CN111044044A - Electric unmanned aerial vehicle routing inspection route planning method and device - Google Patents
Electric unmanned aerial vehicle routing inspection route planning method and device Download PDFInfo
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Abstract
The invention discloses a routing planning method and a device for electric power unmanned aerial vehicle routing inspection, and the method comprises 3 steps of environment modeling, task modeling and routing planning. The electric power unmanned aerial vehicle routing inspection route planning method provided by the invention is characterized in that spatial three-dimensional modeling is carried out on electric power facilities such as towers, lines and fields to be inspected and peripheral environments thereof; sorting parameters such as position coordinates, shooting angles and the like of each task point contained in the routing inspection task to obtain a subtask set; and calculating an optimal route according to the environment modeling result and the task modeling result. The method has the advantages of high safety, high planning speed, high inspection efficiency and the like.
Description
Technical Field
The invention relates to a routing method and a routing device for an electric power unmanned aerial vehicle inspection route.
Background
Along with the power grid technology development, it has become the normality to use unmanned aerial vehicle to carry out transmission line and patrol and examine, and current electric power patrols and examines unmanned aerial vehicle and is operated the flight by the personnel of patrolling and examining usually, or sets up flight path in advance, is operated the flight of unmanned aerial vehicle by intelligent flight control system again.
In the aspect of unmanned aerial vehicle routing inspection route planning, generally, a professional technician manually determines according to own experience and habit. The unmanned aerial vehicle is manually subjected to routing inspection route planning, the unmanned aerial vehicle routing inspection route planning is more convenient, but is also rougher, and a routing inspection scheme which consumes less time and energy and is lowest can not be obtained frequently. If the routing inspection scheme is accurately calculated, a routing inspection scheme which is low in time consumption and energy consumption is obtained, and a long time is needed for calculation and planning.
Therefore, an intelligent planning method for the routing inspection route of the electric unmanned aerial vehicle is needed to efficiently and quickly plan the routing inspection scheme of the unmanned aerial vehicle and obtain a routing inspection route which is safe, reliable, low in consumption, time-consuming and low in energy consumption.
Disclosure of Invention
The invention aims to provide a routing planning method for the inspection of an electric unmanned aerial vehicle, which is used for replacing manual planning.
The invention also aims to provide a routing planning device for the electric power unmanned aerial vehicle inspection, so as to replace manual planning.
Therefore, the invention provides a routing planning method for the inspection of the electric unmanned aerial vehicle on the one hand, which comprises the following steps: a spatial modeling step: carrying out space three-dimensional modeling on the electric power facility to be patrolled and the surrounding environment thereof to obtain space occupation information of the electric power facility to be patrolled and the surrounding environment thereof; and (3) state modeling: setting a safe flight distance and a safe flight height according to field safety factors, and combining information of a space modeling step to obtain a space which the unmanned aerial vehicle is allowed to fly in the task; task modeling step: sorting the relevant parameters of each task point contained in the inspection task to obtain a subtask set, and respectively setting a proper takeoff position range and a proper landing position range which respectively correspond to a pre-takeoff state and a post-landing state; problem modeling step: converting the unmanned plane route planning problem into a tour problem of a simple directed graph, wherein nodes of the simple directed graph comprise: the state of the unmanned aerial vehicle before takeoff is an initial node, the landing state is a termination node, and the state of each subtask is an intermediate node; the weight of the edge between the nodes of the simple directed graph is the lowest cost for the unmanned aerial vehicle to reach another node from one node; and a problem solving step: and solving the optimal solution of the tour problem generated in the problem modeling step by using a graph theory correlation algorithm to obtain an optimal scheme for unmanned aerial vehicle route planning.
According to another aspect of the invention, an electric unmanned aerial vehicle inspection route planning device is provided, which comprises: the spatial modeling module is used for carrying out spatial three-dimensional modeling on the electric power facilities to be patrolled and the surrounding environment thereof to obtain the space occupation information of the electric power facilities to be patrolled and the surrounding environment thereof; the state modeling module is used for setting a safe flying distance and a safe flying height according to field safety factors and obtaining a space which the unmanned aerial vehicle is allowed to fly in the task by combining the three-dimensional modeling information; the task modeling module is used for sorting the relevant parameters of each task point contained in the inspection task to obtain a subtask set, and respectively setting a proper take-off position range and a proper landing position range which respectively correspond to a pre-take-off state and a post-landing state; the problem modeling module is used for converting the unmanned plane route planning problem into a tour problem of a simple directed graph, wherein nodes of the simple directed graph comprise: the state of the unmanned aerial vehicle before takeoff is an initial node, the landing state is a termination node, and the state of each subtask is an intermediate node; the weight of the edge between the nodes of the simple directed graph is the lowest cost for the unmanned aerial vehicle to reach another node from one node; and the problem solving module is used for solving an optimal solution to the tour problem by using a graph theory correlation algorithm to obtain an optimal scheme for unmanned aerial vehicle route planning.
