CN113804213A - AStar fast path planning improvement algorithm - Google Patents
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Abstract
The invention provides an AStar rapid path planning improvement algorithm, which comprises the following steps: the method comprises the following steps: and analyzing the vector road network data, loading the vector road network information of the target map into the system, and performing data processing to obtain a first data, wherein the first data is stored in the system. The invention improves the heuristic map road point searching mode of the AStar original algorithm into vector road network data and grid segmentation and assists the weighting mode, greatly reduces the range and the quantity of the searched data sets, further reduces the searching range through the grid segmentation mode, and obtains more reasonable route planning than the original AStar algorithm by assisting the weighting mode. The algorithm can quickly and accurately obtain the planned path and the correct road network road point sequence, and guide the related targets to pass through the map area according to the planned path. After the region segmentation method and the vector road network data are optimized, the execution efficiency of the AStar algorithm is greatly improved.
Description
Technical Field
The invention relates to the field of automatic path planning, in particular to an AStar rapid path planning improvement algorithm.
Background
Path planning is increasingly used in more and more application scenarios. It is an important branch of modern artificial intelligence and automation processing, is increasingly applied in military and civil fields, and has been developed for a long time. Due to the continuous expansion of the application, the requirement on the execution efficiency is more and more prominent, and how to obtain a reasonable acceptable solution in the contradiction between the algorithm accuracy and the operation efficiency becomes an urgent requirement of each application scenario. The improvement of the invention is just an effective solution provided for various current application requirements.
Disclosure of Invention
The invention aims to provide an AStar rapid path planning improvement algorithm, wherein a heuristic map road point search mode of an improved AStar original algorithm is vector road network data and grid segmentation assisted by weight, so that the range and the quantity of search data sets are greatly reduced, meanwhile, the search range is further reduced by the grid segmentation, and a more reasonable route planning is obtained compared with the original AStar algorithm by the aid of the weight.
In order to achieve the above purpose, the invention provides the following technical scheme: an AStar fast path planning improvement algorithm comprises the following steps:
the method comprises the following steps: analyzing the vector road network data, loading the vector road network information of the target map into the system and performing data processing to obtain a first data, wherein the first data is stored in the system and used for subsequent query and calculation algorithms;
step two: performing data gridding treatment, namely segmenting the first data to obtain second data, and storing the second data in the system for subsequent query and calculation algorithm;
step three: processing the weight information, namely processing the weight information of the second data according to the auxiliary weight data to obtain third data, and storing the third data in the system for subsequent query and calculation algorithm;
step four: data processing, namely receiving external input route point information, positioning a vector network route point closest to the input route point as a corrected input, using data III, performing rapid path planning by using an improved AStar algorithm and returning a processing result, wherein a specific algorithm formula is as follows:
F(n)＝G(n)+H(n)；
f (n) is the next best point, where G (n) represents the true distance from the origin to node n; h (n) represents the distance from the node n to the end point estimated by the heuristic function, the calculation of h (n) adds weight information processing, so the actual meaning is the estimated distance under the condition of weight, and the specific algorithm formula of h (n) is as follows:
H(n)＝T(n)+D(n)+P(n)；
where T (n) is the time weight estimate from node n to the endpoint; d (n) is an estimate of the distance weight from node n to the endpoint; p (n) is an estimate of other available reference weights from node n to the endpoint, where at least one of T (n), D (n), P (n) has a significance.
Furthermore, in the present invention, the specific process of obtaining the first data in the first step includes loading the vector network data by the system, analyzing the vector network data after the vector network data is successfully imported, and recombining the management vector network data to obtain the first data.
Further, in the invention, the specific process of obtaining the second data in the second step loads the gridding processing configuration information, gridding processing is carried out according to the configuration after the loading is successful, the processed data is put into the manager to obtain the second data, and failure information is returned after the loading is failed and the process is finished.
Further, in the invention, in the concrete process of obtaining the third data in the third step, the weight configuration information is emphasized, after the loading is successful, each weight information is configured to each road point of the vector road network according to a specified mode to obtain the third data, and the failure information is returned after the loading is failed and the process is finished.
Further, in the fourth step of the present invention, the data processing flow is as follows, receiving the input parameters, determining the nearest road network point of the input parameters, the effective road network point is greater than or equal to 2, calling the improved AStar algorithm to calculate the road network path, returning the effective path information if the path exists, returning the failure identifier and ending if the path does not exist, and returning the error identifier and ending if the effective road network point is less than 2.
Further, in the invention, the data comprehensive processing flow loads the data comprehensive processing configuration information, after the loading is successful, the gridding data information is merged into the vector road network information, the weight information is merged into the vector road network information, the road network information is counted and put into the manager, and the failure information is returned after the loading is failed and the process is finished.
An electronic device comprising a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the AStar fast path planning improvement algorithm.
A computer readable storage medium having stored thereon computer instructions for causing the computer to execute an AStar fast path planning improvement algorithm as described above.
