and insufficient monitoring in traditional database queries. Ultimately, it achieves the beneficial effects of improving the quality and efficiency of database management, reducing personnel's dangerous exposure, supplementing monitoring blind spots, and reducing operation and maintenance investment.

[0022] The multi-sensor monitoring module includes a temperature and humidity dynamic acquisition unit, a harmful gas concentration detection unit, an obstacle distance recognition unit, and an inspection location positioning unit.

[0023] The temperature and humidity dynamic acquisition unit collects temperature and humidity data in different areas of the storage area in real time through an integrated digital temperature and humidity sensor (such as DHT22), records the acquisition timestamp, and monitors abnormal temperature and humidity in the storage area. The hazardous gas concentration detection unit detects the concentration of hazardous gases such as methane, carbon monoxide, hydrogen sulfide, volatile organic compounds (VOCs), and oxygen through semiconductor or electrochemical gas sensors (such as MQ-4 and MQ-7), and marks the sensor accuracy to avoid personnel contact with hazardous gases.

[0024] The advantages are: through the synchronous monitoring of temperature and humidity, various harmful gases, obstacle distance and robot position, the system can achieve comprehensive and blind-spot-free real-time perception of the warehouse environment and the robot's own status, providing a solid data foundation for the entire system.

[0025] The obstacle distance recognition unit measures the distance between the robot and the obstacle using laser / infrared sensors to prevent the robot from colliding with warehouse facilities; the inspection position positioning unit combines tracking sensors and encoders to determine the robot's real-time position, fill blind spots in warehouse monitoring, and record the inspection trajectory.

[0026] The advantages are: by combining the above-mentioned laser / infrared ranging and tracking coding positioning, centimeter-level accuracy autonomous navigation and obstacle avoidance can be achieved in complex warehouse environments, thereby reducing collision risks and ensuring the integrity and traceability of inspection paths.

[0027] The multi-sensor fusion processing module includes a fused data preprocessing unit and a feature extraction unit; The integrated data preprocessing unit filters out abnormal data (such as sensor false alarms) and standardizes data formats to improve the accuracy of subsequent decision-making data. The feature extraction unit extracts key features (such as gas concentration peaks and humidity abrupt changes) from the fused data, providing core feature parameters for obstacle avoidance and early warning.

[0028] The advantages are: through the two-stage processing flow of data preprocessing and key feature extraction, the quality and usability of the original data are effectively improved, providing reliable input for subsequent decision-making algorithms, thereby enhancing the overall intelligence level and decision accuracy of the system.

[0029] The intelligent obstacle avoidance decision-making module (path decision-making) calculates the required steering angle α for the robot based on the distance and positional deviation of obstacles to avoid collisions. The calculation formula is as follows: In the formula, This indicates the steering angle that the robot needs to adjust, i.e., the robot steering angle (unit: °, positive value for right turn, negative value for left turn). This indicates the safe obstacle avoidance distance (a fixed value, such as 1.5m, set according to the width of the storage area passageway). This represents the actual distance (m) between the obstacle and the robot, collected by the obstacle distance recognition unit. This indicates the positional deviation of the robot from the path centerline (m, collected by the inspection position positioning unit; positive values ​​indicate deviation to the right, and negative values ​​indicate deviation to the left). This represents the obstacle distance correction factor, used to quantify the actual distance between the robot and the obstacle. Deviating from safe distance The steering correction strength at that time, this coefficient is determined through actual measurement and calibration, so that the robot can dynamically adjust the steering amplitude according to the proximity of the obstacle to avoid the risk of collision; This represents the path deviation correction factor, used to quantify the positional deviation between the robot and the path centerline. The steering correction strength at that time, this coefficient is also determined by actual measurement and calibration, to ensure that the robot can quickly respond to path deviation and return to the preset trajectory (actual measurement and calibration to ensure that the steering angle is within ±45°).

[0030] The advantages are: by calculating the required steering angle α for the robot, collisions during the robot's movement can be avoided. When path deviation occurs, the real-time calculated α value can correct the robot's direction of travel and bring it back to the preset trajectory. When obstacles appear ahead, the real-time calculated α value can guide the robot to actively plan a detour path, thereby avoiding collision risks and achieving a balance between safety and accuracy. Ultimately, this achieves the beneficial effect of greatly improving the safety of the equipment itself and the warehouse facilities while ensuring the continuity of inspections.

