CN-121979280-A - Heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring iron tower foundation

CN121979280ACN 121979280 ACN121979280 ACN 121979280ACN-121979280-A

Abstract

The invention discloses a heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring an iron tower foundation, which is characterized by comprising the following steps of S1, constructing a collaborative operation system, wherein the collaborative operation system is a core support for realizing continuous pouring and is composed of three core modules of heterogeneous unmanned aerial vehicle formation, ground control station and task intelligent core and intelligent load interface system, the functions of the modules are complementary and are in collaborative linkage, S2, the construction method is based on the collaborative operation system, and follows a closed-loop digital flow of planning-executing-fault-tolerant to ensure that each step meets the process requirement of concrete for time-limited synchronous pouring, and the construction method is concretely divided into three stages, namely, task planning and feasibility analysis, time synchronous scheduling and autonomous execution, dynamic fault tolerance and seamless replacement. The method has the characteristics of intelligence, high efficiency and high safety, and is suitable for remote areas.

Inventors

DAI HUAWEI
WANG HAITING
YE WEI
GONG GUOFENG
Meng anning
ZHENG QIANG
WANG YONGMING
ZHANG LEI
ZHOU YAOWEN
FANG HONG
YU LIHUA
ZHANG FAN
XU KE
LIU XIAOQING
ZHENG YAN

Assignees

国网湖北省电力有限公司宜昌供电公司

Dates

Publication Date: 20260505
Application Date: 20260116

Claims (9)

