Total station intelligent avoidance method, device, equipment, medium and program product

Abstract

The application is suitable for the technical field of intelligent avoidance, and provides a total station intelligent avoidance method, device, equipment, medium and program product, the method comprises the following steps: controlling the total station to horizontally rotate, triggering ATR search and reading the horizontal angle and the vertical angle at each stop position, and constructing an interference candidate angle record set according to the horizontal angle and the vertical angle; screening the interference candidate angle record set to obtain an effective interference angle data set, and obtaining an interference cluster set through clustering; merging adjacent interference cluster boundaries based on the interference cluster set to generate a filter area list; determining a search range based on the current orientation angle of the total station, determining an effective search sub-interval set according to the search range and the filter area list, triggering ATR search and generating an intelligent avoidance search result, and the application fully concentrates the time and computing power of each ATR search on the effective angle interval of the real prism target, thereby improving the accuracy and search efficiency of ATR search in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of intelligent obstacle avoidance technology, and in particular to an intelligent obstacle avoidance method, device, equipment, medium and program product for a total station. Background Technology

[0002] Total station ATR (Automatic Target Recognition) technology is widely used in high-precision measurement scenarios such as deformation monitoring and construction layout. Its core working principle is to perform a full-range scan within a preset horizontal and vertical angle range, and then use image processing algorithms to identify prisms and automatically lock onto the target. However, in complex environments such as construction sites and bridge construction sites, strong reflective objects such as glass curtain walls, metal support structures, and direct sunlight surfaces are often stably located at fixed angles. Traditional ATR searches have no ability to detect or remember these interference sources, and each search repeatedly performs a complete scan within the same range. This causes the system to waste search time repeatedly at known interference locations, and may even misidentify strong reflective objects as prism targets and incorrectly lock onto them.

[0003] Existing ATR search systems lack the ability to accumulate and utilize historical search failure experience, and cannot eliminate recurring interference angle positions from the search range. This results in the same interference source repeatedly interfering with the search process in each measurement operation, leading to low efficiency and reliability of ATR search. Summary of the Invention

[0004] This invention provides a total station intelligent obstacle avoidance method, device, equipment, medium, and program product. This invention enables the time and computing power of each ATR search to be fully concentrated in the effective angle range of the real prism target, thereby improving the accuracy and efficiency of ATR search in complex environments.

[0005] In a first aspect, embodiments of this application provide a total station intelligent obstacle avoidance method, including: Control the total station to rotate horizontally, trigger ATR search at each stopping position and read the horizontal and vertical angles, and construct a set of interference candidate angle records based on the horizontal and vertical angles; The set of interference candidate angle records is filtered to obtain a dataset of effective interference angles, and the dataset of effective interference angles is clustered to obtain a set of interference clusters. Based on the set of interference clusters, adjacent interference cluster boundaries are merged to generate a list of filtered regions. The search range is determined based on the current orientation angle of the total station. The set of valid search sub-intervals is determined according to the search range and the list of filtered areas. The ATR search is triggered and intelligent avoidance search results are generated.

[0006] Optionally, in a first implementation of the first aspect of the present invention, controlling the total station to rotate horizontally, triggering an ATR search and reading the horizontal and vertical angles at each stopping position, and constructing a set of interference candidate angle records based on the horizontal and vertical angles, includes: Control the total station to rotate horizontally, read the horizontal and vertical angles at each stopping position, and verify the valid angle frames based on the horizontal and vertical angles; ATR search is triggered based on the effective angle frame. When the ATR search fails, the number of consecutive failures and the historical failure probability of the corresponding dwell position are obtained according to the effective angle frame, and the environmental correlation coefficient is calculated. The credibility score is obtained by weighted summing of the number of consecutive failures, the historical failure probability, and the environmental correlation coefficient, and then compared with the high credibility threshold and the observation threshold. When the credibility score is not lower than the high credibility threshold, the horizontal angle, the vertical angle, and the credibility score are encapsulated into an interference candidate angle record and written into the interference candidate angle record set; when the credibility score is lower than the high credibility threshold but not lower than the observation threshold, the horizontal angle, the vertical angle, and the credibility score are written into the observation queue; when the credibility score is lower than the observation threshold, the horizontal angle, the vertical angle, and the credibility score are discarded.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of filtering the set of interference candidate angle records to obtain a dataset of effective interference angles, and clustering the dataset of effective interference angles to obtain a set of interference clusters, includes: The set of interference candidate angle records is filtered to obtain a dataset of valid interference angles; For the effective interference angle dataset, the first unclustered record is taken as the seed point to create a new interference cluster. The mean of the horizontal angle and the mean of the vertical angle of all records in the current interference cluster are taken as the cluster center. All unclustered records are traversed until all records are clustered to obtain the initial cluster set. Calculate the horizontal and vertical angular spans of each interference cluster in the initial cluster set, and re-perform extended clustering based on the horizontal and vertical angular spans to obtain the interference cluster set.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of merging adjacent interference cluster boundaries based on the interference cluster set to generate a list of filtered regions includes: Determine the initial boundary, point density, and average confidence score of each interference cluster in the interference cluster set; The adaptive expansion distance is calculated based on the point density and the average confidence score, and the initial boundary is expanded according to the adaptive expansion distance to obtain the target boundary of each interference cluster. The target boundaries of adjacent interference clusters are merged into a single filtering region, and a list of filtering regions is generated based on all filtering regions.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the step of determining the search range based on the current orientation angle of the total station, determining the set of valid search sub-intervals according to the search range and the filter area list, triggering ATR search and generating intelligent avoidance search results includes: The search range is determined based on the current orientation angle of the total station; The set of valid search sub-intervals is determined based on the search range and the list of filtered regions; ATR search is triggered based on the angle range of each valid search sub-interval in the set of valid search sub-intervals, and the result of the first ATR search that is successful is output as the intelligent avoidance search result.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of triggering ATR search based on the angle range of each valid search sub-interval in the set of valid search sub-intervals, and outputting the result of the first successfully searched sub-interval as the intelligent avoidance search result, includes: Calculate the angular distance of each valid search sub-interval in the set of valid search sub-intervals, and determine the angular range of each valid search sub-interval based on the angular distance; The ATR search is triggered using the angle range as a parameter. When a valid sub-interval is successfully searched by ATR, the result of the first successfully searched sub-interval is output as the intelligent avoidance search result. When all valid sub-intervals fail to be searched by ATR, the filtering area is determined and the filtering area is deleted from the filtering area list.

