Dynamic path planning method for power transmission line maintenance robot

Abstract

The invention discloses a power transmission line maintenance robot dynamic path planning method, and relates to the technical field of robot maintenance, and the method comprises the steps: obtaining a three-dimensional topology model of a power transmission line, and constructing a path diagram; performing initial path planning based on a comprehensive scoring algorithm; surrounding environment data are continuously collected, dynamic obstacle recognition and prediction are carried out, a path is re-planned online, and an optimized path is regenerated; an energy model is introduced, and path-energy consumption joint decision making is carried out; and an abnormal feedback and path rollback mechanism is introduced. The method can achieve efficient planning and dynamic adjustment of the path, has high environmental adaptability and task safety, is suitable for intelligent maintenance operation of the power transmission line under the complex terrain, makes a decision by introducing an energy consumption model to cooperate with path comprehensive scoring, effectively prevents the robot from neglecting energy efficiency in the path planning process, and improves the maintenance efficiency of the power transmission line. The method guarantees the continuity and safety of task execution, and is especially suitable for long-distance, high-fall and energy consumption constraint scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance robot path planning, and in particular to a dynamic path planning method for a power transmission line maintenance robot. Background Art

[0002] As the scale of power systems continues to expand, the number and span of transmission lines are increasing. These lines are located in increasingly complex environments, such as mountainous areas, plateaus, forested areas, and areas crossing rivers. This places higher demands on routine line inspections and troubleshooting. Traditional manual inspection methods are not only inefficient and costly, but also pose significant safety risks in harsh terrain or extreme weather conditions.

[0003] In recent years, transmission line maintenance robot technology has gradually matured. Some robots, equipped with capabilities such as mounting, walking, positioning, and simple operation, have been deployed in actual line inspection tasks. However, existing path planning methods often rely on static terrain maps or preset path points, lacking the ability to perceive and respond to dynamic obstacles in real time. This often prevents rapid path adjustments in unexpected situations, such as line obstructions, impacting operational continuity and safety. Furthermore, existing path planning often prioritizes the shortest path or minimizes energy consumption, failing to comprehensively consider multi-dimensional factors such as slope, safety risk level, energy constraints, and obstacle variations. These factors lack comprehensive scoring and hierarchical decision-making mechanisms. Especially in complex line environments, robots often experience operational interruptions or even equipment damage due to unforeseen risky paths. Furthermore, most current path planning systems lack efficient exception feedback mechanisms and fault tolerance, making them unable to dynamically assess and address deviations based on the actual task. Once a path fails, it is often impossible to effectively roll back to a known safe node for path reconstruction.

[0004] Therefore, there is an urgent need for a transmission line maintenance robot path planning method that can integrate static and dynamic environmental information, combine energy and risk assessment, and have an adaptive abnormal rollback mechanism to achieve more intelligent, efficient, and safe autonomous operation and maintenance capabilities. Summary of the Invention

[0005] The present invention is proposed in view of the fact that most existing path planning methods rely on static terrain maps or preset path points when planning the path of maintenance robots, and lack the ability to perceive and respond to dynamic obstacles in real time; and that existing path planning mostly takes the shortest path or minimum energy consumption as the optimization goal, fails to comprehensively consider multi-dimensional factors, and lacks a comprehensive scoring and hierarchical decision-making mechanism; and that most current path planning systems lack an efficient abnormal feedback mechanism and fault tolerance, and are unable to perform dynamic judgment and processing based on the degree of deviation from the actual task.

[0006] Therefore, the problem to be solved by the present invention is how to provide a dynamic path planning method for a power transmission line maintenance robot.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] First, embodiments of the present invention provide a dynamic path planning method for a power transmission line maintenance robot, comprising: obtaining a three-dimensional topological model of the transmission line, constructing a path map, and displaying a panoramic view of the line on the human-machine interface of the control center; selecting and executing an initial path plan based on a comprehensive scoring algorithm; continuously collecting ambient environmental data, identifying and predicting dynamic obstacles, and replanning the path online to regenerate an optimized path; introducing an energy model to make joint path-energy consumption decisions. A motion energy consumption model is established to make path decisions; and an abnormality feedback and path rollback mechanism are introduced. The robot automatically identifies abnormal events, classifies them into abnormality levels, and reconstructs the path based on the abnormality level.

