Obstacle avoidance control system and method for inspection robot

Abstract

The invention discloses an inspection robot obstacle avoidance control system and method, and the system comprises an obstacle avoidance signal collection node which is provided with a plurality of data collection nodes for collecting various mine obstacle avoidance data; the mine obstacle avoidance data comprises main obstacle avoidance data and auxiliary obstacle avoidance data; the robot comprehensive obstacle avoidance service node comprises a main obstacle avoidance service node and an auxiliary obstacle avoidance service node, the main obstacle avoidance service node performs main obstacle avoidance rule judgment on the main obstacle avoidance data according to a stored main obstacle avoidance service rule set, and the auxiliary obstacle avoidance service node performs auxiliary obstacle avoidance rule judgment according to a stored auxiliary obstacle avoidance service rule set; if the mine obstacle avoidance data conforming to the obstacle avoidance rule appears, generating an obstacle avoidance signal; and the robot inspection node receives the obstacle avoidance signal and controls the robot to execute an obstacle avoidance action according to the obstacle avoidance signal. According to the invention, efficient obstacle avoidance, intelligent decision making, signal redundancy, operation with faults and dynamic management of obstacle avoidance rules of the robot in a complex environment are realized.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to an obstacle avoidance control system and method for an inspection robot. Background Art

[0002] With the intelligent development of coal mining, modern fully mechanized mining faces have widely applied the latest electronic information and automatic control technologies, realizing highly automated coal mining operations.

[0003] Currently, patrol robots have been introduced into coal mining for equipment monitoring and environmental detection during the coal mining process. These patrol robots can perform tasks such as coal mining machine status tracking, coal flow monitoring, and hydraulic support parallelism detection. They also use automation technology to track the coal mining status in real time, ensuring the safety and stability of the entire operation process. The patrol robot's obstacle avoidance control system mainly relies on a variety of sensors, such as infrared sensors, visual sensors, and radar sensors, to detect the surrounding environment and avoid obstacles. It can also perform continuous inspections within the entire working face of the coal mining machine. The patrol robot obstacle avoidance control system in the existing technology usually integrates sensor data acquisition, signal processing, and obstacle avoidance decision-making into a main obstacle avoidance program, or some data acquisition functions are separated into independent sensor acquisition modules.

[0004] However, this integrated inspection robot obstacle avoidance control system has certain drawbacks. The system uses centralized signal processing logic, with all sensor data and business logic concentrated in a single main program. This highly coupled design makes the system overly dependent on individual components. As a result, if an external sensor fails, the functionality of the entire obstacle avoidance system is affected, and even erroneous obstacle avoidance signals may be generated. Furthermore, because all components of the inspection robot obstacle avoidance control system are tightly coupled, when a component in the system (such as a sensor) fails, the robot often cannot operate despite the failure and must be shut down for troubleshooting. This troubleshooting process typically involves testing the communication status and data acquisition functions of each component, and may require replacing parts before restarting the program to restore normal operation. This model not only affects the stability and robustness of the obstacle avoidance program, but can also lead to production stagnation and reduce overall operational efficiency. Therefore, there is still significant room for improvement in the fault tolerance, maintainability, and continuous operation capabilities of the existing inspection robot obstacle avoidance control system.

[0005] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section. Summary of the Invention

[0006] An embodiment of the present invention provides an obstacle avoidance control system for an inspection robot, which is used to achieve efficient obstacle avoidance, intelligent decision-making, signal redundancy, fault-tolerant operation, and dynamic management of obstacle avoidance rules in complex environments, ensuring that the obstacle avoidance control system has high safety, strong robustness, and flexible adaptability.

[0007] The inspection robot obstacle avoidance control system includes:

[0008] The obstacle avoidance signal collection node is provided with multiple data collection nodes for collecting various mine obstacle avoidance data. Each data collection node periodically collects obstacle avoidance data through a timing collection function.

[0009] The mine obstacle avoidance data includes main obstacle avoidance data and auxiliary obstacle avoidance data. The same type of mine obstacle avoidance data may belong to both main obstacle avoidance data and auxiliary obstacle avoidance data.

[0010] An obstacle avoidance signal data caching node, connected to a plurality of the data acquisition nodes, for caching the mine obstacle avoidance data, the mine obstacle avoidance data being periodically cached by the obstacle avoidance signal acquisition nodes through a data caching function;

[0011] A robot comprehensive obstacle avoidance service node includes a primary obstacle avoidance service node and an auxiliary obstacle avoidance service node. The primary obstacle avoidance service node receives the primary obstacle avoidance data, and the auxiliary obstacle avoidance service node receives the auxiliary obstacle avoidance data. The primary obstacle avoidance service node performs a primary obstacle avoidance rule determination on the primary obstacle avoidance data according to a stored primary obstacle avoidance service rule set. The auxiliary obstacle avoidance service node performs a secondary obstacle avoidance rule determination according to a stored secondary obstacle avoidance service rule set. If mine obstacle avoidance data that meets the obstacle avoidance rule appears, an obstacle avoidance signal is generated.

[0012] The robot inspection node receives the obstacle avoidance signal and controls the robot to perform obstacle avoidance actions according to the obstacle avoidance signal.

[0013] In one embodiment, the main obstacle avoidance service node is specifically used to: determine the category label of each mine obstacle avoidance data according to the degree of influence of each mine obstacle avoidance data on the operating status and safety of the robot; determine the main obstacle avoidance data and auxiliary obstacle avoidance data according to the category label of each mine obstacle avoidance data, and establish a main obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the main obstacle avoidance data; compare the rule threshold of each main obstacle avoidance data in the established main obstacle avoidance service rule set with the corresponding main obstacle avoidance data.

[0014] In one embodiment, the auxiliary obstacle avoidance service node is specifically used to: establish an auxiliary obstacle avoidance service rule set based on the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data; and compare the rule thresholds and / or robot inspection task status of each auxiliary obstacle avoidance data in the established auxiliary obstacle avoidance service rule set with the corresponding auxiliary obstacle avoidance data.

[0015] In one embodiment, the main obstacle avoidance service node establishes a coverable rule set based on the intersection of the main obstacle avoidance service rule set and the auxiliary obstacle avoidance service rule set. When the main obstacle avoidance service node fails, the auxiliary obstacle avoidance service node compares the rule threshold of each mine obstacle avoidance data in the coverable rule set with the corresponding mine obstacle avoidance data.

[0016] In one embodiment, the obstacle avoidance signal collection node includes:

[0017] A battery node, configured to collect battery status parameters inside the robot according to a first time interval;

[0018] a methane sensor node, configured to collect a methane gas concentration around the robot according to a second time interval and determine a methane concentration level of the methane gas concentration according to a preset rule;

[0019] A methane emergency stop recovery node is used to collect status information of a methane emergency stop recovery button on the robot according to a third time interval;

[0020] A front infrared proximity switch node is used to collect infrared blocking status of the robot in front of the robot through an infrared sensor;

[0021] A rear infrared proximity switch node is used to collect infrared blocking status of the rear of the robot through an infrared sensor;

[0022] A front radar obstacle avoidance sensor node is used to collect distance information of obstacles within a preset range in front of the robot; determine whether the distance information exceeds a safe collision threshold and generate a front radar obstruction state;

[0023] The rear radar obstacle avoidance sensor node is used to collect distance information of obstacles within a preset range behind the robot; determine whether the distance information exceeds a safe collision threshold and generate a rear radar obstruction state.

[0024] In one embodiment, the obstacle avoidance signal collection node further includes:

[0025] Multiple visual camera nodes are used to collect image information of the environment in front of the robot;

[0026] The RFID reader node is used to scan the RFID tags on the robot's inspection path and generate point information based on the analysis of the RFID tags.

[0027] An embodiment of the present invention also provides an obstacle avoidance control method for a patrol robot, which is used to achieve efficient obstacle avoidance, intelligent decision-making, signal redundancy, fault-tolerant operation, and dynamic management of obstacle avoidance rules in complex environments, ensuring that the obstacle avoidance control system has high safety, strong robustness, and flexible adaptability.

[0028] The inspection robot obstacle avoidance control method is applied to the robot comprehensive obstacle avoidance service node, the robot comprehensive obstacle avoidance service node includes a main obstacle avoidance service node and an auxiliary obstacle avoidance service node, and the inspection robot obstacle avoidance control method includes:

[0029] The primary obstacle avoidance service node receives primary obstacle avoidance data, and the secondary obstacle avoidance service node receives secondary obstacle avoidance data;

[0030] The main obstacle avoidance service node determines the main obstacle avoidance rules on the main obstacle avoidance data according to the stored main obstacle avoidance service rule set, and the auxiliary obstacle avoidance service node determines the auxiliary obstacle avoidance rules according to the stored auxiliary obstacle avoidance service rule set. If mine obstacle avoidance data that meets the obstacle avoidance rules appears, an obstacle avoidance signal is generated, and the obstacle avoidance signal is transmitted to the robot inspection node and the sound and light alarm node, so that the robot inspection node controls the robot to make an emergency stop according to the obstacle avoidance signal, and the sound and light alarm node performs an sound and light alarm.

