CN111443712A - Three-dimensional path planning method based on longicorn group search algorithm - Google Patents

Three-dimensional path planning method based on longicorn group search algorithm Download PDF

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CN111443712A
CN111443712A CN202010241304.2A CN202010241304A CN111443712A CN 111443712 A CN111443712 A CN 111443712A CN 202010241304 A CN202010241304 A CN 202010241304A CN 111443712 A CN111443712 A CN 111443712A
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周冠丞
陈德潮
李密
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Hangzhou Dianzi University
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Abstract

The invention discloses a three-dimensional path planning method based on a longicorn group search algorithm. The method comprises the steps of firstly reading map data, describing a three-dimensional map scene by using uniform and discrete space points, connecting the points by using a geometric patch to form a three-dimensional plane simulating real terrain, projecting three-dimensional map scene points to two dimensions to generate a two-dimensional grid surface, numbering the points on the grid surface, giving independent digital marks to each grid point, calculating the adjacent points of each grid point, randomly connecting the points to generate different paths from a terminal point to a starting point, performing optimization solution on the paths by using an algorithm, calculating the path length, substituting the path length into an altitude information vertical coordinate, solving the path corresponding to the optimal path length value, finally transmitting the path result to an unmanned vehicle, and controlling the unmanned vehicle to perform navigation movement on the three-dimensional plane. The invention optimizes the selection of the path points from the starting point to the end point, so that the path length passed by the unmanned vehicle is shortest and optimal.

