Transmission line unmanned aerial vehicle robot cooperative operation dynamic planning method and system

Abstract

The application discloses a power transmission line unmanned aerial vehicle robot cooperative operation dynamic planning method and system, and the method comprises the following steps: acquiring unmanned aerial vehicle state information, maintenance robot state information, line domain state information, obstacle semantic information, environmental disturbance information and maintenance task information, which are used for generating a line domain operation feasible region set; a cooperative operation access judgment function is constructed, which is used for judging whether to execute a cooperative task sequence; in the process of executing the task, the line domain operation feasible region set is updated in real time, the online correction trigger value is continuously calculated, and when the trigger condition is met, the cooperative task sequence is corrected online; when the maintenance task is completed, the pre-recovery accessibility is lower than the recovery access threshold or the unmanned aerial vehicle state does not meet the subsequent cooperative operation requirements, the robot is controlled to move to the unmanned aerial vehicle recovery feasible region which meets the recovery condition, the unmanned aerial vehicle is controlled to perform the docking recovery, the safe waiting or the return flight. The application improves the safety, continuity and autonomy of the unmanned aerial vehicle and the maintenance robot cooperative operation under complex working conditions.

Description

Technical Field

[0001] This invention relates to intelligent maintenance technology for power lines, specifically to a dynamic planning method and system for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines. Background Technology

[0002] Transmission lines are characterized by high voltage levels, long transmission distances, large spans, complex equipment structures, and harsh operating environments. As the scale of transmission lines continues to expand, traditional manual inspection and maintenance methods face problems such as high safety risks, low work efficiency, high labor intensity, and insufficient environmental adaptability.

[0003] Currently, drones and line maintenance robots are being gradually applied to the operation and maintenance of power transmission lines. Drones have advantages such as rapid approach, flexible maneuverability, and diverse perspectives, making them suitable for performing line inspection, target location, and status awareness tasks. Line maintenance robots can walk along the conductor and complete maintenance tasks that require contact or close-range operation, such as defect detection, hardware status confirmation, bolt tightening, and foreign object removal.

[0004] However, existing technologies still have the following shortcomings:

[0005] First, existing drones and maintenance robots mostly adopt a phased collaborative approach, usually focusing only on how drones deploy robots, how robots go online, how robots walk along the line, or how drones retrieve robots, lacking a unified judgment mechanism for the conditions of the entire process of "deployment, attachment, operation, and retrieval".

[0006] Second, existing planning methods mostly focus on single deployment points, path points, or retrieval points, lacking a method for uniformly screening local feasible areas along the guide that meet different operational conditions. This could lead to robots being deployed to locations that are unfavorable for subsequent operations or retrieval.

[0007] Third, existing path planning methods focus more on path length, obstacle risk and energy consumption, and do not take "whether it can be safely retrieved in the future" as a prerequisite constraint before deployment and maintenance operations. This may result in situations where the robot can reach the maintenance location and complete the maintenance operation, but cannot be safely retrieved by the drone after the task is completed.

[0008] Fourth, the operating environment of power transmission lines is characterized by significant conductor sag, conductor sway, strong wind disturbance, dense hardware, high requirements for live safety distances, and a complex electromagnetic environment. Existing technologies struggle to integrate these factors with the unified planning of drone deployment, robot attachment, maintenance operations, and drone recovery.

[0009] Therefore, there is an urgent need to propose a collaborative dynamic programming method for UAVs and maintenance robots that can make access decisions based on the feasible area of ​​line operation. This method can complete the joint judgment of "deployable, line-attached, operable, and retrievable" before operation, thereby improving the safety and continuity of robotic maintenance operations on transmission lines under complex working conditions. Summary of the Invention

[0010] This invention addresses the shortcomings of existing technologies by providing a dynamic planning method and system for collaborative operations between unmanned aerial vehicles (UAVs) and robots on power transmission lines. It constructs feasible zones for UAV deployment, robot line-attaching and locking, maintenance operations, and UAV recovery. Based on the feasibility of UAV deployment, the success rate of robot line-attaching and locking, the executability of maintenance operations, and pre-recovery reachability, a collaborative operation admission judgment function is built. This ensures that UAV-maintenance robot collaborative tasks are executed only under the conditions of "deployable, line-attached, operable, and recoverable." Furthermore, this invention uses pre-recovery reachability as a prerequisite constraint before deployment and maintenance operations, predicting whether the robot can be safely recovered by the UAV before entering the maintenance operation area, thereby preventing the robot from entering a dangerous state of "reachable and operable but unrecoverable."

[0011] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic programming method for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines includes the following steps: S1, acquires the status information of the UAV, the status information of the maintenance robot, the line domain status information of the power transmission line, the semantic information of the obstacle, the environmental disturbance information, and the maintenance task information; S2, based on UAV status information, maintenance robot status information, line domain status information, obstacle semantic information, environmental disturbance information and maintenance task information, generates a set of feasible line domain operations on the target line; the set of feasible line domain operations includes feasible UAV deployment area, feasible line-locked maintenance robot area, feasible maintenance operation area and feasible UAV recovery area. S3 calculates the feasibility of drone deployment, the success rate of the maintenance robot's wire-locking, the executability of maintenance operations, and the reachability of pre-recovery. S4. Based on the feasibility of drone deployment, the success rate of maintenance robot line locking, the executability of maintenance operations, and the reachability of pre-recovery, construct a collaborative operation access judgment function. S5, when the collaborative task admission judgment function meets the preset admission conditions, a collaborative task sequence is generated; When the collaborative operation admission judgment function does not meet the preset admission conditions, the feasible area for drone deployment, the feasible area for maintenance robot line locking, the feasible area for maintenance operation, or the feasible area for drone recovery is reselected, and the collaborative task sequence is dynamically replanned. S6, during the execution of collaborative tasks, the set of feasible zones for line domain operations is updated in real time, and the online correction trigger value is continuously calculated. When the trigger condition is met, the collaborative task sequence is corrected online. S7: When the maintenance task is completed, the pre-recovery accessibility is lower than the preset recovery access threshold, or the drone status does not meet the requirements for subsequent collaborative operations, control the maintenance robot to move to the drone recovery feasible area that meets the recovery conditions, and control the drone to perform docking recovery, safe waiting, or emergency return.

[0012] To optimize the above technical solution, the specific measures also include: Furthermore, in S2, the set of feasible regions for line-domain operations is represented as: Ω(t) = { Ω_drop(t), Ω_lock(t), Ω_work(t), Ω_recycle(t)} In the formula, Ω(t) represents the set of feasible areas for line-domain operations at time t; Ω_drop(t) represents the feasible area for drone deployment; Ω_lock(t) represents the feasible area for maintenance robot line locking; Ω_work(t) represents the feasible area for maintenance operations; and Ω_recycle(t) represents the feasible area for drone recovery. The feasible zones for drone deployment, maintenance robot line locking, maintenance operations, and drone recovery are generated according to their respective operation types, and the generation method is as follows: Ω_type(t)={ s∈S|S_area_type(s,t)≥S_min_type, and C_type(s,t)=1} In the formula, type represents the type of task action. ; When type=drop, Ω_type(t) corresponds to the feasible area for drone deployment Ω_drop(t); when type=lock, Ω_type(t) corresponds to the feasible area for maintenance robot line locking Ω_lock(t); when type=work, Ω_type(t) corresponds to the feasible area for maintenance operation Ω_work(t); when type=recycle, Ω_type(t) corresponds to the feasible area for drone recovery Ω_recycle(t); S represents the set of candidate line domain positions of the target line; s represents the candidate line domain position on the target line; S_area_type(s,t) represents the comprehensive score of the operation action type type corresponding to the candidate line domain position s at time t; S_min_type represents the minimum feasible score threshold corresponding to the operation action type type; C_type(s,t) represents the hard constraint satisfaction state corresponding to the operation action type type, which is 1 when the candidate line domain position satisfies the live safety distance, spatial scale, attitude matching and task operation constraints required for this type of operation action, and 0 otherwise.

[0013] Furthermore, the comprehensive score S_area_type(s,t) of the candidate line domain position s at time t corresponding to the operation action type is calculated using the following formula: S_area_type(s,t)=w1×S_line(s,t) + w2×S_uav(s,t) + w3×S_robot(s,t)+ w4×S_task(s,t) w5×R_obstacle(s,t) w6×R_env(s,t) w7×R_electric(s,t) In the formula, S_line(s,t) represents line stability, used to characterize the conductor sag, curvature, and oscillation state; S_uav(s,t) represents UAV operational margin, used to characterize the UAV's remaining battery power, payload capacity, and hovering stability; S_robot(s,t) represents maintenance robot matching degree, used to characterize the degree of matching between the robot's posture, gripping mechanism, and operating mechanism and the current position; S_task(s,t) represents task satisfaction, used to characterize whether the current area meets the space, angle, and operating conditions required for the maintenance task; R_obstacle(s,t) represents obstacle risk; R_env(s,t) represents environmental disturbance risk; R_electric(s,t) represents energized safety distance risk; w1~w7 represent the weight coefficients of each evaluation item.