[0031] In one embodiment, the primary obstacle avoidance service node performs primary obstacle avoidance rule determination on the primary obstacle avoidance data according to a stored primary obstacle avoidance service rule set, including:

[0032] The main obstacle avoidance service node determines the category label to which each mine obstacle avoidance data belongs based on the degree of influence of each obstacle avoidance data on the robot's operating status and safety;

[0033] The main obstacle avoidance service node determines the main obstacle avoidance data and the auxiliary obstacle avoidance data according to the category label of each mine obstacle avoidance data, and establishes the main obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the main obstacle avoidance data;

[0034] The primary obstacle avoidance service node compares the rule threshold of each primary obstacle avoidance data with the corresponding primary obstacle avoidance data according to the established primary obstacle avoidance service rule set.

[0035] In one embodiment, the auxiliary obstacle avoidance service node performs auxiliary obstacle avoidance rule determination according to the stored auxiliary obstacle avoidance service rule set, including:

[0036] The auxiliary obstacle avoidance service node establishes an auxiliary obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data;

[0037] The auxiliary obstacle avoidance service node compares the rule threshold of each auxiliary obstacle avoidance data and / or the robot inspection task status with the corresponding auxiliary obstacle avoidance data in the established auxiliary obstacle avoidance service rule set.

[0038] In one embodiment, the method further comprises:

[0039] The primary obstacle avoidance service node establishes an overlayable rule set according to the intersection of the primary obstacle avoidance service rule set and the auxiliary obstacle avoidance service rule set;

[0040] When the primary obstacle avoidance service node fails, the secondary obstacle avoidance service node compares the corresponding mine obstacle avoidance data according to the rule threshold of each mine obstacle avoidance data in the covering rule set.

[0041] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned inspection robot obstacle avoidance control method when executing the computer program.

[0042] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the obstacle avoidance control method of the inspection robot is implemented.

[0043] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned inspection robot obstacle avoidance control method.

[0044] The obstacle avoidance control system and method for inspection robots provided by embodiments of the present invention utilize multiple data collection nodes within the obstacle avoidance signal collection node to independently operate and periodically collect various mine obstacle avoidance data within the mine environment, eliminating the need for a central control unit and ensuring the continuity and integrity of the mine obstacle avoidance data. Furthermore, the mine obstacle avoidance data is divided into primary and secondary obstacle avoidance data. Different types of data can be used as both primary and secondary obstacle avoidance data in different business scenarios, resulting in higher data utilization and preventing the impact of a single data source on obstacle avoidance decisions, thereby improving the reliability of the obstacle avoidance strategy. The obstacle avoidance signal data cache node serves as an intermediate layer, connecting multiple data collection nodes and caching the collected mine obstacle avoidance data. This ensures that the latest obstacle avoidance information is available even in the event of network fluctuations or sensor anomalies, ensuring the stable operation of the obstacle avoidance control system. This application utilizes the Redis in-memory database and the Linux file system for distributed data storage and caching. The obstacle avoidance signal data cache node allows multiple data collection nodes to write concurrently and supports data reading by different obstacle avoidance service nodes. The robot's integrated obstacle avoidance service node can query the latest data from Redis in real time, improving data access speed. The robot's comprehensive obstacle avoidance service node adopts a two-tier structure consisting of a primary obstacle avoidance service node and an auxiliary obstacle avoidance service node, which processes primary and auxiliary obstacle avoidance data respectively, and makes intelligent judgments based on the preset primary and auxiliary obstacle avoidance rule sets. This two-tier structure makes obstacle avoidance decisions more accurate. The primary obstacle avoidance service node can process key obstacle avoidance information, ensuring that the robot is shut down first when encountering major dangers, while the auxiliary obstacle avoidance service node can analyze secondary influencing factors, improving the system's flexibility and fine-grained decision-making capabilities. Through a distributed architecture, a two-tier obstacle avoidance data classification, and a caching mechanism, efficient collection, storage, and intelligent judgment of mine obstacle avoidance data are ensured, enhancing the fault tolerance and adaptability of the obstacle avoidance control system, making the robot safer and more stable during mine operations, while reducing misjudgments and unnecessary shutdowns caused by sensor failures or network fluctuations, and improving overall mine operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0046] Figure 1 Schematic diagram of the structure of the inspection robot obstacle avoidance control system in an embodiment of the present invention;

[0047] Figure 2Schematic diagram of the flow of an obstacle avoidance control method for an inspection robot according to one embodiment of the present invention;

[0048] Figure 3 Schematic diagram of the flow of an obstacle avoidance control method for an inspection robot in another embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the flow of an obstacle avoidance control method for an inspection robot in another embodiment of the present invention;

[0050] Figure 5 Schematic diagram of the flow of an obstacle avoidance control method for an inspection robot in another embodiment of the present invention;

[0051] Figure 6 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0053] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0054] Provide users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, they will enter the expert decision-making process.

[0055] In order to realize the autonomous and safe obstacle avoidance of the inspection robot in a complex environment and to improve the stability, flexibility and fault tolerance of the obstacle avoidance control system, the present application proposes an obstacle avoidance control system for the inspection robot. Existing obstacle avoidance systems usually adopt a centralized architecture, which concentrates the processing logic of all obstacle avoidance data in a main program, resulting in that when a sensor fails or is abnormal, the entire obstacle avoidance system will be affected and unable to continue to operate. At the same time, the traditional obstacle avoidance rules are fixed in configuration and difficult to dynamically adjust according to different operating environments, which reduces the adaptability of the inspection robot in complex scenarios. The present application adopts the ROS2 (Robot Operating System) architecture, and through distributed obstacle avoidance signal acquisition nodes, it realizes the independent operation of multiple acquisition devices, making the obstacle avoidance control system more robust and maintainable. Through the design method of obstacle avoidance rules that can be added and deleted, the obstacle avoidance control system can dynamically adjust the obstacle avoidance strategy according to environmental requirements to meet the needs of different application scenarios.

[0056] like Figure 1 As shown, the inspection robot obstacle avoidance control system includes an obstacle avoidance signal acquisition node, an obstacle avoidance signal data cache node, a robot comprehensive obstacle avoidance service node, a robot inspection node, and an audio-visual alarm node.

[0057] The obstacle avoidance signal collection node 101 is equipped with multiple data collection nodes for collecting various types of mine obstacle avoidance data. Each data collection node periodically collects obstacle avoidance data through a timed collection function. Mine obstacle avoidance data includes primary obstacle avoidance data and auxiliary obstacle avoidance data. The same type of mine obstacle avoidance data can belong to both primary and auxiliary obstacle avoidance data.

[0058] Specifically, multiple data collection nodes are used to collect mine obstacle avoidance data, such as various obstacles, hazardous gases, and battery status in the robot's operating environment. The collected mine obstacle avoidance data must conform to the message format of the ROS2 architecture to facilitate data exchange with other ROS2 components (such as SLAM, path planning, and navigation systems). This application uses the ROS2 architecture-compatible msg data interface to ensure a unified data format for different sensors and readers, allowing for unified parsing by the robot's integrated obstacle avoidance business node.

[0059] The obstacle avoidance signal data cache node 102 is connected to multiple data collection nodes and is used to cache mine obstacle avoidance data. The mine obstacle avoidance data is periodically cached by the obstacle avoidance signal collection node 101 through a data cache function.

[0060] Specifically, the obstacle avoidance signal data cache node 102 is used to receive the mine obstacle avoidance data of multiple data acquisition nodes, and write the mine obstacle avoidance data line into the cache to ensure that the data will not be lost. The Redis memory-type database file system and the Linux file system are used for data storage. The Redis database uses a key-value pair (Key-Value) method to store a variety of mine obstacle avoidance data, and supports high-concurrency real-time access, so that multiple data acquisition nodes can write mine obstacle avoidance data at the same time, and can ensure the real-time nature of data access. The Redis database can be set to automatically delete expired data to prevent excessive memory usage. The Linux file system is used to store long-term mine obstacle avoidance data, and the mine obstacle avoidance data is regularly written to the local disk. Historical data analysis and log recording can be performed on the data to ensure data persistence.