1. A heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring an iron tower foundation is characterized by comprising the following steps: s1, constructing a collaborative operation system: the collaborative operation system is a core support for realizing continuous pouring, and consists of three core modules of heterogeneous unmanned aerial vehicle formation, ground control station and task intelligent core and intelligent load interface system, wherein the functions of the modules are complementary and are in collaborative linkage: (1) The heterogeneous unmanned aerial vehicle formation comprises unmanned aerial vehicle compositions with at least two different functions; the formation comprises at least two unmanned aerial vehicles with different functions, and covers the whole process of environmental perception-material transportation by division work cooperation, namely the mapping unmanned aerial vehicle adopts a light-weight and high-mobility design, carries a laser radar and a high-definition camera, has the core functions of carrying out three-dimensional digital modeling of centimeter-level precision on pouring points and surrounding environments before operation, providing accurate environmental data support for subsequent path planning and safe operation, the material transportation unmanned aerial vehicle is specially designed for medium-load transportation such as concrete, takes high reliability and high transportation efficiency as a core target, has the functions of automatic navigation, accurate lifting, remote control unloading by a ground station pilot and the like, and simultaneously, configures a sufficient number of transportation unmanned aerial vehicles, thereby not only meeting the material requirements of continuous operation, but also providing redundancy support for fault-tolerant backup; (2) Ground control station and task intelligence core: The intelligent task control station comprises a command center of the whole system, a task intelligent core, a core software system, a core algorithm-a time synchronization continuous material flow scheduling algorithm and a time synchronization continuous material flow scheduling algorithm, wherein the command center is used for defining tasks, monitoring processes and intervening emergently, and carrying out telemetry data and real-time video feedback for accurate unloading for a flying vehicle, the core software system is deployed in a GCS and is a 'brain' of the system, and the intelligent task control station integrates the core algorithm-the time synchronization continuous material flow scheduling algorithm and comprises the following specific functions: ① . Loading and displaying a digital twin model, and allowing an operator to designate a concrete mixing area and a pouring point in a virtual environment; ② . Task allocation and scheduling, namely running a dynamic task allocation framework based on multi-agent deep reinforcement learning; ③ . Trajectory planning, namely running a constraint-aware collaborative trajectory planning algorithm to generate a safe and efficient flight path for the fleet; (3) Intelligent load interface system: The system is a key connection module for realizing unmanned operation, an unmanned aerial vehicle end is provided with an automatic mounting/unloading module, and the unmanned aerial vehicle end can be accurately butted with a ground loading device, so that automatic material bearing is realized, the operation efficiency is improved, and the manual operation error is reduced; s2, construction method: based on the collaborative operation system, the construction method follows a closed-loop digital flow of planning, executing and fault tolerance, ensures that each step meets the process requirement of concrete time-limited synchronous pouring, and is specifically divided into three stages: The method comprises the steps of firstly, task planning and feasibility analysis, wherein before operation is started, an operator inputs casting task core parameters at a ground control station, wherein the casting task core parameters comprise total material demand, material critical time windows, single machine load, total number of available unmanned aerial vehicles, flight path distance, unmanned aerial vehicle flight speed, loading/unloading time and the like, and a task intelligent core develops the feasibility analysis through a TSS-CMFSA algorithm, and the flow is as follows: 1) Calculating the complete cycle time of the single unmanned aerial vehicle for completing the process of loading, flying, pouring and returning; 2) Verifying whether the single material delivery time is far less than a critical time window, and ensuring that the concrete keeps active in the transportation process; 3) Optimizing and calculating a scheduling time interval, and setting the scheduling time interval to be consistent with the single machine dumping time so as to realize pouring seamless connection; 4) Calculating the minimum number of active unmanned aerial vehicles required for maintaining continuous flow according to the cycle time and the scheduling interval; 5) Checking whether the total number of available unmanned aerial vehicles meets the total requirement of 'the number of active unmanned aerial vehicles and the number of backup unmanned aerial vehicles'; after the task is started, the system firstly divides the queue of the unmanned aerial vehicle into a standby queue, a backup queue and an active list, and then executes the operation according to the following logic: taking the scheduling time interval calculated in the first stage as an accurate beat, sequentially dispatching unmanned aerial vehicles from a standby queue to take off, and executing transportation tasks; 2) Each transport unmanned aerial vehicle independently flies according to a preset route and sequentially goes through the state periods of loading, independently flying to an operation point, waiting for a manual unloading instruction, remotely unloading and independently returning to a base; 3) Through sequencing accurate scheduling, a plurality of unmanned aerial vehicles form an end-to-end aerial virtual conveyor belt in the air, so that unmanned aerial vehicles are ensured to continuously cast above a casting point all the time, and material supply interruption is avoided; In the operation process, the system continuously monitors the running states of all the movable unmanned aerial vehicles, and immediately starts a fault-tolerant mechanism once abnormality occurs: 1) If a certain active unmanned aerial vehicle fails or deviates from a preset time table seriously, the system moves the active unmanned aerial vehicle out of the active list at the first time and marks the active unmanned aerial vehicle as an abnormal state; 2) Synchronously activating 1 standby unmanned aerial vehicle from the standby queue, and placing the standby unmanned aerial vehicle at the forefront end of the standby queue; 3) And at the next scheduling moment, the standby unmanned aerial vehicle is preferentially dispatched, the working gap left by the fault unmanned aerial vehicle is filled seamlessly, and the continuity and stability of the concrete material flow are not affected.