[0009] As a preferred solution of the dynamic path planning method for a power transmission line maintenance robot described in the present invention, the construction of the path map includes: using a three-dimensional laser radar, a camera and an inertial measurement unit (IMU) sensor to collect terrain and ground object information along the transmission line, and reconstructing a path point set V = {v1, v2, ..., v n} and path edge set E={e ij}, forming a directed graph G = (V, G). Path edge e ij Describes the path point v from which the robot can i Travel to waypoint v j , and the geometric attribute information of path length, slope, and elevation difference is attached for subsequent path scoring and energy consumption calculation.

[0010] As a preferred solution of the dynamic path planning method for a power transmission line maintenance robot described in the present invention, in the path planning process, the path with the smallest cumulative score is preferentially selected as the robot's initial path and executed. The calculation formula is:

[0011]

[0012] in, Represents the set of path segments that the path passes through from the starting point to the end point, d ij represents the length of the path segment; α ij is the slope angle, ρ ij is the risk level corresponding to the path segment; ε ij is the estimated energy consumption; w d 、w g 、w r 、w eare the length of the path segment, the slope angle, the risk level corresponding to the path segment, and the weight of the estimated energy consumption.

[0013] As a preferred solution of the dynamic path planning method for a power transmission line maintenance robot described in the present invention, during the robot's execution of the initial path, obstacle position and density data are obtained in real time, and combined with the original scoring function to form a dynamic path cost function:

[0014] C ij (t)=(w d ·d ij +w g α ij +w r ·ρ ij +w e ·ε ij )+λ·Ω ij (t);

[0015] Among them, C ij (t) is the real-time evaluation of the current path; Ω ij (t) is the obstacle density of the current path segment, and λ is the weight.

[0016] As a preferred solution of the dynamic path planning method for a power transmission line maintenance robot described in the present invention, the total energy consumption of the entire path is estimated using the formula:

[0017]

[0018] Among them, ε total is the total energy consumption expected for the path, m is the mass of the robot, and g is the acceleration due to gravity (9.8 m / s 2 ), μ is the friction coefficient, is the power consumption coefficient of the drive motor, v ij is the uniform speed of the path segment.

[0019] As a preferred solution of the dynamic path planning method for a power transmission line maintenance robot described in the present invention, the decision is made based on the total energy consumption of the path, specifically:

[0020] If ε total If the value is less than or equal to the robot power threshold, it means that the robot's current remaining power can meet the task requirements, and the path is determined to be the optimal path and executed;

[0021] If ε total If the value is greater than the robot's battery threshold, it means that the robot's current remaining battery power cannot meet the task requirements and a new path needs to be replanned.

[0022] When ε appears three times in a row totalWhen the battery level is greater than the robot's battery threshold, it returns to replenish the battery.

[0023] As a preferred solution of the dynamic path planning method for a power transmission line maintenance robot described in the present invention, the abnormality level determination function is:

[0024]

[0025] Where δ(t) is the deviation between the actual path and the expected path, τ1 and τ2 are the set tolerance thresholds;

[0026] Specifically:

[0027] When the system determines that the abnormality level is 1, it continues to execute the current path;

[0028] When the system determines that the abnormality level is 2, the system activates the path rollback mechanism and selects a safe anchor point with a better score from the historical path point set for path rollback;

[0029] When the system determines that the abnormality level is 3, the robot maintenance task is terminated and human intervention is requested through the control center to make a decision.

[0030] Secondly, in order to further solve the problems existing in robot path planning, the present invention provides a dynamic path planning system for a transmission line maintenance robot in an embodiment, which includes: an acquisition module for acquiring a three-dimensional topological model of the transmission line; a path map construction module for constructing a path map; an obstacle recognition module for collecting surrounding environment data and performing dynamic obstacle recognition and prediction; and a decision module for combining an energy model with abnormal feedback to make decisions on the path.