[0031] The environmental risk early warning module (safety assurance) calculates the environmental risk index. To quantify the safety risks in the reservoir area and trigger different levels of early warnings, the calculation formula is as follows: In the formula, This represents the environmental risk index (0-3, with higher values ​​indicating higher risk). This indicates the actual concentration of harmful gases (%VOL, collected by the harmful gas concentration detection unit). This indicates the safety threshold for harmful gases (e.g., 0.5% VOL for methane). , These represent the actual temperature and humidity (°C / % RH, collected by the temperature and humidity dynamic acquisition unit). , λ and β represent the standard temperature and humidity of the reservoir area, respectively, and γ represent the risk weights of gas, temperature and humidity (gas is the core risk and has the highest weight).

[0032] The advantage is that it calculates the environmental risk index. Assess the current environmental safety status of the reservoir area; when the environmental risk index... When the environmental risk index is in the range of 0-1, the current risk is judged to be at a safe level; only data is recorded without triggering an early warning to avoid excessive intervention. When the risk level is within the range of 1-2, the current risk is determined to be mild, triggering an audible and visual alarm and sending early warning data to the remote center to alert personnel; when the environmental risk index... When the risk level is between 2 and 3, the current risk is determined to be severe, triggering an emergency shutdown (the robot returns to the safe zone) and sending a personnel-restricted alarm to quickly prevent dangerous contact. By quantifying the risk, subjective judgment errors are avoided, achieving the beneficial effects of accurate safety risk classification, timely and effective early warning response, protection of personnel life safety, and reduction of accident risk in the storage area.

[0033] The solar-powered power management module (energy support) calculates the robot's remaining available power. To optimize the distribution of solar power replenishment and motor energy consumption, and extend the driving range, the calculation formula is as follows: In the formula, This represents the robot's remaining available power (W; a positive value indicates an energy surplus, while a negative value indicates a need to reduce energy consumption). This indicates the real-time output power of the solar panel (W, collected by the solar power management module). This indicates the solar energy conversion efficiency (a fixed value, such as 0.85, based on the parameters of domestically produced solar panels). This indicates the real-time energy consumption of the motor (W, collected by the robot inspection and start-up module). This indicates energy transmission loss (a fixed value, such as 3W, measured and calibrated).

[0034] The advantage is that it calculates the robot's remaining available power. Assessing the current energy supply and demand balance serves as the core standard for energy consumption adjustment and replenishment strategies; when the robot has remaining available power When the power consumption is >5W, the surplus energy is stored in the backup battery to reserve energy for low-light environments; when the robot has remaining available power... Within the 0-5W range, maintain the current motor speed and sensor acquisition frequency to ensure inspection efficiency; when the robot has remaining available power... When the power consumption is less than 0W, the motor speed is reduced (e.g., from 1m / s to 0.5m / s), the frequency of non-core sensor acquisition is reduced, and energy consumption is reduced. By dynamically optimizing energy allocation, the problems of high energy consumption, short battery life and reliance on external power supply of traditional inspection equipment are solved, achieving the beneficial effects of efficient use of solar energy, extended inspection battery life, reduced operation and maintenance energy costs, and adaptability to scenarios without external power supply.

[0035] The remote data transmission module (interactive collaboration) calculates data compression efficiency. To ensure that the compressed data can be efficiently transmitted to the control center and avoid delays, the calculation formula is as follows: And satisfy ≤ In the formula, Indicates data compression efficiency (%, must be ≥60% to ensure transmission speed). This indicates the original fused data volume (KB, output by the multi-sensor fusion processing module). This indicates the compressed data size (KB, processed by the remote data transmission module). This indicates the data transmission delay (s, which must be ≤1s to avoid delays in real-time monitoring). This indicates network bandwidth (KB / s, based on the bandwidth setting of the unit data sharing platform).