2. The method according to claim 1, wherein the ground control station in the collaborative operation system comprises a task intelligent core for executing a scheduling algorithm, a human-machine interaction interface configured to receive manual unloading instructions from human pilots, at least two material transporting unmanned aerial vehicles, each unmanned aerial vehicle comprising an onboard controller, a payload interface, and a remote unloading executor, wherein the task intelligent core is configured to calculate a scheduling time interval based on task parameters including a single load, sequentially issue task instructions to the unmanned aerial vehicles based on the scheduling time interval, plan an empty conflict-free autonomous flight line for the unmanned aerial vehicles, wherein the onboard controller of each unmanned aerial vehicle is configured to control the unmanned aerial vehicle to fly to an unloading area along the autonomous flight line, wherein the ground control station is further configured to send a unloading signal to the unmanned aerial vehicle in response to the manual unloading instructions after receiving the manual unloading instructions, and trigger the unloading of the unmanned aerial vehicle in the unloading area.
3. The collaborative work and construction method for a heavy-duty unmanned aerial vehicle for casting an iron tower foundation according to claim 1, wherein in the collaborative work system, a ground scheduling server operates as a ground control station, the server comprises a processor and a memory, wherein the memory stores computer executable instructions, and when the instructions are executed, the server is enabled to calculate a scheduling time interval based on task parameters including a single machine load of 40 kg, sequentially send autonomous flight instructions to a plurality of unmanned aerial vehicles to form an 'aerial virtual conveyor' based on the scheduling time interval in the air, receive a human pilot unloading instruction from a human-computer interaction interface, and send an unloading signal to a target unmanned aerial vehicle in response to the unloading instruction.
4. The method for collaborative operation and construction of a heavy-duty unmanned aerial vehicle for casting of iron tower foundations according to claim 1, wherein a computer-readable storage medium is stored in an unmanned aerial vehicle onboard controller in the collaborative operation system, and wherein computer-executable instructions are stored in the medium, which when executed, cause the unmanned aerial vehicle to (a) receive autonomous flight route instructions from a ground control station, (b) autonomously control the unmanned aerial vehicle to fly along the route to a discharge area, (c) suspend autonomous operation and enter a wait instruction state after reaching the discharge area, (d) receive a remote control discharge signal from the ground control station, and (e) drive a discharge actuator to discharge in response to the remote control discharge signal.
5. The collaborative operation and construction method of the heavy-duty unmanned aerial vehicle for pouring the iron tower foundation is characterized in that the construction method in the step S2 comprises the following steps of (a) distributing tasks by a ground control station, wherein the tasks are generated based on task parameters including single-machine load, (b) enabling the unmanned aerial vehicle to take off and hover to a place where materials are loaded and loading the materials, (c) enabling the unmanned aerial vehicle to fly to a discharging area along a set track autonomously, (d) receiving a discharging control instruction from a flight hand at the ground control station when the unmanned aerial vehicle reaches the discharging area, (e) responding to the discharging control instruction to control the unmanned aerial vehicle to discharge, and (f) enabling the unmanned aerial vehicle to return to the air autonomously.
6. The collaborative operation and construction method of a heavy-duty unmanned aerial vehicle for pouring an iron tower foundation according to claim 1, wherein the heterogeneous unmanned aerial vehicle formation comprises at least one mapping unmanned aerial vehicle for site three-dimensional mapping and a transportation formation consisting of a plurality of medium-sized load unmanned aerial vehicles responsible for transporting concrete, and all unmanned aerial vehicles are uniformly commanded and scheduled by a ground control station integrated with a task intelligent core.
7. The method for collaborative operation and construction of heavy-duty unmanned aerial vehicle for casting iron tower foundation according to claim 1, wherein the construction method in step S2 comprises path planning, and in the calculation of path cost, a time-sensitive task window is used as a key constraint condition to ensure that materials are delivered within a specified time limit.
8. The collaborative operation and construction method of a heavy-duty unmanned aerial vehicle for casting iron tower foundations according to claim 1, wherein in the construction method of step S2, the system always maintains at least one hot standby unmanned aerial vehicle in a standby state, and when the unmanned aerial vehicle in the process fails or delays, the standby unmanned aerial vehicle can be automatically activated and inserted into a transport sequence without any problem, so as to maintain continuity of material flow.
