CN115933735A - Astar algorithm-based cross-sea logistics unmanned aerial vehicle path planning method - Google Patents
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Abstract
The invention discloses a logistics unmanned aerial vehicle path planning method for a cross-sea scene, which comprises the following steps: converting the transportation environment of the logistics unmanned aerial vehicle into a grid airspace environment; determining the passing cost of each grid by considering the environmental characteristics of the cross-sea scene, wherein the passing cost comprises distance cost, population risk cost, barrier risk cost and altitude change cost; constructing a path planning objective function by using an analytic hierarchy process based on the grid through cost, constructing a constraint condition according to the performance of the logistics unmanned aerial vehicle, and establishing a logistics unmanned aerial vehicle path planning model under a cross-sea scene; and solving the model by adopting an Astar algorithm with an improved heuristic function to obtain a primary planned path, and performing smoothing treatment on the obtained path based on a second-order Bezier curve to obtain a final planned path of the logistics unmanned aerial vehicle.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle path planning, and provides a path planning method aiming at solving the multi-target path planning problem of a single logistics unmanned aerial vehicle under a cross-sea scene.
Background
In recent years, because unmanned aerial vehicle is safe, convenient, nimble rapid and the like outstanding logistics advantages, the low-altitude unmanned aerial vehicle logistics operation is rapidly started, therefore, how to rapidly plan safe, reliable, economic and rapid transportation path for the logistics unmanned aerial vehicle gradually becomes the current research focus. In the research on unmanned aerial vehicle path planning, many expert scholars provide better path planning schemes for different logistics unmanned aerial vehicle application scenes such as city low-altitude logistics, mountain area rescue goods and materials transportation and the like, and a better method and a new research idea are provided for logistics unmanned aerial vehicle multi-target path planning.
However, when solving the problem of path planning of the cross-sea logistics unmanned aerial vehicle, the traditional modeling and solving method mainly has the following disadvantages: firstly, the unmanned aerial vehicle facing the cross-sea logistics is large in mass and size, the unmanned aerial vehicle with small motion characteristics is heavier, the motion characteristics of the unmanned aerial vehicle need to be fully considered in the rasterization environment, and a grid distance calculation formula in the traditional grid environment is obviously not in line with the actual motion situation of the cross-sea logistics unmanned aerial vehicle.
Disclosure of Invention
The purpose of the invention is as follows: because unmanned aerial vehicle logistics under the cross-sea environment has certain difference with conventional unmanned aerial vehicle logistics in aspects of transportation environment, transportation type and the like, the invention aims to solve the problem of multi-target path planning of the logistics unmanned aerial vehicle under the cross-sea scene, and a series of improvements are made based on the original logistics unmanned aerial vehicle path planning method, so that the obtained logistics unmanned aerial vehicle path is planned more scientifically and reasonably.
The logistics unmanned aerial vehicle has a long range in a cross-sea scene, and the complexity of a path planning algorithm of the logistics unmanned aerial vehicle needs to be reduced as much as possible; in addition, aiming at the difference of population densities below an ocean airspace and a land airspace when the logistics unmanned aerial vehicle flies, the method further considers the threat degree of the logistics unmanned aerial vehicle to the ground population during path planning and takes the logistics unmanned aerial vehicle as one of planning targets.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, the invention provides a cross-sea logistics unmanned aerial vehicle path planning method based on an Astar algorithm, which comprises the following steps:
step S1: dividing grids based on the determined grid granularity and the space information of the logistics unmanned aerial vehicle in the cross-sea scene, and determining barrier grids;
step S2: determining a grid passing cost of each grid, wherein the grid passing cost comprises a distance cost, a population risk cost, an obstacle risk cost and an altitude change cost;
and step S3: determining a path planning objective function by utilizing an analytic hierarchy process based on the grid passing cost;
and step S4: determining constraint conditions according to the motion performance of the logistics unmanned aerial vehicle, and constructing a path planning model under a cross-sea scene by combining the path planning objective function;
step S5: solving the path planning model by adopting an Astar algorithm for improving the pre-estimated value function to obtain the optimal path of the planned logistics unmanned aerial vehicle, and smoothing the optimal path of the planned logistics unmanned aerial vehicle by adopting a second-order Bezier curve to obtain a final logistics unmanned aerial vehicle path planning scheme.
In some embodiments, in step S1, the method for determining the grid granularity includes:
determining the grid granularity tau according to the maximum turning radius of the logistics unmanned aerial vehicle:
τ＝R(v max )
in the formula, v max Representing maximum speed of the drone, R (v) max ) And the maximum speed of the unmanned aerial vehicle corresponds to the turning radius.