[0011] Secondly, embodiments of this application provide a total station intelligent obstacle avoidance device, comprising: The reading module is used to control the horizontal rotation of the total station, trigger ATR search at each stopping position and read the horizontal and vertical angles, and construct a set of interference candidate angle records based on the horizontal and vertical angles; The clustering module is used to filter the set of interference candidate angle records to obtain a set of effective interference angles, and to cluster the set of effective interference angles to obtain a set of interference clusters. The merging module is used to merge the boundaries of adjacent interference clusters based on the interference cluster set and generate a list of filtered regions. The generation module is used to determine the search range based on the current orientation angle of the total station, determine the set of valid search sub-intervals according to the search range and the list of filtered areas, trigger ATR search, and generate intelligent avoidance search results.

[0012] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described intelligent obstacle avoidance method for total stations.

[0013] Fourthly, embodiments of this application provide a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described intelligent obstacle avoidance method for total stations.

[0014] Fifthly, embodiments of this application provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described intelligent obstacle avoidance method for total stations.

[0015] In one of the solutions provided by the above-mentioned intelligent obstacle avoidance methods, devices, equipment, media and program products for total stations, the present invention controls the total station motor to drive the instrument to perform 360° continuous step rotation with a fixed horizontal angle increment. At each stopping position, it actively triggers ATR search and records the search results, transforming the total station from a working mode of passively waiting for search commands to a learning mode of actively detecting the interference distribution of the entire angular space. The present invention fully grasps the angular position information of all interference sources in the current environment before the formal measurement operation, solving the technical problem that traditional ATR search has no awareness of interference sources. This invention introduces a three-factor weighted credibility scoring mechanism based on the number of consecutive failures, historical failure probability, and environmental correlation coefficient. It performs multi-dimensional quantitative evaluation of each ATR search failure event, recording only failure locations with credibility scores reaching a high credibility threshold as interference candidate angles. Simultaneously, medium-credibility failure locations are temporarily stored in an observation queue, while low-credibility failure locations are directly discarded. From the data acquisition stage, noise events such as occasional communication jitter and instantaneous target occlusion are filtered to ensure that the data entering the subsequent clustering modeling process are all statistically significant real interference locations. In the clustering modeling stage, this invention employs a dual-dimensional joint clustering method using horizontal and vertical angles. The difference between the horizontal and vertical angles simultaneously not exceeding their respective clustering thresholds serves as a dual constraint for cluster determination. This overcomes the technical problem of existing technologies that incorrectly merge interference points at different vertical angle positions in the same horizontal direction into the same region when performing one-dimensional clustering based solely on horizontal angles. This ensures that each interference cluster accurately corresponds to the true two-dimensional angular distribution range of a single interference source in physical space, avoiding unnecessary loss of the effective search space due to excessive expansion of the filtering area in the vertical dimension. This invention dynamically calculates the adaptive expansion distance in the horizontal and vertical directions based on the point density and average confidence score of interference clusters. After finely expanding the initial boundary of the interference region, adjacent regions are merged to generate an accurate and complete list of filtered regions, which is then persistently stored. Before the normal ATR search is executed, interference regions are precisely removed from the search range through two-dimensional geometric intersection operations of horizontal and vertical angles. The search range is divided into several effective search sub-intervals, which are then sorted from near to far according to the angular distance between the center angle of each sub-interval and the current instrument orientation, and ATR searches are triggered sequentially. This completely avoids known interference regions while minimizing the instrument rotation travel, so that the time and computing power of each ATR search are fully concentrated on the effective angular interval of the real prism target, thus improving the accuracy and efficiency of ATR searches in complex environments. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. 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 effort.

[0017] Figure 1 This is a structural schematic diagram of a total station intelligent obstacle avoidance system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a total station intelligent obstacle avoidance method according to an embodiment of the present invention; Figure 3 yes Figure 2 A schematic diagram of the implementation process of step S10; Figure 4 yes Figure 2 A schematic diagram of the implementation process of step S20; Figure 5 yes Figure 2 A schematic diagram of the implementation process of step S30; Figure 6 yes Figure 2 A schematic diagram of the implementation process of step S40; Figure 7 This is a schematic diagram of the structure of the intelligent obstacle avoidance device for total station in one embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0023] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0024] To address the problems mentioned above in the background art, this application provides a total station intelligent obstacle avoidance method, device, equipment, medium, and program product. The total station intelligent obstacle avoidance method provided by this invention can be applied to applications such as... Figure 1 The total station intelligent obstacle avoidance system shown includes a client and a server.

