CN112650219A - Unmanned mobile platform path planning method based on full coverage algorithm - Google Patents
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Abstract
The invention discloses a path planning method of an unmanned mobile platform based on a full coverage algorithm, and relates to the technical field of path planning of unmanned mobile platforms. The unmanned mobile platform can collect whether a barrier exists in front or not while controlling the unmanned mobile platform to move by adopting the matching of the vehicle-mounted sensor and the vision system, the unmanned mobile platform can avoid the barrier conveniently and can find out the optimal path covering the farmland, and the optimal path covering the farmland can be covered while avoiding the barrier in the farmland by adopting a full-area covering algorithm, so that the mobility of the unmanned mobile platform in the farmland can be improved, the sowing speed can be changed in real time according to various requirements of different sections and the change of the speed of the vehicle by adopting the matching of the stepping motor and the sowing machine, and the requirement of the precision of agricultural sowing can be improved.
Description
Technical Field
The invention relates to the technical field of unmanned mobile platform path planning, in particular to an unmanned mobile platform path planning method based on a full coverage algorithm.
Background
With the rapid development of artificial intelligence technology, the unmanned vehicle technology is gradually mature and applied to limited scenes, such as the fields of environmental sanitation, mountain mining, logistics and the like, China is used as a big agricultural country, the planting area is large, the mechanization level of agriculture in China is low, the population of farmers is reduced, the burden is increased day by day, and in the coming 5G era, the enhancement of agricultural mechanization, automation and intellectualization is imperative, so that the application of the unmanned vehicle technology to agriculture in China has a great prospect, the application of the unmanned vehicle technology to agriculture is the beginning of the mode of agricultural production in China, the hands of farmers are liberated, and the agricultural production efficiency is improved.
The unmanned vehicle is applied to the farmland to reduce the labor amount, but the movement of the unmanned vehicle is influenced by some barriers existing in the farmland, and in the process of moving and sowing of the unmanned vehicle, the seeds are extremely unevenly distributed in the farmland due to the fact that the moving speed of the unmanned vehicle and the sowing frequency of the sowing machine are not in a direct proportion relation, so that the initial use purpose of seeking the optimal path of the unmanned vehicle when the obstacles in the farmland cannot be avoided is achieved, the applicability of the unmanned mobile platform path planning method based on the full coverage algorithm is poor, and therefore structural innovation is needed to be carried out to solve the specific problem.
Disclosure of Invention
The invention provides a path planning method of an unmanned mobile platform based on a full coverage algorithm, which aims to solve the problem that an unmanned vehicle cannot avoid obstacles in a farmland and find an optimal path for the farmland; wherein another kind of purpose is in order to solve the inhomogeneous problem of seed distribution of seeder to reach the effect that the seed can evenly distributed in the farmland.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: an unmanned mobile platform path planning method based on a full coverage algorithm comprises the following steps:
in a first aspect, the invention provides an unmanned mobile platform based on a full-coverage algorithm, which comprises a farmland area, a vision system, an on-board computer, a controller, an automatic driving system and a seeder, and comprises the following steps:
the method comprises the following steps: moving, namely sending an instruction to an automatic driving system through a visual system according to the vehicle-mounted computer to control the moving direction of the automatic driving system so as to realize automatic driving;
step two: seeding, namely processing a signal according to the running speed of a vehicle in the automatic driving process and transmitting the processed signal to a seeding controller to realize automatic seeding;
step three: and the motion trail is obtained according to the vehicle-mounted sensor in the moving process of the unmanned mobile platform, the vehicle-mounted computer corrects the real-time motion trail information, and transmits an instruction to the controller to control the movement of the unmanned mobile platform.
Preferably, the unmanned mobile platform based on the full-coverage algorithm comprises an unmanned mobile platform seed discharging speed analysis, the seeding frequency of the seeder is in a direct proportion relation with the vehicle running speed, and the seeding frequency of the seeder is calculated by measuring the period of a pulse signal output by a vehicle speed sensor under the condition of low speed.
In a second aspect, the invention provides a path planning method for an unmanned mobile platform based on a full coverage algorithm, which comprises path planning of the full coverage algorithm and design of a local area full coverage algorithm.
Preferably, the path planning of the full coverage algorithm comprises modeling of a farmland environment and design of the full coverage path planning algorithm of the mobile platform, so that the full coverage path planning of the mobile platform is completed.
