CN110006430B - Optimization method of track planning algorithm - Google Patents
Optimization method of track planning algorithm Download PDFInfo
- Publication number
- CN110006430B CN110006430B CN201910230859.4A CN201910230859A CN110006430B CN 110006430 B CN110006430 B CN 110006430B CN 201910230859 A CN201910230859 A CN 201910230859A CN 110006430 B CN110006430 B CN 110006430B
- Authority
- CN
- China
- Prior art keywords
- node
- point
- algorithm
- search
- ship
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- G01C21/203—Specially adapted for sailing ships
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A method for optimizing a track planning algorithm, comprising: s1, acquiring a node set O for judging the waypoints and a node set C for judging the waypoints in the track planning by adopting a track planning algorithm according to the current position and the target point position of the ship; s2, for each node in the set C, obtaining a target boundary box of each node by adopting a Dijkstra search method; s3, when the neighborhood of each node in the traversal set C is searched for the next feasible node, selecting the node which is in the non-obstacle area and the current node target boundary box in the current node neighborhood as the next feasible node; updating the set O to judge the waypoints according to the next feasible node; and S4, traversing the set C to obtain the optimal track route. The method not only avoids the exploration of redundant and invalid nodes by the track planning algorithm, but also avoids the exploration of nodes by the track planning algorithm from expanding to the wrong direction, thereby accelerating the speed of finding the way and improving the running efficiency of the algorithm.
Description
Technical Field
The invention relates to the technical field of navigation control of unmanned ships, in particular to an optimization method of a track planning algorithm.
Background
With the continuous development of shipping industry, a large number of ships frequently move around ports and sea traffic main roads, and the ships develop to be large-scale and high-speed, so that the sea navigation becomes crowded and the navigation efficiency is reduced. The research and development of the geographic information system provide theoretical support and technical support for constructing an intelligent traffic system and improving the marine traffic condition. The selection of the sea route is related to the efficiency problems of ship minimum time cost and the like, thereby being related to the ship shipping cost problem. How to reduce the cost of shipping time, save shipping cost, improve maritime shipping competitiveness becomes the most concerned problem for each shipping company.
The planning of the ship track refers to the self-planning of an optimal path of the ship according to the navigation environment on the premise of ensuring safety, namely the shortest path for avoiding various dynamic or static navigation obstacles during the shipping of the ship. Path planning algorithms based on point finding in graph search space are currently common and effective track planning methods, wherein points in a graph are associated with coordinates in space, such as grids, waypoints, quadtrees, octrees, navigation grids, and the like. However, the path planning algorithm based on the graph search space point finding often has redundant and invalid nodes in the application process, which not only occupies memory, reduces the execution efficiency, but also increases the execution time of the algorithm.
The existing node pruning technology can greatly reduce the number of redundant nodes in a path planning algorithm based on a graph search space, but because the existing node pruning technology is the adjustment of a node exploration strategy, the node exploration and expansion in a wrong direction cannot be avoided.
Therefore, a method for planning a flight path based on a target boundary is needed.
Disclosure of Invention
Technical problem to be solved
In order to solve the above problems in the prior art, the present invention provides an optimization method for a flight path planning algorithm. The method not only avoids the redundant invalid nodes explored by the track planning algorithm, but also avoids the nodes explored by the track planning algorithm from expanding towards the wrong direction, thereby greatly reducing the number of node expansion, accelerating the speed of finding the way, improving the efficiency of algorithm operation, and simultaneously ensuring the superiority of the algorithm.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a method for optimizing a track planning algorithm, comprising:
s1, acquiring a node set O for judging the waypoints and a node set C for judging the waypoints in the course planning by adopting a course planning algorithm according to the current position coordinates of the ship and the position coordinates of the target point.
S2, aiming at each node in the set C, obtaining a target boundary box of each node by adopting a Dijkstra search method.
S3, when the neighborhood of each node in the traversal set C is searched for the next feasible node, selecting the node which is in the non-obstacle area and the current node target boundary box in the current node neighborhood as the next feasible node; and updating the set O to judge the waypoints according to the next feasible node.
And S4, traversing the set C to obtain a final output track route.
As an improvement of the optimization method of the track planning algorithm of the present invention, in step S1, acquiring a node set O for determining waypoints and a node set C for determining waypoints in the track planning by using the track planning algorithm includes:
and selecting the node with the minimum cost in the set O to add into the node set C of the judged waypoint according to the node set O for judging the waypoint and the node cost calculation rule.
As an improvement of the optimization method of the track planning algorithm, the node cost calculation rule is as follows: f is the total cost of the path from the node, G is the cost of the path from the starting point of the ship to the node, and H is the cost of the path from the node to the end point of the ship.
As an improvement of the optimization method of the track planning algorithm of the present invention, in step S3, the step of updating the set O to determine the waypoints according to the next feasible node includes:
and judging whether the next feasible node is positioned in the set O, if so, updating the father node, the G value and the F value of the feasible node based on the path with the smaller G value of the feasible node, otherwise, adding the feasible node into the set O, and marking the father node of the feasible node.