The routing planning method for the electric unmanned aerial vehicle inspection route has the following advantages:
1. according to the space occupation of the routing inspection target, the safe distance is determined by considering factors such as the running state, the environmental state and the like, the safe flight range in the space is obtained, and the safety of the route planning result is ensured;
2. by using an intelligent route planning method, the planning speed is obviously improved compared with the manual planning;
3. the route planning problem is converted into a graph theory problem to be solved, the obtained routing inspection scheme has the advantages of low consumption of time and energy and the like, and the routing inspection efficiency of the unmanned aerial vehicle is improved.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a tower and surrounding environment space modeling according to the present invention;
FIG. 2 is a state modeling diagram according to the present invention;
FIG. 3 is a schematic diagram of mission planning according to the present invention;
FIG. 4 is a directed simple graph building diagram according to the present invention;
fig. 5 is a flow chart of a method for planning a routing of the electric unmanned aerial vehicle inspection according to the invention; and
fig. 6 is a block diagram of the electric unmanned aerial vehicle inspection route planning device according to the invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The method carries out spatial three-dimensional modeling on the power facilities such as towers, lines, fields and the like to be patrolled and the surrounding environment thereof; sorting parameters such as position coordinates, shooting angles and the like of each task point contained in the routing inspection task to obtain a subtask set; and calculating an optimal route according to the environment modeling result and the task modeling result. The method has the advantages of high safety, high planning speed, high inspection efficiency and the like.
With reference to fig. 1 to 4, the invention provides a routing planning method for routing inspection of an electric unmanned aerial vehicle, which comprises 3 steps of environment modeling, task modeling and routing planning.
The environment modeling step comprises 2 links (or substeps) of space modeling and state modeling.
In the spatial modeling link, three-dimensional spatial modeling is carried out on electric power facilities such as towers, lines and sites to be patrolled and environments such as mountain and water tree roads and grounds around the electric power facilities to be patrolled, so that space occupation information of the electric power facilities is obtained.
In the spatial three-dimensional modeling, the routing inspection object and the obstacle with complex shapes are preferably replaced by simple geometric bodies such as a cylinder, a sphere, a cuboid, a cone, a truncated cone, a polyhedron and the like.
In the state modeling link, a safe flying distance and a safe flying height are set according to factors such as line voltage level, power-on state, ambient wind speed, air temperature and air pressure. And combining the information of the space modeling link to obtain the space of the unmanned aerial vehicle allowed to fly in the task.
In the task modeling step, parameters such as position coordinates, shooting angles and the like of all task points included in the inspection task are sorted to obtain a subtask set.
The route planning step comprises 2 links of problem modeling and problem solving.
And in the problem modeling link, converting the unmanned aerial vehicle route planning problem into a tour problem of a simple directed graph.
The nodes of the simple directed graph comprise: the state of the unmanned aerial vehicle before takeoff is an initial node, the landing state is a termination node, and the state of each subtask is an intermediate node. There are and only 1 start node, 1 end node, and at least 1 intermediate node.
The weight of the edges between the nodes of the simple directed graph is the lowest cost for the unmanned aerial vehicle to reach another node from one node, for example, the time consumption is the lowest, the energy consumption is the lowest, or the comprehensive indexes of the time consumption and the energy consumption are the lowest, wherein the starting node only has an edge, and the terminating node only has an edge. Calculating, testing or predicting the weight from the intermediate node to the intermediate node according to the result of the environment modeling and the performance parameters of the unmanned aerial vehicle; the weight from the starting node to a certain intermediate node is the minimum value of the lowest cost from any place in a proper takeoff range to the intermediate node as a takeoff point; the weight from a certain intermediate node to a termination node is the minimum value of the lowest cost from the intermediate node to the landing point by taking any place in a proper landing range as the landing point.
The optimization goal of the tour problem is that the unmanned aerial vehicle starts from the start node, passes through all intermediate nodes, and finally reaches the end node, and the sum of the weights of the passed edges is made to be the lowest.
