Cable tunnel robot control method and system based on multiple auxiliary devices

Abstract

The invention relates to the technical field of adaptive track and attitude control systems of land mobile robots, in particular to a cable tunnel robot control method and system based on multiple auxiliary devices. The invention discloses a cable tunnel robot control method based on multiple auxiliary devices. The cable tunnel robot control method comprises the following steps that S1, terrain profile data and cable channel environment data are obtained, and a basic path unit is determined; and calculating each basic path unit to obtain a terrain consistency coefficient and an environment consistency coefficient, and forming a terrain consistency coefficient sequence and an environment consistency coefficient sequence according to the terrain consistency coefficient and the environment consistency coefficient. According to the invention, a multi-dimensional dynamic stability criterion is constructed, environment fine grit perception and multi-equipment collaborative evaluation are fused, efficient inspection, fine detection and high-stability force control modes are dynamically switched, movement and operation integration is realized, and adaptability, stability and task continuity of the robot in the cable tunnel are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of adaptive trajectory and attitude control system technology for land mobile robots, and in particular to a control method and system for cable tunnel robots based on multiple auxiliary devices. Background Technology

[0002] Cable tunnels, as critical infrastructure for power transmission, house high-voltage cables and various auxiliary equipment, forming a complex, enclosed environment with strong electromagnetic interference. These auxiliary devices, including communication relay robots, detail inspection robots, and manipulator robots, collectively constitute a collaborative operation system. Existing cable tunnel robot control technology acquires environmental information through LiDAR and vision sensors, employing fixed gait planning and threshold response mechanisms. This has achieved basic motion stability and task execution efficiency in inspection tasks on flat areas, promoting the initial automation of tunnel operation and maintenance.

[0003] However, traditional methods have serious shortcomings in dealing with sudden changes in ground material, dynamic electromagnetic interference, and multi-device collaborative scheduling within tunnels. Control strategies based on static environment modeling cannot perceive the consistent changes in terrain friction coefficient and visual features in real time, and lack joint quantification of local environmental stability and global switching risks. This leads to gait instability, positioning drift, and task interruption problems when robots cross drainage ditches or areas with strong electromagnetic fields. Legged robots, especially quadruped robots, although they have the potential to adapt to terrain, also face the risk of instability in complex working conditions due to the lack of effective environmental coupled situational awareness and adaptive gait decision-making.

[0004] Therefore, there is an urgent need to study an adaptive method that can integrate multi-source features of terrain and environment, evaluate the system's stable state in real time, and dynamically adjust the control strategy to improve the robot's motion robustness and task continuity in the special environment of cable tunnels, and ultimately achieve a functional leap from single inspection to collaborative operation. Summary of the Invention

[0005] To overcome the shortcomings of insufficient dynamic stability control of robots in complex environments, this invention provides a control method and system for cable tunnel robots based on multiple auxiliary devices.

[0006] The technical solution of this invention is: a cable tunnel robot control method based on multiple auxiliary devices, comprising the following steps: S1: Obtain terrain profile data and cable channel environmental data and determine basic path units; calculate the terrain consistency coefficient and environmental consistency coefficient for each basic path unit, and construct a terrain consistency coefficient sequence and an environmental consistency coefficient sequence based on the terrain consistency coefficient and environmental consistency coefficient. S2: Generate N consecutive local path segments based on the terrain consistency coefficient sequence and the environmental consistency coefficient sequence; for each local path segment, calculate the mean terrain consistency coefficient and the mean environmental consistency coefficient of all basic path units within it, and determine the cooperative stable zone, cooperative challenge zone, cross challenge zone and uncertain zone based on the mean terrain consistency coefficient and the mean environmental consistency coefficient, combined with the terrain-environment correlation degree. S3: Obtain the terrain change degree and environmental change degree based on adjacent local path segments, and determine the comprehensive change degree based on the terrain change degree and environmental change degree; obtain motion stability, perception stability and task execution degree, and obtain the average real-time stability of local path segments based on the motion stability, perception stability and task execution degree; S4: Determine the switching stability of adjacent local path segments based on the terrain change degree, environmental change degree, and multiple auxiliary devices; perform adaptive control of the cable tunnel robot based on the comprehensive change degree, average real-time stability, and switching stability.

[0007] Preferably, the step of acquiring terrain profile data and cable tunnel environmental data and determining basic path units includes: The terrain profile data includes an estimated ground friction coefficient and elevation variation variance; the cable tunnel environmental data includes electromagnetic field strength values ​​and visual texture self-similarity index. Geographic sampling points are set on the preset inspection path, and the path between adjacent geographic sampling points is defined as a basic path unit.

[0008] Preferably, the step of calculating the terrain consistency coefficient and environmental consistency coefficient for each basic path unit, and constructing a terrain consistency coefficient sequence and an environmental consistency coefficient sequence based on the terrain consistency coefficient and environmental consistency coefficient, includes: The eigenvalue variation coefficients of all topographic profile data and cable channel environmental data within the basic path unit are calculated to obtain the topographic consistency coefficient and environmental consistency coefficient of the basic path unit. The eigenvalue variation coefficient is calculated as the ratio of the standard deviation to the mean. Arrange all basic path units in spatial order to form a basic path unit sequence; The terrain consistency coefficients of each basic path unit in the basic path unit sequence are combined according to the spatial order of the corresponding basic path units to form a terrain consistency coefficient sequence. The environmental consistency coefficients of each basic path unit in the basic path unit sequence are combined according to the spatial order of the corresponding basic path units to form an environmental consistency coefficient sequence.

[0009] Preferably, generating N consecutive local path segments based on the terrain consistency coefficient sequence and the environmental consistency coefficient sequence includes: Calculate the Spearman rank correlation coefficient between the topographic consistency coefficient series and the environmental consistency coefficient series to obtain the degree of correlation between topography and environment. At coordinate points where the terrain consistency coefficient is lower than a preset terrain threshold or the environmental consistency coefficient is lower than a preset environmental threshold, the preset inspection path is divided into N continuous local path segments.

[0010] Preferably, for each local path segment, the average terrain consistency coefficient and the average environmental consistency coefficient of all basic path units within it are calculated. Based on these average terrain consistency coefficients and the average environmental consistency coefficients, and combined with the terrain-environment correlation, collaborative stability zones, collaborative challenge zones, cross-challenge challenge zones, and uncertain zones are determined, including: When the average terrain consistency coefficient is higher than the preset terrain threshold, the average environmental consistency coefficient is higher than the preset environmental threshold, and the terrain-environment correlation is higher than the preset positive correlation threshold, the local path segment is defined as a cooperative stable region. When the average terrain consistency coefficient is lower than the preset terrain threshold, the average environmental consistency coefficient is lower than the preset environmental threshold, and the terrain-environment correlation is higher than the preset positive correlation threshold, the local path segment is defined as a collaborative challenge zone. When the average terrain consistency coefficient is higher than the preset terrain threshold and the average environmental consistency coefficient is lower than the preset environmental threshold, or when the average terrain consistency coefficient is lower than the preset terrain threshold and the average environmental consistency coefficient is higher than the preset environmental threshold, and the terrain-environment correlation is lower than the preset negative correlation threshold, the local path segment is defined as the cross-challenge zone. When the absolute value of the terrain-environment correlation is lower than the preset uncertainty correlation threshold, the local path segment is defined as an uncertainty area.