As an improvement of the optimization method of the track planning algorithm of the present invention, step S4 includes:
traversing the set C; starting from the terminal point of the ship navigation, and reversely tracking along the father node until reaching the starting point of the ship navigation; and outputting the reverse tracking path as an optimal path from the starting point to the end point of the ship navigation.
As an improvement of the optimization method of the track planning algorithm of the present invention, step S1 further includes: judging whether the current position coordinate of the ship and the position coordinate of the target point are in the same connected region by adopting a width-first search algorithm; if so, acquiring a set O and a set C by adopting a flight path planning algorithm; if not, the current position coordinates and the target point coordinates of the ship are reset.
As an improvement of the optimization method of the track planning algorithm of the present invention, in step S2, a Dijkstra search method is used to obtain a target bounding box of each node, including: carrying out destination-free Dijkstra search on the nodes to obtain nodes in Dijkstra search mapping in each search direction of the nodes; constructing a bounding box of each search direction according to the minimum circumscribed rectangle of the nodes in the Dijkstra search map in each search direction; and selecting a search direction boundary box containing a ship target point as a target boundary box of the current node.
The track planning algorithm comprises a path planning algorithm based on a graph search space point searching, such as an A star algorithm, a Dijkstra path planning algorithm, an optimal priority search algorithm, a depth priority search algorithm, a width priority search algorithm, a skip point search algorithm and the like.
(III) advantageous effects
The invention has the beneficial effects that:
1. according to the invention, a target boundary box containing a next feasible node and a terminal point is constructed according to the current node, and the target boundary box is used as a region definition for searching the next local target point by a track planning algorithm, so that the track planning algorithm based on a graph search space can be deeply optimized. The method can avoid the redundant invalid nodes explored by the track planning algorithm, and can also avoid the nodes explored by the track planning algorithm from expanding towards the wrong direction, thereby greatly reducing the number of node expansion, accelerating the speed of routing and improving the efficiency of algorithm operation.
2. The track planning method based on the target boundary has wide application range, is suitable for any track planning algorithm based on a graph search space, and the graph only needs to meet the condition that points in the graph are associated with coordinates in the space, such as grids, a road point diagram, a quadtree, an octree, a navigation grid and the like; the track planning algorithm comprises an A star algorithm, a Dijkstra path planning algorithm, an optimal priority search algorithm, a depth priority search algorithm, a width priority search algorithm and a skip point search algorithm. And the execution efficiency of the related track planning algorithm can be greatly improved.
3. The track planning method based on the target boundary provided by the invention does not change the internal structure of the track planning algorithm, thereby ensuring the superiority of the original algorithm.
Drawings
The invention is described with the aid of the following figures:
fig. 1 is a flowchart of a flight path planning method based on a target boundary and an a-star algorithm in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a bounding box of each search direction of a current node on a grid map in embodiment 1 of the present invention;
FIG. 3 is a grid map applied in a simulation experiment;
FIG. 4 is a schematic diagram of a conventional A-star algorithm for track planning in a simulation experiment;
fig. 5 is a schematic diagram of a conventional a-star algorithm based on target boundaries for flight path planning in a simulation experiment.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
The invention provides an optimization method of a flight path planning algorithm, which comprises the following steps:
and step S1, acquiring a node set O for judging the waypoints and a node set C for judging the waypoints in the course planning by adopting a course planning algorithm according to the current position coordinates of the ship and the position coordinates of the target point.
Specifically, judging whether the current position coordinate of the ship and the position coordinate of the target point are in the same connected region by adopting a width-first search algorithm; if so, acquiring a set O and a set C by adopting a flight path planning algorithm; if not, the current position coordinates and the target point coordinates of the ship are reset.
Specifically, according to a node set O for judging the waypoints and a node cost calculation rule, the node with the minimum cost in the set O is selected to be added into a node set C for judging the waypoints. The node cost calculation rule is as follows: f is the total cost of the path from the node, G is the cost of the path from the starting point of the ship to the node, and H is the cost of the path from the node to the end point of the ship.
S2, aiming at each node in the set C, obtaining a target boundary box of each node by adopting a Dijkstra search method;
specifically, carrying out destination-free Dijkstra search on the nodes to obtain nodes in Dijkstra search mapping in each search direction of the nodes; constructing a bounding box of each search direction according to the minimum circumscribed rectangle of the nodes in the Dijkstra search map in each search direction; and selecting a search direction boundary box containing a ship target point as a target boundary box of the current node.
Step S3, when the neighborhood of each node in the traversal set C is searched for the next feasible node, selecting the node which is in the non-obstacle area and the current node target boundary box in the current node neighborhood as the next feasible node; and updating the set O to judge the waypoints according to the next feasible node.