[0061] like Figure 1As shown, the robot's comprehensive obstacle avoidance service node 103 includes a primary obstacle avoidance service node 130 and a secondary obstacle avoidance service node 131. The primary obstacle avoidance service node 130 receives primary obstacle avoidance data, while the secondary obstacle avoidance service node 131 receives secondary obstacle avoidance data. The primary obstacle avoidance service node 130 determines the primary obstacle avoidance rule based on the stored primary obstacle avoidance service rule set. The secondary obstacle avoidance service node 131 determines the secondary obstacle avoidance rule based on the stored secondary obstacle avoidance service rule set. If mine obstacle avoidance data that meets the obstacle avoidance rules appears, an obstacle avoidance signal is generated. Both the primary and secondary obstacle avoidance service rule sets support user-defined configuration to enhance the flexibility of obstacle avoidance decision-making.

[0062] The robot inspection node 104 is used to receive the obstacle avoidance signal from the robot comprehensive obstacle avoidance service node 103 and control the robot to perform obstacle avoidance actions according to the obstacle avoidance signal.

[0063] Specifically, the robot inspection node 104 continuously monitors the obstacle avoidance signal by subscribing to the ROS2 topic. The obstacle avoidance signal is used to indicate whether the robot needs to make an emergency stop. After the robot inspection node 104 receives the obstacle avoidance signal, if the obstacle is close to the robot, the robot is controlled to immediately perform an emergency stop operation; if the obstacle is far away from the robot, the robot is controlled to slow down or detour. When the obstacle avoidance signal is released, the robot inspection node 104 determines whether to automatically resume the inspection operation based on the internal logic. For example, when an obstacle is detected by radar or infrared, the robot can automatically resume inspection after the mine personnel move the obstacle. If it is in the methane over-limit obstacle avoidance state, even if the methane concentration returns to normal, the robot will not automatically resume inspection. The methane emergency stop recovery button must be manually pressed to resume the robot's inspection operation.

[0064] The sound and light alarm node 105 receives the obstacle avoidance signal from the robot comprehensive obstacle avoidance service node 103 and sends an audible and visual alarm prompt based on the obstacle avoidance signal. When the obstacle avoidance signal disappears, the sound and light alarm is turned off and control is released to the robot inspection node 104, allowing the robot inspection node 104 to continue the inspection task. For example, during the inspection process, the siren can be avoided continuously, and only the flashing light prompt can be retained.

[0065] The present application uses multiple data collection nodes in the obstacle avoidance signal collection node to independently operate and periodically collect various mine obstacle avoidance data in the mine environment without relying on a central control unit, thereby ensuring the continuity and integrity of the mine obstacle avoidance data. In addition, the mine obstacle avoidance data is divided into primary obstacle avoidance data and auxiliary obstacle avoidance data. Different types of data can be used as primary obstacle avoidance data and auxiliary obstacle avoidance data in different business scenarios, making the data utilization rate higher and avoiding the influence of a single data source on obstacle avoidance decisions, thereby improving the reliability of the obstacle avoidance strategy. The obstacle avoidance signal data cache node serves as an intermediate layer, connecting multiple data collection nodes, and caching the collected mine obstacle avoidance data. It can ensure that the latest obstacle avoidance information can still be provided in the event of network fluctuations or sensor abnormalities, ensuring the stable operation of the obstacle avoidance control system. The present application uses the Redis memory-level database and the Linux file system for distributed data storage and caching. The obstacle avoidance signal data cache node allows multiple data collection nodes to write concurrently and supports different obstacle avoidance business nodes to read data. The robot's comprehensive obstacle avoidance business node can query the latest data from Redis in real time, which improves the speed of data access. The robot's comprehensive obstacle avoidance service node adopts a two-tier structure consisting of a primary obstacle avoidance service node and an auxiliary obstacle avoidance service node, which processes primary and auxiliary obstacle avoidance data respectively, and makes intelligent judgments based on the preset primary and auxiliary obstacle avoidance rule sets. This two-tier structure makes obstacle avoidance decisions more accurate. The primary obstacle avoidance service node can process key obstacle avoidance information, ensuring that the robot is shut down first when encountering major dangers, while the auxiliary obstacle avoidance service node can analyze secondary influencing factors, improving the system's flexibility and fine-grained decision-making capabilities. Through a distributed architecture, a two-tier obstacle avoidance data classification, and a caching mechanism, efficient collection, storage, and intelligent judgment of mine obstacle avoidance data are ensured, enhancing the fault tolerance and adaptability of the obstacle avoidance control system, making the robot safer and more stable during mine operations, while reducing misjudgments and unnecessary shutdowns caused by sensor failures or network fluctuations, and improving overall mine operation efficiency.

[0066] In one embodiment, the data acquisition node uses the LifecycleNode (lifecycle management node) in the ROS framework. The data acquisition node is configured and managed through lifecycle management functions. These lifecycle management functions include a configuration function (on_configure), an activation function (on_activate), a deactivation function (on_deactivate), a cleanup function (on_cleanup), and a shutdown function (on_shutdown).

[0067] The configuration function (on_configure) is used to initialize external obstacle avoidance detection devices (such as various sensors, cameras, or readers). Initialization includes configuring the monitoring parameters of the external obstacle avoidance detection devices and setting up the communication interface.

[0068] The activation function (on_activate) is used to start the data collection node so that it can work normally. The activation operation includes publishing a ROS2 topic (Topic) and starting an internal timer.

[0069] The deactivation function (on_deactivate) is used to control the data collection node to enter the deactivated state, that is, to suspend the mine obstacle avoidance data collection and stop publishing the ROS2 topic (Topic).

[0070] The cleanup function (on_cleanup) is used to clean up the cache and release memory resources to prevent old data from affecting the restart of the data acquisition node. The shutdown function (on_shutdown) is used to shut down the data acquisition node.

[0071] Specifically, when the data acquisition node needs to be restarted, the deactivation function (on_deactivate) is first used to control the data acquisition node to enter the deactivated state, that is, to suspend the collection of mine obstacle avoidance data and the publication of ROS2 topics (Topic). Then, the cache and memory resources are cleared through the cleanup function (on_cleanup) to ensure that the data acquisition node will not be affected by old data when it is restarted. Then, the data acquisition node is reinitialized through the configuration function (on_configure). Finally, the data acquisition operation is restarted through the activation function (on_activate), and the status information of the data acquisition node continues to be published through the ROS2 topic (Topic) to restore the normal operation of the data acquisition node. When the data acquisition node needs to be shut down, the shutdown function (on_shutdown) can be executed.

[0072] The data collection node uses auxiliary functions to collect mine obstacle avoidance data and publish the status of external obstacle avoidance detection devices. These auxiliary functions include a data refresh function (flush_file) and an internal callback function (internal_callback). The data refresh function (flush_file) is used to periodically store mine obstacle avoidance data to prevent data loss (for example, recovering data after a power outage). The internal callback function (internal_callback) is used to collect mine obstacle avoidance data when the ROS2 timer reaches a preset collection interval.

[0073] Auxiliary functions also include: a robot obstacle avoidance client function (robot_avoidance_client) and a publishing function (publisher). The robot obstacle avoidance client function (robot_avoidance_client) is used to interact with the robot comprehensive obstacle avoidance service node 103, enabling the robot comprehensive obstacle avoidance service node 103 to query the status information of external obstacle avoidance detection equipment. The publishing function (publisher) uses the ROS2 topic (Topic) publisher to publish the status information of external obstacle avoidance detection equipment. Topic subscription does not collect mine obstacle avoidance data from data collection nodes, but is only used to collect the heartbeat of sensors, cameras, and readers to prevent external obstacle avoidance detection equipment from experiencing abnormalities or disconnections.

[0074] Specifically, the data acquisition node is initialized through the configuration function (on_configure). After the data acquisition node is initialized, the activation function (on_activate) is automatically executed to activate the publishing function (publisher) and start the internal timer (internal_timer). The status information of the external obstacle avoidance detection device is published through the publishing function (publisher), and the internal timer (internal_timer) periodically triggers the internal callback function (internal_callback) to collect mine obstacle avoidance data. The collected mine obstacle avoidance data is regularly stored in the obstacle avoidance signal data cache node 102 through the data refresh function (flush_file). Among them, the internal timer (internal_timer) adopts the ROS2 timer, which is used to periodically trigger the internal callback function (internal_callback) for data collection.

[0075] In this embodiment of the present invention, lifecycle management functions are used to configure and manage data collection nodes. If an external obstacle avoidance detection device, such as a robot sensor, camera, or reader, fails, the corresponding data collection node can be restarted after the device is replaced. This enables a modular, pluggable design for external obstacle avoidance detection devices, ensuring the continued stable operation and easy maintenance of the obstacle avoidance control system.

[0076] In one embodiment, the obstacle avoidance signal collection node 101 includes multiple data collection nodes such as a battery node 110, a methane sensor node 111, a methane emergency stop recovery node 112, a front infrared proximity switch node 113, a rear infrared proximity switch node 114, a front radar obstacle avoidance sensor node 115, and a rear radar obstacle avoidance sensor node 116.

[0077] The battery node 110 is used to collect battery status parameters inside the robot according to the first time interval.

