CN113946161A - Flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling - Google Patents
Flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent inspection control of unmanned aerial vehicles, in particular to a flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling. The retrieval range is divided into retrieval subareas, the global optimal problem is converted into the local optimal problem, the control platform calculates the optimal solution for each retrieval subarea in parallel, and the solving efficiency is improved. Secondly, the invention acquires the distance between the unmanned aerial vehicle and the nest in real time, and takes the minimum distance as a repeated flight section, thereby further accelerating the speed of calculating the flight shortest path of the unmanned aerial vehicle and rapidly obtaining the local shortest path.
Description
Technical Field
The invention relates to the technical field of intelligent inspection control of unmanned aerial vehicles, in particular to a flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling.
Background
Whether in the civil field or the military field. The small-scale clustering of unmanned aerial vehicles/robots is a trend. At present, more logistics robots are applied, and a plurality of robots are cooperated to complete tasks.
The power distribution network is an important component of a power grid and is an important link for butting a large-scale transmission network and users, and the safe and reliable operation of the system directly influences the normal operation of the whole power grid system and the daily production life of the users. The power distribution network line is regularly inspected in a patrol mode, the running condition of the power distribution line, the change conditions of the surrounding environment of the line and the line protection area are known and mastered at any time, and the basis for ensuring power supply safety is achieved.
The distribution network is a power network which receives electric energy from a transmission network or a regional power plant, distributes the electric energy to various users on site through distribution facilities or distributes the electric energy to various users step by step according to voltage, and consists of overhead lines, cables, towers, distribution transformers, isolating switches, reactive power compensators, auxiliary facilities and the like, and plays a role in distributing the electric energy in the power network. How to realize distribution network unmanned aerial vehicle's automation based on wireless charging technology and patrol and examine, it is that the electric wire netting company faces a technical challenge, and its research achievement will improve unmanned aerial vehicle autonomous operation ability by a wide margin, promotes the distribution network comprehensively and patrols and examines efficiency, reduces basic unit team personnel and patrols and examines work load. The effective path planning of the unmanned aerial vehicle is an effective way for improving the inspection efficiency and saving energy, and currently, aiming at the problem that the flight path of the unmanned aerial vehicle is a path planning method tending to avoid an obstacle, an obstacle avoidance algorithm can be divided into global path planning and local path planning, but a multi-nest unmanned aerial vehicle flight path planning method does not exist.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling, which has the following specific technical scheme:
a flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling comprises the following steps:
s1: determining a polling range, wherein a plurality of towers with known coordinates and the known length of each distribution line are arranged in the polling range; the tower comprises a branch tower and a main tower; the branch tower is connected with a tower with only one distribution line section; the main line tower is connected with at least two distribution line sections; a plurality of nests are arranged in the inspection range, and the nests are used for wirelessly charging the unmanned aerial vehicle;
s2: a plurality of fully charged unmanned aerial vehicles respectively start to patrol the distribution line sections within the patrol range;
s3: the control platform initially plans the flight path of the unmanned aerial vehicle;
s4: after the control platform plans the path of each unmanned aerial vehicle, obtaining the routing inspection path of each unmanned aerial vehicle, and further optimizing the flight path planned in the step S3 by taking the minimum sum of the total paths of all unmanned aerial vehicles as an objective function;
s5: and after the control platform optimizes the path of each unmanned aerial vehicle, controlling the flight corresponding to each unmanned aerial vehicle according to the optimization result.
Preferably, the machine nest is arranged on a main road tower.
Preferably, in step S3, specifically, the method includes:
s31: the control platform divides inspection subareas equal to the number of the unmanned aerial vehicles according to the number of the unmanned aerial vehicles, and each inspection subarea comprises a branch tower and a main tower; each unmanned aerial vehicle patrols one patrolling subarea;
s32: and each unmanned aerial vehicle flies back to the starting point after traversing all the distribution lines in the retrieval subarea, and the total distance flown by each unmanned aerial vehicle is obtained.
Preferably, in step S31, the unmanned aerial vehicle selects to start the inspection from one of the branch towers, and the sum of the distances from the power distribution line segment connected to the branch tower to the unmanned aerial vehicle to fly to the branch tower is less than the maximum distance that the unmanned aerial vehicle can fly when the unmanned aerial vehicle is fully charged.
Preferably, the division mode of the inspection subarea is as follows: the distribution line sections in the inspection subareas intersect or are uninterrupted distribution lines.
Preferably, the step S32 includes: the method comprises the steps of acquiring the residual electric quantity and the coordinates of the unmanned aerial vehicle in real time, predicting the distance of the corresponding residual electric quantity which can fly, calculating the nearest distance between the coordinates of the unmanned aerial vehicle and a nest in real time, returning the original route after the unmanned aerial vehicle flies to the nest closest to the unmanned aerial vehicle after charging when the distance of the corresponding residual electric quantity which can fly is larger than the nearest distance to the nest and the difference between the distance of the corresponding residual electric quantity which can fly and the nearest distance to the nest reaches a set threshold value, and continuing to patrol from the coordinates before leaving the nest to charge until the corresponding patrol subarea is patrolled.