[0031] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the dynamic path planning method for a transmission line maintenance robot as described in the first aspect of the present invention.

[0032] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of a dynamic path planning method for a power line maintenance robot as described in the first aspect of the present invention.

[0033] The beneficial effects of the present invention are:

[0034] 1. This invention introduces an obstacle density function and a dynamic scoring mechanism. During execution, it can continuously perceive changes in surrounding obstacles, adjust the path cost function in real time, and then perform online path replanning, thereby improving the robot's navigation robustness and continuous operation capability in complex power transmission line environments.

[0035] 2. This invention accurately estimates the energy consumption of each path segment based on an energy consumption model and makes decisions based on a comprehensive path score. This effectively prevents the robot from ignoring energy efficiency during path planning, ensuring the continuity and safety of task execution. It is particularly suitable for long-distance, high-drop, and energy-constrained scenarios.

[0036] 3. By introducing a position offset and action failure judgment mechanism, this invention can classify unexpected situations that occur during execution into abnormal levels and implement safe path point rollback based on historical path scores, significantly improving the robot's ability to respond to abnormal events such as path blockage and equipment failure.

[0037] 4. This invention constructs a path map model that includes three-dimensional coordinates, slope angle, risk level and terrain information, and combines it with a multi-factor comprehensive scoring function to select paths. Compared with traditional solutions that rely on two-dimensional maps or simple shortest path algorithms, it has significantly improved path adaptability and feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0039] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0043] Example 1

[0044] Reference Figure 1 The present invention provides a dynamic path planning method for a power transmission line maintenance robot, which is suitable for intelligent autonomous maintenance tasks in complex terrain environments of power transmission lines. The method mainly includes the following steps:

[0045] S1: Construct a three-dimensional path model of the transmission line and generate a panoramic path map.

[0046] Specifically, three-dimensional laser radar, camera and inertial measurement unit (IMU) and other sensors are used to collect terrain and ground object information along the transmission line, and the path point set V = {v1, v2, ..., v n} and path edge set E={e ij}, forming a directed graph G = (V, G). Path edge e ij Describes the path point v from which the robot can i Travel to waypoint v j , and attached geometric attribute information such as path length, slope, height difference, etc. for subsequent path scoring and energy consumption calculation.

[0047] S2: Perform initial path planning based on the path scoring function.

[0048] Specifically, based on the constructed three-dimensional path graph, a multi-factor comprehensive scoring function is used to evaluate the performance of each path edge d. ij Scoring, scoring function S ij As shown below:

[0049] S ij =w d ·d ij +w g α ij +w r ·ρ ij +w e ·ε ij ;

[0050] Among them, d ij represents the length of the path segment; α ij is the slope angle, ρ ij The risk level corresponding to the path segment, such as the high-voltage electromagnetic interference area, is assigned a higher value; ε ij is the estimated energy consumption; w d 、w g 、w r 、w e are the path segment length, slope angle, risk level corresponding to the path segment, and weight of estimated energy consumption, which are set manually according to environmental characteristics and meet w d +w g +w r +w e =1;

[0051] Specifically, during the path planning process, the cumulative score value S is given priority. total The minimum path is used as the robot's initial path and executed. The calculation formula is:

[0052]

[0053] in, Indicates that the path starts from the starting point v A To the end v G The set of path segments traversed on the selected path;

[0054] It should be noted that the slope angle α ij Indicates the degree of rise or fall of the path segment in the vertical direction. The calculation formula is:

[0055]

[0056] Among them, α ij Represents path segment e ij From point v i to v j Slope angle; h i is the path point v i Altitude, h j is the path point v j Altitude above sea level; x i 、y i is the path point v i The horizontal coordinate, x j 、y j is the path point v j The horizontal coordinate of

[0057] If α ij >0, indicating an uphill slope; if α ij <0, indicating a downhill slope; if α ij =0, indicating a flat path.