[0078] Specifically, the battery node 110 is used to collect the status of the robot's internal battery management system (BMS), including battery pack voltage, battery pack current, rated battery capacity, remaining battery capacity, and cell status. The cell status includes the voltage, current, and temperature of each battery cell.

[0079] Battery node 110 regularly monitors battery voltage and current to ensure they are within preset thresholds, preventing overvoltage or undervoltage from causing abnormalities in the battery management system (BMS). Battery node 110 calculates the robot's remaining battery life based on the rated battery capacity and remaining battery capacity to ensure the robot does not shut down due to battery depletion during inspection missions. Battery node 110 regularly monitors the voltage, current, and temperature of each cell within the battery pack and determines whether the maximum cell temperature exceeds a preset threshold to prevent battery pack overheating and potential safety incidents.

[0080] Battery node 110 collects battery status parameters at fixed intervals, controlled by an internal timer. This collection interval can be set or adjusted using the sampling interval parameter in the node parameter server, allowing the obstacle avoidance control system to optimize the data collection frequency based on actual needs. The collection interval is typically set to 50ms, or 20 times per second, ensuring real-time battery status parameters. After collection is complete, battery node 110 writes the collected battery status parameters to the obstacle avoidance signal data cache node 102.

[0081] The methane sensor node 111 is configured to collect the methane gas concentration around the robot according to the second time interval and determine the methane concentration level of the methane gas concentration according to a preset rule.

[0082] Specifically, methane sensor node 111 uses a high-precision methane sensor to collect methane gas concentrations around the robot at pre-set sampling intervals. This sampling interval can be set or adjusted using the sampling interval parameter in the node parameter server, allowing the obstacle avoidance control system to optimize the data collection frequency based on actual needs. The default sampling interval is 20ms (i.e., 50 samples per second), ensuring the high sensitivity of the obstacle avoidance control system.

[0083] The methane sensor node 111 classifies methane concentration levels according to the methane gas concentration. When the methane concentration is lower than 0.1%, the methane concentration level is low, and the robot can operate normally. When the methane concentration is in the range of 0.1% to 0.5%, the methane concentration level is medium, and the robot can continue to operate, but the acquisition frequency needs to be increased. When the methane concentration is in the range of 0.5% to 1%, the methane concentration level is high, and the robot reduces its operating speed and issues an audible and visual alarm. When the methane concentration is higher than 1%, the methane concentration exceeds the limit (i.e., there is a risk of explosion), and the robot makes an emergency stop and issues an audible and visual alarm. After the acquisition is completed, the methane sensor node 111 writes the collected methane gas concentration and methane concentration level into the obstacle avoidance signal data cache node 102.

[0084] The methane emergency stop recovery node 112 is used to collect status information of the methane emergency stop recovery button on the robot according to the third time interval.

[0085] Specifically, the methane emergency stop recovery node 112 detects whether the methane emergency stop recovery button is manually pressed according to a fixed signal sampling interval. The fixed signal sampling interval is less than 50ms, that is, the data is collected more than 20 times per second. The status of the methane emergency stop recovery button is stored in binary format, where 0 represents a floating state (i.e., the button is not pressed and the robot is in an emergency stop state), and 1 represents a pressed state (i.e., the button is pressed, the emergency stop is released, and the robot resumes operation). After the collection is completed, the methane emergency stop recovery node 112 writes the collected status information of the methane emergency stop recovery button to the obstacle avoidance signal data cache node 102.

[0086] When the methane sensor detects that the methane gas concentration exceeds the limit, the primary obstacle avoidance service node 130 compares the methane gas concentration and methane concentration level with the corresponding obstacle avoidance rule and generates an obstacle avoidance signal. Based on the obstacle avoidance signal, the robot inspection node 104 controls the robot to perform an emergency stop and performs a power-off process. At this point, a manual press of the methane emergency stop recovery button is required to release the robot from the obstacle avoidance state.

[0087] The front infrared proximity switch node 113 is used to collect the infrared shielding status of the robot in front through an infrared sensor.

[0088] Specifically, the infrared sensor emits infrared light to determine whether there is an obstacle in front of the robot. The default sensing distance of the infrared sensor is 450mm (under 50% reflectivity). When the robot detects a reflected signal in the inspection direction, it determines whether there is an obstacle in front of the robot. The front infrared blockage status is a switching signal, represented in binary format, with 0 indicating unblocked (no obstacle ahead) and 1 indicating blocked (indicating an obstacle ahead).

[0089] After the acquisition is completed, the front infrared proximity switch node 113 writes the acquired front infrared blocking status into the obstacle avoidance signal data cache node 102 .

[0090] The rear infrared proximity switch node 114 is used to collect the infrared shielding status of the rear of the robot through an infrared sensor.

[0091] Specifically, the infrared sensor emits infrared light to determine whether there is an obstacle in front of the robot. The default sensing distance of the infrared sensor is 450mm (under 50% reflectivity). When the robot detects a reflected signal in the inspection direction, it determines whether there is an obstacle in front of the robot. The rear infrared blockage status is a switching signal, represented in binary format, with 0 indicating unblocked (i.e., no obstacle behind) and 1 indicating blocked (indicating an obstacle behind).

[0092] After the acquisition is completed, the rear infrared proximity switch node 114 writes the acquired rear infrared blocking status into the obstacle avoidance signal data cache node 102 .

[0093] The forward radar obstacle avoidance sensor node 115 is used to collect distance information of obstacles within a preset range in front of the robot and determine whether the distance information exceeds the safe collision threshold to generate a forward radar obstruction state. The safe collision threshold can be set or adjusted using the collision threshold parameter in the node parameter server.

[0094] Specifically, a laser radar (LIDAR) or ultrasonic radar is used to periodically scan the area in front of the robot (within a 120° range) to measure the distance and orientation of the nearest obstacle to the robot. If the obstacle is less than 750mm away, it indicates a collision risk ahead of the robot, and a blocked state is generated (i.e., the radar signal value is set to 1). If the obstacle is greater than 750mm away, it indicates that the inspection path ahead of the robot is clear, and an unblocked state is generated (i.e., the radar signal value is set to 0).

[0095] After the acquisition is completed, the front radar obstacle avoidance sensor node 115 writes the acquired front radar obstruction status and obstacle distance into the obstacle avoidance signal data cache node 102 .

[0096] The rear radar obstacle avoidance sensor node 116 is used to collect distance information of obstacles within a preset range behind the robot, and determine whether the distance information exceeds the safety collision threshold to generate a rear radar obstruction state.

[0097] Specifically, a laser radar (LIDAR) or ultrasonic radar is used to periodically scan the area behind the robot (within a 120° range) to measure the distance and orientation of the nearest obstacle to the robot. If the obstacle is less than 750mm away, there is a collision risk behind the robot, and an obstructed state is generated (i.e., the radar signal value is set to 1). If the obstacle is greater than 750mm away, the inspection path behind the robot is clear, and an unobstructed state is generated (i.e., the radar signal value is set to 0).

[0098] After the acquisition is completed, the rear radar obstacle avoidance sensor node 116 writes the acquired rear radar obstruction status and obstacle distance into the obstacle avoidance signal data cache node 102 .

[0099] The infrared proximity switch node or radar obstacle avoidance sensor node in the embodiment of the present invention has a signal redundancy feature. When the front infrared proximity switch node 113 fails, the obstacle avoidance control system can still collect and process obstacle avoidance data according to the front radar obstacle avoidance sensor node 115 to replace the original judgment rules, thereby ensuring that the obstacle avoidance control system still has a certain fault-tolerant operation capability in the event of partial sensor failure, thereby avoiding the complete failure of the robot's obstacle avoidance function due to a single sensor failure.

[0100] In one embodiment, if Figure 1 As shown, the obstacle avoidance signal collection node 101 also includes multiple visual camera detection nodes 117 and RFID reader nodes 118.

[0101] The plurality of visual camera detection nodes 117 are used to collect image information of the environment in front of the robot.

[0102] Specifically, a visual camera is used to capture real-time image information of the robot's surroundings. For example, during a robot inspection, the camera can identify the position, angle, and movement of hydraulic supports, ensuring the robot can avoid them as they move, thus preventing collisions. When the robot is in machine-following inspection mode, the camera can identify the movement trajectory of the shearer and decide whether to follow it based on the shearer's operating status, thereby optimizing the robot's inspection path and improving inspection efficiency.

[0103] The RFID reader node 118 is used to scan the RFID tags on the robot's inspection path and generate point information based on the parsed RFID tags.

[0104] Specifically, an RFID reader / writer continuously scans forward or downward on RFID tags placed along the robot's inspection path, acquiring the data stored on each tag. RFID tags are located at the starting point, intermediate nodes, and end points of the inspection path, as well as robot docking locations and special task points. This data includes the origin, endpoint, and docking location.