[0014] Furthermore, in S3, the feasibility of the drone deployment is calculated using the following formula: F_drop(s,t) = σ[ a1×S_uav(t) + a2×S_position(s,t) + a3×S_line(s,t)+ a4×S_pose(s,t) a5×R_collision(s,t) a6×R_env(t) ] Where F_drop(s,t) represents the feasibility of drone deployment at candidate line domain position s at time t; σ is the normalization function: , x For the normalized quantities, S_uav(t) represents the UAV state margin; S_position(s,t) represents the deployment position matching degree; S_line(s,t) represents the line stability; S_pose(s,t) represents the maintenance robot's line-attached posture matching degree; R_collision(s,t) represents the deployment collision risk; R_env(t) represents the environmental disturbance risk; a1~a6 represent the evaluation weights of each item in the deployment feasibility; When F_drop(s,t) ≥ T_drop, the candidate line domain position s is determined to meet the drone deployment admission condition; where T_drop represents the drone deployment admission threshold.

[0015] Furthermore, in S3, the success rate of the inspection robot's wire-locking is calculated using the following formula: F_lock(s,t) = σ[ b1×S_clamp(s,t) + b2×S_force(s,t) + b3×S_pose(s,t) + b4×S_speed(s,t) b5×R_impact(s,t) In the formula, F_lock(s,t) represents the success rate of the maintenance robot's wire locking at the candidate line domain position s at time t; σ is the normalization function; S_clamp(s,t) represents the clamping closure degree; S_force(s,t) represents the clamping force stability; S_pose(s,t) represents the robot's attitude alignment degree; S_speed(s,t) represents the degree of relative speed decay between the robot and the wire; R_impact(s,t) represents the drop impact risk; b1~b5 represent the evaluation weights of each item in the wire locking success rate. When F_lock(s,t) ≥ T_lock, the maintenance robot is deemed to have successfully locked the wire; where T_lock represents the wire locking threshold.

[0016] Furthermore, in S3, the feasibility of the maintenance operation is calculated using the following formula: F_work(s,t)=σ[c1×S_dof(s,t) + c2×S_space(s,t) + c3×S_task(s,t) +c4×S_stable(s,t) c5×R_env(s,t) c6×R_block(s,t) In the formula, F_work(s,t) represents the executability of the maintenance operation at candidate line location s at time t; σ is the normalization function; S_dof(s,t) represents the degree of freedom matching at the end of the operation; S_space(s,t) represents the local spatial matching degree of the line; S_task(s,t) represents the degree of satisfaction of task operation constraints; S_stable(s,t) represents the robot's posture stability; R_env(s,t) represents the environmental disturbance risk; R_block(s,t) represents the obstacle obstruction risk; c1~c6 represent the evaluation weights of the maintenance operation. When F_work(s,t) ≥ T_work, the location is determined to meet the maintenance operation access conditions; where T_work represents the maintenance operation access threshold.

[0017] Furthermore, in S3, the pre-recovery reachability is calculated using the following formula: F_recycle_pre(s,t) = σ[ d1×S_battery(t) + d2×S_reach(s,t) + d3×S_dock(s,t) d4×R_obstacle_recycle(s,t) d5×R_wind(t) d6×C_return(s,t) In the formula, F_recycle_pre(s,t) represents the pre-recovery reachability of the maintenance robot at candidate line domain position s at time t; σ is the normalization function; S_battery(t) represents the remaining battery margin of the UAV; S_reach(s,t) represents the geometric reachability of the UAV approaching the recovery position; S_dock(s,t) represents the docking attitude matching degree between the UAV and the maintenance robot; R_obstacle_recycle(s,t) represents the obstacle risk of the recovery path; R_wind(t) represents the wind disturbance risk during the recovery process; C_return(s,t) represents the return distance cost from the current position of the maintenance robot to the safe recovery feasible zone; d1~d6 represent the pre-recovery reachability evaluation weights. The distance cost C_return(s,t) is calculated using the following formula: C_return(s,t) = D_return(s,Ω_recycle) / D_max wherein D_return(s,Ω_recycle) denotes the shortest line segment distance for the inspection robot moving from the current position s along the wire to the UAV recovery feasible zone Ω_recycle; D_max denotes the maximum allowable return distance; When F_recycle_pre(s,t) ≥ T_recycle, it is determined that this position satisfies the pre-recovery access condition; wherein T_recycle denotes the recovery access threshold; When F_recycle_pre(s,t) < T_recycle, this position is prohibited from being taken as the preferred inspection operation position, and reselection of the inspection operation feasible zone or replanning of the recovery feasible zone is triggered.

[0018] Further, in S4, the collaborative operation access determination function is specifically: If and only if the following four conditions are satisfied simultaneously: F_drop(s,t) ≥ T_drop; F_lock(s,t) ≥ T_lock; F_work(s,t) ≥ T_work; F_recycle_pre(s,t) ≥ T_recycle, A(s,t) = 1, Otherwise: A(s,t) = 0; wherein: A(s,t) denotes the collaborative operation access determination function of the candidate line segment position s at time t; A(s,t) = 1 denotes that the current collaborative task sequence is allowed to be executed; A(s,t) = 0 denotes that the current collaborative task sequence is prohibited from being executed, and reselection of the line segment operation feasible zone or replanning of the task sequence is triggered; F_drop(s,t) denotes the UAV delivery feasibility of the candidate line segment position s at time t; T_drop denotes the UAV delivery access threshold; F_lock(s,t) denotes the wire-attached locking success degree of the inspection robot at the candidate line segment position s at time t; T_lock denotes the wire-attached locking access threshold; F_work(s,t) denotes the inspection operation executability of the candidate line segment position s at time t; T_work denotes the inspection operation access threshold; F_recycle_pre(s,t) denotes the pre-recovery reachability of the inspection robot located at the candidate line segment position s at time t; T_recycle denotes the recovery access threshold.

[0019] Further, S6 is specifically: The online correction trigger value is calculated using the following formula: K(t) = p1×ΔR_obstacle(t) + p2×ΔR_stable(t) + p3×ΔS_area(t) + p4×ΔF_drop(t) + p5×ΔF_recycle_pre(t) + p6×E_task(t) In the formula: K(t) represents the online correction trigger value at time t; ΔR_obstacle(t) represents the change in obstacle risk; ΔR_stable(t) represents the change in the attitude stability risk of the maintenance robot; ΔS_area(t) represents the change in the comprehensive score of the feasible area of ​​the line operation; ΔF_drop(t) represents the change in the feasibility of UAV deployment; ΔF_recycle_pre(t) represents the change in the pre-recovery reachability; E_task(t) represents the task execution error; p1~p6 represent the weight coefficients of the online correction trigger item. When K(t) ≥ K_th, online correction of the collaborative task sequence is triggered; where K_th represents the preset online correction trigger threshold.

[0020] This invention also proposes a dynamic planning system for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines, comprising: The multi-source perception module is used to acquire the status information of the UAV, the status information of the maintenance robot, the line domain status information of the power transmission line, the semantic information of the obstacle, the environmental disturbance information, and the maintenance task information. The line-domain operation feasible area generation module is used to generate a set of line-domain operation feasible areas on the target line based on UAV status information, maintenance robot status information, line-domain status information, obstacle semantic information, environmental disturbance information, and maintenance task information. The set of line-domain operation feasible areas includes UAV deployment feasible area, maintenance robot line-attached locking feasible area, maintenance operation feasible area, and UAV recovery feasible area. The deployment feasibility calculation module is used to calculate the feasibility of drone deployment; The wire-locking success rate calculation module is used to calculate the wire-locking success rate of the maintenance robot. The maintenance operation feasibility calculation module is used to calculate the feasibility of maintenance operations. The pre-recovery accessibility calculation module is used to calculate the pre-recovery accessibility. The collaborative operation access determination module is used to construct a collaborative operation access determination function based on the feasibility of UAV deployment, the success rate of maintenance robot line locking, the executability of maintenance operations, and the pre-retrieval reachability; determine whether to allow the execution of the collaborative task sequence based on the collaborative operation access determination function; generate a collaborative task sequence when the collaborative operation access determination function meets the preset access conditions; and reselect a feasible area for UAV deployment, feasible area for maintenance robot line locking, feasible area for maintenance operations, or feasible area for UAV retrieval when the collaborative operation access determination function does not meet the preset access conditions, and dynamically replan the collaborative task sequence. The online replanning module is used to update the set of feasible zones for line-domain operations in real time during the execution of collaborative tasks, and continuously calculate the online correction trigger value. When the trigger condition is met, the collaborative task sequence is corrected online. The recovery control module is used to control the maintenance robot to move to a drone recovery zone that meets the recovery conditions when the maintenance task is completed, the pre-recovery accessibility is lower than the preset recovery access threshold, or the drone status does not meet the requirements for subsequent collaborative operations. It also controls the drone to perform docking recovery, safe waiting, or emergency return.

[0021] The beneficial effects of this invention are: First, this invention upgrades the single-point deployment planning to the line-domain operation feasible area planning. It no longer plans only for a single deployment point, docking point, or path point, but simultaneously constructs the drone deployment feasible area, the maintenance robot line-locking feasible area, the maintenance operation feasible area, and the drone recovery feasible area, realizing the unified screening of conditions for the collaborative operation of drones and maintenance robots throughout the entire process.