[0011] Preferably, the step of obtaining the terrain change degree and environmental change degree based on adjacent local path segments, and determining the comprehensive change degree based on the terrain change degree and environmental change degree, includes: The absolute difference between the mean values ​​of the terrain consistency coefficients between adjacent local path segments is used as the degree of terrain variation. The absolute difference between the mean environmental consistency coefficients of adjacent local path segments is used as the degree of environmental variability. The Euclidean distance between adjacent local path segments in the feature space composed of terrain variability and environmental variability is used as the comprehensive variability.

[0012] Preferably, the step of acquiring motion stability, perception stability, and task execution degree, and obtaining the average real-time stability of a local path segment based on the motion stability, perception stability, and task execution degree, includes: The inverse of the variance of the body attitude angle is used as the motion stability, the inverse of the trace of the localization estimation covariance is used as the perception stability, and the success rate of subtask completion is used as the task execution degree. The motion stability, perception stability, and task execution degree are weighted and summed to obtain the real-time stability of each basic path unit; For each local path segment, the arithmetic mean of the real-time stability of all basic path units within it is calculated as the average real-time stability of the local path segment.

[0013] Preferably, determining the switching stability of adjacent local path segments based on the terrain change degree, environmental change degree, and multiple auxiliary devices includes: Based on the terrain and environmental variability, the environmental transition assessment value of adjacent local path segments is obtained using the environmental transition assessment value calculation formula. The environmental transition assessment value calculation formula is as follows: ;in, This is an environmental switching assessment value. As a baseline switching cost, For the degree of topographic variation, For environmental change degree, , These are the weighting coefficients; Based on the real-time status data of multiple auxiliary devices, the normalized average of the communication signal-to-noise ratio of all devices is calculated as the average communication link quality, and the normalized average of the battery capacity and component status of all devices is calculated as the average device health status. Based on the average communication link quality and the average device health status, the system collaborative evaluation value of adjacent local path segments is obtained using the system collaborative evaluation value calculation formula. The system collaborative evaluation value calculation formula is as follows: ;in, This is a system collaborative evaluation value. This represents the average quality of the communication link. This represents the average health status of the equipment. , These are the weighting coefficients; Based on the environmental handover assessment value and the system coordination assessment value, the handover situation stability is obtained using the handover situation stability calculation formula; the handover situation stability calculation formula is: ;in, To switch the state of stability.

[0014] Preferably, the adaptive control of the cable tunnel robot based on the comprehensive variability, average real-time stability, and switching state stability includes: obtaining the control strength coefficient of the tunnel robot according to the control strength coefficient calculation formula, wherein the control strength coefficient calculation formula is: ; in, To control the strength coefficient, For average real-time stability, To assess the overall degree of change, For the response coefficient, The regional risk coefficient is determined by the classification of collaboratively stable areas, collaboratively challenging areas, cross-challenging areas, or uncertain areas. When the control intensity coefficient is lower than the preset low threshold, the high-efficiency inspection mode is activated to control the robot to maintain its travel efficiency and perform routine inspection tasks. When the control intensity coefficient is higher than or equal to the preset low threshold and lower than the preset high threshold, the fine operation mode is activated, and the robot is controlled to enter a stable state and perform fine detection tasks. When the control strength coefficient is higher than or equal to the preset high threshold, the force control operation mode is activated to control the robot to establish a stable base and perform high-precision force control tasks.

[0015] Preferably, the cable tunnel robot control system based on multiple auxiliary devices includes: The environmental feature extraction module is used to acquire terrain profile data and cable channel environmental data, calculate the terrain consistency coefficient and environmental consistency coefficient of basic path units, and construct the terrain consistency coefficient sequence and environmental consistency coefficient sequence. The path segmentation and classification module is used to generate local path segments based on the terrain consistency coefficient sequence and the environmental consistency coefficient sequence, calculate the mean of terrain consistency coefficient and the mean of environmental consistency coefficient, and divide the area into cooperative stable area, cooperative challenge area, cross challenge area and uncertain area based on the terrain and environment correlation. The dynamic situation analysis module is used to calculate the terrain change and environmental change of adjacent local path segments, determine the comprehensive change, and integrate motion stability, perception stability and task execution to obtain the average real-time stability. The control decision module is used to evaluate the stability of the switching situation obtained by the environmental switching and system coordination, introduce the regional risk coefficient, calculate the control strength coefficient, and select the efficient inspection mode, adaptive operation mode or high stability operation mode accordingly, so as to realize the integrated adaptive control from mobile inspection to fine operation.

[0016] The beneficial effects are as follows: This invention achieves intelligent adaptive control of the robot in the complex environment of cable tunnels by constructing a multi-dimensional dynamic stability criterion. It utilizes fine-grained perception and zoning of the environmental situation to provide precise basis for decision-making; and by integrating real-time status and multi-device collaborative evaluation, it forms a forward-looking risk prediction. Finally, based on the control strength coefficient, it integrates and coordinates the control of the mobile chassis and the working tools, dynamically switching between three modes: efficient inspection, adaptive operation, and highly stable operation. This allows the robot to complete routine inspections while moving, perform fine detection within a stable window, and achieve force-controlled operation on a stable base. This effectively overcomes the limitations of traditional methods in adaptability to sudden ground changes and electromagnetic interference, significantly improves the robot's motion robustness, posture stability, and task continuity, and achieves a functional leap from "inspection only" to "integrated inspection and operation," greatly expanding its practical operational capabilities. Attached Figure Description

[0017] Figure 1 This is a flowchart of the cable tunnel robot control method based on multiple auxiliary devices according to the present invention; Figure 2 This is a structural diagram of the cable tunnel robot control system based on multiple auxiliary devices according to the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0019] Example 1: A control method for a cable tunnel robot based on multiple auxiliary devices, such as... Figure 1 As shown, it includes the following steps: S1-1: Acquire topographic profile data and cable tunnel environmental data and determine basic path units, including: The terrain profile data includes an estimated ground friction coefficient and elevation variation variance; the cable tunnel environmental data includes electromagnetic field strength values ​​and visual texture self-similarity index. Geographic sampling points are set on the preset inspection path, and the path between adjacent geographic sampling points is defined as a basic path unit.

