CN114440916A - Navigation method, device, equipment and storage medium - Google Patents
Navigation method, device, equipment and storage medium Download PDFInfo
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- G01C21/34—Route searching; Route guidance
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- 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
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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 travels on the basis of the static planning path, determining whether an impassable road section exists in the static planning path or not on the basis of the current grid coordinate of the target object and the acquired real-time road condition data; if the destination grid coordinate data exists, updating the static planning path 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; and returning to the step of executing the step of determining whether the static planning path has the impassable road section 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, sequence points or curves connecting positions of a starting point and a terminal point are called paths, strategies forming the paths are called path planning, the path planning is wide in application field, and the path planning is represented by GPS navigation or road planning in daily life, such as unmanned boats, unmanned vehicles and the like in high and new technologies.
Especially in campus life scene, how to avoid study rooms in class, examination or other purposes among many study rooms, and navigating to find the nearest available study room becomes the 'just-needed' of students. The conventional campus navigation method is mainly used for carrying out static and offline global path planning based on the overall school map environment, but the global path planning method ignores the variable factors of movable objects (such as bicycles and vehicles) or road occupation conditions in the campus environment, so that the navigation accuracy is low.
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 advancing process.
According to an aspect of the present invention, there is provided a navigation method, the method including:
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 travels on the basis of the static planned path, determining whether an impassable road section exists in the static planned path or not on the basis of the current grid coordinate of the target object and the acquired real-time road condition data;
if the current grid coordinate exists, updating the static planned path 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;
and returning to execute the step of determining whether the static planned path has the impassable road section or not based on the current grid coordinate of the target object and the acquired real-time road condition data until the target object reaches the grid coordinate of the destination.
According to another aspect of the present invention, there is provided a navigation device, the device including:
the static planning path determining module is used for 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;
the non-accessible road section determining module is used for determining whether a non-accessible road section exists in the static planning path or not based on the current grid coordinate of the target object and the acquired real-time road condition data in the process that the target object travels based on the static planning path;
a static planned path updating module, configured to update the static planned path based on the current grid coordinate, a real-time obstacle grid coordinate in the real-time road condition data, and the destination grid coordinate data, if any;
and the navigation ending module is used for returning and executing the step of determining whether the static planned path has the impassable road section or not based on the current grid coordinate of the target object and the acquired real-time road condition data until the target object reaches the grid coordinate 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 content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the navigation method according to any of the embodiments of the 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 implement a navigation method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, whether the impassable road section exists in the static planning path is determined based on the current grid coordinate of the target object and the acquired real-time road condition data in the process that the target object travels based on the static planning path, if yes, the static planning path is updated 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 until the target object reaches the destination grid coordinate, so that the problem of poor navigation accuracy of the static planning path is solved, the safety of the target object in the traveling process is improved, and the time of the target object reaching the destination is effectively shortened.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a navigation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a real-time resultant force provided according to an embodiment of the present invention;
FIG. 3 is a flowchart 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 skip 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 to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and the above-described drawings, 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, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a navigation method according to an embodiment of the present invention, where the navigation method is applicable to a situation of performing route navigation in a campus scenario, and the navigation method may be executed by a navigation device, where the navigation device may be implemented in a form of hardware and/or software, and the navigation device may be configured in the navigation device. As shown in fig. 1, the method includes:
s110, obtaining a grid map model of the target school, and determining a static planning path based on the grid coordinates of the target object at the starting point and the destination in the grid map model.
Specifically, the grid map model abstracts and represents an actual scene environment as a two-dimensional terrain, so that the purpose of simplifying a motion space of a 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 human or a mobile robot, for example. For example, when the target object is a person, the acquired current grid coordinates of the user in the grid map model are used as the starting point grid coordinates of the target object in response to the user starting the navigation software. The destination grid coordinate can be directly input by the user in the navigation software, or can be searched based on the destination type input by the user.
The static planning path is a shortest path from a starting point grid coordinate to a destination grid coordinate, and is formed by a plurality of idle state grids planned by using the principle of searching the idle state grids and avoiding the occupied state grids.