[0025] In one embodiment, such as Figure 2 As shown, a total station intelligent obstacle avoidance method is provided, which is then applied to... Figure 1 Taking the intelligent obstacle avoidance system of a total station as an example, the following steps are included: S10: Control the total station to rotate horizontally, trigger ATR search at each stopping position and read the horizontal and vertical angles, and construct a set of interference candidate angle records based on the horizontal and vertical angles; S20: Filter the set of candidate interference angle records to obtain a dataset of valid interference angles, and cluster the dataset of valid interference angles to obtain a set of interference clusters; S30: Merge the boundaries of adjacent interference clusters based on the interference cluster set to generate a list of filtered regions; S40: Determine the search range based on the current orientation angle of the total station, determine the set of valid search sub-intervals based on the search range and the list of filtered areas, trigger ATR search, and generate intelligent avoidance search results.

[0026] In this embodiment, the present invention controls the total station motor to drive the instrument to perform 360° continuous step rotation with a fixed horizontal angle increment. At each stop position, it actively triggers ATR search and records the search results. This transforms the total station from a working mode that passively waits for search commands to a learning mode that actively detects the distribution of interference in the entire angular space. Before the formal measurement operation, the present invention fully grasps the angular position information of all interference sources in the current environment, solving the technical problem that traditional ATR search has no awareness of interference sources. This invention introduces a three-factor weighted credibility scoring mechanism based on the number of consecutive failures, historical failure probability, and environmental correlation coefficient. It performs multi-dimensional quantitative evaluation of each ATR search failure event, recording only failure locations with credibility scores reaching a high credibility threshold as interference candidate angles. Simultaneously, medium-credibility failure locations are temporarily stored in an observation queue, while low-credibility failure locations are directly discarded. From the data acquisition stage, noise events such as occasional communication jitter and instantaneous target occlusion are filtered to ensure that the data entering the subsequent clustering modeling process are all statistically significant real interference locations. In the clustering modeling stage, this invention employs a dual-dimensional joint clustering method using horizontal and vertical angles. The difference between the horizontal and vertical angles simultaneously not exceeding their respective clustering thresholds serves as a dual constraint for cluster determination. This overcomes the technical problem of existing technologies that incorrectly merge interference points at different vertical angle positions in the same horizontal direction into the same region when performing one-dimensional clustering based solely on horizontal angles. This ensures that each interference cluster accurately corresponds to the true two-dimensional angular distribution range of a single interference source in physical space, avoiding unnecessary loss of the effective search space due to excessive expansion of the filtering area in the vertical dimension. This invention dynamically calculates the adaptive expansion distance in the horizontal and vertical directions based on the point density and average confidence score of interference clusters. After finely expanding the initial boundary of the interference region, adjacent regions are merged to generate an accurate and complete list of filtered regions, which is then persistently stored. Before the normal ATR search is executed, interference regions are precisely removed from the search range through two-dimensional geometric intersection operations of horizontal and vertical angles. The search range is divided into several effective search sub-intervals, which are then sorted from near to far according to the angular distance between the center angle of each sub-interval and the current instrument orientation, and ATR searches are triggered sequentially. This completely avoids known interference regions while minimizing the instrument rotation travel, so that the time and computing power of each ATR search are fully concentrated on the effective angular interval of the real prism target, thus improving the accuracy and efficiency of ATR searches in complex environments.

[0027] In one embodiment, such as Figure 3 As shown, step S10 specifically includes the following steps: S11: Control the total station to rotate horizontally, read the horizontal and vertical angles at each stopping position, and verify the valid angle frame based on the horizontal and vertical angles; S12: Trigger ATR search based on effective angle frames. When ATR search fails, obtain the number of consecutive failures and historical failure probabilities of the corresponding dwell position based on the effective angle frames and calculate the environmental correlation coefficient. S13: The credibility score is obtained by weighted summation of the number of consecutive failures, the probability of historical failures and the environmental correlation coefficient, and the credibility score is compared with the high credibility threshold and the observation threshold. S14: When the confidence score is not lower than the high confidence threshold, the horizontal angle, vertical angle and confidence score are encapsulated into an interference candidate angle record and written into the interference candidate angle record set; when the confidence score is lower than the high confidence threshold but not lower than the observation threshold, the horizontal angle, vertical angle and confidence score are written into the observation queue; when the confidence score is lower than the observation threshold, the horizontal angle, vertical angle and confidence score are discarded.