[0020] It should be noted that in the motion control system of the cable tunnel robot, the initial environmental perception stage requires accurate acquisition of the physical characteristics of the terrain and environment. The estimated ground friction coefficient in the terrain profile data is obtained by directly measuring the friction and normal force of the contact surface using force sensors on the robot, and calculated based on the classical Coulomb's law of friction. This parameter directly determines the robot's traction force and slippage risk. The elevation variation variance is obtained by scanning the ground with a two-dimensional lidar, acquiring a set of elevation points, and then calculating its statistical variance to quantify the smoothness of the terrain surface. The electromagnetic field strength value in the environmental data is collected by a broadband electromagnetic sensor to monitor the intensity of cable radiation interference sources in real time. The visual texture self-similarity index is obtained by capturing ground images with a camera, extracting texture feature values ​​using a gray-level co-occurrence matrix algorithm, and then calculating its autocorrelation coefficient to evaluate the reliability of visual navigation. The preset inspection path is planned using a piecewise linearization method based on the tunnel topology, and geographical sampling points are laid out at equal intervals. The path between adjacent sampling points is divided into basic path units, such as continuous path segments with a length of 0.5 meters. This discretization method effectively solves the motion instability problem caused by insufficient environmental perception granularity in traditional control. By extracting features independently for each unit, it provides a high-precision environmental modeling foundation for subsequent adaptive trajectory planning.

[0021] S1-2: Calculate the terrain consistency coefficient and environmental consistency coefficient for each basic path unit, and construct a terrain consistency coefficient sequence and an environmental consistency coefficient sequence based on the terrain consistency coefficient and environmental consistency coefficient, including: The eigenvalue variation coefficients of all topographic profile data and cable channel environmental data within the basic path unit are calculated to obtain the topographic consistency coefficient and environmental consistency coefficient of the basic path unit. The eigenvalue variation coefficient is calculated as the ratio of the standard deviation to the mean. Arrange all basic path units in spatial order to form a basic path unit sequence; The terrain consistency coefficients of each basic path unit in the basic path unit sequence are combined according to the spatial order of the corresponding basic path units to form a terrain consistency coefficient sequence. The environmental consistency coefficients of each basic path unit in the basic path unit sequence are combined according to the spatial order of the corresponding basic path units to form an environmental consistency coefficient sequence.

[0022] It should be noted that steps S1-2 address the control instability problem caused by traditional static modeling by quantifying the consistency of environmental features. The terrain consistency coefficient is calculated based on the estimated ground friction coefficient and the variance of elevation changes, using its eigenvalue variation coefficient to assess the uniformity of the terrain profile. The environmental consistency coefficient, on the other hand, reflects the fluctuation characteristics of environmental parameters based on the variation coefficients of electromagnetic field strength and visual texture self-similarity index. The eigenvalue variation coefficient is calculated as the ratio of the standard deviation to the mean, thereby eliminating dimensional differences and enabling comparison of the stability of multi-source features. In this calculation: the standard deviation quantifies the magnitude or dispersion of the fluctuations in terrain or environmental feature values ​​within the basic path unit. An increase in the standard deviation indicates that the ground friction coefficient, elevation, electromagnetic field strength, or visual texture parameters within the unit change drastically and are extremely unstable; a decrease in the standard deviation indicates that these parameters are concentrated and stable. The mean represents the average level of the corresponding feature value within the unit. The specific physical meaning of the mean depends on the input data: for example, the mean of terrain features reflects the overall roughness or friction level of the terrain, while the mean of environmental features reflects the overall intensity of electromagnetic interference or visual texture complexity. Therefore, the coefficient of variation (standard deviation / mean) as a whole has the physical meaning that a large coefficient of variation indicates significant fluctuations in characteristics relative to the average level. In this scheme, this directly maps to highly inconsistent, unpredictable, and risky terrain or environment. For example, a basic path unit may contain both extremely smooth and extremely rough surfaces (large terrain variability), or the electromagnetic field may rapidly jump between extremely weak and extremely strong (large environmental variability). A small coefficient of variation indicates that the characteristic values ​​are closely distributed around the average level. This corresponds to a uniform, stable, and predictable terrain or environmental state. For example, the entire unit may be a flat concrete road surface (small terrain variability), and the electromagnetic field strength may be constant (small environmental variability). By normalizing the standard deviation to the mean, the coefficient of variation effectively eliminates the effects of the surface friction coefficient (dimensionless) and the elevation variance (in mm). 2 The different dimensions of electromagnetic field strength (unit μT) create obstacles to comparison, making it possible to use a unified, dimensionless "consistency coefficient" to fairly assess the stability of terrain and environment.

[0023] Basic path units are arranged into a sequence according to spatial order. For example, units 1 to 3 are set along the inspection path to form a coherent spatial reference frame. The following uses unit 2 as an example to illustrate in detail the calculation process from the raw data of a basic path unit to the consistency coefficient: Data Acquisition and Normalization Preprocessing: Acquiring raw data from multiple sampling points within unit 2: Terrain data: friction coefficient [0.6, 0.8, 0.2], elevation variance [4.1, 3.9, 15.0]; Environmental data: electromagnetic intensity [52, 48, 180], texture index [0.75, 0.72, 0.40]; Key step: Normalization. To address the differences in the dimensions and orders of magnitude of different features, Min-Max normalization is used to linearly transform the data of each feature to the [0, 1] interval. Normalized data (example values): Topography-friction coefficient: [0.67, 1.0, 0.0], Topography-elevation variance: [0.02, 0.0, 1.0], Environment-electromagnetic intensity: [0.03, 0.0, 1.0], Environment-texture index: [1.0, 0.91, 0.0]. Construct a mixed dataset and calculate the consistency coefficient: Topography consistency coefficient calculation: Combine the two normalized topography feature data into a mixed dataset: [0.67, 1.0, 0.0, 0.02, 0.0, 1.0]. Calculate the standard deviation (0.49) and mean (0.45) of this dataset; the ratio (coefficient of variation) is the topography consistency coefficient, 1.09. This coefficient comprehensively reflects the overall inconsistency of the topography in this unit. Environment consistency coefficient calculation: Combine the two normalized environmental feature data into another mixed dataset: [0.03, 0.0, 1.0, 1.0, 0.91, 0.0]. The standard deviation (0.50) and mean (0.49) were calculated to obtain an environmental consistency coefficient of 1.02. This coefficient comprehensively reflects the overall volatility of the unit's environment. Sequence construction and situational awareness: The above process was repeated for all units to obtain: a terrain consistency coefficient sequence, for example [0.20, 1.09, 0.15], and an environmental consistency coefficient sequence, for example [0.25, 1.02, 0.30]. The peaks in the sequences (such as in unit 2) visually identify high-risk areas with drastic changes in terrain or environment.

