CN114440916B - Navigation method, device, equipment and storage medium - Google Patents
Navigation method, device, equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
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Abstract
The invention discloses a navigation method, a navigation device, navigation equipment and a storage medium. The method comprises the following steps: acquiring a grid map model of a target school, and determining a static planning path based on a starting point grid coordinate and a destination grid coordinate of a target object in the grid map model; in the process that the target object advances based on the static planning path, determining whether an unvented road section exists in the static planning path based on the current grid coordinates of the target object and the acquired real-time road condition data; if the static planning path exists, updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data; and returning to execute the step of determining whether the non-passable road section exists in the static planning path based on the current grid coordinate of the target object and the acquired real-time road condition data until the current grid coordinate of the target object is the destination grid coordinate. The embodiment of the invention improves the navigation accuracy.
Description
Technical Field
The present invention relates to the field of intelligent navigation technologies, and in particular, to a navigation method, apparatus, device, and storage medium.
Background
The path planning is one of main research contents of motion planning, a sequence point or curve connecting a starting point and an end point is called a path, a strategy for forming the path is called path planning, and the application field of the path planning is wide, such as unmanned ships, unmanned vehicles and the like of high-tech technology, and the path planning is shown as GPS navigation, road planning and the like in daily life.
Especially in campus life scenes, how to avoid the study room in class, examination or other purposes among a plurality of study rooms, navigation searches for the nearest available study room to become the 'just-needed' of students. The existing campus navigation method is mainly based on global route planning which is static and can be performed offline in the whole school map environment, but the global route planning method ignores the variability factors of the occupation condition of movable objects (such as bicycles and vehicles) or roads in the campus environment, so that the navigation accuracy is not high.
Disclosure of Invention
The invention provides a navigation method, a navigation device, navigation equipment and a storage medium, which are used for improving the navigation accuracy and further ensuring the safety in the travelling process.
According to an aspect of the present invention, there is provided a navigation method, the method comprising:
Acquiring a grid map model of a target school, and determining a static planning path based on a starting point grid coordinate and a destination grid coordinate of a target object in the grid map model;
in the process that the target object advances based on the static planning path, determining whether an unvented road section exists in the static planning path based on the current grid coordinates of the target object and the acquired real-time road condition data;
if so, updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data;
and returning to execute the step of determining whether an unvented road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data until the target object reaches the destination grid coordinates.
According to another aspect of the present invention, there is provided a navigation device including:
the static planning path determining module is used for acquiring a grid map model of the target school and determining a static planning path based on a starting point grid coordinate and a destination grid coordinate of the target object in the grid map model;
The non-passable road section determining module is used for determining whether a non-passable road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data in the process that the target object advances based on the static planning path;
the static planning path updating module is used for updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data if the static planning path exists;
and the navigation ending module is used for returning and executing the step of determining whether the non-passable road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data until the target object reaches the grid coordinates of the destination.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the navigation method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a navigation method according to any one of the embodiments of the present invention.
According to the technical scheme, whether an unvented road section exists in the static planning path or not is determined based on the current grid coordinates of the target object and the acquired real-time road condition data in the process that the target object advances based on the static planning path, if so, the static planning path is updated based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data until the target object reaches the destination grid coordinates, so that the problem of poor navigation accuracy of the static planning path is solved, the safety of the target object in the advancing process is improved, and the time for the target object to reach the destination is effectively shortened.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a navigation method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a real-time resultant force provided in accordance with a first embodiment of the present invention;
FIG. 3 is a flow chart of a navigation method according to a second embodiment of the present invention;
FIG. 4 is a flowchart of a method for constructing a grid map model according to a second embodiment of the present invention;
FIG. 5 is a schematic diagram of a jump point search algorithm according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of a navigation device according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a navigation method according to an embodiment of the present invention, where the method may be performed by a navigation device, and the navigation device may be implemented in hardware and/or software, and the navigation device may be configured in the navigation device. As shown in fig. 1, the method includes:
s110, acquiring a grid map model of the target school, and determining a static planning path based on a starting point grid coordinate and a destination grid coordinate of the target object in the grid map model.
The grid map model abstracts and represents the actual scene environment into two-dimensional terrain, so that the purpose of simplifying the motion space of the target object is achieved. The grid map model disperses the school environment into grids with the same size according to a specific resolution, and each grid corresponds to one state, namely an idle state and an occupied state, and is used for indicating whether the grid position is an obstacle or not.