In one embodiment, the algorithm for determining the static planned path may optionally be the a-algorithm. The A-algorithm is a heuristic search algorithm, the search principle of the A-algorithm is that a grid corresponding to the coordinates of a starting point grid is used as a current node grid, sub-node grids around the current node grid are searched from the current node grid, one sub-node grid with the lowest evaluation function is selected from the sub-node grids each time to be used as the current node grid, and the steps are repeatedly executed until the current node grid is a destination grid corresponding to the coordinates of a destination grid. And the path formed by at least two current node grids is a static planning path.
And S120, acquiring the current grid coordinate and the real-time road condition data of the target object in the process that the target object travels based on the static planned path.
In an exemplary embodiment, the current grid coordinates and the real-time traffic data of the target object are obtained once every time the target object travels one grid. Specifically, the real-time traffic data may be traffic data collected based on a preset sampling range, where the preset sampling range is a sampling range formed based on a preset radius and uses the current grid coordinate as a circle center. Illustratively, the preset radius may be 5m or 10 m. The preset radius is not limited, and can be set by user based on actual requirements.
Specifically, the real-time traffic data includes a real-time obstacle type and a real-time obstacle grid coordinate corresponding to the real-time obstacle type. The real-time obstacle type includes, but is not limited to, a dynamic obstacle and a closed road section, and the dynamic obstacle may be a bicycle, a motor vehicle or a garbage can.
S130, judging whether the current grid coordinate is the destination grid coordinate, if so, executing S160, and if not, executing S140.
Specifically, if the current grid coordinate is the destination grid coordinate, it is indicated that the target object has reached the destination grid coordinate, and the navigation is finished; and if the current grid coordinate is not the destination grid coordinate, indicating that the target object does not reach the destination grid coordinate.
And S140, judging whether the static planned path has an unviable road section, if so, executing S150, and if not, executing S120.
Specifically, whether a road section with overlapped coordinates exists in the rest planning paths with the current grid coordinates as the starting point in the static planning path and the real-time obstacle coordinates in the real-time road condition data or not is judged, if yes, the road section with the overlapped coordinates is used as an unviable road section, and if not, the unviable road section does not exist in the static path planning path.
And S150, updating the static planned path 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, and executing S120.
In an embodiment, optionally, the updating the static planned path based on the current grid coordinate, the obstacle grid coordinate data in the real-time traffic data, and the destination grid coordinate data includes: 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 repulsion data corresponding to the current grid coordinate based on the current grid coordinate, the static obstacle grid coordinate in the grid map model and the real-time obstacle grid coordinate 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 repulsion data.
In one embodiment, optionally, the real-time gravity data satisfies the formula:
wherein, Fatt(x) Representing real-time gravity data, Uatt(x) Representing a gravitational field function, lambda representing a gravitational coefficient, x representing a current grid coordinate, xgRepresenting destination grid coordinates.
Optionally, the real-time repulsion data satisfies the formula:
wherein, Fref(x) Representing real-time repulsion data, Uref(x) Denotes a repulsive force field function, mu denotes a repulsive force coefficient, x denotes a current grid coordinate, p denotes a distance between the current grid coordinate and an obstacle grid coordinate, p0Indicating the extent of impact of the repulsion force of the obstacle.
Wherein, in particular,
representing a negative gradient, the obstacle grid coordinates including real-time obstacle grid coordinates and static obstacle grid coordinates.
In one embodiment, optionally, determining an updated navigation path corresponding to the current grid coordinate in the statically planned path based on the real-time gravity data and the real-time repulsion data includes: and determining real-time resultant force data based on the real-time gravitation data and the real-time repulsion data, and determining an updated navigation path corresponding to the current grid coordinate in the static planning path based on the real-time resultant force data.
Fig. 2 is a schematic diagram of a real-time resultant force according to an embodiment of the present invention. Specifically, solid circles represent the obstacle 1 and the obstacle 2, respectively, open circles represent the target object, and solid squares represent the destination. Arrow F in which the obstacle 1 points to the target objectref1Indicates the obstacle 1 and the eyeReal-time repulsion force 1 between objects, arrow F where obstacle 2 points to target objectref2Representing the real-time repulsion force 2, F between the obstacle 2 and the target objectrefIs represented by Fref1And Fref2The real-time resultant force of (1). Arrow F for target object pointing to destinationattRepresenting real-time gravity, FsumIs represented by FattAnd FrefThe real-time resultant force.