[0022] Second, this invention elevates the judgment of post-recovery to pre-recovery constraint. The pre-recovery reachability is calculated before robot deployment and maintenance operations, and recovery safety is taken as a pre-constraint to avoid the robot entering a dangerous state of "operable but not recyclable".

[0023] Third, this invention improves upon single-path optimization to collaborative task sequence optimization under admission criteria. By using a collaborative operation admission criteria function, it jointly judges the feasibility of deployment, the success rate of siding lock, the executability of maintenance operations, and the reachability of pre-recovery, so that task planning is no longer limited to the shortest path, the lowest energy consumption, or a single obstacle avoidance.

[0024] Fourth, this invention is decoupled from specific mechanical locking structures and throwing devices, and is not limited to specific structures such as clamps, guide rails, electromagnets, and telescopic legs. Instead, it provides a reusable collaborative planning method for different maintenance robots and different UAV platforms.

[0025] Fifth, this invention is more suitable for complex power transmission conditions by incorporating conductor sag, curvature, swing, dense hardware areas, energized safety distance, wind disturbance, and accessibility of recovery areas into a unified evaluation. It is applicable to typical operating scenarios such as crossing rivers and seas, large-span crossings, mountainous areas, strong winds, and areas with complex hardware.

[0026] Sixth, the present invention can update the feasible area of ​​the line operation in real time during the operation, and perform online replanning based on environmental changes, obstacle risk changes, robot posture changes, UAV status changes and pre-recovery accessibility changes, thereby improving the continuity and safety of UAV-maintenance robot collaborative operation in complex dynamic environments. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the overall process of the method of the present invention; Figure 2 This is a schematic diagram illustrating the construction of the feasible region set for line-domain operations according to the present invention; Figure 3 This is a schematic diagram of the feasible area for drone deployment and the feasible area for robot-guided locking according to the present invention; Figure 4 This is a schematic diagram of the feasible area for maintenance operations and the feasible area for drone recovery according to the present invention; Figure 5 This is a schematic diagram of the collaborative operation admission determination process of the present invention; Figure 6 This is a schematic diagram illustrating the pre-recovery reachability calculation of the present invention; Figure 7 This is the global collaborative task state diagram of the present invention; Figure 8 This is a schematic diagram of the online replanning mechanism of the present invention; Figure 9 Line graphs showing the feasibility of drone deployment, the success rate of maintenance robot line locking, the feasibility of maintenance operations, and the reachability of pre-recovery for each candidate line location; Figure 10 A line graph showing the change process of the online correction trigger value; Figure 11 This is a line graph showing the changes in pre-recovery reachability between the original task sequence and the replanned task sequence; Figure 12 Histograms comparing simulation results from different methods. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0029] Example 1 This invention proposes a dynamic programming method for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines. The process of this method is as follows: Figure 1 As shown, it includes the following steps: S1 acquires the status information of the UAV, the status information of the maintenance robot, the line domain status information of the power transmission line, the semantic information of obstacles, the environmental disturbance information, and the maintenance task information.

[0030] The drone status information includes the drone's position, attitude, speed, remaining battery power, payload capacity, and hovering stability; the maintenance robot status information includes the robot's spatial position, attitude, joint status, gripping mechanism status, movement speed, and end effector status; the line domain status information includes conductor sag, conductor curvature, conductor swing amplitude, conductor swing speed, line spacing, hardware position, live safety distance, and target work point location; obstacle semantic information includes the category, location, and identification confidence of spacers, vibration dampers, hardware, insulator strings, jumpers, tension clamps, and temporary obstacles; environmental disturbance information includes wind speed, wind direction, electromagnetic interference, light changes, and rain / fog conditions; and maintenance task information includes task objectives, task type, operating posture, contact force requirements, task priority, and task completion conditions.

[0031] At time t, the collaborative operation state of the drone and maintenance robot is represented as: X(t) = { U(t), P_robot(t), Q_robot(t), V_robot(t), L_line(t), O_obs(t), H_env(t), M_task(t)} In the formula: X(t) represents the overall state of collaborative operation at time t; U(t) represents the set of UAV states; P_robot(t) represents the spatial position of the maintenance robot; Q_robot(t) represents the posture, joint state, and clamping mechanism state of the maintenance robot; V_robot(t) represents the movement speed of the maintenance robot; L_line(t) represents the set of line domain states; O_obs(t) represents the set of obstacle semantics; H_env(t) represents the set of environmental disturbances; M_task(t) represents the set of maintenance task constraints.

[0032] S2, based on UAV status information, maintenance robot status information, line domain status information, obstacle semantic information, environmental disturbance information, and maintenance task information, generates a set of feasible line domain operation areas on the target line; such as Figure 2 As shown, the feasible area set for line-domain operations includes the feasible area for drone deployment, the feasible area for maintenance robot line locking, the feasible area for maintenance operations, and the feasible area for drone recovery.

[0033] The feasible region set for line domain operations is represented as: Ω(t) = { Ω_drop(t), Ω_lock(t), Ω_work(t), Ω_recycle(t)} In the formula, Ω(t) represents the set of feasible linear operation areas at time t; Ω_drop(t) represents the feasible area for drone deployment; Ω_lock(t) represents the feasible area for maintenance robot line locking; Ω_work(t) represents the feasible area for maintenance work; and Ω_recycle(t) represents the feasible area for drone recovery. The feasible areas for drone deployment, maintenance robot line locking, maintenance work, and drone recovery are not the same type of area, but correspond to four different types of operational actions: drone deployment, robot line locking, robot maintenance work, and drone recovery. Different types of feasible linear operation areas have different constraints and evaluation focuses.

[0034] The diagram illustrates the feasible area for drone deployment and the feasible area for robot-guided locking. Figure 3 The feasible areas for maintenance operations and the feasible areas for drone recovery are shown in the diagram below. Figure 4 .

[0035] The feasible zones for drone deployment, maintenance robot line locking, maintenance operations, and drone recovery are generated according to their respective operation types, and the generation method is as follows: Ω_type(t)={ s∈S|S_area_type(s,t)≥S_min_type, and C_type(s,t)=1} In the formula, type represents the type of task action. ; When type=drop, Ω_type(t) corresponds to the feasible area for drone deployment Ω_drop(t); when type=lock, Ω_type(t) corresponds to the feasible area for maintenance robot line locking Ω_lock(t); when type=work, Ω_type(t) corresponds to the feasible area for maintenance operation Ω_work(t); when type=recycle, Ω_type(t) corresponds to the feasible area for drone recovery Ω_recycle(t); S represents the set of candidate line domain positions of the target line; s represents the candidate line domain position on the target line; S_area_type(s,t) represents the comprehensive score of the operation action type type corresponding to the candidate line domain position s at time t; S_min_type represents the minimum feasible score threshold corresponding to the operation action type type; C_type(s,t) represents the hard constraint satisfaction state corresponding to the operation action type type, which is 1 when the candidate line domain position satisfies the live safety distance, spatial scale, attitude matching and task operation constraints required for this type of operation action, and 0 otherwise.

[0036] When type = drop, the constraints include at least: the drone hovering space meets the requirements, the delivery path is unobstructed, the delivery point meets the electrified safety distance, the local sway of the wire does not exceed the allowable delivery value, and the drone's remaining battery power meets the delivery and return requirements. When type = lock, the constraints include at least: the robot's posture deviation does not exceed the allowable value of the attached wire, the gripping mechanism's opening and closing range is adapted to the wire diameter, the gripping mechanism can close, the delivery impact does not exceed the allowable value of the mechanism, and the attached wire position meets the electrified safety distance requirements. When type = work, the constraints include at least: the robot has passage space, the end effector has operating space, current obstacles can be crossed or avoided, the required posture for the task is reachable, and the maintenance work position meets the electrified safety distance requirements. When type = recycle, the constraints include at least: the drone can approach the recovery area, the robot can move to the recovery posture, the recovery path is unobstructed or can be avoided, wind disturbance does not exceed the allowable recovery value, and the drone's remaining battery power meets the recovery and return requirements.

[0037] The feasible area for line operation refers to a local spatial area near the target conductor or line component, selected based on conductor sag, curvature, swing state, obstacle distribution, energized safety distance, drone hovering capability, maintenance robot posture matching degree, and task operation constraints, which can meet the requirements of drone deployment, robot line-attached locking, maintenance operation, or drone recovery.

[0038] The feasible zone for drone deployment is mainly used to determine whether the drone can hover stably and safely release the maintenance robot; the feasible zone for maintenance robot line locking is mainly used to determine whether the robot can complete attitude alignment, clamping closure and stable locking on the target guide wire; the feasible zone for maintenance operation is mainly used to determine whether the robot can complete line-riding, obstacle crossing and maintenance operations; the feasible zone for drone recovery is mainly used to determine whether the robot can move to a position that meets the requirements for drone approach, docking, line detachment and recovery.