[0028] In this embodiment, the learning controller establishes a dwell position index, angle frame buffer, search statistics table, interference candidate angle record set, and observation queue. It then sends a horizontal rotation command to the motor control interface, causing the total station to rotate sequentially to each horizontal dwell position according to a preset horizontal step angle; for example, the horizontal step angle can be set to 3°. After reaching a horizontal dwell position, the total station waits for 500ms to allow the horizontal rotation mechanism to stabilize. After the stabilization wait ends, the ATR control unit executes a search within the preset vertical search range corresponding to that horizontal dwell position, thereby obtaining ATR response records with different horizontal and vertical angles. The learning controller identifies the horizontal angle type, vertical angle type, and angle unit according to the total station's communication protocol and uniformly converts the read angles to the internal angle coordinate system. The converted horizontal angle is located in the range [0°, 360°), and the converted vertical angle is limited to the vertical angle range that the total station can execute. The learning controller compares the data generation timestamp with the angle data reception time, using a time difference of no more than 500ms as the angle data timeliness condition, and simultaneously verifies whether the angle data was generated after the stabilization wait at the current dwell position ended. For adjacent horizontal angles, the minimum period angle difference is determined based on the smaller of the clockwise and counterclockwise angle differences. A minimum period angle difference exceeding 10° or a change in adjacent vertical angles exceeding 5° is used as a condition for angle drift detection. This prevents erroneous drift judgments when the total station rotates from a position close to 360° to a position close to 0°. If the angle type or unit cannot be identified, the angle data does not correspond to the current position, the difference between the data generation time and the reception time exceeds 500ms, there is a communication error, a missing field, or the angle exceeds the device's operational range, a valid angle frame is not generated, and the current scanning position is either reread or skipped. Only after all checks pass are the horizontal angle, vertical angle, timestamp, and current environmental state combined into a valid angle frame.

[0029] Using the horizontal and vertical angles carried in the effective angle frame as the angular positions corresponding to the ATR search, a search command is sent to the ATR control unit, and the status of search success, search failure, search timeout, and abnormal reflection response is monitored. When the ATR search is successful, the reflection response characteristics, effective ranging status, target identification flag, and continuous locking status corresponding to the candidate target are read, and the target authenticity verification is performed on the candidate target. When the candidate target passes the target authenticity verification, the current angular position is determined as the effective target position, and this position is not used as an interference candidate position, and the continuous abnormal status of this position is reset. When the candidate target fails the target authenticity verification, the current angular position is determined as a suspected mis-locked position, and the search statistics table is queried according to the angle information in the effective angle frame. The current angular position is used as the statistical key to update the continuous abnormality count, cumulative detection count, and cumulative abnormality count of this position. When an ATR search fails or times out, the system checks for abnormal reflection responses during the search process. If no abnormal reflection response is detected, the current search result is determined as a normal, target-free result, and the interference statistics for the corresponding angle position are not updated. If an abnormal reflection response is detected, the current angle position is determined as a suspected reflective interference location, and the number of consecutive anomalies, cumulative detections, and cumulative anomalies at that location are updated. The historical anomaly probability is obtained based on the proportion of the cumulative anomaly count to the cumulative detection count. The system also acquires the lighting conditions, operating time, and other collected environmental conditions corresponding to the current anomaly event and matches the environmental conditions with the historical anomaly events at the corresponding angle position. When a suspected false lock or abnormal reflection response repeatedly occurs at the corresponding angle position under the same or similar environmental conditions, the environmental correlation coefficient is increased. When there is no stable and repetitive relationship between the search anomaly and the environmental conditions, the environmental correlation coefficient is decreased. This avoids directly identifying normal target-free results, momentary target occlusion, occasional communication jitter, or single temporary lighting changes as fixed interference sources.

[0030] After updating the three evaluation metrics, the credibility score is calculated according to the normalized weighted summation relationship: ,in, To score credibility, The number of consecutive anomalies. For continuous anomaly threshold, This represents the historical anomaly probability. This is the environmental correlation coefficient. and The values ​​are all limited to between 0 and 1. , and The corresponding weights are assigned, and the sum of the three weights is 1. For example, the consecutive anomaly threshold can be set to 3 times, and the three weights can be set to 0.4, 0.4, and 0.2 respectively. When the number of consecutive anomalies reaches or exceeds the consecutive anomaly threshold, the consecutive anomaly level is determined to be 1, so that the confidence score is always between 0 and 1.

[0031] The confidence score is compared with a high confidence threshold and an observation threshold. For example, the high confidence threshold can be set to 0.75, the observation threshold to 0.5, and the consecutive anomaly threshold to 3. When the number of consecutive anomalies reaches 3 and the confidence score is not lower than 0.75, the horizontal angle, vertical angle, confidence score, and corresponding timestamp are encapsulated as interference candidate angle records and written into the interference candidate angle record set with a record identifier. When the confidence score is not lower than 0.75 but the number of consecutive anomalies is less than 3, the corresponding data is written into the observation queue. When the confidence score is lower than 0.75 but not lower than 0.5, the corresponding data is also written into the observation queue and re-evaluated when the total station scans the corresponding position again, combined with the new anomaly detection results. Only when the number of subsequent consecutive anomalies reaches 3 and the confidence score is not lower than 0.75 will the corresponding record be transferred to the interference candidate angle record set. When the confidence score is lower than 0.5, the existing anomaly evidence is considered insufficient, the corresponding data is discarded, and it will not participate in subsequent clustering.

[0032] In one embodiment, such as Figure 4 As shown, step S20 specifically includes the following steps: S21: Filter the record set of interference candidate angles to obtain the effective interference angle dataset; S22: For the effective interference angle dataset, take the first unclustered record as the seed point to create a new interference cluster. Use the mean of the horizontal angle and the mean of the vertical angle of all records in the current interference cluster as the cluster center. Traverse all unclustered records until all records are clustered to obtain the initial cluster set. S23: Calculate the horizontal and vertical angular spans of each interference cluster in the initial cluster set, and re-perform extended clustering based on the horizontal and vertical angular spans to obtain the interference cluster set.