The target object may be a person or a movable robot, among others, by way of example. For example, when the target object is a person, in response to the user starting the navigation software, the obtained current grid coordinates of the user in the grid map model are used as the starting point grid coordinates of the target object. The destination grid coordinates may be directly input by the user in the navigation software, or may be obtained by searching based on the destination type input by the user.
The static planning path is the shortest path from the starting point grid coordinates to the destination grid coordinates formed by a plurality of grids in idle states, which are planned by taking the grids in idle states as the principle of searching grids in idle states and avoiding grids in occupied states.
In one embodiment, the algorithm for determining the static planned path may alternatively be an a-algorithm. The algorithm A is a heuristic search algorithm, wherein the search principle of the algorithm A is that grids corresponding to the grid coordinates of a starting point are used as current node grids, the child node grids around the current node grids are searched from the current node grids, one child node grid with the lowest evaluation function is selected from the child node grids each time to serve as the current node grids, and the steps are repeatedly executed until the current node grid is a destination grid corresponding to the grid coordinates of a destination. The path formed by at least two current node grids is a static planning path.
S120, acquiring current grid coordinates and real-time road condition data of the target object in the process that the target object travels based on the static planning path.
The method comprises the steps of obtaining current grid coordinates and real-time road condition data of a target object once every time the target object travels one grid. The real-time road condition data may be collected based on a preset sampling range, where the preset sampling range is a sampling range formed by taking a current grid coordinate as a center of a circle and based on a preset radius. The preset radius may be 5m or 10m, for example. The preset radius is not limited herein, and may be set in a customized manner based on actual requirements.
The real-time road condition data specifically comprise a real-time obstacle type and a real-time obstacle grid coordinate corresponding to the real-time obstacle type. Among them, the real-time obstacle types include, but are not limited to, dynamic obstacles, which may be, for example, bicycles, automobiles, or garbage cans, and closed road sections.
S130, judging whether the current grid coordinate is a destination grid coordinate, if so, executing S160, and if not, executing S140.
Specifically, if the current grid coordinate is the destination grid coordinate, indicating that the target object has reached the destination grid coordinate, and ending navigation; if the current grid coordinate is not the destination grid coordinate, the target object does not reach the destination grid coordinate.
S140, judging whether an unviewable road section exists in the static planning path, if so, executing S150, and if not, executing S120.
Specifically, whether a road section with overlapped coordinates exists between the real-time obstacle coordinates in the real-time road condition data in the residual planned path taking the current grid coordinates as a starting point in the static planned path is judged, if so, the road section with overlapped coordinates is taken as an unvented road section, and if not, the unvented road section does not exist in the static path planned path.
And S150, updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data, and executing S120.
In one embodiment, optionally, updating the static planned path based on the current grid coordinate, the obstacle grid coordinate data in the real-time road condition data, and the destination grid coordinate data includes: determining real-time gravitation data corresponding to the current grid coordinates based on the current grid coordinates and the destination grid coordinates; determining real-time repulsive force data corresponding to the current grid coordinates based on the current grid coordinates, static obstacle grid coordinates in the grid map model and real-time obstacle grid coordinates in the real-time road condition data; and determining an updated navigation path corresponding to the current grid coordinates in the static planning path based on the real-time gravitation data and the real-time repulsive data.
In one embodiment, optionally, the real-time gravitational data satisfies the formula:
wherein F is att (x) Representing real-time gravitational data, U att (x) Represents gravitational field function, lambda represents gravitational coefficient, x represents current grid coordinate, x g Representing destination grid coordinates.
Optionally, the real-time repulsive data satisfies the formula:
wherein F is ref (x) Representing real-time repulsive force data, U ref (x) Represents the repulsive force field function, μ represents the repulsive force coefficient, x represents the current grid coordinate, ρ represents the distance between the current grid coordinate and the obstacle grid coordinate, ρ 0 Indicating the repulsive force influence range of the obstacle.
Wherein, in particular,representing a negative gradient, the obstacle grid coordinates include real-time obstacle grid coordinates and static obstacle grid coordinates.
In one embodiment, optionally, determining an updated navigation path corresponding to the current grid coordinates in the static planning path based on the real-time gravitational data and the real-time repulsive data includes: real-time resultant force data is determined based on the real-time gravitational data and the real-time repulsive data, and an updated navigation path corresponding to the current grid coordinates in the static planning path is determined based on the real-time resultant force data.