[0039] The comprehensive score S_area_type(s,t) of the candidate line region position s at time t corresponding to the operation action type is calculated by the following formula: S_area_type(s,t)=w1×S_line(s,t) + w2×S_uav(s,t) + w3×S_robot(s,t)+ w4×S_task(s,t) w5×R_obstacle(s,t) w6×R_env(s,t) w7×R_electric(s,t) In the formula, S_line(s,t) represents line stability, used to characterize the conductor sag, curvature, and sway state; S_uav(s,t) represents UAV operational margin, used to characterize the UAV's remaining battery power, payload capacity, and hovering stability; S_robot(s,t) represents maintenance robot matching degree, used to characterize the degree of matching between the robot's posture, gripping mechanism, and operating mechanism and the current position; S_task(s,t) represents task satisfaction, used to characterize whether the current area meets the space, angle, and operating conditions required for the maintenance task; R_obstacle(s,t) represents obstacle risk, including the influence of obstacles such as spacers, vibration dampers, hardware, and jumpers; R_env(s,t) represents environmental disturbance risk, including the influence of wind speed, conductor sway, and electromagnetic interference; R_electric(s,t) represents the risk of energized safe distance; w1~w7 represent the weight coefficients of each evaluation item.

[0040] The line stability S_line(s,t) can be determined by the conductor sag deviation, conductor curvature, conductor swing amplitude, and conductor swing velocity, and is expressed as: S_line(s,t) =1 [ ·R_sag(s,t) + ·R_curv(s,t) + ·R_amp(s,t) + ·R_vel(s,t) ] In the formula: R_sag(s,t) represents the risk of conductor sag deviation; R_curv(s,t) represents the risk of conductor curvature; R_amp(s,t) represents the risk of conductor sway amplitude; R_vel(s,t) represents the risk of conductor sway speed. to Indicates the weight of the line stability evaluation. The conductor sag can be obtained from the line design parameters, conductor temperature, UAV visual measurement, or lidar point cloud fitting; the conductor curvature can be calculated from the local fitting curve of the conductor; the conductor swing amplitude and conductor swing speed can be calculated from continuous image frames, lidar point cloud sequences, or conductor target tracking results.

[0041] The matching degree of the maintenance robot, S_robot(s,t), can be determined by the robot's posture matching degree, the gripping mechanism matching degree, and the working mechanism matching degree, and is expressed as: S_robot(s,t) = ν1·S_pose(s,t) + ν2·S_clamp(s,t) + ν3·S_tool(s,t) In the formula: S_pose(s,t) represents the robot posture matching degree; S_clamp(s,t) represents the clamping mechanism matching degree; S_tool(s,t) represents the working mechanism matching degree; ν1~ν3 represent the evaluation weights of the robot's matching degree, and ν1 +ν2 + ν3 = 1. Among them, S_pose(s,t) can be determined by the angular deviation between the robot's current posture and the target posture; S_clamp(s,t) can be determined by the opening and closing range of the clamping mechanism, the wire diameter, the clamping contact position, and the clamping force feedback; S_tool(s,t) can be determined by the end-effector's degrees of freedom, the reachable space, and the operating posture required by the task.

[0042] The weighting coefficients for different types of operations can vary. For example, for a feasible area for drone deployment, the emphasis is placed on drone hovering stability, deployment location matching, and deployment collision risk; for a feasible area for a maintenance robot to lock onto a guide wire, the emphasis is placed on robot posture matching, gripping mechanism matching, and local stability of the guide wire; for a feasible area for maintenance operations, the emphasis is placed on task operating space, robot operation capabilities, and obstacle crossing conditions; and for a feasible area for drone retrieval, the emphasis is placed on drone accessibility, robot derailment conditions, docking posture matching, and return distance cost.

[0043] The obstacle risk R_obstacle(s,t) is calculated as follows: R_obstacle(s,t) = min{ R1(s,t) + R2(s,t) +Ri(s,t) +… + Rz(s,t),1} In the formula, Ri(s,t) represents the semantic risk value of the i-th obstacle at time s and t in the candidate line domain; z represents the number of obstacles identified near time s in the candidate line domain. The semantic risk value Ri(s,t) of the i-th obstacle at time s and t in the candidate line domain can be determined based on the obstacle category risk coefficient, identification confidence, distance risk, relative speed risk, and live safe distance risk, and is expressed as: Ri(s,t) = ηi×Pi(t)×R_distance_i(s,t)×R_speed_i(t)×R_electric_i(s,t) In the formula: ηi represents the category risk coefficient of the i-th obstacle; Pi(t) represents the identification confidence of the i-th obstacle at time t; R_distance_i(s,t) represents the distance risk between the candidate line domain position s and the i-th obstacle; R_speed_i(t) represents the relative speed risk between the maintenance robot or drone and the i-th obstacle; R_electric_i(s,t) represents the electrified safe distance risk near the i-th obstacle.

[0044] The obstacle category hazard factor ηi can be determined according to the obstacle type; different category hazard factors can be set for insulator strings, tension clamps, jumpers, spacers, vibration dampers, trees, buildings, and temporary foreign objects. The closer the distance, the greater the relative speed, and the smaller the energized safe distance, the higher the corresponding distance risk, relative speed risk, and energized safe distance risk.

[0045] Through the above method, the present invention can generate corresponding feasible linear operation areas for different operation actions, avoiding the simple view of deployment, attachment, operation and retrieval as the same type of location point, thereby improving the precision and safety of UAV-maintenance robot collaborative task planning.

[0046] S3 calculates the feasibility of drone deployment, the success rate of the maintenance robot's line-locking, the executability of maintenance operations, and the reachability of pre-recovery.

[0047] The feasibility of drone deployment is calculated using the following formula: F_drop(s,t) = σ[ a1×S_uav(t) + a2×S_position(s,t) + a3×S_line(s,t)+ a4×S_pose(s,t) a5×R_collision(s,t) a6×R_env(t) ] Where F_drop(s,t) represents the feasibility of drone deployment at candidate line domain position s at time t; σ is the normalization function: , x For the normalized quantities, S_uav(t) represents the UAV state margin; S_position(s,t) represents the deployment position matching degree; S_line(s,t) represents the line stability; S_pose(s,t) represents the maintenance robot's line-attached posture matching degree; R_collision(s,t) represents the deployment collision risk; R_env(t) represents the environmental disturbance risk; a1~a6 represent the evaluation weights of each item in the deployment feasibility; When F_drop(s,t) ≥ T_drop, the candidate line region position s is determined to meet the UAV deployment admission criteria; otherwise, a feasible deployment area is reselected or the UAV hovering attitude is adjusted. Here, T_drop represents the UAV deployment admission threshold.

[0048] S_uav(t) = μ1·S_battery(t) + μ2·S_load(t) + μ3·S_hover(t); S_battery(t) represents the remaining battery power margin of the drone; S_load(t) represents the payload margin of the drone; S_hover(t) represents the hovering stability margin of the drone; μ1~μ3 represent the evaluation weights of the drone's operational margin, and μ1 + μ2 + μ3 = 1. Among them, S_battery(t) can be determined based on the drone's current remaining battery power, expected deployment energy consumption, expected recovery energy consumption, and return energy consumption; S_load(t) can be determined based on the difference between the drone's maximum allowable payload and the total weight of the maintenance robot and mounting mechanism; S_hover(t) can be determined based on the drone's attitude angle fluctuation, position drift, and speed fluctuation.

[0049] The success rate of the inspection robot's attachment lock is calculated using the following formula: F_lock(s,t) = σ[ b1×S_clamp(s,t) + b2×S_force(s,t) + b3×S_pose(s,t) + b4×S_speed(s,t) b5×R_impact(s,t) In the formula, F_lock(s,t) represents the success rate of the maintenance robot's wire locking at the candidate line domain position s at time t; σ is the normalization function; S_clamp(s,t) represents the clamping closure degree; S_force(s,t) represents the clamping force stability; S_pose(s,t) represents the robot's attitude alignment degree; S_speed(s,t) represents the degree of relative speed decay between the robot and the wire; R_impact(s,t) represents the drop impact risk; b1~b5 represent the evaluation weights of each item in the wire locking success rate. When F_lock(s,t) ≥ T_lock, the maintenance robot is deemed to have successfully locked onto the line; otherwise, it performs secondary gripping, attitude adjustment, UAV-assisted stabilization, or emergency recovery. Here, T_lock represents the line locking threshold.

[0050] The feasibility of maintenance work is calculated using the following formula: F_work(s,t)=σ[c1×S_dof(s,t) + c2×S_space(s,t) + c3×S_task(s,t) +c4×S_stable(s,t) c5×R_env(s,t) c6×R_block(s,t) In the formula, F_work(s,t) represents the executability of the maintenance operation at candidate line location s at time t; σ is the normalization function; S_dof(s,t) represents the degree of freedom matching at the end of the operation; S_space(s,t) represents the local spatial matching degree of the line; S_task(s,t) represents the degree of satisfaction of task operation constraints; S_stable(s,t) represents the robot's posture stability; R_env(s,t) represents the environmental disturbance risk; R_block(s,t) represents the obstacle obstruction risk; c1~c6 represent the evaluation weights of the maintenance operation. When F_work(s,t) ≥ T_work, the location is determined to meet the maintenance operation access conditions; otherwise, the operation posture is adjusted, the operation point is reselected, or the task is replanned. Here, T_work represents the maintenance operation access threshold.