Preferably, the farmland environment modeling comprises the following methods:
1) the visual graph method is characterized in that the barrier is represented by a polygon, and the starting point, the end point and each vertex of the polygon are connected by straight lines on the premise that the connecting lines do not intersect with the polygon;
2) the grid method comprises the steps of dividing a working environment into grids with the same size, recording information of space occupied by the grids in each grid, and finally searching a path leading to the grid where an end point is located from the grid where a starting point is located, wherein connecting lines among the grids cannot have intersection points with the grid where an obstacle is located;
3) the free space method uses polygon to represent barrier, and uses the connection line from vertex to other barrier vertex and without intersection point with barrier and the perpendicular line from the vertex to space boundary, and retains effective connection line, and uses the midpoint of every connection line to connect every midpoint, and retains the connection line without intersection point with barrier, so that it can be used as alternative path.
Preferably, the local area total coverage algorithm design includes a BCD local area total coverage algorithm, and the BCD local area total coverage algorithm includes the following steps:
s1, performing grid division on the farmland, performing region division on the farmland, wherein each sub-region does not contain a barrier, making a straight line perpendicular to an X axis through the maximum coordinate point of each barrier in the X axis direction, and the straight line is intersected with the boundary of the farmland or the barrier to finish the division of the region into a plurality of sub-regions;
and S2, planning the raster path, planning the path of each sub-area by using a BCD local area coverage algorithm to complete the full coverage in the sub-areas, and connecting the sub-areas to complete the full coverage path planning of the whole farmland.
Preferably, the BCD local area full coverage algorithm flow includes the following steps:
a. loading a farmland environment model by the mobile platform, and setting parameters of the current position P0 of the mobile platform and the side length L of the grid;
b. the mobile platform acquires the information of the grid area of the accessory at the position and updates the information of the covered farmland in time;
c. if the farmland grid is uncovered or the obstacle grid, the grid is an infeasible area, and the direction in which the mobile platform can run is determined according to the position of the mobile platform and the information of the accessory grid;
d. according to the priority criterion of the BCD algorithm, the vehicle returns to the b when driving a grid distance L in the direction with the highest priority;
e. until an area is completely covered or not completely covered but reaches a dead center position of the area. If the coverage of the area is finished, whether the coverage of the whole farmland is finished or not is judged, and if the coverage of the whole farmland is finished, the algorithm is ended. If the complete coverage of the local area is not completed but the dead point position of the local area is reached, the point of the next area closest to the dead point is selected as the area entrance, and the next area is entered. Finishing primary coverage of all local areas;
f. and calculating the cost, calculating a grid of a southwest corner closest to the uncovered part of each area from the mobile platform when the primary coverage is finished, wherein the grid is an entrance of secondary coverage, and finishing the coverage of the rest part according to a priority rule of BCD operation.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the technical progress that:
1. the invention provides a path planning method of an unmanned mobile platform based on a full coverage algorithm, which is characterized in that through the delicate design, the cooperation of a vehicle-mounted sensor and a vision system is adopted, whether barriers exist in the front or not can be collected while the unmanned mobile platform is controlled to move, the unmanned mobile platform can conveniently avoid the barriers, the optimal path covering a farmland can be found out, the optimal path covering the farmland can be covered while the barriers in the farmland are avoided by adopting the full coverage algorithm, and the mobility of the unmanned mobile platform in the farmland can be improved.
2. The invention provides a path planning method of an unmanned mobile platform based on a full-coverage algorithm, which can change the seeding speed in real time according to various requirements of different sections and the change of the vehicle speed by adopting the combined arrangement of a stepping motor and a seeder, is beneficial to improving the requirement of the accuracy of agricultural seeding, and ensures that the distribution of rice seeds is more uniform due to the proportional relation between the stepping motor and the seeder.
Drawings
FIG. 1 is a flow chart of an embodiment of the unmanned mobile platform of the present invention;
FIG. 2 is a schematic structural diagram of an unmanned mobile platform according to the present invention;
FIG. 3 is a diagram of the relationship between the minimum turning radius of the unmanned mobile platform and the length of the seeder of the present invention;
FIG. 4 is a flow chart of the BCD algorithm of the present invention;
fig. 5 is the BCD defined driving pattern of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples: an unmanned mobile platform path planning method based on a full coverage algorithm comprises the following steps:
in a first aspect, the invention provides an unmanned mobile platform based on a full-coverage algorithm, which comprises a farmland area, a vision system, an on-board computer, a controller, an automatic driving system and a seeder, and comprises the following steps:
the method comprises the following steps: moving, namely sending an instruction to an automatic driving system through a visual system according to the vehicle-mounted computer to control the moving direction of the automatic driving system so as to realize automatic driving;
step two: seeding, namely processing a signal according to the running speed of a vehicle in the automatic driving process and transmitting the processed signal to a seeding controller to realize automatic seeding;
step three: and the motion trail is obtained according to the vehicle-mounted sensor in the moving process of the unmanned mobile platform, the vehicle-mounted computer corrects the real-time motion trail information, and transmits an instruction to the controller to control the movement of the unmanned mobile platform.