Fig. 2 is a schematic diagram of a real-time resultant force provided according to a first embodiment of the present invention. Specifically, filled circles represent obstacle 1 and obstacle 2, respectively, open circles represent target objects, and filled squares represent destinations. Wherein the obstacle 1 points to the arrow F of the target object ref1 An arrow F indicating a real-time repulsive force 1 between the obstacle 1 and the target object, the obstacle 2 pointing toward the target object ref2 Representing a real-time repulsive force 2,F between the obstacle 2 and the target object ref Represents F ref1 And F ref2 Is a real-time repulsive force. Arrow F of target object pointing to destination att Representing real-time gravitational force, F sum Represents F att And F ref Real-time resultant force of (a).
Specifically, if the distance between the current grid coordinate and the destination grid coordinate is large, the real-time gravitational data is large, and conversely, if the distance between the current grid coordinate and the destination grid coordinate is small, the real-time gravitational data is small. If the distance between the target object and the obstacle is out of the repulsive force influence range, the real-time repulsive force data is 0, and if the distance between the target object and the obstacle is in the repulsive force influence range, the closer the target object is to the obstacle, the larger the real-time repulsive force data corresponding to the target object is.
However, there are two problems in the resultant force field constructed based on the above formula of the real-time attractive force data and the formula of the real-time repulsive force data, one is that the real-time attractive force data becomes smaller as the target object approaches the destination grid coordinate, and the real-time repulsive force data becomes larger as the target object approaches the obstacle grid coordinate, so that the situation that the real-time attractive force data and the real-time repulsive force data are offset may occur, that is, the real-time resultant force data is 0, so that the static planning path cannot be updated, and the target object collides with the obstacle. Another problem is the problem of local oscillation, that is, if there are many obstacles around the destination grid coordinates, the real-time repulsive force data generated by the obstacles is far greater than the real-time attractive force data, which causes the target object to move away from the destination grid coordinates, and the target object moving away from the target destination grid coordinates causes the real-time attractive force data to increase immediately, so that the target object moves closer to the destination grid coordinates, and thus, there is a case where the target object continuously and greatly adjusts the traveling direction to form local oscillation.
In another embodiment, optionally, the real-time gravitational data satisfies the formula:
wherein F is att (x) Representing real-time gravitational data, U att (x) Represents gravitational field function, lambda represents gravitational coefficient, x represents current grid coordinate, x g Representing destination grid coordinates.
In another embodiment, optionally, the real-time repulsive data satisfies the formula:
wherein F is ref (x) Representing real-time repulsive force data, U ref (x) Represents the repulsive force field function, μ represents the repulsive force coefficient, x represents the current grid coordinate, ρ represents the distance between the current grid coordinate and the obstacle grid coordinate, ρ 0 Indicating the repulsive force influence range of the obstacle.
In the embodiment of the invention, the problem that the real-time gravitation data are offset with the real-time repulsive force data is solved by setting the different orders of the gravitational field function and the repulsive force field function. The problem of local oscillation can be solved by adjusting the coefficient and the computing architecture of the repulsive force field function, and the stability of the updating condition of the static planning path is improved.
S160, ending the navigation.
According to the technical scheme, whether an unvented road section exists in the static planning path or not is determined based on the current grid coordinates of the target object and the acquired real-time road condition data in the process that the target object advances based on the static planning path, if so, the static planning path is updated based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data until the target object reaches the destination grid coordinates, so that the problem of poor navigation accuracy of the static planning path is solved, the safety of the target object in the advancing process is improved, and the time for the target object to reach the destination is effectively shortened.
Example two
Fig. 3 is a flowchart of a navigation method according to a second embodiment of the present invention, where the "obtaining a grid map model of a target school" in the above embodiment is further refined. As shown in fig. 3, the method includes:
s210, acquiring a static plane map of the target school, and constructing a map grid network based on a preset grid size and the map size of the static plane map.
For example, if the target object is a human, the preset grid size may be 1×1, and if the target object is a motor vehicle, the preset grid size may be 5×5. The preset grid size may be adjusted according to the type of the target object.
Specifically, the map size of the static planar map is determined based on geographic coordinates corresponding to the upper left corner, the lower left corner, the upper right corner and the lower right corner of the static planar map.
And S220, setting grid information of a network grid corresponding to the obstacle geographic coordinates in the static plane map in the map grid network as a first numerical value.