Specifically, if the distance between the current grid coordinate and the destination grid coordinate is large, the real-time gravity data is large, and conversely, if the distance between the current grid coordinate and the destination grid coordinate is small, the real-time gravity data is small. If the distance between the target object and the obstacle is outside the repulsion influence range, the real-time repulsion data is 0, and if the distance between the target object and the obstacle is within the repulsion influence range, the closer the target object is to the obstacle, the larger the corresponding real-time repulsion data is.
However, a resultant force field constructed based on the formula of the real-time gravitational data and the formula of the real-time repulsive force data has two problems, one is that the real-time gravitational data is reduced as the target object approaches the destination grid coordinate, and the real-time repulsive force data is increased as the target object approaches the obstacle grid coordinate, so that the real-time gravitational data and the real-time repulsive force data are offset, that is, the real-time resultant force data is 0, so that a statically planned path cannot be updated, and the target object and the obstacle collide with each other. Another problem is the problem of local oscillation, that is, if there are more obstacles around the destination grid coordinate, the real-time repulsion data generated by the obstacles is much larger than the real-time attraction data, which causes the target object to move away from the destination grid coordinate, and the target object moves away from the destination grid coordinate, which causes the real-time attraction data to increase immediately, so that the target object moves close to the destination grid coordinate, and thus the target object continuously and greatly adjusts the traveling direction to form local oscillation.
In another embodiment, optionally, the real-time gravity data satisfies the formula:
wherein, Fatt(x) Representing real-time gravity data, Uatt(x) Representing a gravitational field function, lambda representing a gravitational coefficient, x representing a current grid coordinate, xgRepresenting destination grid coordinates.
In another embodiment, optionally, the real-time repulsion data satisfies the formula:
wherein, Fref(x) Representing real-time repulsion data, Uref(x) Denotes a repulsive force field function, mu denotes a repulsive force coefficient, x denotes a current grid coordinate, p denotes a distance between the current grid coordinate and an obstacle grid coordinate, p0Indicating the extent of impact of the repulsion force of the obstacle.
In the embodiment of the invention, the problem that the real-time gravitation data and the real-time repulsion data are mutually offset is solved by setting the order of the gravitational field function to be different from the order of the repulsive field function. By adjusting the coefficient of the repulsive force field function and the calculation framework, the problem of local oscillation can be solved, and the stability of the updating condition of the static planning path is improved.
And S160, ending navigation.
According to the technical scheme of the embodiment, whether the impassable road section exists in the static planned path is determined based on the current raster coordinate of the target object and the acquired real-time road condition data in the process that the target object travels based on the static planned path, if yes, the static planned path is updated based on the current raster coordinate, the real-time obstacle raster coordinate in the real-time road condition data and the destination raster coordinate data until the target object reaches the destination raster coordinate, the problem of poor navigation accuracy of the static planned path is solved, the safety of the target object in the traveling process is improved, and the time of the target object reaching 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, which further details "obtaining a grid map model of a target school" in the foregoing embodiment. As shown in fig. 3, the method includes:
s210, obtaining a static plane map of the target school, and constructing a map grid network based on the 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 1m × 1m, and if the target object is a motor vehicle, the preset grid size may be 5m × 5 m. 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 the 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.
S220, grid information of a network grid corresponding to the geographical coordinates of the obstacles in the static planar map in the map grid network is set to be a first numerical value.
Different types of obstacles in the static planar map are usually stored in different map layers, and for example, obstacle geographic coordinates of a surface obstacle in a first map layer, obstacle geographic coordinates of a line obstacle in a second map layer, and obstacle geographic coordinates of a point obstacle in a third map layer are obtained by traversing all the map layers in the static planar map.
For example, the first value may be 1.
And S230, setting grid information of the network grid in the grid network of the map except the grid information of the network grid corresponding to the geographical coordinates of the obstacles in the static planar map as a second numerical value to obtain an initial grid map array.
For example, the second value may be 0.
S240, building a grid map model of the target school based on the initial grid map array.