In some embodiments, in step S1, determining the obstacle grid comprises:
and in response to the existence of the object which cannot be normally passed by the unmanned aerial vehicle in the grid, determining the grid as an obstacle grid.
In some embodiments, determining the grid pass cost for each grid comprises:
s21, determining the distance cost corresponding to each grid;
in three-dimensional grid space, the next action of the unmanned aerial vehicle can have 26 exploration directions, which are adjacentThe Manhattan distance q between grids has different costs when taking different values and respectively corresponds to the center of the space grid from the center of the plane, the edge and the vertex
Three types of position relationships of (1);
distance cost D:
in the formula, D' is
Tau is the grid granularity and represents the proportional relation between each grid and the actual space, namely the actual grid distance; v is the flight speed of the unmanned aerial vehicle, theta is the speed change angle of the unmanned aerial vehicle, and R is the turning radius of the unmanned aerial vehicle;
step S22: determining a grid population risk cost P based on population density conversion according to the difference of population densities on land and sea;
the risk cost of the population of the airspace above the land is 1, and the risk cost of the population of the airspace above the sea surface is 0;
step S23: counting the number of the barrier grids in the set range around each grid, and determining the risk degree cost S of the barrier of the grid;
for an obstacle grid, assign S to 1;
for a non-obstacle grid, assign S to η:
wherein η is the obstacle risk of the non-obstacle grid; d is the danger radius, which represents the cube with d as the edge length, N obstacle (d) Representing the number of obstacle grids in a cube with d as edge length, N surround (d) Representing the total number of grids in a cube with d as the edge length;
step S24: determining the altitude change cost H according to the unmanned aerial vehicle performance:
in the formula, H represents the height change cost of the unmanned aerial vehicle from the ith grid to the ith grid, k represents the height change penalty coefficient of the unmanned aerial vehicle, mg represents the total mass of the logistics unmanned aerial vehicle, and Delta z (i-1,i) Indicating the vertical height variation values of grid i-1 to grid i.
In some embodiments, in step S3, the path planning objective function minZ is:
minZ＝α 1 S+α 2 D+α 3 P+α 4 H
wherein S represents the cost of the risk of the obstacle, D represents the cost of the range distance, P represents the cost of the risk of the population, and H represents the cost of the altitude change; alpha is alpha 1 、α 2 、α 3 、α 4 Respectively representing the corresponding weights of the risk degree cost, the voyage distance cost, the population risk degree cost and the altitude change cost of the obstacle, and alpha 1 +α 2 +α 3 +α 4 ＝1。
Further, in some embodiments, the barrier risk cost, the voyage distance cost, the population risk cost, and the altitude change cost correspond to a weight α 1 、α 2 、α 3 、α 4 As determined by analytic hierarchy methods, including:
step S311: comparing the importance of the barrier risk cost, the range distance cost, the population risk cost and the height change cost of the logistics unmanned aerial vehicle;
step S312: establishing a hierarchical judgment matrix to measure the importance relationship among the risk cost of the barrier, the voyage distance cost, the population risk cost and the altitude change cost;
step S313: carrying out normalization processing on the hierarchical judgment matrix;
step S314: and (4) checking the consistency of the hierarchical judgment matrix, if the check is passed, obtaining weights corresponding to the barrier risk degree cost, the voyage distance cost, the population risk degree cost and the altitude change cost, and otherwise, returning to the step S311.
Further, in some embodiments, in the path planning objective function, the obstacle risk cost, the voyage distance cost, the population risk cost, and the altitude change cost are the obstacle risk cost, the voyage distance cost, the population risk cost, and the altitude change cost after the maximum and minimum standardization processing; the maximum and minimum standardization processing method comprises the following steps:
wherein x is max 、x min The maximum value and the minimum value are respectively the maximum value and the minimum value in the risk degree cost of the obstacle, the range distance cost, the population risk degree cost and the altitude change cost.