[0024] S2-1: Generate N consecutive local path segments based on the terrain consistency coefficient sequence and the environmental consistency coefficient sequence, including: Calculate the Spearman rank correlation coefficient between the topographic consistency coefficient series and the environmental consistency coefficient series to obtain the degree of correlation between topography and environment. The calculation process of Spearman's rank correlation coefficient is as follows: convert the values ​​of the terrain consistency coefficient sequence and the environmental consistency coefficient sequence into sorting numbers to obtain two sorting number sequences, and calculate the Pearson correlation coefficient between the two sorting number sequences. At coordinate points where the terrain consistency coefficient is lower than a preset terrain threshold or the environmental consistency coefficient is lower than a preset environmental threshold, the preset inspection path is divided into N continuous local path segments.

[0025] It should be noted that local path segments, as continuous path segments composed of multiple basic path units, form a hierarchical relationship with the basic path units—the basic path units serve as the smallest environmental perception units (e.g., 0.5-meter segments), while local path segments form macro-environmental partitions by fusing features from multiple units. The Spearman rank correlation coefficient, calculated based on ordinal numbers, effectively overcomes outlier interference and adapts to the characteristics of non-normally distributed data; therefore, it is introduced to analyze the correlation between terrain and environmental consistency sequences. Specifically, the values ​​of the terrain consistency coefficient sequence and the environmental consistency coefficient sequence are converted into ordinal numbers, and then the terrain-environment correlation degree is obtained through the Pearson correlation coefficient. When the sequence value is below a threshold, it indicates that there is a risk of terrain abrupt change or electromagnetic interference at that coordinate point; therefore, the inspection path is divided at such critical points. For example, consider an inspection path containing 8 basic path units. Its terrain consistency coefficient sequence is [0.90, 0.45, 0.88, 0.38, 0.92, 0.29, 0.85, 0.89], and its environmental consistency coefficient sequence is [0.82, 0.50, 0.35, 0.90, 0.87, 0.40, 0.88, 0.84]. The preset terrain and environmental thresholds are both 0.5. In unit 2, the terrain coefficient 0.45 < 0.5, triggering a partition. In unit 3, the environmental coefficient 0.35 < 0.5, triggering a partition. In unit 6, the terrain coefficient 0.29 < 0.5, triggering a partition. (Key Note) Even if both the terrain and environmental coefficients at the same coordinate point are below the threshold, it is only considered as one partition point to avoid duplicate cutting. By dividing the path at all the aforementioned trigger points, four consecutive local path segments are generated: segment 1 (units 1-2), segment 2 (unit 3), segment 3 (units 4-6), and segment 4 (units 7-8). This method overcomes the limitations of traditional fixed path planning, achieving precise segmentation through dynamic perception of environmental coupling changes, and significantly enhancing the robot's trajectory adaptability and control stability in complex tunnel conditions.

[0026] The preset terrain and environmental thresholds are set using a historical data distribution method. First, a database of historical terrain and environmental consistency coefficients containing various typical operating conditions is constructed. Then, the statistical distribution of this database is analyzed, and a low quantile (e.g., a critical quantile that effectively distinguishes between stable and abrupt operating conditions) is selected as the threshold. This method is based on the fact that coefficient values ​​below this threshold correspond to clearly identified environmental instability or high-risk events in historical data, thus ensuring that the threshold setting has an objective statistical basis and can effectively trigger path delineation.

[0027] S2-2: For each local path segment, calculate the average terrain consistency coefficient and the average environmental consistency coefficient of all basic path units within it. Based on these average terrain consistency coefficients and the average environmental consistency coefficients, and combined with the terrain-environment correlation, determine the cooperative stable zone, cooperative challenge zone, cross-challenge zone, and uncertain zone, including: When the average terrain consistency coefficient is higher than a preset terrain threshold, the average environmental consistency coefficient is higher than a preset environmental threshold, and the terrain-environment correlation degree is higher than a preset positive correlation degree threshold, a local path segment is defined as a cooperative stable region; the cooperative stable region means that both terrain features and environmental features remain stable and their changing trends are consistent. When the average terrain consistency coefficient is lower than a preset terrain threshold, the average environmental consistency coefficient is lower than a preset environmental threshold, and the terrain-environment correlation is higher than a preset positive correlation threshold, a local path segment is defined as a collaborative challenge zone; the collaborative challenge zone indicates that both terrain and environmental features are complex and variable and have the same trend of change. When the average terrain consistency coefficient is higher than the preset terrain threshold and the average environmental consistency coefficient is lower than the preset environmental threshold, or when the average terrain consistency coefficient is lower than the preset terrain threshold and the average environmental consistency coefficient is higher than the preset environmental threshold, and the terrain-environment correlation is lower than the preset negative correlation threshold, the local path segment is defined as a cross-challenge zone; the cross-challenge zone indicates that one of the terrain features and environmental features is stable while the other is complex, and the trends of change are opposite. When the absolute value of the terrain-environment correlation is lower than the preset uncertainty correlation threshold, the local path segment is defined as an uncertainty zone; the uncertainty zone indicates that the changing trends of terrain features and environmental features are not clearly correlated.

[0028] It should be noted that the cooperative stability zone is established when the mean values ​​of both terrain and environmental parameters are higher than a set threshold and the correlation is significantly positive. This corresponds to tunnel sections with flat ground and a stable electromagnetic environment, supporting the robot to adopt an efficient motion mode. The cooperative challenge zone is defined when the mean values ​​of both terrain and environmental parameters are lower than the threshold but maintain a high positive correlation. This represents a situation where terrain complexity and environmental interference increase simultaneously, requiring the activation of a high-stability control strategy. The cross-challenge zone occurs when terrain and environmental parameters show a significant negative correlation, reflecting two types of asymmetric risk scenarios: one is stable ground features but a sharp increase in electromagnetic interference; the other is a stable electromagnetic environment but a sharp increase in terrain complexity. These areas require the robot to implement dynamic posture adjustments to cope with severe challenges in a single dimension. The uncertain zone is defined when the correlation between parameters approaches zero, requiring a conservative control strategy to ensure basic motion safety. In summary, the core logic of this zoning strategy lies in jointly judging based on the "direction" and "intensity" of the correlation between terrain and environment: both the cooperative stability zone and the cooperative challenge zone require a significant positive correlation (i.e., the trends of terrain and environmental changes are consistent), and their risk levels are distinguished based on the level of the mean values ​​of the parameters. Cross-challenge zone: This requires a significant negative antagonism in the correlation (i.e., the trends of topographic and environmental changes are opposite) to identify asymmetric risks. Uncertain zone: This focuses on the strength of the correlation. When its absolute value is too small (approaching zero), it indicates a lack of clear synergistic or antagonistic relationship between the two parameters, and the trend of change is unpredictable. Therefore, it is classified as the region with the highest uncertainty.