[0105] The starting point of the inspection path instructs the robot to begin its inspection from the same location each time. Intermediate nodes on the inspection path are used to correct the robot's inspection path to improve navigation accuracy. The end point of the inspection path indicates that the robot has reached the farthest end of the preset inspection path, allowing the robot to perform reverse operations. Special mission points are used to mark dangerous areas in the mine to ensure that the robot can promptly implement appropriate safety and obstacle avoidance strategies.

[0106] In one embodiment, the primary obstacle avoidance service node 130 is specifically configured to: determine a category label for each piece of mine obstacle avoidance data based on the degree of impact of each piece of mine obstacle avoidance data on the robot's operating status and safety; determine primary obstacle avoidance data and auxiliary obstacle avoidance data based on the category label of each piece of mine obstacle avoidance data; establish a primary obstacle avoidance service rule set based on the obstacle avoidance rules corresponding to the primary obstacle avoidance data; and compare the rule threshold of each piece of primary obstacle avoidance data in the established primary obstacle avoidance service rule set with the corresponding primary obstacle avoidance data.

[0107] Specifically, mine obstacle avoidance data includes battery status parameters, methane gas concentration, methane concentration level, status information of the methane emergency stop recovery button, front infrared blocking status, rear infrared blocking status, front radar blocking status, rear radar blocking status, environmental image information and point information, etc.

[0108] All mine obstacle avoidance data is scored based on its urgency and inspection task dependency. Urgency indicates whether it directly impacts the robot's safe operation, while inspection task dependency indicates whether it is used for inspection task optimization rather than direct emergency obstacle avoidance. Both urgency and inspection task dependency are scored on a scale of 1 to 5, but the present invention is not limited to this.

[0109] For example, the urgency of battery status parameters and methane gas concentration are both 5 (directly impacting inspection execution), and the inspection task dependency is 2. The urgency of methane concentration level is 5 (immediately releasing the robot from emergency stop), and the inspection task dependency is 3. The urgency of the front infrared blockage, rear infrared blockage, front radar blockage, and rear radar blockage are all 4 (affecting the robot's inspection path), and the inspection task dependency is 4. The urgency of environmental image information is 3 (affecting the robot's inspection strategy), and the inspection task dependency is 5. The urgency of point information is 2 (affecting the robot's inspection optimization), and the inspection task dependency is 5.

[0110] Mine obstacle avoidance data with an urgency score greater than 4 and an inspection task dependency score less than 4 is classified as primary obstacle avoidance data (such as battery status parameters, methane gas concentration, and methane concentration level), and the category labels of the battery status parameters, methane gas concentration, and methane concentration level are set as the primary obstacle avoidance data label. Mine obstacle avoidance data with an urgency score less than or equal to 3 and an inspection task dependency score greater than 4 is classified as auxiliary obstacle avoidance data (such as environmental image information and point information), and the category labels of the environmental image information and point information are set as the auxiliary obstacle avoidance data label. The remaining mine obstacle avoidance data (such as the front infrared obstruction status, the rear infrared obstruction status, the front radar obstruction status, and the rear radar obstruction status) is both primary and auxiliary obstacle avoidance data, and the category labels of the above-mentioned mine obstacle avoidance data are set as the primary obstacle avoidance data label and the auxiliary obstacle avoidance data label. The primary obstacle avoidance service node 130 automatically classifies the various input mine obstacle avoidance data according to the preset category labels, classifying them into primary obstacle avoidance data and auxiliary obstacle avoidance data.

[0111] Each type of mine obstacle avoidance data has its corresponding obstacle avoidance rules. The following uses some main obstacle avoidance data as an example to explain in detail:

[0112] The obstacle avoidance rule corresponding to the battery status parameter is that when the battery power drops to the range of 10% to 15%, a low-battery alarm is triggered, the current inspection task is suspended, and a return-to-origin instruction is executed.

[0113] The obstacle avoidance rule for methane gas concentration is that when the methane gas concentration exceeds 1% (with a fluctuation range of ±0.05%), a methane over-limit alarm is triggered, and the robot is forced to make an emergency stop, interrupting the current inspection mission. Simultaneously, the methane gas concentration in the mine environment is continuously monitored and the methane over-limit alarm is continuously issued (i.e., the methane over-limit alarm is self-locking). The remaining external obstacle avoidance detection equipment (non-methane-related detection equipment) and the robot's drive motor are powered off.

[0114] The obstacle avoidance rule corresponding to the methane emergency stop and recovery button status information is as follows: when the obstacle avoidance control system is in the methane over-limit alarm state, if the methane emergency stop and recovery switch continuously outputs a high-level signal for more than 5000ms, the methane over-limit alarm is temporarily released from its self-locking state. When the methane gas concentration drops below 1.0% ± 0.05%, the methane over-limit alarm is released. When the methane concentration drops below 0.85%, power is restored to the remaining external obstacle avoidance detection devices and the robot's drive motors.

[0115] The master obstacle avoidance service node 130 writes the obstacle avoidance rules corresponding to the master obstacle avoidance data into the master obstacle avoidance service rule set one by one, and stores the master obstacle avoidance service rule set in the obstacle avoidance signal data cache node 102 .

[0116] When the master obstacle avoidance service node 130 receives the master obstacle avoidance data, it first verifies whether the master obstacle avoidance data is null (None). If the master obstacle avoidance data is not null, the corresponding data range and data format (such as numeric or switch data) are checked to ensure that the master obstacle avoidance data is valid and conforms to the JSON database storage format. If the master obstacle avoidance data is null, the data range is not within the preset range, or the data format is incorrect, the master obstacle avoidance data is invalid, the master obstacle avoidance data is deleted, and the obstacle avoidance detection device corresponding to the master obstacle avoidance data is marked as faulty.

[0117] After verifying the primary obstacle avoidance data, the obstacle avoidance rules are determined. The primary obstacle avoidance service node 130 retrieves the primary obstacle avoidance service rule set from the obstacle avoidance signal data cache node 102 and stores each rule in its obstacle avoidance rule detector. Each obstacle avoidance rule is compared with the corresponding primary obstacle avoidance data using the rule thresholds in the primary obstacle avoidance service rule set. If any obstacle avoidance rule is triggered, the primary obstacle avoidance service node 130 immediately generates an obstacle avoidance signal and records the corresponding obstacle avoidance status.

[0118] After the obstacle avoidance signal is generated, the master obstacle avoidance service node 130 transmits the signal via Servcie communication to the robot inspection node 104 and the sound and light alarm node 105. The robot inspection node 104 controls the robot to perform an emergency stop, and the sound and light alarm node 105 issues an audible and visual alarm. The master obstacle avoidance service node 130 then sends the recorded obstacle avoidance status to the obstacle avoidance signal data cache node 102 for storage. At this point, the obstacle avoidance control system exits the master obstacle avoidance service node 130.

[0119] In one embodiment, the secondary obstacle avoidance service node 131 is specifically configured to establish a secondary obstacle avoidance service rule set based on the obstacle avoidance rules corresponding to the secondary obstacle avoidance data. The secondary obstacle avoidance service rule set compares the corresponding secondary obstacle avoidance data with the rule thresholds and / or robot inspection task status of each secondary obstacle avoidance data. The secondary obstacle avoidance service rule set may include obstacle avoidance rules not covered by the primary obstacle avoidance rule set, thereby optimizing the obstacle avoidance strategy and improving the accuracy of obstacle avoidance decisions.

[0120] Specifically, the obstacle avoidance rules corresponding to some auxiliary obstacle avoidance data are as follows:

[0121] The obstacle avoidance rule corresponding to the point information is that when the point information is the extreme end mark, the robot is controlled to stop or reverse the inspection operation. When the point information is the docking mark, the robot is controlled to make an emergency dock.

[0122] The obstacle avoidance rules corresponding to the environmental image information are: if the hydraulic support in front of the robot moves, the robot is controlled to make an emergency stop. If the shearer in front of the robot moves, the robot is controlled to switch to the machine-following inspection mode, which allows the human to follow the shearer to perform inspection tasks and appropriately controls the robot's speed to ensure that the robot and the shearer maintain a safe operating distance.

[0123] The auxiliary obstacle avoidance service node 131 writes the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data into the auxiliary obstacle avoidance service rule set one by one, and stores the auxiliary obstacle avoidance service rule set in the obstacle avoidance signal data cache node 102 .

[0124] When the auxiliary obstacle avoidance service node 131 receives auxiliary obstacle avoidance data, it first performs data verification on the auxiliary obstacle avoidance data to check whether the auxiliary obstacle avoidance data is a null value (None). If the auxiliary obstacle avoidance data is not a null value, the corresponding data range and data format (such as numerical type, switch data, or image data) are checked to ensure that the auxiliary obstacle avoidance data is valid and conforms to the JSON database storage format. If the auxiliary obstacle avoidance data is a null value, the data range is not within the preset range, or the data format is incorrect, it indicates that the auxiliary obstacle avoidance data is invalid, the auxiliary obstacle avoidance data is deleted, and the obstacle avoidance detection device corresponding to the auxiliary obstacle avoidance data is marked as a fault state.