Furthermore, the unmanned mobile platform based on the full-coverage algorithm comprises an unmanned mobile platform seed discharging speed analysis, the seeding frequency of the seeder is in direct proportion to the vehicle running speed, and the seeding frequency of the seeder is calculated by measuring the period of a pulse signal output by a vehicle speed sensor under the condition of low speed.
Example 1
As shown in fig. 1-3, in the actual working process of the unmanned mobile platform, due to the change of the road condition, the position, the speed and the acceleration of the mobile platform, the preliminarily formed motion trajectory is in an ideal state and is difficult to meet the actual kinematics requirement, so that the vehicle-mounted sensor is required to acquire the real-time motion trajectory information, the vehicle-mounted computer corrects the real-time motion trajectory information, and then transmits a more accurate instruction to the controller to meet the kinematics requirement, and in order to ensure that the sown seeds are uniformly distributed, the vehicle speed of the mobile platform should be selected to run at a low speed.
The seeding speed of the unmanned mobile platform is as follows: v is 3.6 pi D/TK, wherein v represents the advancing speed of the unmanned mobile platform, D is the diameter unit of a wheel, m is the measured period of a pulse signal of a vehicle speed sensor, K is the number of grating wheel holes in the vehicle speed sensor, km/h is the vehicle speed unit, the rice seeds are required to be 40-60 kg in average hectare, the farmland is divided into S/LX sections by using a differential thought, X represents the infinite number, and each section consumes time
t＝s/LXv(1)
t＝1000Q/XqN(2)
In the above formula, S (m)2) Representing an unobstructed seeding area, Q (kg) representing the weight of the rice seeds required, L (m) representing the width of the seeder, i.e. the width of the area covered by the seeder during each straight travel, the length of L being 2 times the minimum turning radius, v (m/s) representing the instantaneous speed of travel of the mobile platform, q (g/s) representing the seeding speed of the seeder, N representing the number of the seeders, and the simultaneous (1) and (2) obtaining the seeding speed of the seeder as
q＝1000XQLv/NxS＝1000QLv/NS
ω＝2πq/m＝2000πLvQ/NSm
m (g) represents the weight of the seed discharged by one rotation of the seeder, and ω (rad/s) represents the rotational speed of the seeder at each instant.
For example, the relation among the running speed of the unmanned mobile platform, the seeding amount and the stepping motor is shown in the table, it can be known that when the running speed of the seeder and the rotating speed of the stepping motor are relatively high, the seeding amount is also increased, and when the running speed of the seeder is 3km/h, the seeding amount is also increased to a certain extent as the rotating speed of the stepping motor is higher, and in general, the seeding frequency of the seeder is in direct proportion to the rotating speed of the stepping motor.
In a second aspect, the invention provides a path planning method for an unmanned mobile platform based on a full coverage algorithm, which comprises path planning of the full coverage algorithm and design of a local area full coverage algorithm.
Further, path planning of the full-coverage algorithm comprises modeling of a farmland environment and design of the full-coverage path planning algorithm of the mobile platform, and the full-coverage path planning of the mobile platform is completed.
Further, the farmland environment modeling comprises the following methods:
1) the visual graph method is characterized in that the barrier is represented by a polygon, and the starting point, the end point and each vertex of the polygon are connected by straight lines on the premise that the connecting lines do not intersect with the polygon;
2) the grid method comprises the steps of dividing a working environment into grids with the same size, recording information of space occupied by the grids in each grid, and finally searching a path leading to the grid where an end point is located from the grid where a starting point is located, wherein connecting lines among the grids cannot have intersection points with the grid where an obstacle is located;
3) the free space method uses polygon to represent barrier, and uses the connection line from vertex to other barrier vertex and without intersection point with barrier and the perpendicular line from the vertex to space boundary, and retains effective connection line, and uses the midpoint of every connection line to connect every midpoint, and retains the connection line without intersection point with barrier, so that it can be used as alternative path.