In some embodiments, determining the constraint according to the physical distribution unmanned aerial vehicle motion performance includes:
s41, determining the horizontal direction speed and direction change constraint of the logistics unmanned aerial vehicle;
in the formula, beta i For the unmanned plane at the current path point (x) i ,y i ,z i ) (x) a turning angle of i-1 ,y i-1 ,z i-1 ) And (x) i+1 ,y i+1 ,z i+1 ) Respectively representing the grid coordinates, beta, of the front path point and the rear path point of the unmanned aerial vehicle max Representing the maximum turning angle of the unmanned aerial vehicle;
s42, determining climbing and descending performance constraints of the logistics unmanned aerial vehicle in the vertical direction;
in the formula u i Indicates the pitch angle u of the unmanned plane at the current path point max Representing the maximum pitch angle of the unmanned aerial vehicle;
s43, determining the flight height constraint of the logistics unmanned aerial vehicle;
H min ≤Z i ≤H max
in the formula Z i Represents the vertical coordinate of the unmanned plane at the current path point grid, H max Indicates that the drone can reach maximum altitude, H min Indicating that the drone may reach a minimum altitude;
s44, determining the load constraint of the logistics unmanned aerial vehicle;
M+Q≤M max
in the formula, M represents the self weight of the unmanned aerial vehicle, Q represents the load cargo weight of the unmanned aerial vehicle, and M max Representing maximum load of the unmanned aerial vehicle;
and S45, determining the maximum range constraint of the logistics unmanned aerial vehicle:
in the formula I i Representing the current flight distance of the grid unmanned aerial vehicle, n representing the number of unmanned aerial vehicles passing through the total grid, l max Representing the maximum range distance at which the drone may fly.
In some embodiments, solving the path planning model using the Astar algorithm for improving the predictive cost function includes:
step S51: converting the path planning target function into an Astar algorithm estimation cost heuristic function form;
h(x)＝ω 1 S next +ω 2 S next D next +ω 3 S next P next +ω 4 H next
in the formula, w 1 、w 2 、w 3 、w 4 Representing the weights of the objects in a heuristic function, S next Represents the next grid obstacle risk penalty, D next Representing Euclidean distance, P, of the candidate grid to the target next Represents the next grid population risk cost, H next Representing the height change cost required for the next grid;
step S52: obtaining CURRENT grid coordinate position CURRENT of logistics unmanned aerial vehicle i Determining a next reachable grid set OPEN and a unreachable grid set CLOSED of the unmanned aerial vehicle;
step S53: selecting the grid with the minimum total cost in the OPEN set as the next grid CURRENT of the logistics unmanned plane i+1 ；
Step S54: removing CURRENT in OPEN set i+1 Adding CURRENT to CLOSED set i+1 Updating the OPEN and CLOSED set;
step S55: judging whether the target location is reached, if so, turning to the next step, otherwise, returning to the step S52;
step S56: and obtaining the optimal path of the planned logistics unmanned aerial vehicle.
In a second aspect, the invention provides a cross-sea logistics unmanned aerial vehicle path planning device based on an Astar algorithm, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
In a third aspect, the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect.
Has the beneficial effects that: the invention provides a cross-sea logistics unmanned aerial vehicle path planning method, which comprises the steps of firstly, establishing a grid environment according to the transportation environment of a logistics unmanned aerial vehicle; then, considering the environmental characteristics of the cross-sea scene to determine the passing cost of each grid, including distance cost, population risk cost, obstacle risk cost and altitude change cost; constructing a path planning objective function through cost based on an analytic hierarchy process and a grid, constructing a constraint condition of unmanned aerial vehicle motion according to the load of the logistics unmanned aerial vehicle and the unmanned aerial vehicle performance, and establishing a logistics unmanned aerial vehicle path planning model under a cross-sea scene; and obtaining a preliminary planned path by adopting an Astar algorithm with an improved pre-estimated cost function, and finally performing smoothing processing based on a second-order Bezier curve according to the obtained path to obtain a final planned path. The improved distance grid and the improved algorithm heuristic function are introduced, and the problems that the linear grid distance cost is not applicable any more and the algorithm complexity is high due to the fact that the unmanned aerial vehicle is large in mass and volume and the range is generally long when the logistics unmanned aerial vehicle path is planned in the cross-sea environment are effectively solved.
Drawings
In order to more clearly illustrate the technical process and the corresponding implementation of the present invention, some key figures will be briefly described below, and it is obvious that the figures in the following description are only some embodiments of the present invention, and other figures can be obtained by those of ordinary skill in the art without creative efforts.
FIG. 1 is a general flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a generalized technical roadmap for an embodiment of the present invention;
FIG. 3 is a graph illustrating the improved grid distance penalty in an embodiment of the present invention;
FIG. 4 is a flow chart of a solution algorithm in an embodiment of the present invention;
FIG. 5 is a schematic diagram of the Bezier curve smoothing in the example.
Fig. 6 is a schematic scenario of path planning of the cross-sea logistics unmanned aerial vehicle in the embodiment.
Detailed Description
The embodiments of the present invention will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be obtained by a person skilled in the art without making any innovative work based on the embodiments of the present invention, are within the scope of protection of the present invention, and the present invention is further described in detail with reference to the accompanying drawings and detailed description.