In the problem solving link, the problems generated in the problem modeling link are solved by using a graph theory related algorithm, an optimal solution is solved, and the optimal solution of the unmanned aerial vehicle route planning is obtained according to the graph theory problem optimal solution.
The optimal solution of the unmanned aerial vehicle route planning comprises a take-off point position, a landing point position and an execution sequence of each subtask of the unmanned aerial vehicle.
The method is described below with reference to fig. 1 to 5 by taking the case of the inspection of the tower by the unmanned aerial vehicle.
S1, environment modeling
S1.1, spatial modeling
The tower and surrounding environment are modeled spatially as shown in fig. 1. In this example, the complex shape of the tower is replaced by a cylinder.
S1.2, State modeling
The safe distance is set according to the operation state, weather, wind speed, etc., and a flyable range is obtained, as shown in fig. 2.
S2 task modeling
And sorting the polling tasks to obtain a subtask set. A suitable range of takeoff and landing positions is determined as shown in figure 3.
S3 route planning
S3.1, problem modeling
And calculating the lowest cost from the state before takeoff to each task point, calculating the lowest cost between each task point and calculating the lowest cost from each task point to the state after landing according to the results of S1.2 and S2. The nodes, edges, weights of the directed simple graph are constructed as shown in fig. 4.
S3.2, solving problem
And (4) solving the tour problem of the graph by using an exhaustion method to obtain a tour path with the minimum total weight. The result is "start-task 2-task 1-terminate".
S4 routing inspection scheme
And determining the task execution sequence of the unmanned aerial vehicle, namely 'task 2-task 1' according to the result of S3.2.
According to the intermediate result of the lowest cost calculated in the S3.1, determining a flying point, namely the position where the lowest cost reaching the task 2 reaches the minimum value from the proper takeoff range; the drop point is determined, i.e., the location from task 1 to the appropriate drop where the lowest cost is at a minimum.
And calculating the intermediate result of the lowest cost according to the S3.1, and determining the accurate flying route between the task points.
According to the unmanned aerial vehicle routing inspection method, the route planning is intelligently carried out on the unmanned aerial vehicle routing inspection task through environment modeling, task modeling and route planning, the unmanned aerial vehicle routing inspection scheme is obtained, and the unmanned aerial vehicle routing inspection method has the advantages of being high in safety, high in planning speed, high in routing inspection efficiency and the like.
The invention also provides a routing planning device for the electric power unmanned aerial vehicle inspection, as shown in fig. 6, comprising: environment modeling 10, task modeling 20, route planning 30. The environment modeling module 10 includes 2 sub-modules, namely, a space modeling module 11 and a state modeling module 12. The route planning module 30 contains 2 sub-modules of problem modeling 31 and problem solving 32.
The space modeling module 11 is used for performing space three-dimensional modeling on the electric power facility to be patrolled and the surrounding environment thereof to obtain space occupation information of the electric power facility to be patrolled and the surrounding environment thereof.
The state modeling module 12 is configured to set a safe flight distance and a safe flight height according to field safety factors, and obtain a space allowed for the unmanned aerial vehicle to fly in the task by combining the space occupation information.
The task modeling module 20 is configured to sort the relevant parameters of each task point included in the inspection task, to obtain a subtask set, and set a suitable takeoff position range and a suitable landing position range, which correspond to the pre-takeoff state and the post-landing state, respectively.
The problem modeling module 31 is configured to convert the unmanned aerial vehicle routing problem into a tour problem of a simple directed graph, where nodes of the simple directed graph include: the state of the unmanned aerial vehicle before takeoff is an initial node, the landing state is a termination node, and the state of each subtask is an intermediate node; the weight of the edges between the nodes of the simple directed graph is the lowest cost for the drone to reach from one node to another.
The problem solving module 32 is configured to solve an optimal solution for the tour problem by using a graph theory correlation algorithm to obtain an optimal scheme for route planning of the unmanned aerial vehicle.