Further, the local area full-coverage algorithm design comprises a BCD local area full-coverage algorithm, and the BCD local area full-coverage algorithm comprises the following steps:
s1, performing grid division on the farmland, performing region division on the farmland, wherein each sub-region does not contain a barrier, making a straight line perpendicular to an X axis through the maximum coordinate point of each barrier in the X axis direction, and the straight line is intersected with the boundary of the farmland or the barrier to finish the division of the region into a plurality of sub-regions;
and S2, planning the raster path, planning the path of each sub-area by using a BCD local area coverage algorithm to complete the full coverage in the sub-areas, and connecting the sub-areas to complete the full coverage path planning of the whole farmland.
Further, the BCD local area full coverage algorithm flow comprises the following steps:
a. loading a farmland environment model by the mobile platform, and setting parameters of the current position P0 of the mobile platform and the side length L of the grid;
b. the mobile platform acquires the information of the grid area of the accessory at the position and updates the information of the covered farmland in time;
c. if the farmland grid is uncovered or the obstacle grid, the grid is an infeasible area, and the direction in which the mobile platform can run is determined according to the position of the mobile platform and the information of the accessory grid;
d. according to the priority criterion of the BCD algorithm, the vehicle returns to the b when driving a grid distance L in the direction with the highest priority;
e. until an area is completely covered or not completely covered but reaches a dead center position of the area. If the coverage of the area is finished, whether the coverage of the whole farmland is finished or not is judged, and if the coverage of the whole farmland is finished, the algorithm is ended. If the complete coverage of the local area is not completed but the dead point position of the local area is reached, the point of the next area closest to the dead point is selected as the area entrance, and the next area is entered. Finishing primary coverage of all local areas;
f. and calculating the cost, calculating a grid of a southwest corner closest to the uncovered part of each area from the mobile platform when the primary coverage is finished, wherein the grid is an entrance of secondary coverage, and finishing the coverage of the rest part according to a priority rule of BCD operation.
Example 2
The visual graph method is convenient to operate, the principle is that the obstacles are represented by polygons, and the starting point, the end point and each vertex of the polygons are connected by straight lines, on the premise that the connecting lines are not intersected with the polygons, the visual graph method has the advantage of simple principle, and has the defect that once the number of the polygons representing the obstacles in the graph is increased, the connecting lines are increased, so that path planning is more complicated, the efficiency is higher in a static environment, but the obstacles are moved, the positions of the vertices of the polygons are changed, the connecting lines are further changed, and the efficiency of the path planning is greatly reduced.
The grid method of the invention is an environment modeling method which is most widely applied at present, the application of the method is more mature than other methods, the working principle of the method is that a working environment is divided into grids with the same size, then information of space occupied by the grids is recorded in each grid, finally, a path leading to a grid of an end point is searched from the grid of a starting point, connecting lines among the grids cannot have intersection points with the grid of an obstacle, the grid method has the advantages of facilitating the design of path planning, the speed and precision of path searching are related to the size of the selected grids, the smaller the grid is, the more precise the path searching is, but the number of the grids is increased, when the path is searched, the speed is reduced, on the contrary, the larger the grid is, the searching speed is improved, but the searched path is not the optimal path, the defect is suitable for searching the space in a large range, when using the grid method, it is particularly important to determine the appropriate grid size.
The principle of the free space method is simple, so the method is widely applied, and the defects are that once the number of obstacles is increased, the calculation amount is increased steeply, the difficulty is increased, and the efficiency is reduced.
Example 3
As shown in fig. 4-5, in the present invention, a BCD local area coverage algorithm is used to perform path planning on each sub-area, complete the full coverage in the sub-area, and then connect the sub-areas to complete the full coverage path planning of the entire farmland, the principle of BCD local area coverage is very simple and is convenient to be implemented in the farmland, the principle of BCD local area coverage is very similar to that of cattle ploughing in the farmland, the South and North directions are preferred to go straight, when an obstacle is encountered, the driving direction is changed, the set distance is driven along the West and East directions, then the obstacle is driven in the preferred direction, and the cycle is repeated until the vehicle is driven to the dead point position, and then the full coverage of the local area is completed.
The present invention has been described in general terms in the foregoing, but it will be apparent to those skilled in the art that modifications and improvements can be made thereto based on the present invention. Therefore, modifications or improvements are within the scope of the invention without departing from the spirit of the inventive concept.