Wherein different types of obstacles in the static planar map are typically stored in different layers, illustratively traversing all layers in the static planar map, obtaining obstacle geographic coordinates of a face obstacle in a first layer, obstacle geographic coordinates of a line obstacle in a second layer, and obstacle geographic coordinates of a point obstacle in a third layer.
Wherein, the first value may be 1, for example.
And S230, setting grid information of the network grids in the map grid network except for the network grids corresponding to the obstacle geographic coordinates in the static plane map as a second value, and obtaining an initial grid map array.
Wherein, the second value may be 0, as an example.
S240, constructing a grid map model of the target school based on the initial grid map array.
In one embodiment, optionally, building a grid map model of the target school based on the initial grid map array includes: at least one closed area array formed by network grids with grid information of a first value in the initial grid map array is obtained, seed points corresponding to the closed area array are determined for each closed area array, and filling operation is carried out on the closed area array by adopting a flooding algorithm, so that a grid map model is obtained.
The seed point may specifically be any network grid in a closed area formed by the closed area array, and grid information of the network grid is a first value.
The flooding algorithm is based on a four-neighborhood algorithm or an eight-neighborhood algorithm, and grid information of grid grids in the four-neighborhood or the eight-neighborhood with the seed point as a center is set to be a first value. Wherein, the four neighborhood comprises upper, left, right and lower, and the eight neighborhood comprises upper, lower, left, right, upper left, lower left, upper right and lower right.
In one embodiment, optionally, building a grid map model of the target school based on the initial grid map array includes: acquiring grid information corresponding to at least one initial network grid in the initial grid map array; wherein the starting network grid comprises a first network grid of each column, a last network grid of each column, a first network grid of each row, and a last network grid of each row in the initial grid map array; for each initial grid, if the grid information is a second value, using the initial grid as a seed point, and adopting a flooding algorithm to perform filling operation on the initial grid map array to obtain a filling grid map array; and executing union operation on at least one filling grid map array to obtain a grid map model of the target school.
Specifically, grid information of a first network grid of each column in the initial grid map array is longitudinally read, if the grid information is a second value, the information belongs to a peripheral communication area, the initial network grid is used as a seed point, and a flooding algorithm is adopted to execute filling operation on the initial grid map array, so that a filled grid map array is obtained; longitudinally reading grid information of the last network grid of each column in the initial grid map array, if the grid information is a second value, indicating that the network grid belongs to a peripheral communication area, taking the initial network grid as a seed point, and executing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array; transversely reading grid information of a first network grid of each row in the initial grid map array, if the grid information is a second value, indicating that the network grid belongs to a peripheral communication area, taking the initial network grid as a seed point, and executing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array; and transversely reading grid information of the last network grid of each row in the initial grid map array, if the grid information is a second value, indicating that the network grid belongs to the peripheral connected region, taking the initial network grid as a seed point, and executing filling operation on the initial grid map array by adopting a flooding algorithm to obtain the filling grid map array.
In this example, assuming that the initial grid map array is 10×10 and the grid information of each initial network grid is the second value, 100 padding operations are performed on the initial grid map array, and the number of the obtained padded grid map arrays is 100.
The method has the advantages that the initial grid map array constructed based on the static plane map of the target school is time-consuming and poor in search result, and the problems of omission or search result errors easily occur because a large number of complex closed areas exist in the campus scene of the target school. According to the embodiment, the seed points are arranged in the peripheral communication area, so that the step of searching the closed area is avoided, the problem that the grid map model obtained through rasterization is a lattice obstacle model is solved, and the accuracy of the grid map model is guaranteed.
Fig. 4 is a flowchart of a method for constructing a grid map model according to a second embodiment of the present invention. Specifically, the TXT file recorded with the initial raster map array map is read, and the initial raster map array map is copied to obtain a new two-dimensional array mapTmp. Judging whether column reading of the mapTmp is finished or not according to the mapTmp, if not, executing reading of a first network grid p of the column, and if grid information of p is a second value, in the embodiment, taking the network grid p as a seed point and executing a flooding algorithm to obtain a filling map grid array, wherein the second value is 0; and executing the last network grid p of the read column, and if the grid information of the p is 0, taking the network grid p as a seed point, and executing a flooding algorithm to obtain the filling map grid array. If the column reading of the mapTmp is finished, judging whether the line reading of the mapTmp is finished, if not, executing the first network grid p of the read line, and if the grid information of the p is 0, executing a flooding algorithm by taking the network grid p as a seed point to obtain a filling map grid array; and executing the last network grid p of the read row, and if the grid information of the p is 0, taking the network grid p as a seed point, and executing a flooding algorithm to obtain the filling map grid array. And traversing n network grids of the map if the line reading of the map is finished, setting the grid information of the network grid of the initial grid map array map corresponding to the i network grid to be 1 if the i network grid is not a filling value, and setting the grid information of the network grid of the initial grid map array map corresponding to the i network grid to be 0 if the i network grid is a filling value.