Example 1
A cross-sea logistics unmanned aerial vehicle path planning method based on an Astar algorithm comprises the following steps:
step S1: dividing grids based on the determined grid granularity and the space information of the logistics unmanned aerial vehicle in the cross-sea scene, and determining barrier grids;
step S2: determining a grid passing cost of each grid, wherein the grid passing cost comprises a distance cost, a population risk cost, an obstacle risk cost and an altitude change cost;
and step S3: determining a path planning objective function by utilizing an analytic hierarchy process based on the grid passing cost;
and step S4: determining constraint conditions according to the motion performance of the logistics unmanned aerial vehicle, and constructing a path planning model under a cross-sea scene by combining the path planning objective function;
step S5: solving the path planning model by adopting an Astar algorithm for improving the pre-estimated value function to obtain the optimal path of the planned logistics unmanned aerial vehicle, and smoothing the optimal path of the planned logistics unmanned aerial vehicle by adopting a second-order Bezier curve to obtain a final logistics unmanned aerial vehicle path planning scheme.
In some embodiments, fig. 1 is a general flowchart of a method for planning a path of a cross-sea logistics unmanned aerial vehicle according to an embodiment of the present invention, and fig. 2 is a technical route diagram of the method, where the method includes:
step S1: determining proper grid granularity and barrier grids based on trees, land, ocean, buildings and the like in the cross-sea environment where the logistics unmanned aerial vehicle is located;
step S2: setting grid passing cost including distance cost, population risk cost, obstacle risk cost and height change cost for each grid;
and step S3: determining a path planning objective function based on an analytic hierarchy process and the grid passing cost;
and step S4: determining constraint conditions according to the motion performance of the logistics unmanned aerial vehicle, and constructing a path planning model under a cross-sea scene by combining the objective function;
step S5: and solving by adopting an Astar algorithm for improving the pre-estimated cost function, and smoothing the solved solution by adopting a second-order Bezier curve to obtain an optimal planning path.
Step S1: determining proper grid granularity and barrier grids based on trees, land, ocean, buildings and the like in the cross-sea environment where the logistics unmanned aerial vehicle is located, and specifically comprising the following steps:
step S11: determining the grid granularity tau according to the maximum turning radius of the logistics unmanned aerial vehicle:
in the formula (1), v max Indicating maximum speed of the drone, R (v) max ) And the maximum speed of the unmanned aerial vehicle corresponds to the turning radius. Step S12: if any objects such as buildings and trees which cannot be normally passed by the unmanned aerial vehicle exist in the grid, the grid is determined as an obstacle grid.
Step S2: setting grid passing cost for each grid, wherein the grid passing cost comprises distance cost, population risk cost, obstacle risk cost and height change cost, and the method specifically comprises the following steps:
step S21: improved grid distance cost setting, in two-dimensional grid space, unmanned aerial vehicle's next action can have 8 kinds of exploration directions, and in three-dimensional grid space, unmanned aerial vehicle's next action can have 26 exploration directions, has different costs when manhattan distance q takes different values between adjacent grids, corresponds space grid center distance face center, arris, summit respectively
Three types of positional relationships (fig. 3).
And in actual operation, when the unmanned aerial vehicle selects the next grid, only if the current speed of the unmanned aerial vehicle is the same as the searching direction of the next grid, the cost is obtained, otherwise, the operation path of the unmanned aerial vehicle is an arc line, and the actual operation length is (assuming that the unmanned aerial vehicle only changes the speed direction and does not change the speed size when turning):
in formula (2), v is unmanned aerial vehicle airspeed, and theta is unmanned aerial vehicle speed change angle, and R is unmanned aerial vehicle turning radius.
The increased length Δ d compared to the linear motion is as in equation (3):
Δd＝2θR-2Rsinθ(3)
to the actual voyage D of commodity circulation unmanned aerial vehicle, have:
in formula (4), D' is an alkyl group
τ is the grid granularity (scale) and represents the actual spatial relationship from each grid to the actual grid, i.e., the actual grid distance.
Step S22: improved grid obstacle risk penalty: for the barrier grid, the assignment is 1, but because the unmanned aerial vehicle turns, climbs and other flight performance constraints, the risk of being too close to the barrier is very large, so introduce the concept of barrier risk degree, and set the "danger radius d" hyperparameter:
in the formula (5), d is a dangerous radius and represents a cube with d as an edge length, and N obstacle (d) Representing the number of obstacle grids in a cube with d as edge length, N surround (d) The total number of grids in a cube with d as the edge length is shown. Eta is the grid obstacle risk.