[0033] In this embodiment, the set of interference candidate angle records is read and filtered according to record completeness, acquisition status, credibility status, and angle position validity. First, each record is checked to see if it contains horizontal angle, vertical angle, credibility score, timestamp, and corresponding search statistics. Records with missing fields, invalid angle values, or that cannot correspond to valid angle frames are deleted. At the same time, records formed during communication interruption, data packet loss, motor overload, or angle sensor alarm are removed based on communication status and device operation logs. Then, records with the same or extremely close angle positions are deduplicated. For example, when the difference between the horizontal angle and the vertical angle of two records is less than 0.01°, they can be identified as duplicate records, and the one with the higher credibility score is retained. At the same time, isolated records that deviate significantly from the overall angle distribution are identified based on the 3σ principle. The data in the observation queue is reviewed, and only records that reappear and have a credibility threshold of high credibility are transferred to the clustering data, thereby obtaining the effective interference angle dataset.

[0034] After filtering, the valid records are arranged according to their periodic angle position at the horizontal angle, and an unclustered flag is set for each record. Records located at the boundary between 0° and 360° and whose minimum periodic horizontal angle difference meets the horizontal angle clustering threshold are treated as adjacent records. Then, the first unclustered record is selected from the valid records as a seed point to establish a new interference cluster. The horizontal and vertical angles of the seed point are used as the initial cluster centers. The remaining unclustered records are then traversed, and the minimum periodic horizontal and vertical angle differences between the record to be processed and the current cluster center are used to determine whether the record to be processed belongs to the current interference cluster. For example, if the horizontal angle clustering threshold is set to 5° and the vertical angle clustering threshold is set to 3°, when the minimum periodic horizontal angle difference between the record to be processed and the current cluster center does not exceed 5° and the vertical angle difference does not exceed 3°, the record is added to the current interference cluster and updated to a clustered state. For each new record added, the horizontal cluster center is updated based on the periodic distribution of horizontal angles within the cluster on the unit circle, and the vertical cluster center is updated based on the arithmetic mean of vertical angles within the cluster. Then, the unclustered records are iterated again until one round of traversal is completed and no new records are added to the current interference cluster. For interference clusters spanning the boundary between 0° and 360°, the horizontal angles within the cluster are expanded to the same continuous angular domain, and the equivalent horizontal angles are recorded for subsequent calculations of horizontal angle span and boundaries. This process is repeated until all records in the effective interference angle dataset are clustered.

[0035] After obtaining the initial cluster set, the maximum and minimum horizontal angles, maximum and minimum vertical angles of each interference cluster are extracted. The difference between the corresponding extreme values ​​yields the horizontal and vertical angle spans, which are then used to determine whether the initial clustering results are too loose. For example, if the horizontal angle span exceeds 15° or the vertical angle span exceeds 9°, the current interference cluster may incorrectly contain multiple adjacent interference sources. In this case, the cluster center is selected as the splitting position based on the angle dimension with the larger span, splitting the original interference cluster into two record subsets. Seed point selection, cluster center update, and unclustered record traversal are then performed again on each record subset, causing the split records to re-form more compact sub-clusters. If the horizontal angle span does not exceed 15° and the vertical angle span does not exceed 9°, the current interference cluster is retained. By sequentially performing span verification and extended clustering on all initial interference clusters, and replacing the original interference clusters with spans exceeding the limits with the newly formed sub-clusters, a set of interference clusters that can correspond to interference sources with different two-dimensional angles is finally obtained.

[0036] In one embodiment, such as Figure 5 As shown, step S30 specifically includes the following steps: S31: Determine the initial boundary, point density, and average confidence score of each interference cluster in the interference cluster set; S32: Calculate the adaptive expansion distance based on point density and average confidence score, and expand the initial boundary according to the adaptive expansion distance to obtain the target boundary of each interference cluster; S33: Merge the target boundaries of adjacent interference clusters into a single filtering region, and generate a list of filtering regions based on all filtering regions.

[0037] In this embodiment, the set of interference clusters is traversed, and for each interference cluster, the horizontal angle, vertical angle, and confidence score of all interference angle records are extracted, and the number of records within the cluster is counted. When the number of records in an interference cluster is less than 3, the interference cluster is set as an observation cluster, and no formal filtering region is generated based on the interference cluster. When the number of records in an interference cluster is not less than 3, the equivalent minimum horizontal angle and equivalent maximum horizontal angle within the cluster are used as the initial horizontal boundaries, and the minimum vertical angle and maximum vertical angle are used as the initial vertical boundaries, and the horizontal angle span and vertical angle span are determined by the corresponding boundaries. When the angle span in any direction is less than 0.01°, the effective angle span in that direction is set to 0.01°, and the original point density is determined by the product of the number of records and the effective angle spans in the two directions, thereby avoiding abnormally increased point density in single-point clusters or zero-span clusters due to extremely small zero constants. The confidence scores of all records within the cluster are summed and divided by the number of records in the cluster to obtain the average confidence score, which reflects the reliability of the overall interference cluster as a stable interference source. The initial horizontal boundary, initial vertical boundary, cluster center, original point density, average confidence score, number of records and generation time are written into the corresponding initial region object.