Description

Three-dimensional path planning method based on longicorn group search algorithm
Technical Field
The invention relates to the field of three-dimensional path planning and intelligent optimization algorithms, in particular to an optimal path planning method for a three-dimensional terrain plane based on a BSO (BeetlSwarm optimization) algorithm.
Background
Path planning is one of the main research contents of motion planning. The motion planning is composed of path planning and trajectory planning, sequence points or curves connecting the starting position and the end position are called paths, and the strategy for forming the paths is called path planning. In the invention, the execution individual of the path planning is an unmanned vehicle capable of freely driving on a plane.
A path from a shortest connecting starting point to a destination point is planned, and the shortest path planning is also called as shortest path planning and is one of the research hotspots of current unmanned driving and artificial intelligence of the Internet of things. Can be applied to future volume production unmanned vehicles, fields such as unmanned aerial vehicle search and rescue, unmanned aerial vehicle delivery goods. The shortest path planning can help the vehicle to save time and energy.
The longicorn group algorithm (BSO) is a novel intelligent simulated population search algorithm, and the bionics principle of the BSO has better applicability, robustness, accuracy and running speed in solving related optimization problems. The applied search principle and local search feature have higher accuracy and higher running speed.
The results obtained by using the traditional solving method cannot achieve global optimization, the stability of the solved results is insufficient, the defects have great influence on the solving progress and the solving speed of the three-dimensional plane path planning, the defects of messy and curved paths and the like can be caused, and even the mobile planning task of the unmanned vehicle fails.
Disclosure of Invention
The invention mainly aims at the technical defect of a path planning algorithm on a three-dimensional plane, and provides an algorithm model for solving a three-dimensional path planning problem by using a longicorn group algorithm. The solving precision and speed of the three-dimensional path planning are effectively improved by the group characteristic and the Levy flight characteristic of the algorithm, a relatively good moving path from the starting point to the end point can be planned, and the path planning capability of the unmanned vehicle is better improved.
In order to achieve the purpose, the invention adopts a three-dimensional path planning method based on a longicorn group search algorithm, which comprises the following steps:
step 1): reading map data, describing a three-dimensional map scene by using a map information data point which is connected by a geometric patch and is uniformly dispersed, and generating and drawing a three-dimensional plane for simulating a real terrain; the read map data model is three-dimensional scatter data, and the map data is described by using an x coordinate value, a y coordinate value and a z coordinate value.
Step 2): projecting the three-dimensional map scene points read in the step 1) to a two-dimensional grid surface, carrying out one-dimensional numbering on points on the grid surface, giving independent digital marks to each grid point, and calculating and storing adjacent points of each grid point;
step 3): carrying out adjacent point connection on the numbering points in the step 2) to generate different paths from the end point to the starting point;
step 4): performing optimization solution on the path in the step 3) by using a celestial cow group algorithm model, considering height information when calculating the path length, processing a discontinuous path, changing the discontinuous path into a continuous path with sequentially communicated adjacent points, solving a path corresponding to an optimal path length value, and performing path image drawing on the terrain by using a plot function;
step 5): and (4) transmitting the result of the step 4) to the unmanned vehicle, and controlling the unmanned vehicle to perform navigation movement on a three-dimensional plane.
Preferably, in step 1), the basic map information is rendered by connecting the scatter points using a three-dimensional patch.
Preferably, in the step 2), the three-dimensional map information is described by different one-dimensional number values, and the three-dimensional walking route of the unmanned vehicle is planned and described by using the one-dimensional number values; its specific numbering method is numi＝(xi-1)*col+yi(ii) a Wherein numiRepresenting the number value of the point. x is the number ofiX-axis coordinate, y, representing the pointiRepresenting the y-axis coordinate of the point, wherein col represents the column number of the two-dimensional matrix of the map;
preferably, in step 3), the adjacent point of one point is a point on 8 direction unit lengths of east, south, west, north, northeast, southeast, northwest and southwest with the point as the center, the 8 points are the points to be selected of the next path, the distance is set as the range of each movable path of the unmanned vehicle, the starting point is set as the first value of the path, and then the adjacent points of each point are randomly selected to be the path points of the advancing direction.
Preferably, in step 4), the specific method for establishing the algorithm model is as follows:
and (3) minimizing: is ═ iminThe calculation method comprises the following steps:
Figure BDA0002431946480000021
constraint conditions are as follows:
xmin≤xi≤xmax
ymin≤yi≤ymax
zi＝zmap
wherein, for an optimized path length,minfor total length of movement, xi，yi，ziIs the value of each optimized path point, i is each time interval,irepresents the length of the path traversed at the i time interval; the inequality constraint represents the trajectory constraint range of the path, where xmin，xmaxThe x-axis span representing the path points, where ymin，ymaxY-axis span representing path points, where zmapThe z-axis values representing the path points.
Preferably, the discontinuous path in step 4) is changed into a continuous path with sequentially communicated adjacent points, specifically: applying a BSO algorithm to solve, exchanging different path sequences during solving, judging whether the path is a communication path, if so, calculating the path length of the path, and if not, reprocessing the path; after a certain number of iterations, a shortest path is selected.