[0051] Pre-recovery reachability calculation illustration as follows Figure 6 The pre-recovery reachability is calculated using the following formula: F_recycle_pre(s,t) = σ[ d1×S_battery(t) + d2×S_reach(s,t) + d3×S_dock(s,t) d4×R_obstacle_recycle(s,t) d5×R_wind(t) d6×C_return(s,t) ] wherein, F_recycle_pre(s,t) represents the pre-recycling reachability of the inspection robot at the candidate line domain position s at time t; σ is a normalization function; S_battery(t) represents the remaining power margin of the drone, which is determined according to the current remaining power of the drone, the estimated energy consumption for approaching the recovery area from the current position, the estimated energy consumption for docking and recovery, and the estimated energy consumption for return flight; S_reach(s,t) represents the geometric reachability of the drone approaching the recovery position, which is determined according to the spatial distance from the current position of the drone to the feasible recovery area, the distribution of obstacles on the approach path, the live-line safety distance and the drone flight path constraints; S_dock(s,t) represents the docking attitude matching degree between the drone and the inspection robot, which is determined according to the position of the drone mounting mechanism, the position of the docking part of the inspection robot, the relative attitude angle deviation and the relative speed; R_obstacle_recycle(s,t) represents the obstacle risk of the recovery path, which is determined according to the type of obstacles on the recovery path, the distance of obstacles, the recognition confidence and the degree of avoidability; R_wind(t) represents the wind disturbance risk during the recovery process, which is determined according to the current wind speed, wind direction, gust intensity and the hovering stability of the drone; C_return(s,t) represents the return distance cost from the current position of the inspection robot to the safe feasible recovery area, and d1 to d6 represent the evaluation weights of the pre-recycling reachability; The return distance cost C_return(s,t) is calculated by the following formula: C_return(s,t) = D_return(s,Ω_recycle) / D_max wherein, D_return(s,Ω_recycle) represents the shortest line domain distance for the inspection robot to move from the current position s along the wire to the drone feasible recovery area Ω_recycle; D_max represents the maximum allowable return distance; When F_recycle_pre(s,t) ≥ T_recycle, it is determined that this position meets the pre-recycling access condition; wherein, T_recycle represents the recycling access threshold; When F_recycle_pre(s,t)<T_recycle, this position is prohibited from being used as the preferred inspection operation position, and the reselection of the feasible inspection operation area or the replanning of the feasible recovery area is triggered.

[0052] S4, constructing a cooperative operation access judgment function according to the drone delivery feasibility, the line-attached locking success degree of the inspection robot, the inspection operation executability and the pre-recycling reachability; the cooperative operation access judgment function is specifically as follows: If and only if the following four conditions are met simultaneously: F_drop(s,t) ≥ T_drop; F_lock(s,t) ≥ T_lock; F_work(s,t) ≥ T_work; F_recycle_pre(s,t) ≥ T_recycle,A(s,t) = 1, otherwise: A(s,t) = 0; In the formula: A(s,t) represents the collaborative operation admission decision function of candidate line domain position s at time t; A(s,t) = 1 indicates that the current collaborative task sequence is allowed to be executed; A(s,t) = 0 indicates that the current collaborative task sequence is prohibited from being executed, and triggers the reselection of the feasible area of ​​the line domain operation or the replanning of the task sequence; F_drop(s,t) represents the feasibility of drone deployment at candidate line domain position s at time t; T_drop represents the drone deployment admission threshold; F_lock(s,t) represents the success rate of the maintenance robot's line-locking at candidate line domain position s at time t; T_lock represents the line-locking admission threshold; F_work(s,t) represents the executability of the maintenance operation at candidate line domain position s at time t; T_work represents the maintenance operation admission threshold; F_recycle_pre(s,t) represents the pre-recovery reachability of the maintenance robot at candidate line domain position s at time t; T_recycle represents the recovery admission threshold.

[0053] In one implementation, the values ​​of T_drop, T_lock, T_work, and T_recycle can be set to 0.6 to 0.9. For high-risk scenarios such as ultra-high voltage strong winds, large-span crossings, or complex hardware areas, the corresponding access thresholds can be increased. For scenarios with low wind speeds, fewer obstacles, and sufficient linear space, the corresponding access thresholds can be appropriately reduced, but they must not be lower than the preset safety lower limit.

[0054] S5, when the collaborative operation admission judgment function meets the preset admission conditions, a collaborative task sequence is generated; the collaborative task sequence includes: drone approach, robot deployment, robot alignment, line-riding, obstacle crossing, maintenance operation, pre-recovery assessment, robot detachment, drone recovery, and drone return. The collaborative task sequence is as follows: Figure 7 .

[0055] When the collaborative operation admission determination function does not meet the preset admission conditions, a feasible area for drone deployment, a feasible area for maintenance robot line locking, a feasible area for maintenance operations, or a feasible area for drone recovery is reselected, and the collaborative task sequence is dynamically replanned; the collaborative operation admission determination process is as follows: Figure 5 .

[0056] S6, during the execution of collaborative tasks, updates the feasible zone set of line-domain operations in real time and continuously calculates the online correction trigger value. When the trigger condition is met, the collaborative task sequence is corrected online; the online replanning mechanism is illustrated as follows. Figure 8 Specifically: The online correction trigger value is calculated using the following formula: K(t) = p1×ΔR_obstacle(t) + p2×ΔR_stable(t) + p3×ΔS_area(t) + p4×ΔF_drop(t) + p5×ΔF_recycle_pre(t) + p6×E_task(t) In the formula: K(t) represents the online correction trigger value at time t; ΔR_obstacle(t) represents the change in obstacle risk; ΔR_stable(t) represents the change in the attitude stability risk of the maintenance robot; ΔS_area(t) represents the change in the comprehensive score of the feasible area of ​​the line operation; ΔF_drop(t) represents the change in the feasibility of UAV deployment; ΔF_recycle_pre(t) represents the change in the pre-recovery reachability; E_task(t) represents the task execution error; p1~p6 represent the weight coefficients of the online correction trigger item. When K(t) ≥ K_th, online correction of the collaborative task sequence is triggered; where K_th represents the preset online correction trigger threshold.

[0057] S7: When the maintenance task is completed, the pre-recovery accessibility is lower than the preset recovery access threshold, or the drone status does not meet the requirements for subsequent collaborative operations, control the maintenance robot to move to the drone recovery feasible area that meets the recovery conditions, and control the drone to perform docking recovery, safe waiting, or emergency return.

[0058] Example 2 This invention proposes a dynamic planning system for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines, corresponding to the method in Embodiment 1, comprising: A multi-source sensing module is used to acquire UAV status information, maintenance robot status information, power transmission line line domain status information, obstacle semantic information, environmental disturbance information, and maintenance task information. In one embodiment, a maintenance robot mounting and releasing mechanism is installed below the UAV, and the maintenance robot is equipped with a clamping mechanism, a walking mechanism, an end effector, an attitude sensor, a clamping force sensor, and a communication unit. The UAV carries the maintenance robot to the vicinity of the target work area of ​​the power transmission line and acquires information such as the target conductor position, conductor sag, conductor curvature, conductor swing state, distribution of surrounding obstacles, hardware positions, and live safety distance through visual sensors, lidar, positioning unit, inertial measurement unit, and conductor status sensing unit.

[0059] The line-domain operation feasible area generation module is used to generate a set of line-domain operation feasible areas on the target line based on UAV status information, maintenance robot status information, line-domain status information, obstacle semantic information, environmental disturbance information, and maintenance task information. The set of line-domain operation feasible areas includes UAV deployment feasible area, maintenance robot line-attached locking feasible area, maintenance operation feasible area, and UAV recovery feasible area. For feasible drone deployment zones, the system focuses on determining whether candidate line area locations meet conditions such as stable drone hovering, robot release posture, local conductor stability, safe deployment distance, and controllable deployment collision risk. Specifically, when the conductor sway amplitude near the candidate line area location is small, the conductor curvature change is gentle, the drone has sufficient remaining power and payload capacity, the deployment path is unobstructed, and the energized safe distance requirement is met, the candidate line area location can be identified as a feasible drone deployment zone.

[0060] For the feasible area for wire locking by the maintenance robot, the system focuses on determining whether the candidate wire location meets the following conditions: robot posture alignment, clamping mechanism closure, stable clamping force, controllable relative speed after deployment, and adaptation of the local wire structure. Specifically, when the maintenance robot can complete wire alignment at this location, the clamping mechanism can close and form a stable clamping force, and the deployment impact and relative speed are within the allowable range, the candidate wire location can be identified as the feasible area for wire locking by the maintenance robot.