[0058] S3: Dynamic path scoring and replanning based on real-time environmental data.

[0059] Specifically, when the robot executes the initial path, the system scans the surrounding environment with a period of Δt to obtain the obstacle position and density data in real time. The path segment obstacle density function Ω is introduced. ij (t):

[0060]

[0061] in, Indicates time t on path segment e ij The number of obstacles observed on the ijis the path length. Combined with the original scoring function to form a dynamic path cost function C ij (t):

[0062] C ij (t) = S ij +λ·Ω ij (t);

[0063] Among them, λ is the weight of the obstacle density of the path segment, which is set by the staff;

[0064] By evaluating the cost functions of the current path and candidate paths in real time, dynamic adjustment and optimization of the path can be achieved to generate an optimized path.

[0065] S4: Joint decision-making based on energy consumption model;

[0066] Specifically, the following energy consumption estimation model ε is designed ij :

[0067]

[0068] Where m is the mass of the robot, g is the acceleration due to gravity (9.8m / s 2 ), μ is the friction coefficient, is the power consumption coefficient of the drive motor, v ij is the uniform speed of the path segment;

[0069] Similarly, the total energy consumption of the entire path is estimated to be total for:

[0070]

[0071] The model comprehensively considers factors such as slope resistance, friction, and driving power consumption to ensure the authenticity and quantification of energy consumption assessment;

[0072] The decision is made based on the total energy consumption of the path, specifically:

[0073] If ε total If the value is less than or equal to the robot power threshold, it means that the robot's current remaining power can meet the task requirements, and the path is determined to be the optimal path and executed;

[0074] If ε total If the value is greater than the robot's battery threshold, it means that the robot's current remaining battery power cannot meet the task requirements and a new path needs to be replanned.

[0075] When ε appears three times in a row total When the battery level is greater than the robot's battery threshold, it returns to replenish the battery.

[0076] It should be noted that, taking into account the partial loss of power, the power threshold = the current remaining power of the robot × the coefficient, and the coefficient is set by the staff (generally set to 0.8).

[0077] S5: Introduce abnormal feedback level classification and path rollback mechanism.

[0078] The system introduces an offset function to determine the deviation δ(t) between the actual path and the expected path:

[0079] δ(t)=||P(t)-P * (t)||;

[0080] Among them, P(t) is the current position of the robot, P * (t) is the expected position, and the number of consecutive action failures is defined as f fail (t), then the abnormality level determination function is:

[0081]

[0082] Among them, τ1 and τ2 are the tolerance thresholds set by the staff.

[0083] Specifically:

[0084] When the system determines that the abnormality level is 1, it continues to execute the current path;

[0085] When the system determines that the abnormality level is 2, the system activates the path rollback mechanism and selects a safe anchor point with a better score from the historical path point set for path rollback. The point selection logic is as follows:

[0086] v rollback =max{v k ∈V past |C ik <η};

[0087] Among them, V past is the set of historical passing points; η is the safety path scoring threshold to avoid obstacles during the rollback process; C ik From the current point to v k The dynamic path score is calculated based on the cost function in step S3;

[0088] When the system determines that the abnormality level is 3, the robot maintenance task is terminated and human intervention is requested through the control center to make a decision;

[0089] It should be noted that this mechanism ensures that the robot has strong adaptability and robustness in the event of emergencies or drastic changes in the environment.

[0090] This embodiment also provides a dynamic path planning system for a transmission line maintenance robot, including: an acquisition module for acquiring a three-dimensional topological model of the transmission line; a path map construction module for constructing a path map; an obstacle recognition module for collecting surrounding environment data and performing dynamic obstacle recognition and prediction; and a decision module for combining an energy model with abnormal feedback to make decisions on the path.

[0091] This embodiment also provides a computer device, which is suitable for a dynamic path planning method for a transmission line maintenance robot, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a dynamic path planning method for a transmission line maintenance robot proposed in the above embodiment.