[0038] After the initial region is constructed, the original point density of each interference cluster is normalized according to the preset lower and upper limits of point density to obtain a dimensionless point density with values ​​between 0 and 1. And calculate the adaptive expansion distance in the horizontal and vertical directions respectively: ,in, and These are the horizontal expansion distance and the vertical expansion distance, respectively. and These represent the basic expansion distances in two directions. For normalized point density, The average confidence score is calculated. For example, the horizontal base expansion distance can be set to 2°, and the vertical base expansion distance can be set to 1.5°. This allows interference clusters with lower point density or lower average confidence scores to have a larger expansion distance, while interference clusters with higher point density and higher average confidence scores use an expansion range close to the base expansion distance. The initial horizontal lower boundary is expanded by the corresponding horizontal expansion distance in the decreasing direction, and the initial horizontal upper boundary is expanded by the same distance in the increasing direction. The vertical boundary is processed in the same way to obtain the target boundary. If the expanded horizontal boundary crosses the intersection of 0° and 360°, the corresponding area is represented as two related sub-regions with the same area identifier. If the expanded vertical boundary exceeds the effective vertical angle range of the total station, it is truncated according to the device's executable vertical angle boundary.

[0039] After obtaining all target boundaries, the regions are sorted according to their horizontal angular positions. Adjacent regions are then sequentially checked for overlap or only small gaps in both the horizontal and vertical directions. For example, the allowed horizontal gap can be set to 1°, and the allowed vertical gap to 0.5°. When two regions meet the overlap or adjacency conditions in both directions, they are merged into a single filter region. The lower boundaries of the merged region are taken as the smaller of the corresponding lower boundaries of the original regions, and the upper boundaries are taken as the larger of the corresponding upper boundaries. Simultaneously, the record counts of the two regions are summed, and the cluster center and average confidence score are updated with weights based on the number of records in each region. After each merge, the merged region participates in subsequent adjacent detection until no more regions meet the merging conditions. This avoids retaining sub-intervals with excessively small widths and little practical search significance between two closely spaced interfering regions. Finally, a region identifier is generated for each filter region, and the horizontal boundary, vertical boundary, center angle, average confidence score, point density, record count, creation time, and update time are written into the filter region list.

[0040] In one embodiment, such as Figure 6 As shown, step S40 specifically includes the following steps: S41: Determine the search range based on the current orientation angle of the total station; S42: Determine the set of valid search sub-intervals based on the search range and the list of filtered regions; S43: Trigger ATR search based on the angle range of each valid search sub-interval in the set of valid search sub-intervals, and output the result of the first ATR search successful sub-interval as the intelligent avoidance search result.

[0041] In this embodiment, the intelligent avoidance module receives the normal ATR search command and reads the current horizontal and vertical orientation angles of the total station through the angle data acquisition interface. Simultaneously, it verifies whether the current angle data is within a valid range and corresponds to the current search command. After successful verification, the current orientation angle is used as the search center, and the initial search range for this ATR search is determined by combining the preset horizontal and vertical search radii. For example, the horizontal search radius can be set to 30°, and the vertical search radius can be set to 10°, meaning that 30° of horizontal search space is reserved on the left and right sides of the current orientation, and 10° of vertical search space is reserved on the upper and lower sides of the current vertical orientation. When the horizontal search range crosses the 0° or 360° boundary, the cross-boundary range is divided into two consecutive angle intervals located at the end of 360° and the beginning of 0° to ensure that all horizontal angles fall within [0°]. 360 Within the range of vertical search, when the vertical search range exceeds the vertical rotation range allowed by the total station, it is truncated according to the executable boundary of the device.

[0042] After determining the search range, based on the current station identifier, equipment identifier, instrument position, instrument height, and horizontal angular orientation reference, a list of filterable regions that match the current station status and are in an active state is read from the persistent storage area. The region identifier, horizontal boundary, vertical boundary, and active status of each filterable region are verified. If the filterable region list is empty, or if there are no filterable regions overlapping with the current search range, the entire search range is determined as a valid search sub-region and enters the ATR search. If overlap exists, the overlap range between each filterable region and the search range in the horizontal and vertical directions is calculated. Only when a filterable region simultaneously overlaps with the search range in both horizontal and vertical angles is the corresponding two-dimensional overlapping region determined as the area to be avoided. All two-dimensional overlapping regions are merged according to their spatial location, and the merged two-dimensional overlapping regions are sequentially deducted from the search range. The remaining angular regions located to the left, right, top, and bottom of the deducted two-dimensional overlapping regions, and possessing actual search space, are written into the set of valid search sub-regions. For each valid search sub-region, record the starting horizontal angle, ending horizontal angle, lower vertical search boundary, upper vertical search boundary, sub-region number, and corresponding source region identifier. Delete empty or fragmented regions whose horizontal or vertical angle width is insufficient to perform an ATR search. When no remaining search space exists after deducting all two-dimensional overlapping regions, set the set of valid search sub-regions to empty and output the status that the current search range is completely covered by the filtered regions.

[0043] After constructing the set of valid search sub-intervals, the search order is determined based on the positional relationship of each sub-interval relative to the current orientation of the total station. Sub-intervals with smaller rotation distances or closer to the expected target direction are prioritized for entry into the search sequence. Sub-intervals adjacent to high-confidence filtering areas can be appropriately placed later. Subsequently, the horizontal and vertical angle ranges of the first valid search sub-interval are encapsulated as ATR search parameters and the search is triggered. If the search of this sub-interval fails, the search proceeds to the next sub-interval. When any sub-interval is successfully searched and locks onto the prism target, the search of the remaining sub-intervals is immediately terminated. The search success flag, target horizontal angle, target vertical angle, hit sub-interval flag, and lock status are encapsulated as intelligent avoidance search results and output. This ensures that the ATR search is only performed within the valid angle range after deducting known interference areas, while avoiding meaningless subsequent scans after the target has been found. When no target is found in all valid search sub-intervals, the search failure status is output.