Step S23: and the grid population risk degree cost is generally set to be 1 in the above-land airspace population risk degree cost and 0 in the above-sea airspace population risk degree cost, and is used for measuring the falling risk caused by the failure, falling or other reasons of the unmanned aerial vehicle.
Step S24: grid height variation cost: defining the height cost H of the unmanned plane from the ith-1 grid to the ith:
in the formula (6), k represents the punishment coefficient of the height change of the unmanned aerial vehicle, mg represents the total mass of the logistics unmanned aerial vehicle, and Delta z (i-1,i) The vertical height variation values of the grids i-1 to i are represented.
The step S3 of determining the path planning objective function through the cost based on the analytic hierarchy process and the grid specifically includes:
step S31: the method for weighting the target function based on the analytic hierarchy process comprises the following steps of:
step S311: comparing the flight distance of the logistics unmanned aerial vehicle, the risk degree of obstacles, the threat degree of population and the importance of height change cost;
step S312: constructing a hierarchical judgment matrix to measure the importance relationship among the targets;
step S313: carrying out normalization processing on the hierarchical judgment matrix;
step S314: and (5) performing consistency check on the hierarchical judgment matrix, if the hierarchical judgment matrix passes the consistency check, obtaining the final weight, and if the hierarchical judgment matrix does not pass the consistency check, returning to the step S311.
Step S32: each target is standardized, the unmanned aerial vehicle range, the obstacle risk degree, the population threat degree and the maximum and minimum altitude change cost of the planned path are estimated and determined, and the unmanned aerial vehicle range, the obstacle risk degree and the population threat degree are converted into the maximum and minimum standardized form in an objective function to be embodied, wherein the conversion formula is as follows, wherein x is a parameter to be standardized:
the final form of the objective function is:
minZ＝α 1 S+α 2 D+α 3 P+α 4 H (8)
in the formulas (7) and (8), S represents the cost of the risk degree of the obstacle, D represents the cost of the range distance, P represents the cost of the risk degree of the population, and H represents the cost of the height change. Alpha is alpha 1 、α 2 、α 3 、α 4 The weight corresponding to each cost is represented by alpha 1 +α 2 +α 3 +α 4 And =1. Wherein alpha is 1 、α 2 、α 3 、α 4 Obtained by an analytic hierarchy process.
Wherein the step S4 of determining the constraint conditions according to the movement performance of the logistics unmanned aerial vehicle specifically comprises the following steps:
s41, determining horizontal speed and direction change constraint of the logistics unmanned aerial vehicle;
horizontal velocity direction change constraint:
in the formula (9) < beta >) i For the unmanned plane at the current path point (x) i ,y i ,z i ) (x) a turning angle of i-1 ,y i-1 ,z i-1 ) And (x) i+1 ,y i+1 ,z i+1 ) Respectively representing the grid coordinates, beta, of the front path point and the rear path point of the unmanned aerial vehicle max Representing the maximum turning angle of the drone.
S42, determining the change constraint of the vertical speed and the direction of the logistics unmanned aerial vehicle;
in the formula (10), u i Represents the pitch angle u of the unmanned plane at the current path point max The maximum pitch angle of the drone is indicated.
S43, determining the flight height constraint of the logistics unmanned aerial vehicle;
z in formula (11) i Indicating the vertical coordinate (height) of the unmanned plane at the current path point grid, H max Indicates that the drone can reach maximum altitude, H min Indicating that the drone may reach a minimum altitude, both values are determined by the drone performance and local airspace policy.
S44, determining the load constraint of the logistics unmanned aerial vehicle;
in formula (12), M represents the self weight of the unmanned aerial vehicle, Q represents the weight of the cargo loaded on the unmanned aerial vehicle, and M max Representing maximum unmanned aerial vehicle load.
And S45, determining the maximum range constraint of the logistics unmanned aerial vehicle:
in the formula (13), l i Representing the current flight distance of the grid unmanned aerial vehicle, n representing the number of unmanned aerial vehicles passing through the total grid, l max Representing the maximum range distance at which the drone may fly.