[0125] After verifying the auxiliary obstacle avoidance data, the obstacle avoidance rule is determined. The auxiliary obstacle avoidance service node 131 obtains the auxiliary obstacle avoidance service rule set from the obstacle avoidance signal data cache node 102 and stores each rule in its obstacle avoidance rule detector. Each obstacle avoidance rule is compared with the corresponding auxiliary obstacle avoidance data using the rule threshold in the auxiliary obstacle avoidance service rule set. If any obstacle avoidance rule is triggered, the auxiliary obstacle avoidance service node 131 immediately generates an obstacle avoidance signal and records the corresponding obstacle avoidance status.

[0126] After generating an obstacle avoidance signal, the auxiliary obstacle avoidance service node 131 transmits the signal via Servcie communication to the robot inspection node 104 and the sound and light alarm node 105. The robot inspection node 104 controls the robot to make an emergency stop or execute the corresponding inspection command, and the sound and light alarm node 105 issues an audible or visual alarm. The auxiliary obstacle avoidance service node 131 then sends the recorded obstacle avoidance status to the obstacle avoidance signal data cache node 102 for storage. At this point, the obstacle avoidance control system exits the auxiliary obstacle avoidance service node 131.

[0127] In one embodiment, the primary obstacle avoidance service node 130 establishes a coverable rule set based on the intersection of the primary and secondary obstacle avoidance service rule sets. If the primary obstacle avoidance service node 130 fails, the secondary obstacle avoidance service node 131 compares the corresponding mine obstacle avoidance data with the rule thresholds of each mine obstacle avoidance data in the coverable rule set.

[0128] Specifically, the front infrared blockage status, rear infrared blockage status, front radar blockage status, and rear radar blockage status in the mine obstacle avoidance data are both primary and auxiliary obstacle avoidance data. The corresponding obstacle avoidance rules are as follows:

[0129] The obstacle avoidance rule corresponding to the front infrared occlusion state is that when the front infrared occlusion state is 1, the robot is controlled to make an emergency stop, suspend the current inspection task and trigger a collision alarm.

[0130] The obstacle avoidance rule corresponding to the rear infrared occlusion state is that when the rear infrared occlusion state is 1, the robot is controlled to make an emergency stop, suspend the current inspection task and trigger a collision alarm.

[0131] The obstacle avoidance rule for the forward radar blockage status is: When the forward radar blockage status is 1 and the obstacle distance is between 500mm and 750mm, a collision alarm is triggered and the robot's operating speed is reduced to below 100mm / s. When the forward radar blockage status is 1 and the obstacle distance is less than 500mm, the robot is controlled to make an emergency stop, suspending the current inspection task and triggering a collision alarm.

[0132] Obstacle avoidance rules for the rear radar blockage status: When the rear radar blockage status is 1 and the obstacle distance is between 500mm and 750mm, a collision alarm is triggered and the robot's operating speed is reduced to below 100mm / s. When the rear radar blockage status is 1 and the obstacle distance is less than 500mm, the robot is controlled to make an emergency stop, suspending the current inspection task and triggering a collision alarm.

[0133] The master obstacle avoidance service node 130 writes the obstacle avoidance rules corresponding to the obstacle avoidance data into the overridable rule set one by one, and stores the overridable rule set in the obstacle avoidance signal data cache node 102 .

[0134] If the primary obstacle avoidance service node 130 is not faulty, it compares the corresponding mine obstacle avoidance data with the rule thresholds for each mine obstacle avoidance data in the covered rule set. If the primary obstacle avoidance service node 130 is faulty, the secondary obstacle avoidance service node 131 compares the corresponding mine obstacle avoidance data with the rule thresholds for each mine obstacle avoidance data in the covered rule set.

[0135] In an embodiment of the present invention, a robot comprehensive obstacle avoidance service node 103 capable of adding and deleting obstacle avoidance rules is adopted, combined with a dual management mechanism of a main obstacle avoidance service node and an auxiliary obstacle avoidance service node, so that the obstacle avoidance control system can flexibly adapt to the obstacle avoidance needs in different scenarios. Through the classified management of obstacle avoidance rules, the main obstacle avoidance service node is responsible for executing basic safety obstacle avoidance rules, ensuring that the robot can immediately stop running in an emergency to avoid safety accidents. The auxiliary obstacle avoidance service node supports scalable obstacle avoidance rules, allowing the robot to optimize inspection strategies, such as detours, speed adjustment, or dynamic obstacle avoidance, thereby improving the adaptability and robustness of the obstacle avoidance control system. In addition, the obstacle avoidance rules adopt an OR logical judgment method, and are combined with an obstacle avoidance data verification mechanism to ensure the validity of the input data and improve the accuracy of obstacle avoidance decisions. In addition, the obstacle avoidance control system supports dynamic shielding of obstacle avoidance rules, so that the robot has the ability to operate with faults, and can continue to perform inspection tasks even when some sensors fail, reducing downtime.

[0136] The embodiment of the present invention further provides an inspection robot obstacle avoidance control method, which is applied to the robot comprehensive obstacle avoidance service node 103. The robot comprehensive obstacle avoidance service node 103 includes a primary obstacle avoidance service node 130 and an auxiliary obstacle avoidance service node 131.

[0137] like Figure 2 As shown, the inspection robot obstacle avoidance control method includes steps 201 to 202.

[0138] Step 201: The primary obstacle avoidance service node 130 receives primary obstacle avoidance data, and the secondary obstacle avoidance service node 131 receives secondary obstacle avoidance data.

[0139] Specifically, the primary obstacle avoidance service node 130 receives all mine obstacle avoidance data, classifies the mine obstacle avoidance data, and sends the secondary obstacle avoidance data to the secondary obstacle avoidance service node 131 .

[0140] Step 202: The main obstacle avoidance business node 130 determines the main obstacle avoidance rules for the main obstacle avoidance data according to the stored main obstacle avoidance business rule set, and the auxiliary obstacle avoidance business node 131 determines the auxiliary obstacle avoidance rules according to the stored auxiliary obstacle avoidance business rule set. If mine obstacle avoidance data that meets the obstacle avoidance rules appears, an obstacle avoidance signal is generated and transmitted to the robot inspection node 104 and the sound and light alarm node 105, so that the robot inspection node 104 controls the robot to make an emergency stop according to the obstacle avoidance signal, and the sound and light alarm node 105 performs a sound and light alarm.

[0141] Specifically, after the obstacle avoidance signal is generated, the primary obstacle avoidance service node 130 and / or the secondary obstacle avoidance service node 131 transmit the obstacle avoidance signal via Servcie communication to the robot inspection node 104 and the sound and light alarm node 105, respectively. The robot inspection node 104 controls the robot to perform an emergency stop, and the sound and light alarm node 105 issues an audible and visual alarm. The primary obstacle avoidance service node 130 and / or the secondary obstacle avoidance service node 131 transmit the recorded obstacle avoidance status to the obstacle avoidance signal data cache node 102 for storage. At this point, the obstacle avoidance control system exits the primary obstacle avoidance service node 130 and / or the secondary obstacle avoidance service node 131.

[0142] like Figure 3 As shown, the primary obstacle avoidance service node 130 performs primary obstacle avoidance rule determination on the primary obstacle avoidance data according to the stored primary obstacle avoidance service rule set, including steps 301 to 303 .

[0143] Step 301: The main obstacle avoidance service node 130 determines the category label to which each mine obstacle avoidance data belongs based on the degree of impact of each obstacle avoidance data on the robot's operating status and safety.

[0144] Specifically, mine obstacle avoidance data includes battery status parameters, methane gas concentration, methane concentration level, status information of the methane emergency stop recovery button, front infrared blocking status, rear infrared blocking status, front radar blocking status, rear radar blocking status, environmental image information and point information, etc.

[0145] All mine obstacle avoidance data is scored based on its urgency and inspection task dependency. Urgency indicates whether it directly impacts the robot's safe operation, while inspection task dependency indicates whether it is used for inspection task optimization rather than direct emergency obstacle avoidance. Both urgency and inspection task dependency are scored on a scale of 1 to 5, but the present invention is not limited to this.

[0146] For example, the urgency of battery status parameters and methane gas concentration are both 5 (directly impacting inspection execution), and the inspection task dependency is 2. The urgency of methane concentration level is 5 (immediately releasing the robot from emergency stop), and the inspection task dependency is 3. The urgency of the front infrared blockage, rear infrared blockage, front radar blockage, and rear radar blockage are all 4 (affecting the robot's inspection path), and the inspection task dependency is 4. The urgency of environmental image information is 3 (affecting the robot's inspection strategy), and the inspection task dependency is 5. The urgency of point information is 2 (affecting the robot's inspection optimization), and the inspection task dependency is 5.