[0029] The specific process for setting the preset correlation threshold is as follows: A large number of terrain-environment correlation (Spearman rank correlation coefficient) samples are calculated based on historical data to form a correlation distribution. A high quantile is selected from this distribution for the positive correlation threshold to ensure that only when terrain and environmental changes show a sufficiently strong positive correlation are they identified as "cooperative" areas (stable or challenging areas). A low quantile is selected for the preset negative correlation threshold to identify significant negative correlations, thereby defining "cross-challenge areas." The "preset uncertain correlation threshold" is a specific numerical criterion. This value is determined based on statistical analysis of historical terrain-environment correlation data: First, a statistically insignificant correlation interval is determined (e.g., the interval where p>0.05 is obtained through hypothesis testing is (-0.25, +0.25)). Then, a conservative absolute value (e.g., 0.2) is selected within this interval as the threshold actually used in the algorithm. Therefore, during the operation of the control system, this threshold manifests as a specific numerical value, but its setting has clear statistical significance. This method makes the classification of correlations statistically significant, where the positive correlation threshold corresponds to the significant positive correlation level of the parameter sequences, and the negative correlation threshold characterizes the obvious negative correlation between parameters. This environmental partitioning method based on multi-dimensional feature correlation analysis effectively solves the problem of insufficient adaptability of traditional control strategies to dynamic environments, and provides a precise environmental situational awareness foundation for the motion control of robots in complex cable tunnel conditions.

[0030] S3-1: Obtain the terrain variation degree and environmental variation degree based on adjacent local path segments, and determine the comprehensive variation degree based on the terrain variation degree and environmental variation degree, including: The absolute difference between the mean values ​​of the terrain consistency coefficients between adjacent local path segments is used as the degree of terrain variation. The absolute difference between the mean environmental consistency coefficients of adjacent local path segments is used as the degree of environmental variability. The Euclidean distance between adjacent local path segments in the feature space composed of terrain variability and environmental variability is used as the comprehensive variability. The adjacent local path segments refer to the current local path segment and the target local path segment.

[0031] It should be noted that this step overcomes the shortcomings of traditional methods in perceiving abrupt changes in stability between continuous paths by quantifying the dynamic changes in environmental characteristics. The selection of adjacent local path segments stems from the robot's need for continuous motion. The terrain change degree is characterized by the absolute difference of the mean terrain consistency coefficient between segments, representing the magnitude of the abrupt change in terrain features. The environmental change degree is reflected by the absolute difference of the mean environmental consistency coefficient, reflecting the intensity of fluctuations in electromagnetic and visual conditions. When the absolute difference is small, it indicates that the characteristics of two adjacent local path segments in terms of terrain or environment are similar and the changes are gradual. This means that when the robot moves from one area to the next, the environmental transition it faces is smooth, and the switching risk is low. When the absolute difference is large, it indicates that there are significant differences or abrupt changes in terrain or environment between adjacent segments. This corresponds to the robot about to cross an environmental boundary (such as moving from flat ground to a gravel area, or from a weak electromagnetic area to a strong interference area). The uncertainty and risk during the switching process are high, requiring the control system to pay more attention and prepare stronger countermeasures. Therefore, by calculating and evaluating this absolute difference, the "latent" abrupt change risk in the continuous environmental space can be transformed into a quantifiable "explicit" indicator, providing a key basis for subsequent stability assessment and control decisions. The mean absolute difference can effectively eliminate directional bias and highlight the magnitude of change. The feature space consists of a two-dimensional measurement framework composed of terrain and environmental change degree. Euclidean distance integrates two-dimensional change information in this space to form a comprehensive change degree, thereby comprehensively characterizing the overall variability of the environmental situation. The magnitude of this Euclidean distance, i.e., the comprehensive change degree, has the following physical meaning: When the comprehensive change degree is small, it indicates that when the robot switches from the current local path segment to the target segment, the overall change in the terrain and environmental conditions is relatively gentle. This represents a low-risk, smooth environmental transition, and the robot does not need to make drastic posture or gait adjustments. When the comprehensive change degree is large, it indicates that there is a drastic jump between adjacent segments in both the terrain and environmental dimensions, or in one of them. This indicates that the robot is facing a high-risk, abrupt, and complex environmental switch, and must trigger a higher-intensity control strategy (such as switching to a highly stable gait) to cope with the resulting risk of motion instability. Therefore, the comprehensive variability, through a scalar value, comprehensively quantifies the overall challenge level of environmental transitions and is a core element in achieving unified decision-making from multi-dimensional perception. This method overcomes the limitations of traditional static environment modeling, providing accurate quantitative basis for real-time adjustment of robot trajectory and posture, and significantly enhancing motion adaptation capabilities in complex tunnel conditions.

[0032] S3-2: Obtain motion stability, perception stability, and task execution degree, and obtain the average real-time stability of the local path segment based on the motion stability, perception stability, and task execution degree, including: The inverse of the variance of the body attitude angle is used as the motion stability, the inverse of the trace of the localization estimation covariance is used as the perception stability, and the success rate of subtask completion is used as the task execution degree. The motion stability, perception stability, and task execution degree are weighted and summed to obtain the real-time stability of each basic path unit; For each local path segment, the arithmetic mean of the real-time stability of all basic path units within it is calculated as the average real-time stability of the local path segment.

[0033] It should be noted that motion stability is characterized by the reciprocal of the body attitude angle variance, representing the robot's dynamic balance capability. For quadruped robots, the attitude angle variance directly reflects their gait stability and anti-tipping ability on complex terrain; perception stability, based on the reciprocal of the localization estimation covariance trace, measures the reliability of the environmental perception system, reflecting the constraint of localization accuracy on navigation performance; task execution efficiency assesses the continuity of operations through the success rate of sub-task completion, directly related to task execution effectiveness. Real-time stability is formed by weighted fusion of the above three-dimensional indicators to create a quantitative value of the instantaneous stability of the basic path unit, where the sum of the weight coefficients in the weighted summation is 1. The average real-time stability of local path segments is calculated using the arithmetic mean, as it can balance and integrate stability fluctuations between units, forming a unified stability representation of macroscopic path segments. This mechanism breaks through the limitations of traditional single-dimensional evaluation, providing a holographic stability basis for robot trajectory planning and attitude adjustment, significantly improving control accuracy and task robustness in dynamic tunnel environments.