The beneficial effects are that the technical scheme of this application possesses following technological effect:
the invention improves the heuristic map road point searching mode of the AStar original algorithm into vector road network data and grid segmentation and assists the weighting mode, greatly reduces the range and the quantity of the searched data sets, further reduces the searching range through the grid segmentation mode, and obtains more reasonable route planning than the original AStar algorithm by assisting the weighting mode. The algorithm can quickly and accurately obtain the planned path and the correct road network road point sequence, and guide the related targets to pass through the map area according to the planned path. After the region segmentation method and the vector road network data are optimized, the execution efficiency of the AStar algorithm is greatly improved.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a flow chart illustrating the analysis of vector road network data according to the present invention.
FIG. 3 is a schematic diagram of a data gridding process according to the present invention.
FIG. 4 is a schematic diagram of a process flow of weight information according to the present invention.
FIG. 5 is a schematic diagram of a data processing flow according to the present invention.
FIG. 6 is a schematic diagram of the data synthesis process of the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings. In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
The embodiment provides an AStar fast path planning improvement algorithm, which comprises the following steps,
the method comprises the following steps: analyzing the vector road network data, loading the vector road network information of the target map into the system and performing data processing to obtain a first data, wherein the first data is stored in the system and used for subsequent query and calculation algorithms;
step two: performing data gridding treatment, namely segmenting the first data to obtain second data, and storing the second data in the system for subsequent query and calculation algorithm;
step three: processing the weight information, namely processing the weight information of the second data according to the auxiliary weight data to obtain third data, and storing the third data in the system for subsequent query and calculation algorithm;
step four: data processing, namely receiving external input route point information, positioning a vector network route point closest to the input route point as a corrected input, using data III, performing rapid path planning by using an improved AStar algorithm and returning a processing result, wherein a specific algorithm formula is as follows:
F(n)＝G(n)+H(n)；
f (n) is the next best point, where G (n) represents the true distance from the origin to node n; h (n) represents the distance from the node n to the end point estimated by the heuristic function, the calculation of h (n) adds weight information processing, so the actual meaning is the estimated distance under the condition of weight, and the specific algorithm formula of h (n) is as follows:
H(n)＝T(n)+D(n)+P(n)；
where T (n) is the time weight estimate from node n to the endpoint; d (n) is an estimate of the distance weight from node n to the endpoint; p (n) is an estimate of other available reference weights from node n to the endpoint, where at least one of T (n), D (n), P (n) has a significance.
Furthermore, in the present invention, the specific process of obtaining the first data in the first step includes loading the vector network data by the system, analyzing the vector network data after the vector network data is successfully imported, and recombining and managing the vector network data to obtain the first data.
Further, in the invention, the specific process of obtaining the second data in the second step loads the gridding processing configuration information, gridding processing is carried out according to the configuration after the loading is successful, the processed data is put into the manager to obtain the second data, and failure information is returned after the loading is failed and the process is finished.
Further, in the invention, in the concrete process of obtaining the third data in the third step, the weight configuration information is emphasized, after the loading is successful, each weight information is configured to each road point of the vector road network according to a specified mode to obtain the third data, and the failure information is returned after the loading is failed and the process is finished.
Further, in the fourth step of the present invention, the data processing flow is as follows, receiving the input parameters, determining the nearest road network point of the input parameters, the effective road network point is greater than or equal to 2, calling the improved AStar algorithm to calculate the road network path, returning the effective path information if the path exists, returning the failure identifier and ending if the path does not exist, and returning the error identifier and ending if the effective road network point is less than 2.
Further, in the invention, the data comprehensive processing flow loads the data comprehensive processing configuration information, after the loading is successful, the gridding data information is merged into the vector road network information, the weight information is merged into the vector road network information, the road network information is counted and put into the manager, and the failure information is returned after the loading is failed and the process is finished.
The core data structure of the present invention is as follows:
1. waypoint information structure
// waypoint relationship structure definition
typedef struct roadPointRelationship
{
PointInfo*ptInfo；
boul reactable; // whether arrival is allowed
}roadPointRelationship；
The reactable in the roadPointRelationship structure is used for marking the reachability of the waypoint pointed by the ptInfo and the associated waypoint, and is used for directed path planning.
The ptInfo in the roadPointRelationship structure is a pointer of a waypoint information structure related to the associated waypoint, and the reachABLE describes the arrival relationship between the readInfo and the associated waypoint.
// directed graph coordinates and related information structures
typedef struct PointInfo
{
std::vector<PointInfo*>parentPts；
std::vector<PointInfo*>childrenPts；
std::vector<roadPointRelationship>PtRelationship；
double lat；
double lon；
}PointInfo；
PtRelationship in the PointInfo structure is used for saving the traffic state of a route point directly connected with the PtRelationship, and the PtRelationship is only used when a directed path is planned.
parentPts in the PointInfo structure store the parent waypoint of the current waypoint, and the parent here is only a relative concept and is not absolutely immutable.
The child nodes of the current node are stored in child Pts in the PointInfo structure, and the child nodes are only a relative concept and are not absolutely invariable.
Lon in the PointInfo structure is the longitude coordinate information of the current waypoint.
Lat in the PointInfo structure is latitude coordinate information of the current waypoint.