Step S5: solving by adopting an Astar algorithm of an improved estimated cost function, and smoothing the solved solution by adopting a second-order Bezier curve to obtain an optimal planning path, wherein the method specifically comprises the following steps of:
step S51: determining an estimated cost heuristic function form (the actual cost function is the same as the target function) of the Astar algorithm;
on the basis of the traditional Astar algorithm, a suitable heuristic function and an actual cost function are found for the path planning problem, and as shown in FIG. 4, the specific steps are as follows:
determining an actual cost function: generally the same as the path planning objective function, i.e. taking
g(x)＝α 1 S+α 2 D+α 3 P+α 4 H(14)
In equation (14), S represents an obstacle risk cost, D represents a range distance cost, P represents a population risk cost, and H represents a height change cost. Alpha is alpha 1 、α 2 、α 3 、α 4 The weight corresponding to each cost is represented by alpha 1 +α 2 +α 3 +α 4 ＝1。
Determining a heuristic function: the heuristic function of the traditional Astar algorithm adopts Euclidean distance as the estimated cost, but for path planning under multiple targets, the estimated cost cannot be matched with the actual cost, so that the algorithm complexity is greatly increased. The heuristic function under multi-target path planning is as follows:
h(x)＝ω 1 S next +ω 2 S next D next +ω 3 S next P next +ω 4 H next (15)
in the formula (15), w 1 、w 2 、w 3 、w 4 Representing the weights of the objects in a heuristic function, S next Represents the next grid obstacle risk cost, D next Representing Euclidean distance, P, from the grid to be selected to the target next Represents the next grid population risk cost, H next Representing the height change penalty required for the next grid.
Step S52: determining the CURRENT position of the logistics unmanned aerial vehicle (recorded as CURRENT) i ) And a next reachable grid set OPEN and unreachable grid set CLOSED of the UAV;
step S53: selecting the grid with the minimum total cost in the OPEN set as the next grid (CURRENT) of the logistics unmanned plane i+1 )；
Step S54: removing CURRENT in OPEN set i+1 Adding CURRENT to CLOSED set i+1 Updating the OPEN and CLOSED sets;
step S55: judging whether the target location is reached, if so, turning to the next step, and otherwise, returning to the step S52;
step S56: and obtaining the optimal path of the planned logistics unmanned aerial vehicle, and obtaining a final solution by adopting second-order Bezier curve smoothing processing. As shown in fig. 5, the second order bezier curve equation is as follows:
in the formulae (16) and (17), X i Representing grid coordinates, X i ＝(x i ,y i ,z i ) T is the interval [0,1]The variation parameter of (2).
And completing all path planning tasks of the cross-sea logistics unmanned aerial vehicle, wherein fig. 6 is a schematic scene of path planning of the cross-sea logistics unmanned aerial vehicle in the embodiment.
According to the specific embodiment of the invention, the invention discloses the following technical achievements:
the invention discloses a cross-sea logistics unmanned aerial vehicle path planning method, which comprises the following steps: firstly, establishing a grid environment according to the transportation environment of the logistics unmanned aerial vehicle; then, considering the cross-sea scene environment characteristics to determine the passing cost of each grid, wherein the passing cost comprises distance cost, population risk cost, obstacle risk cost and altitude change cost; constructing a path planning objective function through cost based on an analytic hierarchy process and a grid, constructing a constraint condition of unmanned aerial vehicle motion according to the load of the logistics unmanned aerial vehicle and the unmanned aerial vehicle performance, and establishing a logistics unmanned aerial vehicle path planning model under a cross-sea scene; and obtaining a preliminary planned path by adopting an Astar algorithm with an improved pre-estimated cost function, and finally performing smoothing processing based on a second-order Bezier curve according to the obtained path to obtain a final planned path. The invention introduces the improved distance grid and the improved heuristic function of the algorithm, and effectively solves the problems that the distance cost of the linear grid is not applicable any more due to the large mass and volume of the unmanned aerial vehicle when the path of the logistics unmanned aerial vehicle is planned in the cross-sea environment, the algorithm complexity is high due to the generally long range, and the like.
The cross-sea logistics unmanned aerial vehicle path planning method mainly considers the particularity of the cross-sea environment and the performance of the logistics unmanned aerial vehicle, and innovations are made mainly from the following aspects: 1. the grid distance cost is set, the turning curve of the large-scale logistics unmanned aerial vehicle is obvious aiming at the characteristic of small turning rate of the large-scale logistics unmanned aerial vehicle, and the linear grid distance cost in the traditional method is unbalanced, so that improvement is made; 2, threat degree of land population safety after the unmanned aerial vehicle has an accident is measured by increasing grid population threat degree cost. 3. Aiming at the cross-sea logistics transportation with relatively long voyage, the calculation time complexity is reduced by adopting an improved Astar algorithm heuristic function, and the core idea is that the estimated cost is closer to the actual cost (target function), so that the redundant path search is reduced.
Example 2
In a second aspect, the embodiment provides a cross-sea logistics unmanned aerial vehicle path planning device based on an Astar algorithm, which comprises a processor and a storage medium;
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to embodiment 1.