Preferably, the unmanned aerial vehicle adopts Dijkstra algorithm to obtain the shortest path traveled by the inspection sub-area corresponding to the inspection, and the traveled track covers all power distribution line sections of the inspection sub-area.
Preferably, the divided retrieval subareas are updated by a genetic algorithm until an optimal routing inspection subarea dividing mode is found, so that the sum of the distances traveled by all the unmanned aerial vehicles is minimum.
Preferably, if the path along which the unmanned aerial vehicle flies to the nest closest to the corresponding unmanned aerial vehicle is the remaining part which is not patrolled in the power distribution line section which is patrolled, the unmanned aerial vehicle does not need to return to the coordinate position before the unmanned aerial vehicle leaves the nest to be charged after being charged, and the unmanned aerial vehicle continuously patrols the corresponding patrol sub-area by taking the position of the charged nest as the starting point.
The invention has the beneficial effects that: the invention provides a flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling, which is characterized in that the minimum sum of total routes of all unmanned aerial vehicles is used as an objective function to carry out optimization, and the optimal solution is obtained from local optimization to global optimization, so that the inspection efficiency is improved, and the energy is saved. The retrieval range is divided into retrieval subareas, the global optimal problem is converted into the local optimal problem, the control platform calculates the optimal solution for each retrieval subarea in parallel, and the solving efficiency is improved. Secondly, the invention obtains the distance between the unmanned aerial vehicle and the nest in real time, takes the minimum distance as a repeated flight section, and if the path of the unmanned aerial vehicle flying to the nest with the closest corresponding distance is the rest part which is not inspected in the power distribution line section which is inspected, the unmanned aerial vehicle does not need to return to the coordinate before the unmanned aerial vehicle is charged and the unmanned aerial vehicle continuously inspects the corresponding inspection sub-area by taking the position of the charged nest as the starting point, thereby further accelerating the speed of calculating the shortest flight path of the unmanned aerial vehicle and rapidly obtaining the local shortest path.
The invention adopts Dijkstra algorithm to solve the shortest path of the routing inspection subarea, wherein the Dijkstra algorithm is a typical single-source shortest path algorithm and is used for calculating the shortest path from one node to all other nodes. The method is mainly characterized in that the expansion is carried out layer by layer towards the outer part by taking the starting point as the center until the end point is reached. The Dijkstra algorithm is a very representative shortest path algorithm, can quickly converge and improves the operation speed.
The invention adopts the genetic algorithm to update the divided retrieval subareas, and the genetic algorithm has good convergence and can quickly obtain the global optimal solution.
The distribution line sections in the routing inspection subareas are intersected or are uninterrupted distribution lines, so that the repetition rate of routing inspection of the distribution line sections can be reduced, the optimal route planning and the shortest path can be obtained quickly, and the calculation efficiency of the method is improved.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a schematic diagram of the inspection range.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
A flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling comprises the following steps:
s1: determining a polling range, wherein a plurality of towers with known coordinates and the known length of each distribution line are arranged in the polling range; the tower comprises a branch tower and a main tower; the branch tower is connected with a tower with only one distribution line section; the main tower is connected with at least two distribution line sections; be provided with a plurality of nests in patrolling and examining the scope, the nest is used for carrying out wireless charging to unmanned aerial vehicle. Wherein the machine nest is arranged on a main line tower. As shown in fig. 1, if the coordinates of two adjacent towers are known, the length of the power distribution line section between the two adjacent towers is calculated by the following formula, and the patrol inspection range becomes an undirected graph with a distance.
S2: a plurality of fully charged unmanned aerial vehicles respectively start to patrol the distribution line sections within the patrol range;
s3: the control platform initially plans the flight path of the unmanned aerial vehicle; the method specifically comprises the following steps:
s31: the control platform divides inspection subareas equal to the number of the unmanned aerial vehicles according to the number of the unmanned aerial vehicles, and each inspection subarea comprises a branch tower and a main tower; each unmanned aerial vehicle patrols one patrolling subarea; the unmanned aerial vehicle selects one branch tower as a starting point to start to patrol, and the sum of the distances between the power distribution line section connected with the branch tower and the unmanned aerial vehicle to fly to the branch tower is smaller than the maximum distance which can be flown when the unmanned aerial vehicle is fully charged. The division mode of the inspection subarea is as follows: the distribution line sections in the inspection subareas intersect or are uninterrupted distribution lines.