[0044] In one embodiment, step S43 specifically includes the following steps: Calculate the angular distance of each valid search subinterval in the set of valid search subintervals, and determine the angular range of each valid search subinterval based on the angular distance; Trigger ATR search with angle range as parameter. When a valid search sub-interval is found and the ATR search is successful, output the result of the first successfully searched sub-interval as the intelligent avoidance search result. When all valid search sub-intervals fail the ATR search, determine the filtering area and delete the filtering area from the filtering area list.

[0045] In this embodiment, the starting horizontal angle, ending horizontal angle, lower boundary of the vertical angle, upper boundary of the vertical angle, and sub-interval identifier of each sub-interval in the valid search sub-interval set are read. The center horizontal angle of the sub-interval is determined based on the starting and ending horizontal angles. Then, the center horizontal angle is compared with the current horizontal orientation angle of the total station, and the minimum rotation angle difference between the two is taken as the angular distance of the corresponding sub-interval. When the sub-interval or the current orientation is located at the boundary between 0° and 360°, the horizontal angle is periodically processed, and the smaller of the clockwise and counterclockwise angle differences is taken as the angular distance to avoid misjudging two actually adjacent angular positions as being far apart. Then, all valid search sub-intervals are arranged in ascending order of angular distance, so that the total station prioritizes searching the sub-intervals that are closer to the current orientation. For each sub-interval, its horizontal and vertical angle start and end boundaries are read, and the boundaries are encapsulated as the corresponding ATR search angle range. The horizontal angle range is limited to the sub-interval, and the vertical angle range is the vertical search range retained when the valid search sub-intervals are generated, thus ensuring that the ATR search will not re-enter the filtered area that has been subtracted.

[0046] After determining the search order and angle range, the angle range of the first sorted sub-interval is sent to the ATR control unit, which controls the total station to rotate to that sub-interval and perform target scanning. At the same time, it listens for search success, search failure, and search timeout status. When the current sub-interval search fails, its sub-interval identifier and failure status are recorded, and the angle range of the next sub-interval is used as the new search parameter to trigger the ATR search. When any sub-interval search is successful and the prism target is locked, the search of the remaining sub-intervals is stopped immediately. The sub-interval identifier, search angle range, target horizontal angle, target vertical angle, and target locking status of the first successfully searched sub-interval are encapsulated as intelligent avoidance search results and output, thereby reducing the total station's invalid rotation travel and repeated search time. If no target is found after traversing all valid search sub-intervals, the filter areas excluded from the original search range are retrieved based on the region association information saved when generating the set of valid search sub-intervals. Filter areas that overlap with the original search range and actually cause sub-interval truncation or segmentation are identified as areas to be deleted. This is because when all non-filtered angle ranges fail to be searched, the current filter area may no longer be suitable as a fixed avoidance range due to changes in target location, construction environment, or early learning misjudgments. After identifying the areas to be deleted, the corresponding record is removed from the filter area list based on its region identifier, and the region boundary, number of regions, and update time in persistent storage are updated synchronously, so that subsequent ATR searches can re-cover the previously excluded angle ranges. If the current search range is affected by multiple filter areas simultaneously, the corresponding filter areas actually participating in this segmentation are determined based on the source region identifier carried by each valid search sub-interval, avoiding the deletion of other regions unrelated to this search. This maintains intelligent avoidance efficiency while allowing the filter area list to be corrected according to changes in the actual working environment.

[0047] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0048] In one embodiment, a total station intelligent obstacle avoidance device is provided, which corresponds one-to-one with the total station intelligent obstacle avoidance method described in the above embodiments. For example... Figure 7 As shown, the total station's intelligent obstacle avoidance device includes: The reading module 701 is used to control the horizontal rotation of the total station, trigger ATR search at each stopping position and read the horizontal and vertical angles, and construct a set of interference candidate angle records based on the horizontal and vertical angles; Clustering module 702 is used to filter the set of interference candidate angle records to obtain a set of valid interference angles, and to cluster the valid interference angles to obtain a set of interference clusters. The merging module 703 is used to merge the boundaries of adjacent interference clusters based on the interference cluster set and generate a list of filtered regions. The generation module 704 is used to determine the search range based on the current orientation angle of the total station, determine the set of valid search sub-intervals based on the search range and the list of filtered areas, trigger ATR search, and generate intelligent avoidance search results.

[0049] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0050] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0051] This application also provides a computer device, such as... Figure 8 As shown, the computer device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, it implements the steps in any of the above method embodiments, or when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiments.

[0052] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0053] Those skilled in the art will understand that Figure 8The computer device described is merely an example and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0054] The aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), or Field Programmable Gate Arrays (FPGAs). Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0055] The memory can be an internal storage unit of the computer device, such as a hard drive or RAM. The memory can also be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal and external storage units of the computer device.

[0056] This application also provides a readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.