In one embodiment, optionally, constructing a grid map model of the target school based on the initial grid map array includes: the method comprises the steps of obtaining at least one closed area array formed by network grids with grid information in an initial grid map array as a first numerical value, determining seed points corresponding to the closed area array aiming at each closed area array, and performing filling operation on the closed area array by adopting a flooding algorithm to obtain a grid map model.
Specifically, the seed point may be any network grid in the closed region formed by the closed region array, and the grid information of the network grid is the first numerical 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 the center is set as a first numerical value. Wherein, the four neighborhoods include upper, left, right and lower, and the eight neighborhoods include upper, lower, left, right, upper left, lower left, upper right and lower right.
In one embodiment, optionally, constructing 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 an initial grid map array; the initial grid map array comprises a first grid of each column, a last grid of each column, a first grid of each row and a last grid of each row; aiming at each initial network grid, if the grid information is a second numerical value, taking the initial network grid as a seed point, and performing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array; and executing union set taking operation on at least one filling grid map array to obtain a grid map model of the target school.
Specifically, the grid information of a first network grid in each row in the initial grid map array is longitudinally read, if the grid information is a second numerical value, the initial network grid is taken as a seed point, filling operation is performed on the initial grid map array by adopting a flooding algorithm, and a filled grid map array is obtained; longitudinally reading the grid information of the last network grid in each row in the initial grid map array, if the grid information is a second numerical value, indicating that the grid information belongs to a peripheral connected region, taking the initial network grid as a seed point, and performing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array; transversely reading the grid information of the first network grid in each line of the initial grid map array, if the grid information is a second numerical value, indicating that the grid information belongs to a peripheral connected region, taking the initial network grid as a seed point, and performing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array; and transversely reading the grid information of the last network grid in each line in the initial grid map array, if the grid information is a second numerical value, indicating that the grid information belongs to a peripheral connected region, taking the initial network grid as a seed point, and performing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array.
For example, assuming that the initial grid map array is 10 × 10 and the grid information of each initial grid is the second value, 100 times of filling operations are respectively performed on the initial grid map array, and the number of the obtained filled grid map arrays is 100.
The initial grid map array constructed based on the static planar map of the target school has the advantages that due to the fact that a large number of complex closed areas exist in the campus scene of the target school, the time consumed for searching the whole initial grid map array to obtain the closed area array is shortened, the searching result is poor, and the problems of omission or error of the searching result are prone to occurring. In 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 ensured.
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 grid map array map is read, and the initial grid map array map is copied to obtain a new two-dimensional array mapTmp. For mapTmp, judging whether the column reading of mapTmp is finished, if not, executing reading of a first network grid p of the column, and if the grid information of p is a second numerical value, in this embodiment, taking the network grid p as a seed point, executing a flooding algorithm, and obtaining a map filling grid array; and executing reading of the last network grid p of the column, and if the grid information of p is 0, executing a flooding algorithm by taking the network grid p as a seed point to obtain a grid array of the filling map. If the column reading of the mapTmp is finished, judging whether the row reading of the mapTmp is finished, if not, executing reading of a first network raster p of a row, if the raster information of p is 0, executing a flooding algorithm by taking the network raster p as a seed point to obtain a filling map raster array; and executing reading of the last network grid p of the row, and if the grid information of p is 0, executing a flooding algorithm by taking the network grid p as a seed point to obtain a grid array of the filling map. And if the line reading of the mapTmp is finished, traversing n network grids of the mapTmp, if the ith network grid is not the filling value, setting the grid information of the network grid of the initial grid map array map corresponding to the ith network grid to be 1, and if the ith network grid is the filling value, setting the grid information of the network grid of the initial grid map array map corresponding to the ith network grid to be 0.
And S250, determining a static planning path based on the grid coordinates of the starting point and the destination of the target object in the grid map model.
In one embodiment, optionally, determining the static planned path based on the start grid coordinate and the destination grid coordinate of the target object in the grid map model includes: and determining a static planning path by adopting a jumping point search algorithm based on the grid coordinates of the target object in the grid map model at the starting point and the grid coordinates at the destination.
The Jump Point Search algorithm (JPS) is a two-dimensional network-based path planning algorithm, and compared with the a-algorithm, the Jump Point Search algorithm essentially reduces the number of nodes searched in the midway by searching for "Jump points" and improves the Search speed.