Example 3
In a third aspect, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The specific implementation example of this specification describes the implementation process in a progressive manner, and the description of the above embodiment is only used to help understanding the method and core idea of the present invention; also, for those skilled in the art, variations in the embodiments and applications may be made in accordance with the principles of the present invention. In view of the foregoing, the description is not to be taken in a limiting sense.
Claims (10)
1. A logistics unmanned aerial vehicle path planning method under a cross-sea scene is characterized by comprising the following steps:
step S1: dividing grids based on the determined grid granularity and the space information of the logistics unmanned aerial vehicle in the cross-sea scene, and determining barrier grids;
step S2: determining a grid passing cost of each grid, wherein the grid passing cost comprises a distance cost, a population risk cost, an obstacle risk cost and a height change cost;
and step S3: determining a path planning objective function by utilizing an analytic hierarchy process based on the grid passing cost;
and step S4: determining constraint conditions according to the motion performance of the logistics unmanned aerial vehicle, and constructing a path planning model under a cross-sea scene by combining the path planning objective function;
step S5: solving the path planning model by adopting an Astar algorithm for improving the pre-estimated value function to obtain the optimal path of the planned logistics unmanned aerial vehicle, and smoothing the optimal path of the planned logistics unmanned aerial vehicle by adopting a second-order Bezier curve to obtain a final logistics unmanned aerial vehicle path planning scheme.
2. The method for planning a path of a logistics unmanned aerial vehicle in a cross-sea scene according to claim 1, wherein in step S1, the method for determining the grid granularity comprises:
determining the grid granularity tau according to the maximum turning radius of the logistics unmanned aerial vehicle:
τ＝R(v max )
in the formula, v max Representing maximum speed of the drone, R (v) max ) Representing the turning radius corresponding to the maximum speed of the unmanned aerial vehicle;
and/or, in step S1, determining an obstacle grid, comprising:
and in response to the existence of the object which cannot be normally passed by the unmanned aerial vehicle in the grid, determining the grid as an obstacle grid.
3. The method of claim 1, wherein determining a grid pass cost for each grid comprises:
s21, determining the distance cost corresponding to each grid;
in a three-dimensional grid space, the next action of the unmanned aerial vehicle can have 26 exploration directions, and when the Manhattan distance q between adjacent grids takes different values, the Manhattan distance q has different costs and respectively corresponds to the center of the space grid and the center, the edge and the vertex of the plane
Three types of positional relationships of (1);
distance cost D:
in the formula, D' is
Tau is the granularity of the grids and represents the proportional relation between each grid and the actual space, namely the actual grid distance; v is the flight speed of the unmanned aerial vehicle, theta is the speed change angle of the unmanned aerial vehicle, and R is the turning radius of the unmanned aerial vehicle;
step S22: determining a grid population risk cost P based on population density conversion according to the difference of population densities on land and sea;
the risk degree cost of the population of the airspace above the land is 1, and the risk degree cost of the population of the airspace above the sea surface is 0;
step S23: counting the number of the barrier grids in the set range around each grid, and determining the risk degree cost S of the barrier of the grid;
for the barrier grid, assign S to 1;
for a non-obstacle grid, the assignment S is η:
wherein η is the obstacle risk of the non-obstacle grid; d is the danger radius, which represents the cube with d as the edge length, N obstacle (d) Representing the number of obstacle grids in a cube with d as edge length, N surround (d) Representing the total number of grids in a cube with d as the edge length;
step S24: determining the altitude change cost H according to the unmanned aerial vehicle performance:
in the formula, H represents the height change cost of the unmanned aerial vehicle from the ith grid to the ith grid, k represents the height change penalty coefficient of the unmanned aerial vehicle, and Mg represents the logistics unmanned aerial vehicleTotal mass,. DELTA.z (i-1,i) The vertical height variation values of the grids i-1 to i are represented.
4. The method for path planning of a logistics unmanned aerial vehicle under a cross-sea scene as claimed in claim 1, wherein in step S3, the path planning objective function minZ is:
minZ＝α 1 S+α 2 D+α 3 P+α 4 H
wherein S represents the cost of the risk of the obstacle, D represents the cost of the range distance, P represents the cost of the risk of the population, and H represents the cost of the altitude change; alpha is alpha 1 、α 2 、α 3 、α 4 Respectively representing the corresponding weights of the risk degree cost, the voyage distance cost, the population risk degree cost and the altitude change cost of the obstacle, and alpha 1 +α 2 +α 3 +α 4 ＝1。
5. The logistics unmanned aerial vehicle path planning method under the cross-sea scene of claim 4, wherein the weight α corresponding to the barrier risk degree cost, the voyage distance cost, the population risk degree cost and the altitude change cost 1 、α 2 、α 3 、α 4 Determined by analytic hierarchy process, comprising:
step S311: comparing the importance of the barrier risk cost, the range distance cost, the population risk cost and the height change cost of the logistics unmanned aerial vehicle;
step S312: constructing a level judgment matrix to measure the importance relationship among the risk degree cost of the obstacle, the voyage distance cost, the population risk degree cost and the altitude change cost;
step S313: carrying out normalization processing on the hierarchical judgment matrix;
step S314: and (4) checking the consistency of the hierarchical judgment matrix, if the check is passed, obtaining weights corresponding to the barrier risk degree cost, the voyage distance cost, the population risk degree cost and the altitude change cost, and otherwise, returning to the step S311.