[0061] For feasible maintenance areas, the system focuses on determining whether candidate line locations meet the conditions for robot line-riding, obstacle crossing, end-effector operation space, task operation posture, and maintenance task execution. Specifically, when the maintenance robot can move stably in the vicinity of the location, cross or avoid obstacles such as spacers, vibration dampers, hardware, and jumpers, and the end-effector has the degrees of freedom, spatial scale, and operation posture required to complete tasks such as defect detection, hardware status confirmation, bolt tightening, or foreign object removal, the candidate line location can be identified as a feasible maintenance area.

[0062] For a feasible area for drone recovery, the system focuses on determining whether the candidate line area location meets the following conditions: drone proximity and reachability, robot detour or docking posture matching, controllable risks of obstacles along the recovery path, controllable wind disturbance risks, and sufficient remaining battery power for recovery and return. Specifically, when the drone can safely approach the area, the maintenance robot can move to a suitable detour or docking position, and the risks of obstacles along the recovery path, wind disturbance risks, and safe distance from charged objects are all within permissible limits, the candidate line area location can be identified as a feasible area for drone recovery.

[0063] Because different operational actions have different constraints, the system independently scores and filters the feasible areas for four types of line domain operations, and uses the filtering results as the basis for subsequent planning of drone deployment, maintenance robot attachment, maintenance operations, and drone recovery tasks. In this way, the present invention does not simply search for a single deployment point, a single operation point, or a single recovery point, but rather forms feasible areas corresponding to different operational actions within the local line domain of the conductor, thereby improving the safety and precision of collaborative task planning.

[0064] The deployment feasibility calculation module is used to calculate the feasibility of drone deployment; The wire-locking success rate calculation module is used to calculate the wire-locking success rate of the maintenance robot. The maintenance operation feasibility calculation module is used to calculate the feasibility of maintenance operations. The pre-recovery accessibility calculation module is used to calculate the pre-recovery accessibility. The collaborative operation access determination module is used to construct a collaborative operation access determination function based on the feasibility of UAV deployment, the success rate of maintenance robot line locking, the executability of maintenance operations, and the pre-retrieval reachability; determine whether to allow the execution of the collaborative task sequence based on the collaborative operation access determination function; generate a collaborative task sequence when the collaborative operation access determination function meets the preset access conditions; and reselect a feasible area for UAV deployment, feasible area for maintenance robot line locking, feasible area for maintenance operations, or feasible area for UAV retrieval when the collaborative operation access determination function does not meet the preset access conditions, and dynamically replan the collaborative task sequence. The online replanning module is used to update the set of feasible zones for line-domain operations in real time during the execution of collaborative tasks, and continuously calculate the online correction trigger value. When the trigger condition is met, the collaborative task sequence is corrected online. The recovery control module is used to control the maintenance robot to move to a drone recovery zone that meets the recovery conditions when the maintenance task is completed, the pre-recovery accessibility is lower than the preset recovery access threshold, or the drone status does not meet the requirements for subsequent collaborative operations. It also controls the drone to perform docking recovery, safe waiting, or emergency return.

[0065] The implementation methods of each module and its function in the system are completely consistent with the steps of the method in Implementation Example 1, so they will not be repeated here.

[0066] After the robot is attached to the conductor, it moves along the conductor and performs obstacle crossing. During the operation, the system continuously identifies spacers, vibration dampers, fittings, insulator strings, jumpers, tension clamps, and temporary obstacles, and calculates the semantic risk of obstacles based on obstacle type, identification confidence level, real-time distance, relative speed, and energized safe distance. Simultaneously, the system continuously updates the feasible area set for line-domain operations and calculates the pre-retrieval reachability corresponding to the current position of the maintenance robot in real time.

[0067] If the robot continues to move forward and the pre-retrieval reachability falls below the preset retrieval threshold, the system will trigger a replanning mechanism in advance. This mechanism will control the robot to stop entering the risk area and, based on the current route status, drone status, and the distribution of feasible retrieval zones, will choose to return to a safe retrieval zone, switch to an alternative maintenance work zone, or implement a safe waiting strategy. In this way, the system can continuously determine whether the robot can be safely retrieved before and during maintenance tasks, preventing the robot from entering a dangerous state of being "reachable and operable but not retrievable."

[0068] Once the robot reaches the feasible maintenance area, the system calculates the executability of the maintenance operation based on the end-effector's degrees of freedom, the local spatial scale of the line, task operation constraints, robot posture stability, environmental disturbances, and the degree of obstacle obstruction. If the executability of the maintenance operation meets the admission criteria, the robot performs defect detection, foreign object removal, hardware status verification, bolt tightening, or other maintenance operations; if not, the system reselects the feasible maintenance area or adjusts the task sequence.

[0069] After the maintenance task is completed, the system controls the robot to move to the feasible recovery zone based on the pre-recovery accessibility and the drone's status. Once the drone approaches the feasible recovery zone, the robot performs a disconnection or docking maneuver, and the drone completes recovery and returns to base. If the drone has insufficient remaining power, increased wind disturbance, increased risk of obstacles on the recovery path, or a mismatch in docking attitude, the system controls the robot to wait safely, move to an alternative feasible recovery zone, or trigger an emergency return-to-base procedure.

[0070] Throughout the operation, if the following issues occur: increased wire sway, increased obstacle risk, robot posture instability, decreased semantic recognition confidence, increased task execution error, decreased deployment feasibility, or decreased pre-retrieval reachability, the system calculates an online replanning trigger value. When the online replanning trigger value exceeds a preset threshold, the system automatically performs actions such as reselecting a deployment feasibility zone, adjusting the wire locking strategy, correcting the straddling path, switching the obstacle crossing strategy, switching the maintenance operation feasibility zone, reselecting the retrieval feasibility zone, or emergency return.

[0071] Through the above implementation methods, this invention achieves collaborative deployment, line attachment, operation, pre-retrieval assessment, retrieval, and online replanning of UAVs and maintenance robots under complex operating conditions of power transmission lines. This implementation method can continuously determine whether the four conditions of "deployable, line-attached, operable, and retrievalable" are simultaneously met before deployment, before operation, and during operation, thereby improving the safety, continuity, autonomy, and engineering adaptability of collaborative operations between UAVs and maintenance robots on power transmission lines.

[0072] Example 3 This embodiment provides a numerical calculation example for the candidate line domain location admission determination and pre-recovery pre-constraints. The following data are numerical simulation examples obtained based on the formulas and preset operating conditions of this invention, used to illustrate the acquisition, calculation, and determination process of each parameter, and are not intended to limit specific equipment models, line voltage levels, weights, thresholds, or engineering measurement results.

[0073] A single overhead transmission line with a span of 320m was selected as the target line. Candidate line locations s1 to s7 were established every 20m within a conductor range of 20m to 140m from one of the towers. The maintenance task was to remove foreign objects approximately 80m from one of the towers. The UAV had an initial remaining battery of 86%, a maximum allowable payload of 8.0kg, and a total mass of 5.6kg for the maintenance robot and its mounting mechanism. The conductor diameter was 28.6mm, the initial average wind speed was 3.4m / s, and the conductor sway amplitude was 0.08m. A spacer and a vibration damper were identified near the candidate line locations with confidence levels of 0.96 and 0.93, respectively.

[0074] In this embodiment, each original evaluation quantity is first normalized to 0-1 according to its corresponding range. The normalization function is adopted. The admission thresholds and key evaluation weights are shown in Table 1.

[0075] Table 1 shows the main weights and thresholds in the numerical examples.

[0076] Based on UAV vision, LiDAR point cloud, robot state variables, and task constraints, the feasibility of UAV deployment, the success rate of the maintenance robot's line-locking, the executability of the maintenance operation, and the pre-retrieval reachability are calculated for each candidate line domain location. The results are shown in Table 2 and... Figure 9 As shown.

[0077] Table 2. Four types of feasibility and admission results for candidate line domain locations.

[0078] Table 2 shows that s2, s3, and s5 simultaneously satisfy all four admission criteria, A(s,t)=1. The executability of the maintenance operation at location s4 is 0.88, the highest among all candidate locations, but its pre-recovery reachability is only 0.64, lower than the recovery admission threshold of 0.70. Therefore, A(s4,t)=0, and s4 is prohibited as a preferred maintenance operation location. This result indicates that if the operation location is selected solely based on executability or path length, it may result in a "workable but unrecoverable" state. Introducing a pre-recovery reachability constraint can eliminate this risky location before task execution.

[0079] Among the candidate positions that meet the four admission criteria, the admission margin M(s) = min{F_drop} can be used. T_drop, F_lock T_lock, F_work T_work, F_recycle_pre The T_recycle} is sorted. In this embodiment, the admission margin of s3 is 0.06, which is higher than that of s2 (0.01) and s5 (0.04). Therefore, s3 is selected as the preferred cooperative operation position, and a cooperative task sequence of "UAV approach - deployment - line locking - line walking - foreign object removal - movement to the feasible recovery area - docking and recovery - return" is generated.

[0080] For s3, the shortest linear distance from the maintenance robot to the nearest feasible drone recovery zone is 24m, and the maximum allowable return distance D_max is 80m. Therefore, the return distance cost C_return = 24 / 80 = 0.30. Considering the drone's battery margin, geometric reachability, docking attitude matching degree, recovery path obstacle risk, and wind disturbance risk, F_recycle_pre = 0.76 is calculated, which meets the recovery access conditions.