Preferably, in the BSO algorithm, first, when the location is updated, the new location is obtained by weighting after updating in the PSO and BAS modes; that is, the whisker direction and step size of BAS are utilized, while the velocity of the particles is also utilized:
Figure BDA0002431946480000031
wherein the content of the first and second substances,
Figure BDA0002431946480000032
indicating the position of the particle s at k +1 iterations;
Figure BDA0002431946480000033
indicating the velocity of each longicorn of the particle multiplied by a weight; the size of the lambda can be controlled in the longicorn swarm algorithm, the ratio of the influence of the particle swarm algorithm and the longicorn beard algorithm is weighted,
Figure BDA0002431946480000034
the displacement of the longicorn along the speed direction is increased by an increment, and the increment is derived from the thought of a longicorn whisker algorithm; the velocity term is updated using the following formula:
Figure BDA0002431946480000035
wherein c is1And c2Is a constant of a learning factor, r1And r2Is [0,1 ]]Random variable between, Pis kRepresenting the optimal position of the individual, Pgs kRepresents a global optimal location;
the increment of displacement for each iteration is further expressed as:
Figure BDA0002431946480000036
whereinkRepresents the step size of the travel at each time, f (-) represents the fitness function;
wherein
Figure BDA0002431946480000037
And
Figure BDA0002431946480000038
is the position detected by the left and right tentacles of the longicorn, and is expressed as:
Figure BDA0002431946480000039
Figure BDA00024319464800000310
wherein d represents the longicorn tentacle length.
The path planning problem is solved by using a space cattle group search algorithm, and the data sequence vector of the path is substituted into the algorithm for optimization. And after a certain number of iterations, outputting the solved path, and drawing the path on the map.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the BSO algorithm of the present invention;
fig. 3 is a schematic diagram of an optimal path of the three-dimensional map according to the present invention.
Detailed Description
As shown in fig. 1, a mechanical arm motion planning method based on a longicorn whisker optimization strategy specifically comprises the following steps:
1) and reading the map information, and drawing the map information according to the map scatter coordinate points provided by the map information.
2) And (3) minimizing: is ═ iminThe calculation method comprises the following steps:
Figure BDA0002431946480000041
constraint conditions are as follows:
xmin≤xi≤xmax
ymin≤yi≤ymax
zi＝zmap
wherein, for an optimized path length,minfor total length of movement, xi，yi，ziIs the value of each optimized path point, i is each time interval,irepresents the length of the path traversed at the i time interval; the inequality constraint represents the trajectory constraint range of the path, where xmin，xmaxThe x-axis span representing the path points, where ymin，ymaxY-axis span representing path points, where zmapThe z-axis values representing the path points.
3) Applying the planning scheme proposed in the step 3) to a search algorithm of a Tianniu group to solve:
projecting the three-dimensional map information to two dimensions, and numbering the two-dimensional grid map information, wherein the numbering method is numi＝(xi-1)*col+yi. Wherein numiNumber value, x, representing the pointiX-axis coordinate, y, representing the pointiRepresenting the y-axis coordinate of the point. And calculating the adjacent points of each point, taking all the adjacent points of one point as the points to be selected of the next path, setting the distance to be the range of the path which can be moved by the unmanned vehicle each time, setting the starting point as the first value of the path, and then randomly and sequentially selecting the adjacent points of each point as the path points in the advancing direction. In order to guarantee path continuity, the algorithm model is provided.
4) Initializing population information, initializing algorithm parameters, and calculating the path length fitness value of each population individual, wherein each solution vector consists of different continuous paths. Substituting the obtained solution into a longicorn herd algorithm to solve. As shown in figure 2 of the drawings, in which,
in the BSO algorithm, first, when a location is updated, new locations are obtained by weighting after each update in the PSO and BAS modes. That is, we use the whisker direction and step size of BAS, and also the velocity of the particles (in BSO, longicorn):
Figure BDA0002431946480000051
wherein the content of the first and second substances,
Figure BDA0002431946480000052
indicating the position of the particle s at k +1 iterations.
Figure BDA0002431946480000053
Indicating the velocity of the particle multiplied by the weight per longicorn. The size of the lambda can be controlled in the longicorn swarm algorithm, and the specific gravity of the influence of the particle swarm algorithm and the longicorn beard algorithm，
Figure BDA0002431946480000054
The method is an increment of the displacement of the longicorn along the speed direction and is derived from the thought of the longicorn whisker algorithm. The velocity term is updated using the following formula:
Figure BDA0002431946480000055
wherein c is1And c2Is a constant of a learning factor, r1And r2Is [0,1 ]]Random variable between, PisRepresenting the optimal position of the individual, PgsRepresenting a global optimal position.
The increment of displacement for each iteration is further expressed as:
Figure BDA0002431946480000056
wherein
Figure BDA0002431946480000057
And
Figure BDA0002431946480000058
is the position detected by the left and right tentacles of the longicorn and can be expressed as:
Figure BDA0002431946480000059
Figure BDA00024319464800000510
after the position of each population is updated by using a cow group algorithm, the path continuity of each population needs to be ensured, and the following method is used for processing: each population path is iterated, each unconnected path is processed, and the path switching point changes it into a connected path, as shown in fig. 3.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present invention shall be included in the protection scope of the claims of the present invention.