S250, determining a static planning path based on the initial point grid coordinates and the destination grid coordinates of the target object in the grid map model.
In one embodiment, optionally, determining the static planned path based on the starting point grid coordinates and the destination grid coordinates of the target object in the grid map model includes: and determining a static planning path based on the starting point grid coordinates and the destination grid coordinates of the target object in the grid map model by adopting a jump point searching algorithm.
Compared with the A-type algorithm, the jump point searching algorithm essentially reduces the number of midway searching nodes by searching jump points, and improves the searching speed.
The JPS algorithm is defined as: the natural neighbors are neighbors that take into account the direction and cost of the parent node of the current point to the current point, with minimal cost in the direction without obstructions. There must be an adjacent point as an obstacle, and the parent node of the current point spends less time reaching the neighbor through the current point than it does not.
Fig. 5 is a schematic diagram of a jump point search algorithm according to a second embodiment of the present invention. Wherein P is a starting point, and X is a current point searched. In the a-graph of fig. 5, in the absence of an obstacle, X is a point in the positive direction of P points, the cost of P to reach these nodes through X is higher than the cost of P without X, the evaluation cost shows that the gray stripe marked node is a nonsensical node, and the natural neighbor of X has only one point occupied by the five-pointed star mark in the positive direction. Similarly, when X is a point on the diagonal of point P, as shown in fig. 5 b, it is known that the natural neighbors of X are three points occupied by five stars in the figure. In figure 5, c, the black square is an obstacle and the natural neighbor of P passing X is a point occupied by a five-pointed star. Forced neighbors are points occupied by the circle-labeled c-graph that can only be reached by P through X, there are no other nodes that reach and cost less than X, so X in c-graph has one five-star-labeled natural neighbor and one circle-labeled forced neighbor. Similarly, there is no path through other nodes to the circle marker point and less than X in the d-graph of fig. 5, so there are three five-star-marked natural neighbors and one circle-marked forced neighbor in the d-graph.
The definition of the jump points comprises: 1) The current point is a starting point or a target point, and the current point is a jump point; 2) The current point has forced neighbors, and the current point is a jump point; 3) The parent node of the current point is on the diagonal of the current point and the current point is the hop point when the hop point is reached by the forward movement.
The neighboring point of the current point is N, the father node of the current point is P, and the JPS algorithm further provides that: 1) In the jump point searching process, the situation that both the positive direction and the diagonal direction can be searched occurs, and the positive direction is searched first, and then the diagonal direction is searched; 2) In the searching process, if N can be reached by P through other paths and the path cost is smaller than that of a path from P to N through X, the next step of reaching X does not reach N; 3) Only hops can join the Open _ list because they can change the path planning direction.
The path planning flow of the JPS algorithm is as follows:
1) Adding the starting point S into an Open set Openjlist;
2) Sorting the Open_list according to the cost value, taking out the minimum point P, judging whether the P is a target point, if so, ending the path searching, otherwise, entering the step 3;
3) Deleting P in the Openjlist and adding P to the close_list;
4) Judging the direction of P, wherein the judging direction is divided into a positive direction and a diagonal direction, and various conditions can be extended for analysis to obtain a jump point J;
5) Judging whether J is in the Openjlist, if so, modifying the parent node and the cost value of J in the Openjlist, if not, adding the J point into the Openjlist, and looping into the second step until the path finding is finished.