According to the unmanned aerial vehicle routing inspection method, the route planning is intelligently carried out on the unmanned aerial vehicle routing inspection task through environment modeling, task modeling and route planning, the unmanned aerial vehicle routing inspection scheme is obtained, and the unmanned aerial vehicle routing inspection method has the advantages of being high in safety, high in planning speed, high in routing inspection efficiency and the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The electric unmanned aerial vehicle inspection route planning method is characterized by comprising the following steps:
carrying out space three-dimensional modeling on the electric power facility to be patrolled and the surrounding environment thereof to obtain space occupation information of the electric power facility to be patrolled and the surrounding environment thereof;
setting a safe flight distance and a safe flight height according to site safety factors, and combining space occupation information to obtain a space which the unmanned aerial vehicle is allowed to fly in the task;
in the space allowing flight, sorting the relevant parameters of each task point contained in the inspection task to obtain a subtask set, and respectively setting a takeoff position range and a landing position range which respectively correspond to a pre-takeoff state and a post-landing state;
converting the unmanned plane route planning problem into a tour problem of a simple directed graph, wherein nodes of the simple directed graph comprise: the state of the unmanned aerial vehicle before takeoff is an initial node, the state after landing is a termination node, and the state of each subtask is an intermediate node; the weight of the edge between the nodes of the simple directed graph is the lowest cost for the unmanned aerial vehicle to reach another node from one node; and
and solving the optimal solution of the tour problem to obtain an optimal scheme for unmanned aerial vehicle route planning.
2. The electric unmanned aerial vehicle inspection tour route planning method according to claim 1, wherein the optimization goal of the tour problem is that the unmanned aerial vehicle starts from a start node, passes through all intermediate nodes, and finally reaches a stop node, and the sum of the weights of the passed edges is made to be the lowest.
3. The electric unmanned aerial vehicle inspection line planning method according to claim 1, wherein the lowest cost for reaching another node from one node is the lowest consumed time, the lowest consumed energy, or both the lowest consumed time and the lowest consumed energy.
4. The electric unmanned aerial vehicle inspection line planning method according to claim 1,
the weight from the intermediate node to the intermediate node is obtained by calculation, experiment or estimation of the result of environment modeling and the performance parameters of the unmanned aerial vehicle;
the weight from the starting node to a certain intermediate node is the minimum value of the lowest cost from any place in the takeoff range to the intermediate node as the takeoff point;
the weight from one intermediate node to the termination node is the minimum value of the lowest cost from the intermediate node to the landing point by taking any place in the landing range as the landing point.
5. The electric unmanned aerial vehicle inspection line planning method according to claim 1, wherein the electric facilities to be inspected include towers, lines and fields, and the surrounding environment includes mountains, water, trees, roads and ground.
6. The electric unmanned aerial vehicle inspection line planning method according to claim 1, wherein the site safety factors include line voltage level, energization state, ambient wind speed, air temperature and air pressure.
7. The electric unmanned aerial vehicle inspection line planning method according to claim 1, wherein the relevant parameters of each task point include position coordinates and shooting angles.
8. The electric unmanned aerial vehicle inspection tour line planning method according to claim 1, wherein the optimal solution for unmanned aerial vehicle route planning includes a takeoff point position, a landing point position, and an execution sequence of each subtask of the unmanned aerial vehicle.
9. The utility model provides an electric power unmanned aerial vehicle patrols and examines circuit planning device which characterized in that includes:
the spatial modeling module is used for carrying out spatial three-dimensional modeling on the electric power facilities to be patrolled and the surrounding environment thereof to obtain the space occupation information of the electric power facilities to be patrolled and the surrounding environment thereof;
the state modeling module is used for setting a safe flying distance and a safe flying height according to field safety factors and obtaining a space which the unmanned aerial vehicle is allowed to fly in the task by combining the three-dimensional modeling information;
the task modeling module is used for sorting the relevant parameters of each task point contained in the inspection task to obtain a subtask set, and respectively setting a proper take-off position range and a proper landing position range which respectively correspond to a pre-take-off state and a post-landing state;
the problem modeling module is used for converting the unmanned plane route planning problem into a tour problem of a simple directed graph, wherein nodes of the simple directed graph comprise: the state of the unmanned aerial vehicle before takeoff is an initial node, the landing state is a termination node, and the state of each subtask is an intermediate node; the weight of the edge between the nodes of the simple directed graph is the lowest cost for the unmanned aerial vehicle to reach another node from one node; and
and the problem solving module is used for solving an optimal solution to the tour problem by using a graph theory correlation algorithm to obtain an optimal scheme for unmanned aerial vehicle route planning.
10. The electric unmanned aerial vehicle inspection tour line planning device of claim 9, wherein the optimal solution for unmanned aerial vehicle route planning includes a takeoff point position, a landing point position, and an execution sequence of each subtask of the unmanned aerial vehicle.
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