[0092] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0093] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a dynamic path planning method for a power transmission line maintenance robot as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0094] In summary, the present invention introduces an obstacle density function and a dynamic scoring mechanism, which can continuously perceive changes in surrounding obstacles during the execution process, adjust the path cost function in real time, and then perform online path replanning, thereby improving the robot's navigation robustness and continuous operation capability in complex power transmission line environments; secondly, the present invention accurately estimates the energy consumption of each path segment based on the energy consumption model, and makes decisions in conjunction with the comprehensive path scoring, effectively avoiding the robot's neglect of energy efficiency during the path planning process, ensuring the continuity and safety of task execution, and is particularly suitable for long-distance, high-drop, and energy-constrained scenarios; in addition, the present invention introduces a position offset and action failure judgment mechanism, which can classify the abnormality level of emergencies that occur during the execution process, and implement safe path point rollback based on historical path scoring values, greatly improving the robot's ability to respond to abnormal events such as path blockage and equipment failure.

[0095] Example 2

[0096] This is the second embodiment of the present invention. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related descriptions of the present invention in an actual environment are provided.

[0097] The transmission line inspection path in the hilly forest area in this embodiment starts at A and ends at G. There are 7 path points (A to G) in total. There are multiple feasible paths. The robot parameters are: mass m = 50 kg; average inspection speed v = 1.5 m / s; friction coefficient μ = 0.2; motor energy consumption coefficient Gravitational acceleration g = 9.8 m / s 2 ;

[0098] Step S1: Collect information between path points to construct the following feasible path segments:

[0099]

[0100] Step S2, set weight w d =0.25, w g =0.25, w r =0.25, w e =0.25.

[0101] Take the path A-B-C-G as an example:

[0102] 1. Slope calculation (taking A→B as an example):

[0103]

[0104] 2. Estimated energy consumption:

[0105] E AB =50×9.8×0.2×10+1.1×1.5=980+1.65=981.65J;

[0106] 3. Single segment scoring:

[0107] S AB =0.25×10+0.25×0.179+0.25×2+0.25×981.65≈248.5;

[0108] Calculate the cumulative scores of each section A→B→C→G in this way:

[0109] Path Segment Rating Estimates AB 248.5 BC 192.3 CG 370.1 total 810.9

[0110] Compare to another path A→D→E→G:

[0111] Path Segment Rating Estimates AD 241.2 DE 210.6 EG 360.4 total 812.2

[0112] From the above table, we can see that the initial path should be A→B→C→G.

[0113] In step S3, it is detected that the CG segment encounters sudden dense obstacles. The obstacle density of the path segment is increased from 0.15 to 0.6. The cost function is modified as follows (the obstacle density weight λ is taken as 50):

[0114] C CG =S CG +50×0.6=370.1+30=400.1;

[0115] The new total score of the initial path is 840.9, which is higher than the other path (812.2). The system triggers path replanning and switches to A→D→E→G.

[0116] Step S4: Calculate the total energy consumption of the path A→D→E→G (each path is 15m long):

[0117] ε total ≈3×(50×9.8×0.2×15+1.1×1.5)≈3×1471.65≈4414.95J;

[0118] At this time, the current remaining power is = 6000J, and the power threshold = 0.8×6000=4800J;

[0119] Since 4414.95 < 4800 (power threshold), the system determines that "power is sufficient", determines that this path is the optimal path, and executes it.

[0120] The above shows that the present invention accurately estimates the energy consumption of each path segment based on the energy consumption model, and makes decisions in conjunction with the comprehensive path score, effectively preventing the robot from ignoring energy efficiency during the path planning process and ensuring the continuity and safety of task execution.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A dynamic path planning method for a power transmission line maintenance robot, characterized by: include: S1, obtains the three-dimensional topological model of the transmission line, constructs a path map, and displays the line panorama on the human-machine interface of the control center; S2, selects the initial path plan based on the comprehensive scoring algorithm and executes it; S3 continuously collects surrounding environment data, performs dynamic obstacle identification and prediction, and replans the path online to regenerate the optimized path; S4, introduce the energy model to make a joint decision on path and energy consumption. Establish a motion energy consumption model to make path decisions; S5 introduces an exception feedback and path rollback mechanism. The robot automatically identifies abnormal events, classifies them into abnormality levels, and reconstructs the path based on the abnormality level.