S32: and each unmanned aerial vehicle flies back to the starting point after traversing all the distribution lines in the retrieval subarea, and the total distance flown by each unmanned aerial vehicle is obtained. The method specifically comprises the following steps: the control platform acquires the residual electric quantity and the coordinates of the unmanned aerial vehicle in real time, predicts the distance of the corresponding residual electric quantity which can fly, calculates the closest distance between the coordinates of the unmanned aerial vehicle and the corresponding nest in real time, and when the distance of the corresponding residual electric quantity which can fly is larger than the closest distance from the nest and the difference between the distance of the corresponding residual electric quantity which can fly and the closest distance of the unmanned aerial vehicle from the nest reaches a set threshold value, the unmanned aerial vehicle flies to the nest closest to the charge and returns to the original route, and continues to patrol from the coordinates before leaving the nest and charging until the corresponding patrol subarea is patrolled. If the path of the unmanned aerial vehicle flying to the nest closest to the corresponding position is the remaining part which is not patrolled in the power distribution line section which is patrolled, the unmanned aerial vehicle does not need to return to the coordinate position before the unmanned aerial vehicle is charged and leaves the nest, and the unmanned aerial vehicle continuously patrols and examines the corresponding patrolling sub-area by taking the position of the charged nest as the starting point.
According to the invention, each unmanned aerial vehicle and the nest are mutually networked and can carry out information interaction, the unmanned aerial vehicle and the nest carry out information interaction before the unmanned aerial vehicle flies to the nest for charging, the unmanned aerial vehicle is determined to have an idle parking position, and the unmanned aerial vehicle returns to the selected nest for charging; if no vacant position exists, the control platform obtains the remaining time of the unmanned aerial vehicle which is charging the nest from full charge, if the waiting time exceeds the threshold value of the set waiting time, the nest which is closest to the unmanned aerial vehicle and has a vacant parking space in the remaining nests is selected for charging, and the process is circulated until the most suitable nest is found for charging.
The method for calculating the distance which can fly corresponding to the residual electric quantity comprises the following steps: and establishing a mathematical model of the percentage of the remaining battery power changing along with time, calculating the time difference between the current power and the alarm power of the unmanned aerial vehicle according to the mathematical model, and multiplying the flight speed of the unmanned aerial vehicle by the time difference to obtain the distance that the corresponding remaining battery power of the unmanned aerial vehicle can fly.
The distance between the unmanned aerial vehicle and the nest is calculated by adopting a two-point distance formula, namely the calculation is carried out according to the coordinates of the unmanned aerial vehicle and the coordinates of the nest.
The unmanned aerial vehicle adopts Dijkstra algorithm or ant colony algorithm to obtain the shortest path traveled by the inspection sub-area corresponding to the inspection, and the traveled track covers all power distribution line sections of the inspection sub-area.
The unmanned aerial vehicle adopts Dijkstra algorithm to obtain the shortest path that the inspection sub-area corresponding to inspection passes through, and the specific principle is as follows: dijkstra (Dijkstra) is a typical single-source shortest path algorithm used to compute the shortest path from one node to all other nodes. The method is mainly characterized in that the expansion is carried out layer by layer towards the outer part by taking the starting point as the center until the end point is reached. The Dijkstra algorithm is a very representative shortest path algorithm, and G = (V, E) is a weighted directed graph, a vertex set V in the graph is divided into two groups, the first group is a vertex set (denoted by S, only one source point is in S initially, every time a shortest path is obtained, the vertex set is added to the set S until all vertices are added to S, the algorithm is ended), the second group is a vertex set (denoted by U) of the rest undetermined shortest paths, and the vertices of the second group are added to S in sequence according to the increasing order of the shortest path length. In the joining process, the shortest path length from the source point v to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U. In addition, each vertex corresponds to a distance, the distance of the vertex in S is the shortest path length from v to the vertex, and the distance of the vertex in U is the current shortest path length from v to the vertex, only including the vertex in S as the middle vertex.
(1) Initially, S only contains a starting point S; u contains vertices other than s, and the distance of a vertex in U is "distance from the starting point s to the vertex" [ for example, the distance of vertex v in U is the length of (s, v), then s and v are not adjacent, then the distance of v is ∞ ].
(2) Selecting a vertex k with the shortest distance from the U, and adding the vertex k into the S; at the same time, vertex k is removed from U.
(3) And updating the distance from each vertex in the U to the starting point s. The reason for updating the distances of the vertexes in U is that the distances of other vertexes can be updated by using k because k is determined to be the vertex for obtaining the shortest path in the previous step; for example, the distance of (s, v) may be greater than the distance of (s, k) + (k, v).
(4) And (4) repeating the steps (2) and (3) until all the vertexes are traversed.
When the paths cover all distribution line sections corresponding to the inspection subareas and the shortest path of the inspection whole inspection subarea is obtained, the obtained scheme is a local optimal scheme of the inspection subarea, and the scheme comprises the inspection path, the path length and the charged machine nest coordinate parameters.