9. The method for collaborative operation and construction of a heavy-duty unmanned aerial vehicle for casting of an iron tower foundation according to claim 1 is characterized by being decomposed into three interrelated mathematical models, namely a dynamic task allocation model, a space-time trajectory planning model and a serialization material flow control model; 1) Dynamic task allocation model when one unmanned plane is in idle state, the decision engine will be used for the unmanned plane from the task pool to be processed Selecting an optimal task The selection is based on a multi-objective optimization function, the core of which is to calculate the integrated score S (d j , t i ) of the task t i for the unmanned aerial vehicle d j : Wherein the composite score function S (d j , t i ) is defined as: wherein: The weight coefficient representing the priority level is used, Representing the intrinsic priority score of task t i , The time-lapse weight coefficient is represented, Representing the timeliness score of task t i relative to drone d j , The coefficient of the weight of the efficiency is represented, A model match score indicating the unmanned plane d j and task t i , A distance efficiency score indicating the performance of task t i by unmanned plane d j , the specific meaning of each component is as follows, a priority score Refers to the inherent priority of task t i , and the timeliness score is Wherein, the Is the remaining time of the task t i , Representing the latest deadline of the task, Representing the current time of the system, the formula ensures that the more time-critical task scores are higher, Is a very small positive number which is used to determine the number of the cells, Typically 0.001-0.01, for avoiding zero errors; model match score: Wherein, the Representing the type of drone required for task t i , C match 、C mismatch is a preset maximum positive and negative number for forcing the assurance that only the correct type of drone can perform a particular task, efficiency score: wherein: Representing the current three-dimensional coordinate position of the drone d j , The method comprises the steps of representing the starting point coordinate position of a task t i , wherein the score is inversely proportional to Euclidean distance of a current position of the unmanned aerial vehicle to a task starting point, encouraging the selection of tasks with closer distances to improve efficiency, the weight coefficient w p ,w t ,w e is respectively corresponding to weights of priority, timeliness and efficiency, w p ,w t ,w e is respectively 0.1-0.2, 0.5-0.6 and 0.2-0.3, the sum of the three is 1, the system has self-adaption capability, the weight w t can be increased in proportion to make the timeliness of the system attach more importance to the task in the future if the task t i is found to timeout after being executed, in addition, task allocation must meet dependency constraint, and the task can be allocated only when a dependent task set D i exists for the task t i when all dependent tasks are completed Wherein: indicating that all of the dependent tasks t k , Representing a set of pre-dependent tasks for task t i , Representing the current execution state of the dependent task t k , Indicating the completed state of the task; 2) Space-time trajectory planning model to ensure the safety of multi-machine flight, the system adopts space-time The algorithm performs track planning, and the planning problem is modeled as a four-dimensional space-time grid In (1) search problems in which In the discrete-time dimension of the device, For three-dimensional space dimension, the objective of the planner is to find a space-time point from the starting point To the end point space-time point Is a feasible path of (a) Minimizing path costs while meeting physical barrier constraints, i.e., any point on the path Must be located in free space, wherein, Respectively represent four-dimensional space-time coordinates of the starting point, Respectively representing four-dimensional space-time coordinates of the end points, Each representing a sequence of discrete spatiotemporal nodes contained in the planned path, Four-dimensional space-time coordinates of a kth node in the path are respectively represented; where world is a three-dimensional binary occupancy grid map, Represents a conflict-free flight trajectory generated by planning for any node P k in the path P, wherein the space-time conflict constraint refers to any point on the path The reservation by other unmanned aerial vehicles cannot be performed at the planning moment; Wherein the method comprises the steps of Is the current time-space reservation table which is a dynamic set and records all time-space points occupied by the planned path, and once the path P is successfully planned, all time-space points are added into the reservation table immediately The method ensures that the follow-up planning cannot conflict with the method, thereby realizing the safety of multi-machine collaborative flight; 3) Serialized material flow control model Aiming at the continuous tasks of pouring the iron tower foundation, the system organizes a plurality of medium-sized load unmanned aerial vehicles into an air virtual conveyor belt, and the core of the system is to establish a time-synchronous material flow, wherein the total amount of pouring tasks is assumed to be Q, the unit is kg, the initial setting time of concrete is T deadline , the unit is s, and the minimum average material flow rate R min required is Let the payload of a single frame Type-M drone be q in kg, in the preferred embodiment of the invention q be set to 40kg, the time required for a single complete transport cycle be C in s, the number of drones the system needs to deploy N is at least: Compared with a heavy-load unmanned aerial vehicle with more complicated technology and higher cost, the system has stronger economy and realizability, which means that the system needs to schedule a larger-scale unmanned aerial vehicle and execute higher-frequency circulation in order to keep constant material flow rate, which further highlights the core value of the task intelligent core and TSS-CMFSA algorithm in the aspect of managing large-scale, high-density and time-sequence sensitive unmanned aerial vehicles; in order to form continuous material flow, the system adopts equidistant sequencing scheduling, the first unmanned aerial vehicle takes off at the time t 0 , and the take-off time t k of the subsequent unmanned aerial vehicle is determined by the following formula Wherein, the Representing sequence number of first unmanned aerial vehicle in job formation, scheduling interval Is that Representing the total number of unmanned aerial vehicles participating in cyclic operation, and enabling the time interval of materials reaching a pouring point to be constant by adopting the scheduling mode Thereby guaranteeing the continuity and uniformity of the pouring process, effectively avoiding the concrete cold joint caused by material supply interruption, and guaranteeing the integral quality of the foundation structure.