2. A method for dynamic path planning of a power transmission line maintenance robot according to claim 1, characterized in that: The construction of the path map includes: using a three-dimensional laser radar, a camera and an inertial measurement unit (IMU) sensor to collect terrain and ground object information along the transmission line, and reconstructing a path point set V = {v1, v2, ..., v n } and path edge set E={e ij }, forming a directed graph G = (V, G). Path edge e ij Describes the robot's journey from path point v i Travel to waypoint v j , and the geometric attribute information of path length, slope, and elevation difference is attached for subsequent path scoring and energy consumption calculation.

3. A method for dynamic path planning of a power transmission line maintenance robot according to claim 2, characterized in that: During the path planning process, the path with the smallest cumulative score is selected as the robot's initial path and executed. The calculation formula is: Among them, S total Cumulative score for the path; Represents the set of path segments that the path passes through from the starting point to the end point, d ij represents the length of the path segment; α ij is the slope angle, ρ ij is the risk level corresponding to the path segment; ε ij is the estimated energy consumption; w d 、w g 、w r 、w e are the length of the path segment, the slope angle, the risk level corresponding to the path segment, and the weight of the estimated energy consumption.

4. A method for dynamic path planning of a power transmission line maintenance robot according to claim 3, characterized in that: When the robot executes the initial path, it obtains obstacle position and density data in real time and combines it with the original scoring function to form a dynamic path cost function: C ij (t)=(w d ·d ij +w g ·a ij +w r ·r ij +w e ·e ij )+λ·Ω ij (t); Among them, C ij (t) is the real-time evaluation of the current path; Ω ij (t) is the obstacle density of the current path segment, and λ is the weight.

5. A method for dynamic path planning of a power transmission line maintenance robot according to claim 4, characterized in that: The total energy consumption of the entire path is estimated as follows: Among them, ε total is the total energy consumption expected for the path, m is the mass of the robot, and g is the acceleration due to gravity (9.8 m / s 2 ), μ is the friction coefficient, is the power consumption coefficient of the drive motor, v ij is the uniform speed of the path segment.

6. A method for dynamic path planning of a power transmission line maintenance robot according to claim 5, characterized in that: The decision is made based on the total energy consumption of the path, specifically: If ε total If the value is less than or equal to the robot power threshold, it means that the robot's current remaining power can meet the task requirements, and the path is determined to be the optimal path and executed; If ε total If the value is greater than the robot's battery threshold, it means that the robot's current remaining battery power cannot meet the task requirements and a new path needs to be replanned. When ε appears three times in a row total When the battery level is greater than the robot's battery threshold, it returns to replenish the battery.

7. A method for dynamic path planning of a power transmission line maintenance robot according to claim 6, characterized in that: The abnormality level determination function is: Where δ(t) is the deviation between the actual path and the expected path, τ1 and τ2 are the set tolerance thresholds; Specifically: When the system determines that the abnormality level is 1, it continues to execute the current path; When the system determines that the abnormality level is 2, the system activates the path rollback mechanism and selects a safe anchor point with a better score from the historical path point set for path rollback; When the system determines that the abnormality level is 3, the robot maintenance task is terminated and human intervention is requested through the control center to make a decision.

8. A dynamic path planning system for a power transmission line maintenance robot, based on a dynamic path planning method for a power transmission line maintenance robot according to any one of claims 1 to 7, characterized in that: include, An acquisition module, used to acquire a three-dimensional topological model of a transmission line; Path map building module, used to build a path map; Obstacle recognition module, used to collect surrounding environment data and perform dynamic obstacle recognition and prediction; The decision module is used to combine the energy model and abnormal feedback to make decisions on the path.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the dynamic path planning method for a power transmission line maintenance robot according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a dynamic path planning method for a power transmission line maintenance robot according to any one of claims 1 to 7 are implemented.

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