Claims (7)
1. The utility model provides an unmanned mobile platform based on full coverage algorithm, includes farmland area, vision system, on-vehicle computer, controller, autopilot system and seeder, its characterized in that: the unmanned mobile platform based on the full coverage algorithm comprises the following steps:
the method comprises the following steps: moving, namely sending an instruction to an automatic driving system through a visual system according to the vehicle-mounted computer to control the moving direction of the automatic driving system so as to realize automatic driving;
step two: seeding, namely processing a signal according to the running speed of a vehicle in the automatic driving process and transmitting the processed signal to a seeding controller to realize automatic seeding;
step three: and the motion trail is obtained according to the vehicle-mounted sensor in the moving process of the unmanned mobile platform, the vehicle-mounted computer corrects the real-time motion trail information, and transmits an instruction to the controller to control the movement of the unmanned mobile platform.
2. The unmanned mobile platform based on full coverage algorithm of claim 1, wherein: the unmanned mobile platform based on the full-coverage algorithm comprises an unmanned mobile platform seed discharging speed analysis, the seeding frequency of the seeder and the vehicle running speed are in a direct proportion relation, and the seeding frequency of the seeder is obtained through calculation by measuring the period of a pulse signal output by a vehicle speed sensor under the condition of low speed.
3. A path planning method for an unmanned mobile platform based on a full coverage algorithm is characterized by comprising the following steps: the unmanned mobile platform path planning method based on the full-coverage algorithm comprises path planning of the full-coverage algorithm and design of the local area full-coverage algorithm.
4. The unmanned mobile platform path planning method based on full coverage algorithm as claimed in claim 3, wherein: the path planning of the full-coverage algorithm comprises modeling of a farmland environment and design of the full-coverage path planning algorithm of the mobile platform, and the full-coverage path planning of the mobile platform is completed.
5. The unmanned mobile platform path planning method based on full coverage algorithm as claimed in claim 3, wherein: the farmland environment modeling comprises the following methods:
1) the visual graph method is characterized in that the barrier is represented by a polygon, and the starting point, the end point and each vertex of the polygon are connected by straight lines on the premise that the connecting lines do not intersect with the polygon;
2) the grid method comprises the steps of dividing a working environment into grids with the same size, recording information of space occupied by the grids in each grid, and finally searching a path leading to the grid where an end point is located from the grid where a starting point is located, wherein connecting lines among the grids cannot have intersection points with the grid where an obstacle is located;
3) the free space method uses polygon to represent barrier, and uses the connection line from vertex to other barrier vertex and without intersection point with barrier and the perpendicular line from the vertex to space boundary, and retains effective connection line, and uses the midpoint of every connection line to connect every midpoint, and retains the connection line without intersection point with barrier, so that it can be used as alternative path.
6. The unmanned mobile platform path planning method based on full coverage algorithm as claimed in claim 3, wherein: the local area full-coverage algorithm design comprises a BCD local area full-coverage algorithm, and the BCD local area full-coverage algorithm comprises the following steps:
s1, performing grid division on the farmland, performing region division on the farmland, wherein each sub-region does not contain a barrier, making a straight line perpendicular to an X axis through the maximum coordinate point of each barrier in the X axis direction, and the straight line is intersected with the boundary of the farmland or the barrier to finish the division of the region into a plurality of sub-regions;
and S2, planning the raster path, planning the path of each sub-area by using a BCD local area coverage algorithm to complete the full coverage in the sub-areas, and connecting the sub-areas to complete the full coverage path planning of the whole farmland.
7. The unmanned mobile platform path planning method based on full coverage algorithm as claimed in claim 3, wherein: the BCD local area full coverage algorithm flow comprises the following steps:
a. loading a farmland environment model by the mobile platform, and setting parameters of the current position P0 of the mobile platform and the side length L of the grid;
b. the mobile platform acquires the information of the grid area of the accessory at the position and updates the information of the covered farmland in time;
c. if the farmland grid is uncovered or the obstacle grid, the grid is an infeasible area, and the direction in which the mobile platform can run is determined according to the position of the mobile platform and the information of the accessory grid;
d. according to the priority criterion of the BCD algorithm, the vehicle returns to the b when driving a grid distance L in the direction with the highest priority;
e. until an area is completely covered or not completely covered but reaches a dead center position of the area. If the coverage of the area is finished, whether the coverage of the whole farmland is finished or not is judged, and if the coverage of the whole farmland is finished, the algorithm is ended. If the complete coverage of the local area is not completed but the dead point position of the local area is reached, the point of the next area closest to the dead point is selected as the area entrance, and the next area is entered. Finishing primary coverage of all local areas;
f. and calculating the cost, calculating a grid of a southwest corner closest to the uncovered part of each area from the mobile platform when the primary coverage is finished, wherein the grid is an entrance of secondary coverage, and finishing the coverage of the rest part according to a priority rule of BCD operation.
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