[0034] It should be further explained that the specific values ​​of the weight coefficients in the weighted summation operation are determined through the analytic hierarchy process (AHP) or regression analysis based on historical performance data. For example, for the weights in the real-time stability calculation, during the offline phase, the optimal weights are obtained by analyzing the contribution ratios of motion stability, perception stability, and task execution to the overall task success rate from a large amount of historical data, and then using multiple linear regression fitting. The weight coefficients in the environment switching evaluation formula... and By analyzing historical operation and maintenance data, the number of task failures or performance degradation caused by terrain changes and environmental disturbances are statistically analyzed, and then allocated according to their relative proportions. This data-driven weight determination method ensures the scientific and objective nature of the adaptive control strategy. Similarly, the weight coefficients in the system collaborative evaluation value calculation formula are also analyzed. and The specific values ​​are determined by analyzing the criticality of communication assurance and equipment health in historical collaborative tasks. For example, during the offline analysis phase, data mining methods (such as principal component analysis or expert scoring) are used to assess the contribution or influence weight of communication link quality and equipment health status on the overall collaborative success rate of the system in historical task data, thereby determining the criticality of communication assurance and equipment health in historical task data. and The value of is determined by historical data analysis. If historical data analysis indicates that maintaining a reliable communication connection is more critical than the power surplus of individual devices in a specific tunnel environment, then it is the average communication link quality. Assign higher weights Conversely, assign the average health status of the equipment. weight This approach ensures that the assessment of the system's cooperative state accurately reflects the most critical cooperative constraints within the current task context.

[0035] S4-1: Determine the switching stability of adjacent local path segments based on the terrain variability, environmental variability, and multiple auxiliary devices, including: Based on the terrain and environmental variability, the environmental transition assessment value of adjacent local path segments is obtained using the environmental transition assessment value calculation formula. The environmental transition assessment value calculation formula is as follows: ;in, This is an environmental switching assessment value. As a baseline switching cost, For the degree of topographic variation, For environmental change degree, , These are the weighting coefficients; Based on the real-time status data of multiple auxiliary devices, the normalized average of the communication signal-to-noise ratio of all devices is calculated as the average communication link quality, and the normalized average of the battery capacity and component status of all devices is calculated as the average device health status. Based on the average communication link quality and the average device health status, the system collaborative evaluation value of adjacent local path segments is obtained using the system collaborative evaluation value calculation formula. The system collaborative evaluation value calculation formula is as follows: ;in, This is a system collaborative evaluation value. This represents the average quality of the communication link. This represents the average health status of the equipment. , These are the weighting coefficients; Based on the environmental handover assessment value and the system coordination assessment value, the handover situation stability is obtained using the handover situation stability calculation formula; the handover situation stability calculation formula is: ;in, To switch the state of stability.

[0036] It should be noted that the environmental switching assessment value is used to characterize the comprehensive risk brought about by sudden environmental changes between adjacent path segments. In its calculation formula, the terrain change degree and environmental change degree reflect the fluctuation intensity of terrain features and electromagnetic vision conditions, respectively. When the terrain change degree or environmental change degree increases, the environmental switching assessment value rises accordingly, indicating an increased switching risk. The baseline switching cost is determined through the following steps: First, the average time cost and average energy cost of the robot completing the task under typical operating conditions (such as cooperative stable zone and cooperative challenge zone) are extracted from historical data; then, both are normalized separately; finally, they are weighted and summed to form a comprehensive cost, which serves as the baseline switching cost.

[0037] Real-time status data for multiple auxiliary devices includes communication signal-to-noise ratio (SNR), device battery capacity, and component status. The average communication link quality, normalized by SNR, reflects communication reliability, while the average device health status, combining battery capacity and component status, characterizes device availability. The system collaborative evaluation value measures the effectiveness of multi-device collaborative operation. In its calculation formula, the average communication link quality reflects communication stability, while the average device health status reflects the device's operating status. When either the average communication link quality or the average device health status increases, the system collaborative evaluation value also increases, indicating enhanced collaborative effectiveness. The normalization process employs the Min-Max linear normalization method.

[0038] The stability of the switching situation is a combination of the environmental switching evaluation value and the system coordination evaluation value, characterizing the stability of the robot when traversing path segments. The closer this value is to 1, the more stable the switching situation. The theoretical value range is (0,1). When the environmental switching evaluation value... Approaching 0 or system collaborative evaluation value When it approaches 1, A value close to 1 indicates the optimal switching posture; conversely, when... Increase and When decreasing, A value approaching 0 indicates an extremely high switching risk. In its calculation formula, an increase in the environmental switching assessment value leads to a decrease in switching stability, while an increase in the system coordination assessment value promotes a higher switching stability. This mechanism, by dynamically integrating environmental risk and system state, overcomes the limitation of traditional control's lack of quantitative basis for switching decisions, providing precise stability criteria for adaptive gait adjustment, and significantly enhancing the robot's motion continuity and task reliability in complex tunnel environments.

[0039] S4-2: Adaptive control of the cable tunnel robot based on the comprehensive variability, average real-time stability, and switching state stability includes: obtaining the control strength coefficient of the tunnel robot according to the control strength coefficient calculation formula, whereby: ; in, To control the strength coefficient, For average real-time stability, To assess the overall degree of change, For the response coefficient, The regional risk coefficient is determined by the classification of collaboratively stable areas, collaboratively challenging areas, cross-challenging areas, or uncertain areas. When the control intensity coefficient is lower than the preset low threshold, the high-efficiency inspection mode is activated to control the robot to maintain its travel efficiency and perform routine inspection tasks. When the control intensity coefficient is higher than or equal to the preset low threshold and lower than the preset high threshold, the fine operation mode is activated, and the robot is controlled to enter a stable state and perform fine detection tasks. When the control strength coefficient is higher than or equal to the preset high threshold, the force control operation mode is activated to control the robot to establish a stable base and perform high-precision force control tasks.

[0040] The core of the formula lies in the introduction of a regional risk coefficient. The environmental situation perception defined in claim 5 is directly converted into a benchmark gain for control strength through a multiplier greater than or equal to 1.

[0041] The specific method for determining the regional risk coefficient is as follows: The value of is determined through risk cost quantification analysis based on historical operation and maintenance data. The specific process is as follows: Construct a historical database: Collect and archive historical task data, recording the average task time growth rate (the increase in time relative to the baseline time (e.g., time in an ideal flat environment) and the average unexpected energy consumption growth rate (the increase in energy consumption relative to the baseline energy consumption) caused by environmental challenges when the robot operates in different region types (cooperative stable region, cooperative challenge region, cross-challenge region, uncertain region) under different regional conditions. Quantify regional risk costs: Calculate the geometric mean of the above two growth rates for each region type to obtain a comprehensive normalized risk cost index. Mapped to... The comprehensive risk cost index for the coordinated stability zone is benchmarked at 1.0, while other zones... The value is set in a manner proportional to its comprehensive risk cost index. This method ensures... The value of directly and objectively reflects the inherent risk level of different environmental zones.

[0042] Technical effect: final control strength coefficient The range is [ (+∞). This mechanism means that when a robot enters an inherently high-risk area (such as...), When the collaborative challenge zone is 2.0, the starting point for control strength has been significantly raised. This forces the system to adopt robust strategies earlier and more proactively, even if the current dynamic indicators are still acceptable. This decision-making model, which multiplies the inherent environmental risks with real-time dynamic risks, greatly enhances the system's forward-looking safety awareness and overall robustness in complex tunnel environments.