For many of the cases in step 4) above, include:
1. if the direction is positive and the left rear part of the P cannot walk to the left, the P point searches for a jump point J which is not in the close_list according to the left front part and the left side;
2. if the current direction is positive and the current positive direction can be continuously searched, P searches a jump point J which is not in the close_list according to the current direction;
3. if the direction is positive and the right rear part of the P cannot walk to the right, the P point searches a jump point J which is not in the close_list according to the right front part and the right side;
4. if the direction is diagonal and the horizontal component direction of P can be walked, the P point searches for a jump point J which is not in the close_list according to the horizontal component direction;
5. if the direction is diagonal and the current positive direction can continue searching, P searches a jump point J which is not in the close_list according to the current direction;
6. if it is diagonal and the vertical component direction of P can go, then P points look for hops J not in close_list according to the vertical component direction.
Wherein, the left front, left rear, left or right is relative to standing in the forward direction to reach P, assuming that the forward direction is south, the left and right are east and west, respectively, the left front is southeast, and the left rear is northeast. When the positive directions are different, the directions corresponding to the front left, the rear left, the left or the right are also different.
S260, acquiring current grid coordinates and real-time road condition data of the target object in the process that the target object travels based on the static planning path.
S270, judging whether the current grid coordinate is the destination grid coordinate, if so, executing S291, and if not, executing S280.
S280, judging whether an unviewable road section exists in the static planning path, if so, executing S290, and if not, executing S260.
And S290, updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data, and executing S260.
S291, ending the navigation.
According to the technical scheme, a static plane map of a target school is obtained, and a map grid network is constructed based on a preset grid size and the map size of the static plane map; setting grid information of a network grid corresponding to obstacle geographic coordinates in a static plane map in a map grid network as a first numerical value; setting grid information of network grids in the map grid network except for the geographic coordinates of the obstacle in the static plane map as a second value to obtain an initial grid map array; the grid map model of the target school is built based on the initial grid map array, the problem of building the grid map model is solved, and the filling operation is carried out on the initial grid map array by taking boundary points of the initial grid map array as seed points through a generalization algorithm, so that the problem of complicated closed region acquisition process is solved, and the accuracy of the grid map model is improved.
Example III
Fig. 6 is a schematic structural diagram of a navigation device according to a third embodiment of the present invention. As shown in fig. 6, the apparatus includes: a static planned path determination module 310, an unvented road segment determination module 320, a static planned path update module 330, and a navigation end module 340.
The static planning path determining module 310 is configured to obtain a grid map model of the target school, and determine a static planning path based on a starting point grid coordinate and a destination grid coordinate of the target object in the grid map model;
the non-passable road section determining module 320 is configured to determine, during the process of the target object traveling based on the static planned path, whether a non-passable road section exists in the static planned path based on the current grid coordinates of the target object and the acquired real-time road condition data;
a static planned path updating module 330, configured to update the static planned path, if any, based on the current grid coordinate, the real-time obstacle grid coordinate in the real-time road condition data, and the destination grid coordinate data;
the navigation ending module 340 is configured to return to executing the step of determining whether the non-passable road section exists in the static planning path based on the current grid coordinates of the target object and the obtained real-time road condition data until the target object reaches the destination grid coordinates.
According to the technical scheme, whether an unvented road section exists in the static planning path or not is determined based on the current grid coordinates of the target object and the acquired real-time road condition data in the process that the target object advances based on the static planning path, if so, the static planning path is updated based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data until the target object reaches the destination grid coordinates, so that the problem of poor navigation accuracy of the static planning path is solved, the safety of the target object in the advancing process is improved, and the time for the target object to reach the destination is effectively shortened.
Based on the above embodiment, the static planning path updating module 330 is optionally specifically configured to:
determining real-time gravitation data corresponding to the current grid coordinates based on the current grid coordinates and the destination grid coordinates;
determining real-time repulsive force data corresponding to the current grid coordinates based on the current grid coordinates, static obstacle grid coordinates in the grid map model and real-time obstacle grid coordinates in the real-time road condition data;
and determining an updated navigation path corresponding to the current grid coordinates in the static planning path based on the real-time gravitation data and the real-time repulsive data.
On the basis of the above embodiment, optionally, the real-time gravitational data satisfies the formula:
wherein F is att (x) Representing real-time gravitational data, U att (x) Represents gravitational field function, lambda represents gravitational coefficient, x represents current grid coordinate, x g Representing destination grid coordinates.
On the basis of the above embodiment, optionally, the real-time repulsive force data satisfies the formula:
wherein F is ref (x) Representing real-time repulsive force data, U ref (x) Represents the repulsive force field function, μ represents the repulsive force coefficient, x represents the current grid coordinate, ρ represents the distance between the current grid coordinate and the obstacle grid coordinate, ρ 0 Indicating the repulsive force influence range of the obstacle.