The JPS algorithm defines: the natural neighbor is a neighbor point which takes the least cost in the direction without the obstacle in consideration of the direction and cost from the parent node of the current point to the current point. And if the cost of the parent node of the current point reaching the neighbor through the current point is less than the cost of the parent node not reaching the neighbor through the current point, the neighbor is a forced neighbor.
Fig. 5 is a schematic diagram of a skip point search algorithm according to a second embodiment of the present invention. Wherein, P is the starting point, and X is the searched current point. In the a diagram in fig. 5, in the case of no obstacle, X is a point in the positive direction of point P, the cost of P reaching these nodes through X is higher than the cost of P not passing through X, the nodes whose evaluation cost is known as gray stripe marks are meaningless nodes, and the natural neighborhood of X has only one point occupied by a five-pointed star mark in the positive direction. Similarly, when X is a point on the diagonal of P, as shown in b of FIG. 5, it can be seen that the natural neighbors of X are the three points occupied by the five-pointed star in the figure. In the diagram c in fig. 5, the black square is an obstacle and the natural neighborhood that P passes X is a point occupied by a five-pointed star. Forced neighbors are points occupied by circle marks in the c-diagram, which can only be reached by P through X, and there is no case that the points are reached through other nodes and the cost is less than X, so that X in the c-diagram has a natural neighbor marked by a five-pointed star and a forced neighbor marked by a circle. Similarly, there is no path through other nodes to the circle marker point in the d-graph in fig. 5 and it takes less than X, so there are three five-pointed star marked natural neighbors and one circle marked forced neighbor in the d-graph.
The definition of the jumping point comprises the following steps: 1) if the current point is a starting point or a target point, the current point is a jumping point; 2) if the current point has a forced neighbor, the current point is a hop point; 3) the father node of the current point is on the diagonal of the current point, and the current point is a jumping point when the current point can reach the jumping point through moving in the positive direction.
The neighbor 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 jumping point searching process, the positive direction and the diagonal direction can be searched, and the positive direction is searched first, and then the diagonal direction is searched; 2) if N can be reached by P through other ways and the path cost is less than that of the path from P to N through X in the searching process, the next step of reaching X cannot reach N; 3) only hops can join the Open _ list because they can change the path planning direction.
The path planning process of the JPS algorithm is as follows:
1) adding the starting point S into the Open set Open _ list;
2) sorting Open _ list according to the cost value to obtain a 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 Open _ list and adding P into the Close _ list;
4) judging the direction of P, wherein the direction which can be judged can be divided into a positive direction and a diagonal direction, and analyzing various conditions to obtain a jump point J;
5) and judging whether J is in the Open _ list, if so, modifying the parent node and the cost value of the J in the Open _ list, if not, adding the J into the Open _ list, and circularly entering the second step until the path searching is finished.
The various cases in the step 4) include:
1. if the direction is the positive direction and the left rear part of the P can not be moved and the left part can be moved, the point P searches for a jump point J which is not in the Close _ list according to the left front part and the left part;
2. if the direction is the positive direction and the current positive direction can be continuously searched, P searches for a jump point J which is not in the Close _ list according to the current direction;
3. if the direction is the positive direction and the right rear of the P can not move to the right, the point P searches for a jump point J which is not in Close _ list according to the right front and the right;
4. if the direction is the diagonal direction and the horizontal component direction of P can be moved, the point P searches for a jump point J which is not in Close _ list according to the horizontal component direction;
5. if the direction is the diagonal direction and the current positive direction can be continuously searched, P searches for a jump point J which is not in the Close _ list according to the current direction;
6. if the direction is diagonal and the vertical component direction of P is available, the point P looks for the jump point J which is not in Close _ list according to the vertical component direction.
Wherein, left front, left rear, left or right is relative to the forward direction standing to reach P, assuming the forward direction is south, then left and right are east and west respectively, left front is south-east, left rear is north-east. When the positive direction is different, the corresponding directions of the left front, the left back, the left or the right are also different.
And S260, acquiring the current grid coordinate and the real-time road condition data of the target object in the process that the target object travels based on the static planned path.
S270, judging whether the current grid coordinate is the destination grid coordinate, if so, executing S291, and if not, executing S280.