Description

Heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring iron tower foundation Technical Field The invention belongs to the technical field of transportation and pouring of tower foundation concrete of an iron tower, and particularly relates to a heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring of an iron tower foundation. Background In the construction of power towers in remote or complex terrains (e.g., mountainous areas, woodlands), the transportation and placement of tower foundation concrete is a critical and arduous challenge. The prior art mainly has the following limitations: 1. The traditional ground transportation has large limit, and temporary roads need to be built depending on heavy trucks, mixer trucks and other modes, so that the cost is high, the construction period is long, and the damage to the natural environment is huge. In areas of environmental protection or in areas of poor geological conditions, this approach is almost impossible. 2. Traditional air transportation schemes are expensive and dangerous, and although an alternative, the use of helicopters to hoist concrete is extremely costly to operate, has limited single volume of transportation, and is highly susceptible to weather conditions (e.g., wind, rain, fog). In addition, when the helicopter works near narrow valleys or obstacles, the safety risk is extremely high. 3. A single unmanned plane technical bottleneck is that although a heavy-duty unmanned plane with single load capacity reaching tens to hundreds of kilograms appears in the market, for a large amount of continuous concrete supply required by foundation pouring, the single machine round-trip transportation efficiency is low, and the strict timeliness requirement that the concrete must be poured before initial setting is difficult to meet. Simply enlarging the size of a single machine presents a series of problems of stability, energy consumption and cost. The prior unmanned aerial vehicle application field is mismatched, namely the prior unmanned aerial vehicle application in the power industry is mainly focused on light-weight inspection and mapping, and the design is initially information acquisition, but not physical transportation and operation of heavy materials. However, the existing multi-unmanned aerial vehicle cooperative technology is mostly applied to light parcel delivery or academic research, and the control logic and hardware system thereof cannot meet the scheduling requirement of industrial heavy-load transportation, especially continuous material flow (such as concrete) with strict time window requirements. In view of the above, the prior art lacks an integrated solution that can safely, efficiently, cost effectively and environmentally provide continuous, on-time concrete supply for a tower foundation in remote areas. Disclosure of Invention The invention aims to provide a heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring an iron tower foundation, which has the characteristics of high efficiency and high safety and is suitable for remote areas. The invention has the core idea that a plurality of medium-sized load unmanned aerial vehicles are organized into an air virtual conveyor belt with synchronous time so as to realize continuous and uninterrupted conveying of concrete from a ground mixing station to a tower foundation pouring point. The technical scheme core of the invention comprises two parts of a collaborative operation system and a construction method based on the collaborative operation system. In order to achieve the purpose, the technical scheme adopted by the invention is that the heavy-duty unmanned aerial vehicle collaborative operation and construction method for pouring the foundation of the iron tower is characterized by comprising the following steps: s1, constructing a collaborative operation system: the collaborative operation system is a core support for realizing continuous pouring, and consists of three core modules of heterogeneous unmanned aerial vehicle formation, ground control station and task intelligent core and intelligent load interface system, wherein the functions of the modules are complementary and are in collaborative linkage: (1) And forming a team by unmanned aerial vehicles with at least two different functions. The formation comprises at least two types of unmanned aerial vehicles with different functions, and the full flow of environmental perception-material transportation is covered through division cooperation, namely, a mapping unmanned aerial vehicle (Type-S) is designed by adopting light weight and high mobility, and a laser radar (LiDAR) and a high-definition camera are carried, so that the three-dimensional digital modeling of the pouring point and the surrounding environment with centimeter-level precision is carried out before operation, and accurate environmental data s