[0081] Example 4 This embodiment illustrates the online correction triggering and task sequence replanning process when dynamic wind disturbances and temporary obstacles occur. In the initial stage, the task is executed using s3 as selected in Embodiment 3. During the period from t=0 to 40s, the average wind speed slowly increases from 2.8m / s to 3.6m / s, the online correction trigger value K(t) remains below 0.06, and the original collaborative task sequence continues to execute.

[0082] At t=50s, a gust of wind caused the instantaneous wind speed to rise to 7.2m / s, and a temporarily suspended foreign object was detected approximately 4.5m ahead of the original recovery path. After normalization, the change in obstacle risk was 0.62, the change in robot attitude stability risk was 0.55, the change in the comprehensive score of the feasible area for line operation was 0.48, the change in UAV deployment feasibility was 0.40, the change in pre-recovery reachability was 0.68, and the task execution error was 0.42. Using p1~p6 in Table 1, K(50)=0.5426 was calculated, which is greater than K_th=0.45, triggering online replanning.

[0083] Table 3. Changes in Online Correction Triggering and Pre-Recovery Reachability under Dynamic Perturbations

[0084] After triggering replanning, the system first controls the maintenance robot to pause its movement in the original work direction and maintain gripping. Then, based on the real-time updated Ω_recycle(t), it selects a feasible recovery zone 18m away from the current position and controls the robot to retreat back to this area along the guide wire. At the same time, the approach direction of the drone recovery is adjusted from above the guide wire to diagonally above it. At t=60s, the pre-recovery reachability corresponding to the new task sequence recovers to 0.79, which is higher than the recovery access threshold of 0.70; K(t) drops to 0.3007, and the system switches to the execution state of the new task sequence.

[0085] Figure 10 The process of K(t) changing is shown. Figure 11 The changes in pre-recovery reachability between the original task sequence and the reprogrammed task sequence are shown. The original task sequence remained below the recovery admission threshold after t=50s, while the reprogrammed task sequence recovered to above the threshold after t=60s, thus avoiding recovery failure that might have been caused by continuing to enter the original recovery path.

[0086] Example 5 This embodiment uses multi-scenario numerical simulation to illustrate the overall effect of the technical solution of the present invention. The simulation random seed is set to 20260729, generating a total of 300 working conditions; each working condition sets 10 candidate line domain positions. The wind speed range is 2-10 m / s, the number of obstacles near the candidate positions is 0-6, the conductor swing amplitude is 0.03-0.30 m, the initial remaining battery power of the UAV is 55%-95%, the distance of the maintenance task position from the tower is 40-160 m, and the return distance of the robot to the nearest feasible recovery zone is 10-80 m. The same input data is used for all three methods in each working condition.

[0087] Method A is a planning method primarily focused on the feasibility of maintenance operations and travel distance, without setting four joint access criteria or online replanning. Method B sets three access criteria: UAV deployment, siding lock, and maintenance operation, but does not use pre-retrieval reachability as a prerequisite constraint, only determining retrieval conditions after the task is completed. The method of this invention simultaneously sets four joint access criteria, pre-retrieval prerequisite constraints, and online replanning. Task completion rate is defined as the proportion of work conditions that complete deployment, siding, and maintenance operations out of the total number of work conditions; safe retrieval rate is defined as the proportion of work conditions that complete maintenance and safely retrieve the robot out of the total number of work conditions; unplanned interruption rate is defined as the proportion of work conditions that cause task interruption due to deployment failure, siding failure, failure of operating conditions, or retrieval failure out of the total number of work conditions.

[0088] Table 4 Comparison results of numerical simulations

[0089] As shown in Table 4 and Figure 12 As shown, under the simulation parameters and scenario set, the task completion rate of the method of the present invention is 97.7%, the safe recovery rate is 95.3%, and the unplanned interruption rate is 4.7%. Compared with method B, the task completion rate of the method of the present invention is increased by 4.0 percentage points, the safe recovery rate is increased by 10.3 percentage points, and the unplanned interruption rate is reduced by 10.3 percentage points. This difference mainly comes from: eliminating unreachable locations for pre-recovery before deployment and operation; and timely switching of feasible recovery areas and task sequences when environmental disturbances cause changes in recovery conditions.

[0090] The above numerical results are used to illustrate the coordination relationship between the various technical features of the present invention and their technical effects under given simulation conditions. The weights, thresholds, and results in actual engineering can be calibrated according to voltage level, UAV and robot model, line structure, meteorological conditions, and operation tasks, but the same line domain operation feasible zone generation, four access judgments, pre-recovery pre-constraints, and online replanning process are still adopted.

[0091] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application 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.

[0092] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A dynamic programming method for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines, characterized in that, Includes the following steps: S1, acquires the status information of the UAV, the status information of the maintenance robot, the line domain status information of the power transmission line, the semantic information of the obstacle, the environmental disturbance information, and the maintenance task information; S2, based on UAV status information, maintenance robot status information, line domain status information, obstacle semantic information, environmental disturbance information and maintenance task information, generates a set of feasible line domain operations on the target line; the set of feasible line domain operations includes feasible UAV deployment area, feasible line-locked maintenance robot area, feasible maintenance operation area and feasible UAV recovery area. S3 calculates the feasibility of drone deployment, the success rate of the maintenance robot's wire-locking, the executability of maintenance operations, and the reachability of pre-recovery. S4. Based on the feasibility of drone deployment, the success rate of maintenance robot line locking, the executability of maintenance operations, and the reachability of pre-recovery, construct a collaborative operation access judgment function. S5, when the collaborative task admission judgment function meets the preset admission conditions, a collaborative task sequence is generated; When the collaborative operation admission judgment function does not meet the preset admission conditions, the feasible area for drone deployment, the feasible area for maintenance robot line locking, the feasible area for maintenance operation, or the feasible area for drone recovery is reselected, and the collaborative task sequence is dynamically replanned. S6, during the execution of collaborative tasks, the set of feasible zones for line domain operations is updated in real time, and the online correction trigger value is continuously calculated. When the trigger condition is met, the collaborative task sequence is corrected online. S7: When the maintenance task is completed, the pre-recovery accessibility is lower than the preset recovery access threshold, or the drone status does not meet the requirements for subsequent collaborative operations, control the maintenance robot to move to the drone recovery feasible area that meets the recovery conditions, and control the drone to perform docking recovery, safe waiting, or emergency return.

2. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, In S2, the set of feasible regions for line-domain operations is represented as follows: Ω(t) = { Ω_drop(t), Ω_lock(t), Ω_work(t), Ω_recycle(t)} In the formula, Ω(t) represents the set of feasible areas for line-domain operations at time t; Ω_drop(t) represents the feasible area for drone deployment; Ω_lock(t) represents the feasible area for maintenance robot line locking; Ω_work(t) represents the feasible area for maintenance operations; and Ω_recycle(t) represents the feasible area for drone recovery. The feasible zones for drone deployment, maintenance robot line locking, maintenance operations, and drone recovery are generated according to their respective operation types, and the generation method is as follows: Ω_type(t)={ s∈S|S_area_type(s,t)≥S_min_type, and C_type(s,t)=1} In the formula, type represents the type of task action. ; When type=drop, Ω_type(t) corresponds to the feasible area for drone deployment Ω_drop(t); when type=lock, Ω_type(t) corresponds to the feasible area for maintenance robot line locking Ω_lock(t); when type=work, Ω_type(t) corresponds to the feasible area for maintenance operation Ω_work(t); when type=recycle, Ω_type(t) corresponds to the feasible area for drone recovery Ω_recycle(t); S represents the set of candidate line domain positions of the target line; s represents the candidate line domain position on the target line; S_area_type(s,t) represents the comprehensive score of the operation action type type corresponding to the candidate line domain position s at time t; S_min_type represents the minimum feasible score threshold corresponding to the operation action type type; C_type(s,t) represents the hard constraint satisfaction state corresponding to the operation action type type, which is 1 when the candidate line domain position satisfies the live safety distance, spatial scale, attitude matching and task operation constraints required for this type of operation action, and 0 otherwise.

3. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 2, characterized in that, The comprehensive score S_area_type(s,t) of the candidate line domain position s at time t corresponding to the operation action type type is calculated by the following formula: S_area_type(s,t)=w1×S_line(s,t) + w2×S_uav(s,t) + w3×S_robot(s,t) + w4×S_task(s,t) w5×R_obstacle(s,t) w6×R_env(s,t) w7×R_electric(s,t) In the formula, S_line(s,t) represents line stability, used to characterize the conductor sag, curvature, and oscillation state; S_uav(s,t) represents UAV operational margin, used to characterize the UAV's remaining battery power, payload capacity, and hovering stability; S_robot(s,t) represents maintenance robot matching degree, used to characterize the degree of matching between the robot's posture, gripping mechanism, and operating mechanism and the current position; S_task(s,t) represents task satisfaction, used to characterize whether the current area meets the space, angle, and operating conditions required for the maintenance task; R_obstacle(s,t) represents obstacle risk; R_env(s,t) represents environmental disturbance risk; R_electric(s,t) represents energized safety distance risk; w1~w7 represent the weight coefficients of each evaluation item.

4. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, In S3, the feasibility of the drone deployment is calculated using the following formula: F_drop(s,t) = σ[ a1×S_uav(t) + a2×S_position(s,t) + a3×S_line(s,t) +a4×S_pose(s,t) a5×R_collision(s,t) a6×R_env(t) ] Where F_drop(s,t) represents the feasibility of drone deployment at candidate line domain position s at time t; σ is the normalization function: , x For the normalized quantities, S_uav(t) represents the UAV state margin; S_position(s,t) represents the deployment position matching degree; S_line(s,t) represents the line stability; S_pose(s,t) represents the maintenance robot's line-attached posture matching degree; R_collision(s,t) represents the deployment collision risk; R_env(t) represents the environmental disturbance risk; a1~a6 represent the evaluation weights of each item in the deployment feasibility; When F_drop(s,t) ≥ T_drop, the candidate line domain position s is determined to meet the drone deployment admission condition; where T_drop represents the drone deployment admission threshold.

5. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, In S3, the success rate of the inspection robot's wire-locking is calculated using the following formula: F_lock(s,t) = σ[ b1×S_clamp(s,t) + b2×S_force(s,t) + b3×S_pose(s,t) +b4×S_speed(s,t) b5×R_impact(s,t) ] In the formula, F_lock(s,t) represents the success rate of the maintenance robot's wire locking at the candidate line domain position s at time t; σ is the normalization function; S_clamp(s,t) represents the clamping closure degree; S_force(s,t) represents the clamping force stability; S_pose(s,t) represents the robot's attitude alignment degree; S_speed(s,t) represents the degree of relative speed decay between the robot and the wire; R_impact(s,t) represents the drop impact risk; b1~b5 represent the evaluation weights of each item in the wire locking success rate. When F_lock(s,t) ≥ T_lock, the maintenance robot is deemed to have successfully locked the wire; where T_lock represents the wire locking threshold.

6. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, In S3, the feasibility of the maintenance operation is calculated using the following formula: F_work(s,t)=σ[c1×S_dof(s,t) + c2×S_space(s,t) + c3×S_task(s,t) +c4×S_stable(s,t) c5×R_env(s,t) c6×R_block(s,t) ] In the formula, F_work(s,t) represents the executability of the maintenance operation at candidate line location s at time t; σ is the normalization function; S_dof(s,t) represents the degree of freedom matching at the end of the operation; S_space(s,t) represents the local spatial matching degree of the line; S_task(s,t) represents the degree of satisfaction of task operation constraints; S_stable(s,t) represents the robot's posture stability; R_env(s,t) represents the environmental disturbance risk; R_block(s,t) represents the obstacle obstruction risk; c1~c6 represent the evaluation weights of the maintenance operation. When F_work(s,t) ≥ T_work, the location is determined to meet the maintenance operation access conditions; where T_work represents the maintenance operation access threshold.

7. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, In S3, the pre-recovery reachability is calculated using the following formula: F_recycle_pre(s,t) = σ[ d1×S_battery(t) + d2×S_reach(s,t) + d3×S_dock(s,t) d4×R_obstacle_recycle(s,t) d5×R_wind(t) d6×C_return(s,t) ] In the formula, F_recycle_pre(s,t) represents the pre-recovery reachability of the maintenance robot at candidate line domain position s at time t; σ is the normalization function; S_battery(t) represents the remaining battery margin of the UAV; S_reach(s,t) represents the geometric reachability of the UAV approaching the recovery position; S_dock(s,t) represents the docking attitude matching degree between the UAV and the maintenance robot; R_obstacle_recycle(s,t) represents the obstacle risk of the recovery path; R_wind(t) represents the wind disturbance risk during the recovery process; C_return(s,t) represents the return distance cost from the current position of the maintenance robot to the safe recovery feasible zone; d1~d6 represent the pre-recovery reachability evaluation weights. The distance cost C_return(s,t) is calculated using the following formula: C_return(s,t) = D_return(s,Ω_recycle) / D_max In the formula, D_return(s,Ω_recycle) represents the shortest line-region distance for the inspection robot to move from the current position s along the wire to the feasible UAV recovery area Ω_recycle; D_max represents the maximum allowable return distance; When F_recycle_pre(s,t) ≥ T_recycle, it is determined that this position satisfies the pre-recovery access condition; wherein T_recycle represents the recovery access threshold; When F_recycle_pre(s,t)<T_recycle, this position is prohibited from being taken as the preferred inspection operation position, and reselection of the inspection operation feasible area or replanning of the recovery feasible area is triggered.

8. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, In S4, the described cooperative operation access judgment function is specifically as follows: If and only if the following four conditions are satisfied simultaneously: F_drop(s,t) ≥ T_drop; F_lock(s,t) ≥ T_lock; F_work(s,t) ≥ T_work; F_recycle_pre(s,t) ≥ T_recycle, A(s,t) = 1, Otherwise: A(s,t) = 0; In the formula: A(s,t) represents the cooperative operation access judgment function for the candidate line-region position s at time t; A(s,t) = 1 indicates that the current cooperative task sequence is allowed to be executed; A(s,t) = 0 indicates that the current cooperative task sequence is prohibited from being executed, and reselection of the line-region operation feasible area or replanning of the task sequence is triggered; F_drop(s,t) represents the UAV placement feasibility of the candidate line-region position s at time t; T_drop represents the UAV placement access threshold; F_lock(s,t) represents the on-wire locking success degree of the inspection robot at the candidate line-region position s at time t; T_lock represents the on-wire locking access threshold; F_work(s,t) represents the inspection operation executability of the candidate line-region position s at time t; T_work represents the inspection operation access threshold; F_recycle_pre(s,t) represents the pre-recovery reachability of the inspection robot at the candidate line-region position s at time t; T_recycle represents the recovery access threshold.

9. The dynamic planning method for collaborative operation of UAV robots on power transmission lines as described in claim 1, characterized in that, S6 is specifically as follows: The on-line correction trigger value is calculated by the following formula: K(t) = p1×ΔR_obstacle(t) + p2×ΔR_stable(t) + p3×ΔS_area(t) + p4×ΔF_drop(t) + p5×ΔF_recycle_pre(t) + p6×E_task(t) In the formula: K(t) represents the online correction trigger value at time t; ΔR_obstacle(t) represents the change in obstacle risk; ΔR_stable(t) represents the change in the attitude stability risk of the maintenance robot; ΔS_area(t) represents the change in the comprehensive score of the feasible area of ​​the line operation; ΔF_drop(t) represents the change in the feasibility of UAV deployment; ΔF_recycle_pre(t) represents the change in the pre-recovery reachability; E_task(t) represents the task execution error; p1~p6 represent the weight coefficients of the online correction trigger item; When K(t) ≥ K_th, online correction of the collaborative task sequence is triggered; where K_th represents the preset online correction trigger threshold.

10. A dynamic planning system for collaborative operation of unmanned aerial vehicles (UAVs) and robots on power transmission lines, characterized in that, include: The multi-source perception module is used to acquire the status information of the UAV, the status information of the maintenance robot, the line domain status information of the power transmission line, the semantic information of the obstacle, the environmental disturbance information, and the maintenance task information. The line-domain operation feasible area generation module is used to generate a set of line-domain operation feasible areas on the target line based on UAV status information, maintenance robot status information, line-domain status information, obstacle semantic information, environmental disturbance information, and maintenance task information. The set of line-domain operation feasible areas includes UAV deployment feasible area, maintenance robot line-attached locking feasible area, maintenance operation feasible area, and UAV recovery feasible area. The deployment feasibility calculation module is used to calculate the feasibility of drone deployment; The attached line locking success rate calculation module is used to calculate the success rate of the attached line locking of the maintenance robot; The maintenance operation feasibility calculation module is used to calculate the feasibility of maintenance operations. The pre-recovery accessibility calculation module is used to calculate the pre-recovery accessibility. The collaborative operation access determination module is used to construct a collaborative operation access determination function based on the feasibility of UAV deployment, the success rate of maintenance robot line locking, the executability of maintenance operations, and the pre-retrieval reachability; determine whether to allow the execution of the collaborative task sequence based on the collaborative operation access determination function; generate a collaborative task sequence when the collaborative operation access determination function meets the preset access conditions; and reselect a feasible area for UAV deployment, feasible area for maintenance robot line locking, feasible area for maintenance operations, or feasible area for UAV retrieval when the collaborative operation access determination function does not meet the preset access conditions, and dynamically replan the collaborative task sequence. The online replanning module is used to update the set of feasible zones for line-domain operations in real time during the execution of collaborative tasks, and continuously calculate the online correction trigger value. When the trigger condition is met, the collaborative task sequence is corrected online. The recovery control module is used to control the maintenance robot to move to a drone recovery zone that meets the recovery conditions when the maintenance task is completed, the pre-recovery accessibility is lower than the preset recovery access threshold, or the drone status does not meet the requirements for subsequent collaborative operations. It also controls the drone to perform docking recovery, safe waiting, or emergency return.

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