[0043] The specific values ​​of the preset low threshold and preset high threshold for the control intensity coefficient need to be determined. After establishing the value system, cluster analysis (such as K-means) or percentile statistics are performed on a large amount of control strength coefficient data recorded in historical tasks to determine the value. Fine-tuning is then carried out in conjunction with field tests in a controlled tunnel environment: the robot traverses multiple preset working conditions covering all types of areas. The final threshold determination needs to be verified in conjunction with the robot's actual motion instability phenomena (such as slippage or overturning) and task execution failures (such as task interruption) to ensure the timeliness and reliability of gait mode switching.

[0044] High-efficiency inspection mode: When At low speeds, with the core objective of improving surveying efficiency, the robot is controlled to move continuously using a low-energy gait, while simultaneously scheduling the robotic arm and gripper to perform basic inspection tasks such as wide-area visual scanning and infrared temperature measurement. For example, for quadruped robots, their efficient "walking gait" or "trotting gait" is activated to maximize movement speed while ensuring the stability of the robot body, thereby achieving rapid coverage.

[0045] Adaptive job mode: when When in the intermediate range, a brief stable window is created by dynamically adjusting the gait. Within this window, the robotic arm is manipulated to complete precise detection tasks requiring accurate positioning, such as ground current measurement and partial discharge signal acquisition. For example, the quadruped robot switches to a "triangular support gait" or pauses its gait, allowing the three legs to form a stable support and freeing up the working space corresponding to one leg, thus creating a stable operating environment for the robotic arm for several seconds.

[0046] High stability operating mode: When When the load is high, priority is given to ensuring movement safety and controlling the robot to establish a stable working base. For example, for a quadruped robot, the 'stop gait' or 'silent standing' strategy is triggered. By lowering the center of gravity, increasing the support polygon, and locking the joints, a near-static working platform is formed. Then, force feedback control technology is used to manipulate the robotic arm and gripper to complete high-precision force control tasks such as valve operation and switch switching.

[0047] The response coefficient This parameter is used to adjust the system's sensitivity to dynamic stability challenges. Its value is calibrated through simulation experiments based on historical data: stability challenges of varying intensities are simulated in a simulation environment, with the timeliness and accuracy of gait switching as the optimization objective. The optimal value is determined using a trial-and-error method or optimization algorithm (such as gradient descent). value.

[0048] Example 2: Based on Example 1, a cable tunnel robot control system based on multiple auxiliary devices, such as... Figure 2 As shown, it includes: The environmental feature extraction module is used to acquire terrain profile data and cable channel environmental data, calculate the terrain consistency coefficient and environmental consistency coefficient of basic path units, and construct the terrain consistency coefficient sequence and environmental consistency coefficient sequence. The path segmentation and classification module is used to generate local path segments based on the terrain consistency coefficient sequence and the environmental consistency coefficient sequence, calculate the mean of terrain consistency coefficient and the mean of environmental consistency coefficient, and divide the area into cooperative stable area, cooperative challenge area, cross challenge area and uncertain area based on the terrain and environment correlation. The dynamic situation analysis module is used to calculate the terrain change and environmental change of adjacent local path segments, determine the comprehensive change, and integrate motion stability, perception stability and task execution to obtain the average real-time stability. The control decision module is used to evaluate the stability of the switching situation obtained by the environmental switching and system coordination, introduce the regional risk coefficient, calculate the control strength coefficient, and select the efficient inspection mode, adaptive operation mode or high stability operation mode accordingly, so as to realize the integrated adaptive control from mobile inspection to fine operation.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cable tunnel robot control method based on multiple auxiliary devices, characterized by, The method comprises the following steps: S1: obtaining terrain profile data and cable channel environment data and determining basic path units; calculating terrain consistency coefficients and environment consistency coefficients for each basic path unit, and forming terrain consistency coefficient sequences and environment consistency coefficient sequences according to the terrain consistency coefficients and the environment consistency coefficients; S2: generating N continuous local path segments according to the terrain consistency coefficient sequences and the environment consistency coefficient sequences; for each local path segment, calculating the mean values of the terrain consistency coefficients and the environment consistency coefficients of all the basic path units inside, and determining the synergistic stable zone, the synergistic challenge zone, the cross challenge zone and the uncertain zone according to the mean values of the terrain consistency coefficients and the environment consistency coefficients in combination with the terrain environment correlation degree; S3: obtaining terrain variation degrees and environment variation degrees according to adjacent local path segments, and determining comprehensive variation degrees according to the terrain variation degrees and the environment variation degrees; obtaining motion stability degrees, perception stability degrees and task execution degrees, and obtaining the average real-time stability degrees of the local path segments according to the motion stability degrees, the perception stability degrees and the task execution degrees; S4: determining the switching situation stability degrees of adjacent local path segments according to the terrain variation degrees, the environment variation degrees and the multiple auxiliary devices; and adaptively controlling the cable tunnel robot according to the comprehensive variation degrees, the average real-time stability degrees and the switching situation stability degrees.

2. The multi-aided device based cable tunnel robot control method according to claim 1, wherein, The method for obtaining terrain profile data and cable channel environment data and determining basic path units comprises the following steps: The terrain profile data comprises ground friction coefficient estimates and elevation variation variances, and the cable channel environment data comprises electromagnetic field intensity values and visual texture self-similarity indexes. Geographical sampling points are set on a preset inspection path, and the path between adjacent geographical sampling points is defined as a basic path unit.

3. The multi-aided device based cable tunnel robot control method of claim 1, wherein, The method for calculating terrain consistency coefficients and environment consistency coefficients for each basic path unit and forming terrain consistency coefficient sequences and environment consistency coefficient sequences according to the terrain consistency coefficients and the environment consistency coefficients comprises the following steps: The terrain consistency coefficients and the environment consistency coefficients of the basic path units are obtained by calculating the characteristic value variation coefficients of all the terrain profile data and the cable channel environment data in the basic path units, and the calculation method of the characteristic value variation coefficients is the ratio of the standard deviation to the average value. All the basic path units are arranged in spatial order to form a basic path unit sequence. The terrain consistency coefficients of each basic path unit in the basic path unit sequence are combined in the spatial order of the corresponding basic path units to form a terrain consistency coefficient sequence. The environment consistency coefficients of each basic path unit in the basic path unit sequence are combined in the spatial order of the corresponding basic path units to form an environment consistency coefficient sequence.