Claims (8)

1. A three-dimensional path planning method based on a longicorn group search algorithm is characterized by comprising the following steps:
1) reading map data, describing a three-dimensional map scene by using a map information data point which is connected by a geometric patch and is uniformly dispersed, and generating and drawing a three-dimensional plane for simulating a real terrain;
2) projecting the three-dimensional map scene points read in the step 1) to a two-dimensional grid surface, carrying out one-dimensional numbering on points on the grid surface, giving independent digital marks to each grid point, and calculating and storing adjacent points of each grid point;
3) carrying out adjacent point connection on the numbering points in the step 2) to generate different paths from the end point to the starting point;
4) performing optimization solution on the path in the step 3) by using a celestial cow group algorithm model, considering height information when calculating the path length, processing a discontinuous path, changing the discontinuous path into a continuous path with sequentially communicated adjacent points, solving a path corresponding to an optimal path length value, and performing path image drawing on the terrain by using a plot function;
5) and (4) transmitting the result of the step 4) to the unmanned vehicle, and controlling the unmanned vehicle to perform navigation movement on a three-dimensional plane.
2. The method of claim 1, wherein in step 1), the read map data model is three-dimensional scatter data, and the map data is described using x-coordinate values, y-coordinate values, and z-coordinate values.
3. The method of claim 1, wherein in step 1), the basic map information is rendered using a three-dimensional patch to connect the scatter points.
4. The method of claim 1, wherein the method is performed in a batch modeCharacterized in that in the step 2), three-dimensional map information is described by different one-dimensional number values, and the three-dimensional walking route of the unmanned vehicle is planned and described by using the one-dimensional number values; its specific numbering method is numi＝(xi-1)*col+yi(ii) a Wherein numiA number value representing the point; x is the number ofiX-axis coordinate, y, representing the pointiRepresents the y-axis coordinate of the point and col represents the number of columns of the two-dimensional matrix of the map.
5. The method according to claim 1, wherein in step 3), the adjacent point of one point is a point on a unit length of 8 directions of east, south, west, north, northeast, southeast, northwest and southwest with the point as a center, the 8 points are points to be selected of the next path, the distance is set as a range of each movable path of the unmanned vehicle, the starting point is set as a first value of the path, and then the adjacent points of each point are randomly selected in sequence to be the path points of the advancing direction.
6. The method according to claim 1, wherein in step 4), the specific method for establishing the algorithm model is as follows:
and (3) minimizing: is ═ iminThe calculation method comprises the following steps:
Figure FDA0002431946470000021
constraint conditions are as follows:
xmin≤xi≤xmax
ymin≤yi≤ymax
zi＝zmap
wherein, for an optimized path length,minfor total length of movement, xi，yi，ziIs the value of each optimized path point, i is each time interval,irepresents the length of the path traversed at the i time interval; the inequality constraint represents the trajectory constraint range of the path, where xmin，xmaxThe x-axis span representing the path points, where ymin，ymaxY-axis span representing path points, where zmapThe z-axis values representing the path points.
7. The method of claim 1, wherein: step 4), changing the discontinuous path into a continuous path with sequentially communicated adjacent points, which specifically comprises the following steps: applying a BSO algorithm to solve, exchanging different path sequences during solving, judging whether the path is a communication path, if so, calculating the path length of the path, and if not, reprocessing the path; after a certain number of iterations, a shortest path is selected.
8. The method of claim 1, wherein: in the BSO algorithm, firstly, when the position is updated, the position is weighted to obtain a new position after being respectively updated according to the PSO and BAS modes; that is, the whisker direction and step size of BAS are utilized, while the velocity of the particles is also utilized:
Figure FDA0002431946470000022
wherein the content of the first and second substances,
Figure FDA0002431946470000023
indicating the position of the particle s at k +1 iterations;
Figure FDA0002431946470000024
indicating the velocity of each longicorn of the particle multiplied by a weight; the size of the lambda can be controlled in the longicorn swarm algorithm, the ratio of the influence of the particle swarm algorithm and the longicorn beard algorithm is weighted,
Figure FDA0002431946470000025
the displacement of the longicorn along the speed direction is increased by an increment, and the increment is derived from the thought of a longicorn whisker algorithm; the velocity term is updated using the following formula:
Figure FDA0002431946470000026
wherein c is1And c2Is a constant of a learning factor, r1And r2Is [0,1 ]]Random variable between, Pis kRepresenting the optimal position of the individual, Pgs kRepresents a global optimal location;
the increment of displacement for each iteration is further expressed as:
Figure FDA0002431946470000031
whereinkRepresents the step size of the travel at each time, f (-) represents the fitness function;
wherein
Figure FDA0002431946470000032
And
Figure FDA0002431946470000033
is the position detected by the left and right tentacles of the longicorn, and is expressed as:
Figure FDA0002431946470000034
Figure FDA0002431946470000035
wherein d represents the longicorn tentacle length.
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CN113985922B (en) * 2021-11-10 2023-11-17 浙江建德通用航空研究院 Unmanned aerial vehicle hierarchical path planning method under multi-target constraint
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