S4: after the control platform plans the path of each unmanned aerial vehicle, obtaining the routing inspection path of each unmanned aerial vehicle, and further optimizing the flight path planned in the step S3 by taking the minimum sum of the total paths of all unmanned aerial vehicles as an objective function; specifically, the divided retrieval subareas are updated by a genetic algorithm until an optimal routing inspection subarea dividing mode is found, so that the sum of the distances traveled by all unmanned aerial vehicles is minimum.
S5: and after the control platform optimizes the path of each unmanned aerial vehicle, controlling the flight corresponding to each unmanned aerial vehicle according to the optimization result.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the unit is only one division of logical functions, and other division manners may be used in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling is characterized by comprising the following steps: the method comprises the following steps:
s1: determining a polling range, wherein a plurality of towers with known coordinates and the known length of each distribution line are arranged in the polling range; the tower comprises a branch tower and a main tower; the branch tower is connected with a tower with only one distribution line section; the main line tower is connected with at least two distribution line sections; a plurality of nests are arranged in the inspection range, and the nests are used for wirelessly charging the unmanned aerial vehicle;
s2: a plurality of fully charged unmanned aerial vehicles respectively start to patrol the distribution line sections within the patrol range;
s3: the control platform initially plans the flight path of the unmanned aerial vehicle;
s4: after the control platform plans the path of each unmanned aerial vehicle, obtaining the routing inspection path of each unmanned aerial vehicle, and further optimizing the flight path planned in the step S3 by taking the minimum sum of the total paths of all unmanned aerial vehicles as an objective function;
s5: and after the control platform optimizes the path of each unmanned aerial vehicle, controlling the flight corresponding to each unmanned aerial vehicle according to the optimization result.
2. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling as claimed in claim 1, wherein: the machine nest is arranged on a main line tower.
3. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling according to claim 2, characterized in that: the step S3 specifically includes:
s31: the control platform divides inspection subareas equal to the number of the unmanned aerial vehicles according to the number of the unmanned aerial vehicles, and each inspection subarea comprises a branch tower and a main tower; each unmanned aerial vehicle patrols one patrolling subarea;
s32: and each unmanned aerial vehicle flies back to the starting point after traversing all the distribution lines in the retrieval subarea, and the total distance flown by each unmanned aerial vehicle is obtained.
4. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling according to claim 3, characterized in that: in the step S31, the unmanned aerial vehicle selects to start the inspection from one of the branch towers, and the sum of the distances from the power distribution line segment connected to the branch tower to the unmanned aerial vehicle flies to the branch tower is less than the maximum distance that the unmanned aerial vehicle can fly when the unmanned aerial vehicle is fully charged.
5. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling according to claim 3, characterized in that: the division mode of the inspection subarea is as follows: the distribution line sections in the inspection subareas intersect or are uninterrupted distribution lines.
6. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling according to claim 3, characterized in that: the step S32 includes:
the method comprises the steps of acquiring the residual electric quantity and the coordinates of the unmanned aerial vehicle in real time, predicting the distance of the corresponding residual electric quantity which can fly, calculating the nearest distance between the coordinates of the unmanned aerial vehicle and a nest in real time, returning the original route after the unmanned aerial vehicle flies to the nest closest to the unmanned aerial vehicle after charging when the distance of the corresponding residual electric quantity which can fly is larger than the nearest distance to the nest and the difference between the distance of the corresponding residual electric quantity which can fly and the nearest distance to the nest reaches a set threshold value, and continuing to patrol from the coordinates before leaving the nest to charge until the corresponding patrol subarea is patrolled.
7. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling according to claim 3, characterized in that: the unmanned aerial vehicle adopts Dijkstra algorithm to obtain the shortest path traveled by the inspection sub-area corresponding to the inspection, and the traveled path covers all distribution line sections of the inspection sub-area.
8. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling according to claim 3, characterized in that: and updating the divided retrieval subareas by adopting a genetic algorithm until an optimal routing inspection subarea division mode is found, so that the sum of the distances traveled by all the unmanned aerial vehicles is minimum.
9. The flight path planning method based on multi-nest multi-unmanned aerial vehicle scheduling of claim 6, wherein: if the path of the unmanned aerial vehicle flying to the nest closest to the corresponding position is the remaining part which is not patrolled in the power distribution line section which is patrolled, the unmanned aerial vehicle does not need to return to the coordinate position before the unmanned aerial vehicle is charged and leaves the nest, and the unmanned aerial vehicle continuously patrols and examines the corresponding patrolling sub-area by taking the position of the charged nest as the starting point.
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| CN115913341A (en) * | 2023-01-09 | 2023-04-04 | 广东电网有限责任公司佛山供电局 | Inspection method, inspection system, machine library and storage medium |
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