And S280, judging whether the static planned path has the impassable road section, if so, executing S290, and if not, executing S260.
And S290, updating the static planned path 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, and executing S260.
And S291, ending navigation.
According to the technical scheme, the map grid network is constructed by acquiring the static planar map of the target school and based on the preset grid size and the map size of the static planar map; setting grid information of a network grid corresponding to the geographical coordinates of the obstacles in the static planar map in the map grid network as a first numerical value; setting grid information of a network grid in the map grid network except the grid information of the network grid corresponding to the geographical coordinates of the obstacles in the static planar map as a second numerical 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, filling operation is performed on the initial grid map array by using boundary points of the initial grid map array as seed points and adopting a generalization algorithm, the problem that the closed area obtaining process is complicated 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 statically planned path determination module 310, an unvaryable road segment determination module 320, a statically planned path update module 330, and an end of navigation module 340.
The static planned path determining module 310 is configured to obtain a grid map model of a target school, and determine a static planned path based on a start point grid coordinate and a destination grid coordinate of a target object in the grid map model;
the impassable road section determining module 320 is configured to determine whether an impassable road section exists in the static planned path based on the current grid coordinate of the target object and the acquired real-time road condition data in the process that the target object travels based on the static planned path;
a static planned path updating module 330, configured to update the static planned path 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, if any;
and the navigation ending module 340 is configured to return to execute the step of determining whether an impassable road section exists in the static planned path based on the current grid coordinate of the target object and the acquired real-time road condition data until the target object reaches the destination grid coordinate.
According to the technical scheme of the embodiment, whether the impassable road section exists in the static planned path is determined based on the current raster coordinate of the target object and the acquired real-time road condition data in the process that the target object travels based on the static planned path, if yes, the static planned path is updated based on the current raster coordinate, the real-time obstacle raster coordinate in the real-time road condition data and the destination raster coordinate data until the target object reaches the destination raster coordinate, the problem of poor navigation accuracy of the static planned path is solved, the safety of the target object in the traveling process is improved, and the time of the target object reaching the destination is effectively shortened.
On the basis of the foregoing embodiment, optionally, the static planned path updating module 330 is specifically configured to:
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 repulsion data corresponding to the current grid coordinate based on the current grid coordinate, the static obstacle grid coordinate in the grid map model and the real-time obstacle grid coordinate 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 repulsion data.
On the basis of the above embodiment, optionally, the real-time gravity data satisfies the formula:
wherein, Fatt(x) Representing real-time gravity data, Uatt(x) Representing a gravitational field function, lambda representing a gravitational coefficient, x representing a current grid coordinate, xgRepresenting destination grid coordinates.
On the basis of the above embodiment, optionally, the real-time repulsive force data satisfies the formula:
wherein, Fref(x) Representing real-time repulsion data, Uref(x) Denotes a repulsive force field function, mu denotes a repulsive force coefficient, x denotes a current grid coordinate, p denotes a distance between the current grid coordinate and an obstacle grid coordinate, p0Indicating the extent of impact of the repulsion force of the obstacle.
On the basis of the foregoing 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 coordinate and the destination grid coordinate of the target object in the grid map model by adopting a jumping point searching algorithm.
On the basis of the foregoing 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 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 first grid information setting unit is used for setting grid information of a network grid corresponding to the geographical coordinates of the obstacles in the static planar map in the map grid network as a first numerical value;
the second grid information setting unit is used for setting grid information of a network grid in the map grid network except for the grid information of the network grid corresponding to the geographical coordinates of the obstacles in the static plane map as a second numerical value 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 foregoing embodiment, optionally, the grid map model building unit is specifically configured to:
acquiring grid information corresponding to at least one initial network grid in an initial grid map array; the initial grid map array comprises a first grid of each column, a last grid of each column, a first grid of each row and a last grid of each row;
aiming at each initial network grid, if the grid information is a second numerical value, taking the initial network grid as a seed point, and performing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array;
and executing union set taking 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 corresponding functional modules and beneficial effects of the execution method.
Example four
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. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, 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 communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can 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.
A number of 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, or the like; 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, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the navigation method.