[0147] The main obstacle avoidance service node 130 classifies mine obstacle avoidance data with an urgency score greater than 4 and an inspection task dependency score less than 4 as primary obstacle avoidance data (such as battery status parameters, methane gas concentration, and methane concentration level), and sets the category labels of the battery status parameters, methane gas concentration, and methane concentration level as primary obstacle avoidance data labels. The main obstacle avoidance service node 130 classifies mine obstacle avoidance data with an urgency score less than or equal to 3 and an inspection task dependency score greater than 4 as auxiliary obstacle avoidance data (such as environmental image information and point information), and sets the category labels of the environmental image information and point information as auxiliary obstacle avoidance data labels. The remaining mine obstacle avoidance data (such as the front infrared occlusion status, the rear infrared occlusion status, the front radar occlusion status, and the rear radar occlusion status) is both primary and auxiliary obstacle avoidance data, and the category labels of the above-mentioned mine obstacle avoidance data are set as the primary obstacle avoidance data label and the auxiliary obstacle avoidance data label.

[0148] Step 302: The primary obstacle avoidance service node 130 determines primary obstacle avoidance data and auxiliary obstacle avoidance data according to the category labels of the obstacle avoidance data of each mine, and establishes a primary obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the primary obstacle avoidance data.

[0149] Specifically, the primary obstacle avoidance service node 130 automatically categorizes the input mine obstacle avoidance data into primary obstacle avoidance data and auxiliary obstacle avoidance data based on pre-set category labels. The primary obstacle avoidance service node 130 writes each obstacle avoidance rule corresponding to the primary obstacle avoidance data into a primary obstacle avoidance service rule set and stores the primary obstacle avoidance service rule set in the obstacle avoidance signal data cache node 102.

[0150] Step 303: The primary obstacle avoidance service node 130 compares the corresponding primary obstacle avoidance data with the rule threshold of each primary obstacle avoidance data in the established primary obstacle avoidance service rule set.

[0151] Specifically, the primary obstacle avoidance service node 130 first verifies the primary obstacle avoidance data to see if it is null. If it is not, the corresponding data range and data format (e.g., numeric or switch data) are checked to ensure that the primary obstacle avoidance data is valid and conforms to the JSON database storage format. If the primary obstacle avoidance data is null, the data range is not within the preset range, or the data format is incorrect, indicating that the primary obstacle avoidance data is invalid, the primary obstacle avoidance data is deleted, and the obstacle avoidance detection device corresponding to the primary obstacle avoidance data is marked as faulty.

[0152] After verifying the primary obstacle avoidance data, the obstacle avoidance rules are determined. The primary obstacle avoidance service node 130 retrieves the primary obstacle avoidance service rule set from the obstacle avoidance signal data cache node 102 and enters each rule into its obstacle avoidance rule detector. Each obstacle avoidance rule is evaluated using an OR operator. The obstacle avoidance rule detector compares each primary obstacle avoidance data entry with the corresponding primary obstacle avoidance data using the rule threshold in the primary obstacle avoidance service rule set.

[0153] For example, when the received primary obstacle avoidance data is a methane gas concentration of 1.2%, it is compared with the obstacle avoidance rule corresponding to the methane gas concentration. At this time, the methane gas concentration exceeds the limit, and the primary obstacle avoidance service node 130 generates an obstacle avoidance signal.

[0154] like Figure 4 As shown, the above-mentioned auxiliary obstacle avoidance service node 131 performs auxiliary obstacle avoidance rule determination according to the stored auxiliary obstacle avoidance service rule set, including steps 401 to 402.

[0155] Step 401: The auxiliary obstacle avoidance service node 131 establishes an auxiliary obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data.

[0156] Specifically, the obstacle avoidance rules corresponding to some auxiliary obstacle avoidance data are as follows:

[0157] The obstacle avoidance rule corresponding to the point information is that when the point information is the extreme end mark, the robot is controlled to stop or reverse the inspection operation. When the point information is the docking mark, the robot is controlled to make an emergency dock.

[0158] The obstacle avoidance rules corresponding to the environmental image information are: if the hydraulic support in front of the robot moves, the robot is controlled to make an emergency stop. If the shearer in front of the robot moves, the robot is controlled to switch to the machine-following inspection mode, which allows the human to follow the shearer to perform inspection tasks and appropriately controls the robot's speed to ensure that the robot and the shearer maintain a safe operating distance.

[0159] The auxiliary obstacle avoidance service node 131 writes the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data into the auxiliary obstacle avoidance service rule set one by one, and stores the auxiliary obstacle avoidance service rule set in the obstacle avoidance signal data cache node 102 .

[0160] Step 402: The auxiliary obstacle avoidance service node 131 compares the auxiliary obstacle avoidance data with the corresponding auxiliary obstacle avoidance data according to the rule threshold of each auxiliary obstacle avoidance data in the established auxiliary obstacle avoidance service rule set and / or the robot inspection task status.

[0161] Specifically, the auxiliary obstacle avoidance service node 131 first verifies the auxiliary obstacle avoidance data to see if it is null. If it is not, the node verifies the corresponding data range and data format (such as numeric, switch data, or image data) to ensure that the auxiliary obstacle avoidance data is valid and conforms to the JSON database storage format. If the auxiliary obstacle avoidance data is null, the data range is not within the preset range, or the data format is incorrect, indicating that the auxiliary obstacle avoidance data is invalid, the auxiliary obstacle avoidance data is deleted, and the obstacle avoidance detection device corresponding to the auxiliary obstacle avoidance data is marked as faulty.

[0162] After verifying the auxiliary obstacle avoidance data, the obstacle avoidance rules are determined. The auxiliary obstacle avoidance service node 131 obtains the auxiliary obstacle avoidance service rule set from the obstacle avoidance signal data cache node 102 and enters each rule into its obstacle avoidance rule detector. Each obstacle avoidance rule is judged in an OR fashion. The obstacle avoidance rule detector compares each auxiliary obstacle avoidance data entry with the corresponding auxiliary obstacle avoidance data according to the rule threshold in the auxiliary obstacle avoidance service rule set.

[0163] For example, when the received auxiliary obstacle avoidance data is a limit endpoint identifier, the limit endpoint identifier is compared with its corresponding obstacle avoidance rule, and the auxiliary obstacle avoidance service node 131 generates an obstacle avoidance signal (to control the robot to stop running or reverse driving).

[0164] like Figure 5 As shown, the inspection robot obstacle avoidance control method further includes steps 501 to 502.

[0165] Step 501: The primary obstacle avoidance service node 130 establishes an overlayable rule set according to the intersection of the primary obstacle avoidance service rule set and the secondary obstacle avoidance service rule set.

[0166] Specifically, the front infrared blockage status, rear infrared blockage status, front radar blockage status, and rear radar blockage status in the mine obstacle avoidance data are both primary and auxiliary obstacle avoidance data. The corresponding obstacle avoidance rules are as follows:

[0167] The obstacle avoidance rule corresponding to the front infrared occlusion state is that when the front infrared occlusion state is 1, the robot is controlled to make an emergency stop, suspend the current inspection task and trigger a collision alarm.

[0168] The obstacle avoidance rule corresponding to the rear infrared occlusion state is that when the rear infrared occlusion state is 1, the robot is controlled to make an emergency stop, suspend the current inspection task and trigger a collision alarm.

[0169] The obstacle avoidance rule for the forward radar blockage status is: When the forward radar blockage status is 1 and the obstacle distance is between 500mm and 750mm, a collision alarm is triggered and the robot's operating speed is reduced to below 100mm / s. When the forward radar blockage status is 1 and the obstacle distance is less than 500mm, the robot is controlled to make an emergency stop, suspending the current inspection task and triggering a collision alarm.

[0170] Obstacle avoidance rules for the rear radar blockage status: When the rear radar blockage status is 1 and the obstacle distance is between 500mm and 750mm, a collision alarm is triggered and the robot's operating speed is reduced to below 100mm / s. When the rear radar blockage status is 1 and the obstacle distance is less than 500mm, the robot is controlled to make an emergency stop, suspending the current inspection task and triggering a collision alarm.

[0171] The master obstacle avoidance service node 130 writes the obstacle avoidance rules corresponding to the obstacle avoidance data into the overridable rule set one by one, and stores the overridable rule set in the obstacle avoidance signal data cache node 102 .

[0172] Step 502: When the primary obstacle avoidance service node 130 fails, the secondary obstacle avoidance service node 131 compares the corresponding mine obstacle avoidance data according to the rule threshold of each mine obstacle avoidance data in the covering rule set.