4. The multi-aided device based cable tunnel robot control method of claim 1, wherein, The method for generating N continuous local path segments according to the terrain consistency coefficient sequences and the environment consistency coefficient sequences comprises the following steps: The terrain environment correlation degree is obtained by calculating the Spearman rank correlation coefficient between the terrain consistency coefficient sequences and the environment consistency coefficient sequences. When the terrain consistency coefficient value is lower than the preset terrain threshold or the environment consistency coefficient value is lower than the preset environment threshold, the preset inspection path is divided, and N continuous local path segments are generated.

5. The multi-aided device based cable tunnel robot control method of claim 1, wherein, For each local path segment, the average value of the terrain consistency coefficient and the average value of the environment consistency coefficient of all basic path units inside are calculated, and the collaborative stable area, the collaborative challenge area, the cross challenge area and the uncertain area are determined according to the average value of the terrain consistency coefficient and the average value of the environment consistency coefficient in combination with the terrain environment correlation degree, including: When the average value of the terrain consistency coefficient is higher than the preset terrain threshold, the average value of the environment consistency coefficient is higher than the preset environment threshold, and the terrain environment correlation degree is higher than the preset positive correlation degree threshold, the local path segment is defined as the collaborative stable area; When the average value of the terrain consistency coefficient is lower than the preset terrain threshold, the average value of the environment consistency coefficient is lower than the preset environment threshold, and the terrain environment correlation degree is higher than the preset positive correlation degree threshold, the local path segment is defined as the collaborative challenge area; When the average value of the terrain consistency coefficient is higher than the preset terrain threshold and the average value of the environment consistency coefficient is lower than the preset environment threshold, or the average value of the terrain consistency coefficient is lower than the preset terrain threshold and the average value of the environment consistency coefficient is higher than the preset environment threshold, and the terrain environment correlation degree is lower than the preset negative correlation degree threshold, the local path segment is defined as the cross challenge area; When the absolute value of the terrain environment correlation degree is lower than the preset uncertain correlation degree threshold, the local path segment is defined as the uncertain area.

6. The multi-aided device based cable tunnel robot control method of claim 1, wherein, The terrain change degree and the environment change degree are obtained according to adjacent local path segments, and the comprehensive change degree is determined according to the terrain change degree and the environment change degree, including: The absolute difference value of the average value of the terrain consistency coefficient between adjacent local path segments is taken as the terrain change degree; The absolute difference value of the average value of the environment consistency coefficient between adjacent local path segments is taken as the environment change degree; The Euclidean distance of adjacent local path segments in the feature space composed of the terrain change degree and the environment change degree is taken as the comprehensive change degree.

7. The multi-aided device based cable tunnel robot control method of claim 1, wherein, The motion stability degree, the perception stability degree and the task execution degree are obtained, and the average real-time stability degree of the local path segment is obtained according to the motion stability degree, the perception stability degree and the task execution degree, including: The reciprocal of the body posture angle variance is taken as the motion stability degree, the reciprocal of the positioning estimation covariance trace is taken as the perception stability degree, and the sub-task completion success rate is taken as the task execution degree; The motion stability degree, the perception stability degree and the task execution degree are weighted and summed to obtain the real-time stability degree of each basic path unit; For each local path segment, the arithmetic mean of the real-time stability degrees of all basic path units inside is calculated as the average real-time stability degree of the local path segment.

8. The multi-aided device based cable tunnel robot control method of claim 1, wherein, The switching situation stability degree of adjacent local path segments is determined according to the terrain change degree, the environment change degree and the multiple auxiliary devices, including: According to the terrain change degree and the environment change degree, an environment switching evaluation value of an adjacent local path segment is obtained by using an environment switching evaluation value calculation formula, and the environment switching evaluation value calculation formula is: ; wherein, is the environment switching evaluation value, is a reference switching cost, is the terrain change degree, is the environment change degree, , is a weight coefficient. According to the real-time state data of the multiple auxiliary devices, a normalized average of communication signal-to-noise ratios of all the devices is calculated as a communication link quality average, and a normalized average of battery capacities and component states of all the devices is calculated as a device health state average; according to the communication link quality average and the device health state average, a system synergy evaluation value of an adjacent local path segment is obtained by using a system synergy evaluation value calculation formula; the system synergy evaluation value calculation formula is: ; wherein, is the system synergy evaluation value, is the communication link quality average, is the device health state average, , is a weight coefficient. According to the environment switching evaluation value and the system cooperative evaluation value, a switching situation stability degree is obtained by using a switching situation stability degree calculation formula, the switching situation stability degree calculation formula being: ; wherein, is the switching situation stability degree.

9. The multi-aided device based cable tunnel robot control method according to claim 8, wherein, The adaptive control of the cable tunnel robot is performed according to the comprehensive change degree, the average real-time stability degree and the switching situation stability degree, including: the control intensity coefficient of the tunnel robot is obtained according to the control intensity coefficient calculation formula, and the control intensity coefficient calculation formula is: ； wherein, is a control intensity coefficient, is an average real-time stability, is a comprehensive change degree, is a response coefficient, is a regional risk coefficient, determined by the classification of the synergistic stability zone, the synergistic challenge zone, the cross challenge zone, or the uncertain zone; When the control intensity coefficient is lower than the preset low threshold, a high-efficiency inspection mode is enabled, and the robot is controlled to maintain travel efficiency and perform a routine inspection task; When the control intensity coefficient is higher than or equal to the preset low threshold and lower than the preset high threshold, a fine operation mode is enabled, and the robot is controlled to enter a stable state and perform a fine detection task; When the control intensity coefficient is higher than or equal to the preset high threshold, a force control operation mode is enabled, and the robot is controlled to establish a stable base and perform a high-precision force control operation task.

10. A cable tunnel robot control system based on multiple auxiliary devices for implementing the cable tunnel robot control method based on multiple auxiliary devices according to any one of claims 1 to 9, characterized by Comprise: An environment feature extraction module for obtaining terrain profile data and cable channel environment data, calculating terrain consistency coefficients and environment consistency coefficients of basic path units, and constructing terrain consistency coefficient sequences and environment consistency coefficient sequences; A path segmentation and classification module for generating local path segments according to the terrain consistency coefficient sequences and the environment consistency coefficient sequences, calculating terrain consistency coefficient mean values and environment consistency coefficient mean values, and dividing cooperative stable zones, cooperative challenge zones, cross challenge zones and uncertain zones in combination with terrain environment correlation degrees; A dynamic situation analysis module for calculating terrain variation degrees and environment variation degrees of adjacent local path segments, determining comprehensive variation degrees, and obtaining average real-time stability degrees by fusing motion stability degrees, perception stability degrees and task execution degrees; A control decision module for evaluating environment switching and system cooperation to obtain switching situation stability degrees, introducing regional risk coefficients, calculating control intensity coefficients, and selecting a high-efficiency inspection mode, an adaptive operation mode or a high-stability operation mode accordingly to realize integrated adaptive control from mobile inspection to fine operation.

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