6. The logistics unmanned aerial vehicle path planning method under the cross-sea scene of claim 4, wherein in the path planning objective function, the obstacle risk cost, the voyage distance cost, the population risk cost and the altitude change cost are the obstacle risk cost, the voyage distance cost, the population risk cost and the altitude change cost after maximum and minimum standardization processing; the maximum and minimum standardization processing method comprises the following steps:
wherein x is max 、x min The maximum value and the minimum value are respectively the maximum value and the minimum value in the risk degree cost of the obstacle, the range distance cost, the population risk degree cost and the altitude change cost.
7. The method for planning the path of the logistics unmanned aerial vehicle under the cross-sea scene according to claim 1, wherein the determining the constraint condition according to the movement performance of the logistics unmanned aerial vehicle comprises:
s41, determining horizontal direction speed and direction change constraint of the logistics unmanned aerial vehicle;
in the formula, beta i For the unmanned plane at the current path point (x) i ,y i ,z i ) (x) the turning angle of (c) i-1 ,y i-1 ,z i-1 ) And (x) i+1 ,y i+1 ,z i+1 ) Respectively representing the grid coordinates, beta, of the front path point and the rear path point of the unmanned aerial vehicle max Representing the maximum turning angle of the unmanned aerial vehicle;
s42, determining climbing and descending performance constraints of the logistics unmanned aerial vehicle in the vertical direction;
in the formula u i Indicates the pitch angle u of the unmanned plane at the current path point max Representing the maximum pitch angle of the unmanned aerial vehicle;
s43, determining the flight height constraint of the logistics unmanned aerial vehicle;
H min ≤Z i ≤H max
in the formula Z i Represents the vertical coordinate H of the unmanned plane at the grid of the current path point max Indicates that the drone can reach maximum altitude, H min Indicating that the drone may reach a minimum altitude;
s44, determining the load constraint of the logistics unmanned aerial vehicle;
M+Q≤M max
in the formula, M represents the self weight of the unmanned aerial vehicle, Q represents the load weight of the unmanned aerial vehicle, and M max Representing maximum load of the unmanned aerial vehicle;
and S45, determining the maximum range constraint of the logistics unmanned aerial vehicle:
in the formula I i Representing the range distance of the unmanned plane on the current grid, n representing the number of unmanned planes passing through the total grid, l max Representing the maximum range distance at which the drone may fly.
8. The method for path planning of logistics unmanned aerial vehicle in a cross-sea scenario of claim 1, wherein solving the path planning model with an Astar algorithm for improving a predictive cost function comprises:
step S51: converting the path planning target function into an Astar algorithm estimation cost heuristic function form;
h(x)＝ω 1 S next +ω 2 S next D next +ω 3 S next P next +ω 4 H next
in the formula, w 1 、w 2 、w 3 、w 4 Representing the weights of the objects in a heuristic function, S next Represents the next grid obstacle risk cost, D next Representing Euclidean distance, P, from the grid to be selected to the target next Represents the next grid population risk cost, H next Representing the height change cost required for the next grid;
step S52: obtaining CURRENT grid coordinate position CURRENT of logistics unmanned aerial vehicle i Determining a next reachable grid set OPEN and a unreachable grid set CLOSED of the unmanned aerial vehicle;
step S53: selecting the grid with the minimum total cost in the OPEN set as the next grid CURRENT of the logistics unmanned plane i+1 ；
Step S54: removing CURRENT in OPEN set i+1 Adding CURRENT to CLOSED set i+1 Updating the OPEN and CLOSED sets;
step S55: judging whether the target location is reached, if so, turning to the next step, otherwise, returning to the step S52;
step S56: and obtaining the optimal path of the planned logistics unmanned aerial vehicle.
9. A cross-sea logistics unmanned aerial vehicle path planning device based on an Astar algorithm is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 8.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 8.
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