2. Grid information structure
L _ lon in the AreaRectagle structure represents the upper left-corner longitude coordinate information, l _ lat represents the upper left-corner latitude coordinate information, r _ lon represents the lower right-corner longitude coordinate information, and r _ lat represents the lower right-corner latitude coordinate information.
PtsInAtraa in the AreaRectangle structure stores information about all points within this grid.
The IsInRect function in the area rectangle structure is used to determine whether the specified coordinate point belongs to the grid range.
3. Vector road network information route structure
// directed graph segments and related information structures
typedef struct LineInfo
{
PointInfo*begin；
PointInfo*end；
boool onewayFlag; // one-way road
std::vector<PointInfo*>roadPts；
std::vector<double>pointDistance；//F
}LineInfo；
Begin in the LineInfo structure stores a way start end way point information pointer.
The end in the LineInfo structure stores the end endpoint information pointer of the route.
onewayFlag in the LineInfo structure is used to mark whether the road segment is a one-way road.
The roadPts in the LineInfo structure stores all the waypoint information in the road section;
the pointDistance in the LineInfo structure stores the distance information between the front and back points in the road section.
Another embodiment of the present invention discloses an electronic device, which includes a memory and a processor, the memory and the processor are communicatively connected with each other, for example, through a bus or other means, the memory stores computer instructions, and the processor executes the computer instructions to execute the AStar fast path planning improvement algorithm.
The processor is preferably, but not limited to, a Central Processing Unit (CPU). For example, the Processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof. The memory is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as program instructions/modules corresponding to an AStar fast path planning improvement algorithm in the embodiment of the present invention, and the processor executes various functional applications and data processing of the processor by running the non-transitory software program, instructions, and modules stored in the memory, so as to implement the AStar fast path planning improvement algorithm in the above method embodiments.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory is preferably, but not limited to, a high speed random access memory, for example, but may also be a non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may also optionally include memory located remotely from the processor, which may be connected to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which is stored in a computer readable storage medium and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (8)
1. An AStar fast path planning improvement algorithm is characterized in that: the method comprises the following steps:
the method comprises the following steps: analyzing the vector road network data, loading the vector road network information of the target map into the system and performing data processing to obtain a first data, wherein the first data is stored in the system and used for subsequent query and calculation algorithms;
step two: performing data gridding treatment, namely segmenting the first data to obtain second data, and storing the second data in the system for subsequent query and calculation algorithm;
step three: processing the weight information, namely processing the weight information of the second data according to the auxiliary weight data to obtain third data, and storing the third data in the system for subsequent query and calculation algorithm;
step four: data processing, namely receiving external input route point information, positioning a vector network route point closest to the input route point as a corrected input, using data III, performing rapid path planning by using an improved AStar algorithm and returning a processing result, wherein a specific algorithm formula is as follows:
F(n)＝G(n)+H(n)；
f (n) is the next best point, where G (n) represents the true distance from the origin to node n; h (n) represents the distance from the node n to the end point estimated by the heuristic function, the calculation of h (n) adds weight information processing, so the actual meaning is the estimated distance under the condition of weight, and the specific algorithm formula of h (n) is as follows:
H(n)＝T(n)+D(n)+P(n)；
where T (n) is the time weight estimate from node n to the endpoint; d (n) is an estimate of the distance weight from node n to the endpoint; p (n) is an estimate of other available reference weights from node n to the endpoint, where at least one of T (n), D (n), P (n) has a significance.
2. The algorithm of claim 1, wherein the algorithm comprises: the specific process of obtaining the first data in the first step comprises the steps of loading the vector network data by the system, analyzing the vector network data after the vector network data is successfully imported, and recombining and managing the vector network data to obtain the first data.
3. The algorithm of claim 2, wherein the algorithm comprises: and a specific process of obtaining the second data is to load gridding processing configuration information, gridding processing is carried out according to the configuration after the loading is successful, the processed data is put into a manager to obtain the second data, and failure information is returned after the loading is failed and the process is finished.
4. The AStar fast path planning improvement algorithm of claim 3, wherein: and in the third step, weighting the weight configuration information, configuring each weight information to each road point of the vector road network according to a specified mode after the loading is successful to obtain the third data, returning failure information after the loading is failed, and ending.
5. The algorithm of claim 1, wherein the algorithm comprises: and in the fourth step, the data processing flow is as follows, the input parameters are received, the nearest road network point of the input parameters is judged, the effective road network point is more than or equal to 2, the improved AStar algorithm is called to calculate the road network path, the path returns effective path information, if the path does not exist, the failure mark is returned and ended, and the effective road network point is less than 2, the error mark is returned and ended.
6. The algorithm of claim 1, wherein the algorithm comprises: and a data comprehensive processing flow, loading data comprehensive processing configuration information, merging gridding data information into vector road network information after successful loading, merging weight information into vector road network information, counting road network information and putting the road network information into a manager, returning failure information after failure of loading, and ending.
7. An electronic device, comprising a memory and a processor, wherein the memory and the processor are communicatively coupled, and the memory stores computer instructions, and the processor executes the computer instructions to perform an AStar fast path planning improvement algorithm according to any one of claims 1 to 6.
8. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the AStar fast path planning refinement algorithm of any of claims 1-6.
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