Based on the above embodiment, optionally, the static planned path determining module 310 includes:
and the static planning path determining unit is used for determining a static planning path based on the starting point grid coordinates and the destination grid coordinates of the target object in the grid map model by adopting a jump point searching algorithm.
Based on the above embodiment, optionally, the static planned path determining module 310 includes:
the map grid network construction unit is used for acquiring a static plane map of the target school and constructing a map grid network based on a preset grid size and the map size of the static plane map;
A first raster information setting unit configured to set raster information of a network raster corresponding to an obstacle geographical coordinate in a static planar map in a map raster network to a first numerical value;
a second grid information setting unit, configured to set grid information of a network grid in the map grid network, except for a grid corresponding to the obstacle geographic coordinates in the static planar map, to a second value, so as to obtain an initial grid map array;
and the grid map model building unit is used for building a grid map model of the target school based on the initial grid map array.
On the basis of the above embodiment, optionally, the grid map model building unit is specifically configured to:
acquiring grid information corresponding to at least one initial network grid in the initial grid map array; wherein the starting network grid comprises a first network grid of each column, a last network grid of each column, a first network grid of each row, and a last network grid of each row in the initial grid map array;
for each initial grid, if the grid information is a second value, using the initial grid as a seed point, and adopting a flooding algorithm to perform filling operation on the initial grid map array to obtain a filling grid map array;
And executing union operation on at least one filling grid map array to obtain a grid map model of the target school.
The navigation device provided by the embodiment of the invention can execute the navigation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 7 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as navigation methods.
In some embodiments, the navigation method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more steps of the navigation method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the navigation method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program used to implement the navigation method of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Example five
The fifth embodiment of the present invention also provides a computer readable storage medium storing computer instructions for causing a processor to execute a navigation method, the method comprising:
acquiring a grid map model of a target school, and determining a static planning path based on a starting point grid coordinate and a destination grid coordinate of a target object in the grid map model;
in the process that the target object advances based on the static planning path, determining whether an unvented road section exists in the static planning path based on the current grid coordinates of the target object and the acquired real-time road condition data;
if the static planning path exists, updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data;
and returning to execute the step of determining whether the non-passable road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data until the target object reaches the destination grid coordinates.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. The navigation method is characterized by being applied to path navigation in a campus scene and comprising the following steps:
acquiring a static plane map of a target school, and constructing a map grid network based on a preset grid size and the map size of the static plane map; the preset grid size is adjusted according to the type of the target object;
setting grid information of a network grid corresponding to obstacle geographic coordinates in the static planar map in the map grid network as a first numerical value; wherein, different types of barriers in the static plane map are stored in different layers;
setting grid information of network grids in the map grid network except for the network grid corresponding to the obstacle geographic coordinates in the static plane map as a second value to obtain an initial grid map array;
acquiring grid information corresponding to at least one initial network grid in the initial grid map array; wherein the initial network grid comprises a first network grid of each column, a last network grid of each column, a first network grid of each row, and a last network grid of each row in the initial grid map array;
For each initial grid, if the grid information is a second value, using the initial grid as a seed point, and adopting a flooding algorithm to execute filling operation on the initial grid map array to obtain a filling grid map array;
executing union operation on at least one filling grid map array to obtain a grid map model of a target school;
acquiring a grid map model of the target school, and determining a static planning path based on a starting point grid coordinate and a destination grid coordinate of the target object in the grid map model; the method comprises the steps that a school environment is scattered into grids with the same size according to a preset resolution ratio by the grid map model, each grid corresponds to a state, and the states comprise an idle state and an occupied state;
in the process that the target object travels based on the static planning path, determining whether an unvented road section exists in the static planning path based on the current grid coordinates of the target object and the acquired real-time road condition data comprises the following steps: judging whether a road section with overlapped coordinates exists in the real-time obstacle coordinates in the real-time road condition data in the rest planned paths taking the current grid coordinates as a starting point or not, if so, taking the road section with overlapped coordinates as an unviewable road section, and if not, and otherwise, not exist in the static planned paths; the real-time road condition data comprise a real-time obstacle type and a real-time obstacle grid coordinate corresponding to the real-time obstacle type; the real-time obstacle types include dynamic obstacles and closed road segments;
If so, updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data;
and returning to execute the step of determining whether an unvented road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data until the target object reaches the destination grid coordinates.