[0057] This application provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0059] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0060] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0061] In the embodiments provided in this application, it should be understood that the disclosed apparatus / devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0062] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A total station intelligent obstacle avoidance method, characterized in that, include: Control the total station to rotate horizontally, trigger ATR search at each stopping position and read the horizontal and vertical angles, and construct a set of interference candidate angle records based on the horizontal and vertical angles; The set of interference candidate angle records is filtered to obtain a dataset of effective interference angles, and the dataset of effective interference angles is clustered to obtain a set of interference clusters. Based on the set of interference clusters, adjacent interference cluster boundaries are merged to generate a list of filtered regions. The search range is determined based on the current orientation angle of the total station. The set of valid search sub-intervals is determined according to the search range and the list of filtered areas. The ATR search is triggered and intelligent avoidance search results are generated.

2. The intelligent obstacle avoidance method for total station as described in claim 1, characterized in that, The total station is controlled to rotate horizontally, triggering an ATR search and reading the horizontal and vertical angles at each stopping position. Based on these horizontal and vertical angles, a set of interference candidate angle records is constructed, including: Control the total station to rotate horizontally, read the horizontal and vertical angles at each stopping position, and verify the valid angle frames based on the horizontal and vertical angles; ATR search is triggered based on the effective angle frame. When the ATR search fails, the number of consecutive failures and the historical failure probability of the corresponding dwell position are obtained according to the effective angle frame, and the environmental correlation coefficient is calculated. The credibility score is obtained by weighted summing of the number of consecutive failures, the historical failure probability, and the environmental correlation coefficient, and then compared with the high credibility threshold and the observation threshold. When the credibility score is not lower than the high credibility threshold, the horizontal angle, the vertical angle, and the credibility score are encapsulated into an interference candidate angle record and written into the interference candidate angle record set; when the credibility score is lower than the high credibility threshold but not lower than the observation threshold, the horizontal angle, the vertical angle, and the credibility score are written into the observation queue; when the credibility score is lower than the observation threshold, the horizontal angle, the vertical angle, and the credibility score are discarded.

3. The intelligent obstacle avoidance method for total stations as described in claim 2, characterized in that, The process involves filtering the set of candidate interference angle records to obtain a dataset of valid interference angles, and then clustering the dataset of valid interference angles to obtain a set of interference clusters, including: The set of interference candidate angle records is filtered to obtain a dataset of valid interference angles; For the effective interference angle dataset, the first unclustered record is taken as the seed point to create a new interference cluster. The mean of the horizontal angle and the mean of the vertical angle of all records in the current interference cluster are taken as the cluster center. All unclustered records are traversed until all records are clustered to obtain the initial cluster set. Calculate the horizontal and vertical angular spans of each interference cluster in the initial cluster set, and re-perform extended clustering based on the horizontal and vertical angular spans to obtain the interference cluster set.

4. The intelligent obstacle avoidance method for total station as described in claim 1, characterized in that, The step of merging adjacent interference cluster boundaries based on the interference cluster set to generate a list of filtered regions includes: Determine the initial boundary, point density, and average confidence score of each interference cluster in the interference cluster set; The adaptive expansion distance is calculated based on the point density and the average confidence score, and the initial boundary is expanded according to the adaptive expansion distance to obtain the target boundary of each interference cluster. The target boundaries of adjacent interference clusters are merged into a single filtering region, and a list of filtering regions is generated based on all filtering regions.

5. The intelligent obstacle avoidance method for total station as described in claim 1, characterized in that, The process involves determining the search range based on the current orientation angle of the total station, determining a set of valid search sub-intervals based on the search range and the filter area list, triggering an ATR search, and generating intelligent avoidance search results, including: The search range is determined based on the current orientation angle of the total station; The set of valid search sub-intervals is determined based on the search range and the list of filtered regions; ATR search is triggered based on the angle range of each valid search sub-interval in the set of valid search sub-intervals, and the result of the first ATR search that is successful is output as the intelligent avoidance search result.

6. The intelligent obstacle avoidance method for total station as described in claim 5, characterized in that, The step of triggering ATR search based on the angle range of each valid search sub-interval in the set of valid search sub-intervals, and outputting the first successfully searched sub-interval result as the intelligent avoidance search result, includes: Calculate the angular distance of each valid search sub-interval in the set of valid search sub-intervals, and determine the angular range of each valid search sub-interval based on the angular distance; The ATR search is triggered using the angle range as a parameter. When a valid sub-interval is successfully searched by ATR, the result of the first successfully searched sub-interval is output as the intelligent avoidance search result. When all valid sub-intervals fail to be searched by ATR, the filtering area is determined and the filtering area is deleted from the filtering area list.

7. A total station intelligent obstacle avoidance device, characterized in that, The steps for implementing the total station intelligent obstacle avoidance method as described in any one of claims 1 to 6 include: The reading module is used to control the horizontal rotation of the total station, trigger ATR search at each stopping position and read the horizontal and vertical angles, and construct a set of interference candidate angle records based on the horizontal and vertical angles; The clustering module is used to filter the set of interference candidate angle records to obtain a set of effective interference angles, and to cluster the set of effective interference angles to obtain a set of interference clusters. The merging module is used to merge the boundaries of adjacent interference clusters based on the interference cluster set and generate a list of filtered regions. The generation module is used to determine the search range based on the current orientation angle of the total station, determine the set of valid search sub-intervals according to the search range and the list of filtered areas, trigger ATR search, and generate intelligent avoidance search results.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the total station intelligent obstacle avoidance method as described in any one of claims 1 to 6.

9. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the total station intelligent obstacle avoidance method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the total station intelligent obstacle avoidance method as described in any one of claims 1 to 6.

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