[0173] Specifically, if the primary obstacle avoidance service node 130 is not faulty, the primary obstacle avoidance service node 130 compares the corresponding mine obstacle avoidance data based on the rule threshold of each mine obstacle avoidance data in the covered rule set. If the primary obstacle avoidance service node 130 is faulty, the secondary obstacle avoidance service node 131 compares the corresponding mine obstacle avoidance data based on the rule threshold of each mine obstacle avoidance data in the covered rule set.

[0174] In an embodiment of the present invention, a dual management mechanism that allows the addition and deletion of obstacle avoidance rules and combines the main obstacle avoidance business node and the auxiliary obstacle avoidance business node is adopted, so that the obstacle avoidance control system can flexibly adapt to the obstacle avoidance needs in different scenarios. Through the classified management of obstacle avoidance rules, the main obstacle avoidance business node is responsible for executing basic safety obstacle avoidance rules, ensuring that the robot can immediately stop running in an emergency to avoid safety accidents. The auxiliary obstacle avoidance business node supports extensible obstacle avoidance rules, allowing the robot to optimize inspection strategies, such as detours, speed adjustments, or dynamic obstacle avoidance, thereby improving the adaptability and robustness of the obstacle avoidance control system. In addition, the obstacle avoidance rules adopt an OR logical judgment method, combined with an obstacle avoidance data verification mechanism, to ensure the validity of the input data and improve the accuracy of obstacle avoidance decisions.

[0175] Figure 6 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 6As shown, the electronic device 600 includes a processor 601 , a memory 602 and a bus 603 .

[0176] The processor 601 and the memory 602 communicate with each other via a bus 603 .

[0177] The processor 601 is configured to call program instructions in the memory 602 to execute the methods provided by the above method embodiments.

[0178] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the obstacle avoidance control method of the inspection robot is implemented.

[0179] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned inspection robot obstacle avoidance control method.

[0180] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0184] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An inspection robot obstacle avoidance control system, characterized in that: include: The obstacle avoidance signal collection node is provided with multiple data collection nodes for collecting various mine obstacle avoidance data. Each data collection node periodically collects obstacle avoidance data through a timing collection function. The mine obstacle avoidance data includes main obstacle avoidance data and auxiliary obstacle avoidance data. The same type of mine obstacle avoidance data may belong to both main obstacle avoidance data and auxiliary obstacle avoidance data. An obstacle avoidance signal data caching node, connected to a plurality of the data acquisition nodes, for caching the mine obstacle avoidance data, the mine obstacle avoidance data being periodically cached by the obstacle avoidance signal acquisition nodes through a data caching function; A robot comprehensive obstacle avoidance service node includes a primary obstacle avoidance service node and an auxiliary obstacle avoidance service node. The primary obstacle avoidance service node receives the primary obstacle avoidance data, and the auxiliary obstacle avoidance service node receives the auxiliary obstacle avoidance data. The primary obstacle avoidance service node performs a primary obstacle avoidance rule determination on the primary obstacle avoidance data according to a stored primary obstacle avoidance service rule set. The auxiliary obstacle avoidance service node performs a secondary obstacle avoidance rule determination according to a stored secondary obstacle avoidance service rule set. If mine obstacle avoidance data that meets the obstacle avoidance rule appears, an obstacle avoidance signal is generated. The robot inspection node receives the obstacle avoidance signal and controls the robot to perform obstacle avoidance actions according to the obstacle avoidance signal.

2. The system according to claim 1, wherein: The main obstacle avoidance business node is specifically used to: determine the category label of each mine obstacle avoidance data according to the degree of influence of each mine obstacle avoidance data on the robot's operating status and safety; determine the main obstacle avoidance data and auxiliary obstacle avoidance data according to the category label of each mine obstacle avoidance data, and establish a main obstacle avoidance business rule set according to the obstacle avoidance rules corresponding to the main obstacle avoidance data; compare the rule threshold of each main obstacle avoidance data in the established main obstacle avoidance business rule set with the corresponding main obstacle avoidance data.

3. The system according to claim 2, characterized in that The auxiliary obstacle avoidance service node is specifically used to: establish an auxiliary obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data; and compare the rule threshold and / or robot inspection task status of each auxiliary obstacle avoidance data in the established auxiliary obstacle avoidance service rule set with the corresponding auxiliary obstacle avoidance data.

4. The system according to claim 3, characterized in that The main obstacle avoidance service node establishes a coverable rule set based on the intersection of the main obstacle avoidance service rule set and the auxiliary obstacle avoidance service rule set. When the main obstacle avoidance service node fails, the auxiliary obstacle avoidance service node compares the rule threshold of each mine obstacle avoidance data in the coverable rule set with the corresponding mine obstacle avoidance data.

5. The system according to claim 1, wherein: The obstacle avoidance signal acquisition node includes: A battery node, configured to collect battery status parameters inside the robot according to a first time interval; a methane sensor node, configured to collect a methane gas concentration around the robot according to a second time interval and determine a methane concentration level of the methane gas concentration according to a preset rule; A methane emergency stop recovery node is used to collect status information of a methane emergency stop recovery button on the robot according to a third time interval; The front infrared proximity switch node is used to collect the infrared occlusion status of the robot in front of the infrared sensor; The rear infrared proximity switch node is used to collect infrared occlusion status of the robot's rear through an infrared sensor; The front radar obstacle avoidance sensor node is used to collect distance information of obstacles within a preset range in front of the robot; determine whether the distance information exceeds the safe collision threshold and generate a front radar obstruction state; The rear radar obstacle avoidance sensor node is used to collect distance information of obstacles within a preset range behind the robot; determine whether the distance information exceeds the safe collision threshold and generate a rear radar obstruction state.

6. The system according to claim 1, wherein: The obstacle avoidance signal acquisition node also includes: Multiple visual camera nodes are used to collect image information of the environment in front of the robot; The RFID reader node is used to scan the RFID tags on the robot's inspection path and generate point information based on the analysis of the RFID tags.

7. A patrol robot obstacle avoidance control method, characterized in that: Applied to the robot comprehensive obstacle avoidance service node, the robot comprehensive obstacle avoidance service node includes a main obstacle avoidance service node and an auxiliary obstacle avoidance service node, and the inspection robot obstacle avoidance control method includes: The primary obstacle avoidance service node receives primary obstacle avoidance data, and the secondary obstacle avoidance service node receives secondary obstacle avoidance data; The main obstacle avoidance service node determines the main obstacle avoidance rules on the main obstacle avoidance data according to the stored main obstacle avoidance service rule set, and the auxiliary obstacle avoidance service node determines the auxiliary obstacle avoidance rules according to the stored auxiliary obstacle avoidance service rule set. If mine obstacle avoidance data that meets the obstacle avoidance rules appears, an obstacle avoidance signal is generated, and the obstacle avoidance signal is transmitted to the robot inspection node and the sound and light alarm node, so that the robot inspection node controls the robot to make an emergency stop according to the obstacle avoidance signal, and the sound and light alarm node performs an sound and light alarm.

8. The inspection robot obstacle avoidance control method according to claim 7, characterized in that: The primary obstacle avoidance service node performs a primary obstacle avoidance rule determination on the primary obstacle avoidance data according to the stored primary obstacle avoidance service rule set, including: The main obstacle avoidance service node determines the category label to which each mine obstacle avoidance data belongs based on the degree of influence of each obstacle avoidance data on the robot's operating status and safety; The main obstacle avoidance service node determines the main obstacle avoidance data and the auxiliary obstacle avoidance data according to the category label of each mine obstacle avoidance data, and establishes the main obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the main obstacle avoidance data; The primary obstacle avoidance service node compares the rule threshold of each primary obstacle avoidance data with the corresponding primary obstacle avoidance data according to the established primary obstacle avoidance service rule set.

9. The inspection robot obstacle avoidance control method according to claim 8, characterized in that: The auxiliary obstacle avoidance service node performs auxiliary obstacle avoidance rule determination according to the stored auxiliary obstacle avoidance service rule set, including: The auxiliary obstacle avoidance service node establishes an auxiliary obstacle avoidance service rule set according to the obstacle avoidance rules corresponding to the auxiliary obstacle avoidance data; The auxiliary obstacle avoidance service node compares the rule threshold of each auxiliary obstacle avoidance data and / or the robot inspection task status with the corresponding auxiliary obstacle avoidance data in the established auxiliary obstacle avoidance service rule set.

10. The inspection robot obstacle avoidance control method according to claim 9, characterized in that: Also includes: The primary obstacle avoidance service node establishes an overlayable rule set according to the intersection of the primary obstacle avoidance service rule set and the auxiliary obstacle avoidance service rule set; When the primary obstacle avoidance service node fails, the secondary obstacle avoidance service node compares the corresponding mine obstacle avoidance data according to the rule threshold of each mine obstacle avoidance data in the covering rule set.

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