2. The method of claim 1, wherein updating the static planned path based on the current grid coordinates, obstacle grid coordinate data in the real-time road condition data, and the destination grid coordinate data comprises:
determining real-time gravitation data corresponding to the current grid coordinate based on the current grid coordinate and the destination grid coordinate;
determining real-time repulsive force data corresponding to the current grid coordinates based on the current grid coordinates, static obstacle grid coordinates in the grid map model and real-time obstacle grid coordinates in the real-time road condition data;
and determining an updated navigation path corresponding to the current grid coordinate in the static planning path based on the real-time gravitation data and the real-time repulsive data.
3. The method of claim 2, wherein the real-time gravitational data satisfies the formula:
wherein F is att (x) Representing real-time gravitational data, U att (x) Represents gravitational field function, lambda represents gravitational coefficient, x represents current grid coordinate, x g Representing destination grid coordinates.
4. The method of claim 2, wherein the real-time repulsive data satisfies the formula:
wherein F is ref (x) Representing real-time repulsive force data, U ref (x) Represents the repulsive force field function, μ represents the repulsive force coefficient, x represents the current grid coordinate, ρ represents the distance between the current grid coordinate and the obstacle grid coordinate, ρ 0 Indicating the repulsive force influence range of the obstacle.
5. The method of claim 1, wherein the determining a static planned path based on starting point grid coordinates and destination grid coordinates of the target object in the grid map model comprises:
and determining a static planning path by adopting a jump point searching algorithm based on the starting point grid coordinates and the destination grid coordinates of the target object in the grid map model.
6. A navigation device, which is applied to path navigation in campus scenes, comprising:
A static planned path determination module, comprising: the system comprises a map grid network construction unit, a first grid information setting unit, a second grid information setting unit and a grid map model construction unit;
the map grid network construction unit is used for acquiring a static plane map of a target school and constructing a map grid network based on a preset grid size and the map size of the static plane map; the preset grid size is adjusted according to the type of the target object;
the first grid information setting unit is used for setting grid information of a network grid corresponding to the geographic coordinates of the obstacle in the static plane map in the map grid network to be a first numerical value; wherein, different types of barriers in the static plane map are stored in different layers;
the second grid information setting unit is used for setting grid information of the network grids in the map grid network except for the grid information corresponding to the obstacle geographic coordinates in the static plane map as a second value to obtain an initial grid map array;
the grid map model building unit is used for:
acquiring grid information corresponding to at least one initial network grid in the initial grid map array; wherein the initial network grid comprises a first network grid of each column, a last network grid of each column, a first network grid of each row, and a last network grid of each row in the initial grid map array;
For each initial grid, if the grid information is a second value, using the initial grid as a seed point, and adopting a flooding algorithm to execute filling operation on the initial grid map array to obtain a filling grid map array;
executing union operation on at least one filling grid map array to obtain a grid map model of a target school;
the static planning path determining module is specifically configured to obtain a grid map model of the target school, and determine a static planning path based on a starting point grid coordinate and a destination grid coordinate of the target object in the grid map model; the method comprises the steps that a school environment is scattered into grids with the same size according to a preset resolution ratio by the grid map model, each grid corresponds to a state, and the states comprise an idle state and an occupied state;
the non-passable road section determining module is used for determining whether a non-passable road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data in the process that the target object advances based on the static planning path;
The non-passable road section determining module is specifically configured to determine whether a road section with overlapped coordinates exists in the real-time obstacle coordinates in the real-time road condition data in the remaining planned paths taking the current grid coordinates as a starting point in the static planned path, if so, the road section with overlapped coordinates is used as a non-passable road section, and if not, the non-passable road section does not exist in the static planned path; the real-time road condition data comprise a real-time obstacle type and a real-time obstacle grid coordinate corresponding to the real-time obstacle type; the real-time obstacle types include dynamic obstacles and closed road segments;
the static planning path updating module is used for updating the static planning path based on the current grid coordinates, the real-time obstacle grid coordinates in the real-time road condition data and the destination grid coordinate data if the static planning path exists;
and the navigation ending module is used for returning and executing the step of determining whether the non-passable road section exists in the static planning path or not based on the current grid coordinates of the target object and the acquired real-time road condition data until the target object reaches the grid coordinates of the destination.
7. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the navigation method of any one of claims 1-5.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the navigation method of any one of claims 1-5 when executed.
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