In some embodiments, the navigation method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as 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. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the navigation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the navigation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the navigation method of the present invention may 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 performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, where a computer instruction is stored, where the computer instruction is used to enable a processor to execute a navigation method, where the method includes:
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 travels on the basis of the static planning path, determining whether an impassable road section exists in the static planning path or not on the basis of the current grid coordinate of the target object and the acquired real-time road condition data;
if the destination grid coordinate data exists, updating the static planning path 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;
and returning to execute the step of determining whether the static planning path has the impassable road section or not based on the current grid coordinate of the target object and the acquired real-time road condition data until the target object reaches the grid coordinate of the destination.
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. A 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) by 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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. A client and server are generally 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 host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A navigation 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 travels on the basis of the static planned path, determining whether an impassable road section exists in the static planned path or not on the basis of the current grid coordinate of the target object and the acquired real-time road condition data;
if the current grid coordinate exists, updating the static planned path 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;
and returning to execute the step of determining whether the static planned path has the impassable road section or not based on the current grid coordinate of the target object and the acquired real-time road condition data until the target object reaches the grid coordinate of the destination.
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 traffic data, and the destination grid coordinate data comprises:
determining real-time gravity data corresponding to the current grid coordinate based on the current grid coordinate and the destination grid coordinate;
determining real-time repulsion data corresponding to the current grid coordinate based on the current grid coordinate, the static obstacle grid coordinate in the grid map model and the real-time obstacle grid coordinate 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 repulsion data.
3. The method of claim 2, wherein the real-time gravity data satisfies the formula:
wherein, Fatt(x) Representing real-time gravity data, Uatt(x) Representing a gravitational field function, lambda representing a gravitational coefficient, x representing a current grid coordinate, xgRepresenting destination grid coordinates.
4. The method according to claim 2, wherein the real-time repulsion data satisfies the formula:
wherein, Fref(x) Representing real-time repulsion data, Uref(x) Denotes a repulsive force field function, mu denotes a repulsive force coefficient, x denotes a current grid coordinate, p denotes a distance between the current grid coordinate and an obstacle grid coordinate, p0Indicating the extent of impact of the repulsion force of the obstacle.
5. The method of claim 1, wherein determining a static planned path based on the starting point grid coordinates and the destination grid coordinates of the target object in the grid map model comprises:
and determining a static planning path based on the initial point grid coordinate and the destination grid coordinate of the target object in the grid map model by adopting a jumping point search algorithm.
6. The method of any one of claims 1-5, wherein obtaining the grid map model of the target school comprises:
acquiring a static planar map of a target school, and constructing a map grid network based on a preset grid size and the map size of the static planar map;
setting grid information of a network grid corresponding to the geographical coordinates of the obstacles in the static planar map in the map grid network as a first numerical value;
setting grid information of a network grid in the map grid network except for the grid information of the network grid corresponding to the geographical coordinates of the obstacles in the static plane map as a second numerical value to obtain an initial grid map array;
and constructing a grid map model of the target school based on the initial grid map array.
7. The method of claim 6, wherein constructing a grid map model of a target school based on the initial grid map array comprises:
obtaining grid information corresponding to at least one initial network grid in the initial grid map array; wherein the initial grid map array includes a first grid of each column, a last grid of each column, a first grid of each row, and a last grid of each row;
for each initial network grid, if the grid information is a second numerical value, taking the initial network grid as a seed point, and performing filling operation on the initial grid map array by adopting a flooding algorithm to obtain a filled grid map array;
and executing union set taking operation on at least one filling grid map array to obtain a grid map model of the target school.
8. A navigation device, comprising:
the static planning path determining module is used for 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;
the non-accessible road section determining module is used for determining whether a non-accessible road section exists in the static planning path or not based on the current grid coordinate of the target object and the acquired real-time road condition data in the process that the target object travels based on the static planning path;
a static planned path updating module, configured to update the static planned path based on the current grid coordinate, a real-time obstacle grid coordinate in the real-time road condition data, and the destination grid coordinate data, if any;
and the navigation ending module is used for returning and executing the step of determining whether the static planned path has the impassable road section or not based on the current grid coordinate of the target object and the acquired real-time road condition data until the target object reaches the grid coordinate of the destination.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
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 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the navigation method of any one of claims 1-7 when executed.
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