[0036] The advantage is that it improves data compression efficiency through calculation. This is used as a key indicator for evaluating data transmission load and real-time performance, to assess the data throughput pressure of the current network channel, and when data compression efficiency... When the data compression efficiency is ≥80%, the complete data packet is transmitted; when the data compression efficiency is ≥80%, the complete data packet is transmitted. When the data compression efficiency is between 60% and 80%, the selected key data frames are transmitted; when the data compression efficiency is... When the sampling rate is less than 60%, the system can ensure the accessibility of core information by reducing the data sampling rate or deleting invalid data in the storage location. This allows the system to prioritize the low-latency and high-reliability transmission of critical data for early warning and control commands within limited bandwidth resources, ultimately achieving the beneficial effects of remote, accurate judgment and rapid response.

[0037] The operation method of an intelligent four-wheel drive warehouse inspection robot based on multi-sensor fusion includes the following operation steps: Step 1: Build a robot inspection system. The system includes a robot inspection start-up module, a multi-sensor monitoring module, a multi-sensor fusion processing module, an intelligent obstacle avoidance decision-making module, an environmental risk early warning module, a solar power management module, a remote data transmission module, and a robot integrated control module. Step 2: The robot inspection start-up module is responsible for starting the system and inspecting the warehouse area path by moving in four-wheel drive tracking mode; Step 3: The multi-sensor monitoring module collects real-time data on temperature, humidity, gas, obstacles, and location. Step 4: The multi-sensor fusion processing module performs data preprocessing, feature extraction, fusion, and state assessment. Step 5: The intelligent obstacle avoidance decision module calculates the required steering angle α for the robot based on the fused data, thereby achieving automatic obstacle avoidance; Step Six: The environmental risk early warning module calculates the environmental risk index. This triggers the early warning mechanism; Step 7: The solar power management module calculates the robot's remaining available power. Dynamically adjust the power consumption allocation strategy of each module to optimize energy use and extend the robot's battery life; Step 8: The remote data transmission module calculates the data compression efficiency. Under the condition of meeting the transmission delay requirements, the compressed data is sent to the remote control center to receive control commands from the robot integrated control module; Step 9: The robot integrated control module integrates all module inputs, arbitrates and makes decisions based on preset logic and remote commands, controls the robot's behavior, and realizes intelligent inspection. This method forms an automated closed loop integrating perception, decision-making, execution, and feedback from system startup to intelligent inspection, significantly improving inspection efficiency.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion, characterized in that, It includes a robot inspection start-up module, a multi-sensor monitoring module, a multi-sensor fusion processing module, an intelligent obstacle avoidance decision-making module, an environmental risk early warning module, a solar power management module, a remote data transmission module, and a robot integrated control module; The robot system is based on a four-wheel drive chassis, an open-source main control board, multi-source sensors and structural components to construct the physical entity of the robot system. It adopts a modular design to complete the mechanical structure assembly and signal and power line layout, providing a stable hardware foundation for the system. The robot inspection start module is responsible for the system power-on self-test and initialization parameter loading, and issues a start command to drive the robot system into the preset inspection standby state. The multi-sensor monitoring module is responsible for collecting multi-dimensional environmental data of the reservoir area in real time, providing a data source for fusion processing; The multi-sensor fusion processing module is responsible for fusing the raw data collected by the multi-sensor monitoring module to obtain fused data. The intelligent obstacle avoidance decision-making module calculates the steering angle that the robot needs to adjust based on the fused data. Dynamic programming for collision-free paths; The environmental risk early warning module calculates the environmental risk index based on the fused data. Quantify the safety risks in the storage area and automatically trigger early warning signals of different levels; The solar power management module calculates the robot's remaining available power based on the fused data. It dynamically optimizes the energy distribution between solar power and components such as motors to maximize range; The remote data transmission module calculates the data compression efficiency based on the collected raw data. It performs efficient compression and encryption of the fused data; The robot integrated control module summarizes, arbitrates, and makes decisions on the output information of all upstream modules, and finally generates coordinated control commands to realize the robot's intelligent inspection.

2. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 1, characterized in that: The multi-sensor monitoring module includes a temperature and humidity dynamic acquisition unit, a harmful gas concentration detection unit, an obstacle distance recognition unit, and an inspection location positioning unit.

3. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 2, characterized in that: The temperature and humidity dynamic acquisition unit collects temperature and humidity data in different areas of the storage area in real time through an integrated digital temperature and humidity sensor and performs anomaly monitoring; the hazardous gas concentration detection unit detects the concentration of hazardous gases such as methane, carbon monoxide, hydrogen sulfide, volatile organic compounds and oxygen through semiconductor or electrochemical gas sensors.

4. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 2, characterized in that: The obstacle distance recognition unit measures the distance between the obstacle and the robot using a laser / infrared sensor; the inspection position positioning unit combines a tracking sensor and an encoder to determine the robot's real-time position, fill in blind spots in warehouse monitoring, and record the inspection trajectory.

5. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 1, characterized in that: The multi-sensor fusion processing module includes a fused data preprocessing unit and a feature extraction unit; The fused data preprocessing unit filters out abnormal data and unifies the data format; The feature extraction unit extracts feature parameters from the fused data.

6. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 1, characterized in that: The intelligent obstacle avoidance decision-making module avoids collisions by adjusting the robot's steering angle α, and its calculation formula is as follows: In the formula, This indicates the steering angle that the robot needs to adjust. Indicates the safe obstacle avoidance distance. Indicates the actual distance between the obstacle and the robot. This indicates the positional deviation of the robot from the centerline of the path. This represents the obstacle distance correction factor. This represents the path deviation correction factor.

7. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 1, characterized in that: The environmental risk early warning module uses an environmental risk index. Different levels of alerts are triggered, and the calculation formula is as follows: In the formula, This indicates the environmental risk index. Indicates the actual concentration of harmful gases. Indicates the safety threshold for harmful gases. , These represent the actual temperature and humidity, respectively. , λ, β, and γ represent the standard temperature and humidity of the reservoir area, respectively, and represent the risk weights of gas, temperature, and humidity, respectively.

8. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 1, characterized in that: The solar power management module uses the robot's remaining available power. The formula for optimizing the allocation of solar energy supplementation and motor energy consumption is as follows: In the formula, This indicates the robot's remaining available power. This indicates the real-time output power of the solar panel. Indicates solar energy conversion efficiency. This indicates the real-time energy consumption of the motor. This indicates energy transmission loss.

9. The intelligent four-wheel drive warehouse inspection robot system based on multi-sensor fusion according to claim 1, characterized in that: The remote data transmission module improves data compression efficiency. To ensure that the compressed data can be efficiently transmitted to the control center, the calculation formula is as follows: And satisfy ≤ In the formula, Indicates data compression efficiency. Indicates the original fused data volume. Indicates the amount of data after compression. Indicates data transmission delay. This indicates network bandwidth.

10. An operation method for an intelligent four-wheel drive warehouse inspection robot based on multi-sensor fusion, characterized in that, The following steps are included: Step 1: Build a robot inspection system. The system includes a robot inspection start-up module, a multi-sensor monitoring module, a multi-sensor fusion processing module, an intelligent obstacle avoidance decision-making module, an environmental risk early warning module, a solar power management module, a remote data transmission module, and a robot integrated control module. Step 2: The robot inspection start-up module is responsible for starting the system and inspecting the warehouse area path by moving in four-wheel drive tracking mode; Step 3: The multi-sensor monitoring module collects real-time data on temperature, humidity, gas, obstacles, and location. Step 4: The multi-sensor fusion processing module performs data preprocessing, feature extraction, fusion, and state assessment. Step 5: The intelligent obstacle avoidance decision module calculates the required steering angle α for the robot based on the fused data, thereby achieving automatic obstacle avoidance; Step Six: The environmental risk early warning module calculates the environmental risk index. This triggers the early warning mechanism; Step 7: The solar power management module calculates the robot's remaining available power. Dynamically adjust the power consumption allocation strategy of each module to optimize energy use and extend the robot's battery life; Step 8: The remote data transmission module calculates the data compression efficiency. Under the condition of meeting the transmission delay requirements, the compressed data is sent to the remote control center to receive control commands from the robot integrated control module; Step 9: The robot integrated control module integrates all module inputs, arbitrates and makes decisions based on preset logic and remote commands, controls the robot's behavior, and realizes intelligent inspection.

Images