And step S4, traversing the set C to obtain a final output track route.
Specifically, when the nodes in the set C are traversed to be the target points of the ship, the finally output track routes are acquired.
According to the optimization method of the track planning algorithm, the target boundary frame containing the next feasible node and the target point of the ship is constructed according to the current node and is used as the regional definition of the node for searching the route point judgment by the track planning algorithm, so that the redundant invalid node searched by the track planning algorithm can be avoided, the node searched by the track planning algorithm can be prevented from expanding towards the wrong direction, the number of node expansion is greatly reduced, the route searching speed is accelerated, and the algorithm operation efficiency is improved. The method is different from the traditional strategy of directly acquiring all non-closed reachable neighbor nodes of the current target point for expansion in the path planning algorithm based on the graph search space.
The optimization method of the track planning algorithm is suitable for any track planning algorithm based on a graph search space, wherein points in a graph are associated with coordinates in the space, such as grids, a road point diagram, a quad tree, an octree, a navigation grid and the like; the flight path planning algorithm comprises an A star algorithm, a Dijkstra path planning algorithm, an optimal priority search algorithm, a depth priority search algorithm, a width priority search algorithm and a jumping point search algorithm. The method is widely applied, and the execution efficiency of the related track planning algorithm can be greatly improved.
It should be particularly noted that the track planning method based on the target boundary provided by the present invention is not only suitable for the track planning of ships, but also suitable for the path planning under other situations, such as the path planning of vehicles and unmanned planes, the path planning in games, and the like.
Example 1
The following describes the optimization method of the track planning algorithm provided by the present invention specifically based on a grid map and an a-star algorithm, as shown in fig. 1.
Step S1, dividing the grid map into feasible areas and infeasible areas according to the obstacle environment information; and in the grid map, setting a starting point and an end point of ship navigation according to the current position and the navigation task of the ship.
And step S2, establishing two empty lists, namely openlist and closed list, wherein openlist is used for storing waypoints judged by the openlist, and closed list is used for storing waypoints judged by the closed list.
Step S3, judging whether the starting point and the terminal point of the ship navigation are in the same communication area, if so, executing step S4; if not, the process returns to step S1 to reset the starting point and the ending point of the ship navigation.
Numbering grids of feasible regions communicated with a starting point of ship navigation and grids of feasible regions communicated with an end point of the ship navigation according to a width-first search algorithm; if the numbers of the ship navigation start point and the ship navigation end point are consistent, the ship navigation start point and the ship navigation end point are in the same communication area; and if the numbers of the ship navigation start point and the ship navigation end point are not consistent, the ship navigation start point and the ship navigation end point are not in the same communication area. Before the track planning is carried out each time, whether the starting point and the terminal point of the ship navigation can be reached or not needs to be judged, and the track planning needs to be carried out only if the starting point and the terminal point can be reached, so that the route searching cost when the starting point and the terminal point can not be reached is avoided, the route searching time is saved, the route searching speed is accelerated, and the operation efficiency of a track planning algorithm is further improved.
And step S4, putting the starting point of the ship navigation and the reachable nodes in the 8 fields into openlist, and putting the starting point and the ending point of the ship navigation into closelist.
Step S5, selecting the node with the minimum cost in openlist as the current node according to the node cost calculation rule; adding the current node into closelist, and deleting the current node in openlist; judging whether the current node is the terminal point of the ship navigation, if so, reversely tracking along the father node by taking the terminal point of the ship navigation as the starting point until the starting point of the ship navigation, and outputting by taking the reversely tracked path as the optimal path from the starting point to the terminal point of the ship navigation; otherwise, step S6 is executed.
Specifically, the node cost calculation rule is as follows: f is the total cost of the path from the node, G is the cost of the path from the starting point of the ship to the node, and H is the cost of the path from the node to the end point of the ship.
And step S6, acquiring a target boundary box of the current node by adopting a Dijkstra search method according to the current node.
Specifically, as shown in fig. 2, performing a non-destination Dijkstra search in 3 search directions of a current node, that is, according to 3 node edges of the current node, to obtain nodes in the Dijkstra search map in each search direction of the current node, constructing each search direction bounding box according to a minimum bounding rectangle of the nodes in the Dijkstra search map in each search direction, and selecting a search direction bounding box including a ship navigation destination as a target bounding box of the current node.
Initial information, A, B, C, is preset on 3 node edges of the current node, and during Dijkstra search, the initial information of the current node edge is transferred to the nodes in the Dijkstra search map along with the Dijkstra search. When Dijkstra search is completed, nodes in each Dijkstra search map are marked with initial information of the current node edge, and accordingly, a minimum circumscribed rectangle containing the nodes of the same node edge of the current node is constructed and serves as a bounding box of each search direction of the current node.
S7, selecting a node which is in a non-obstacle area and in a current node target boundary box in the current node neighborhood as a next feasible node; and judging whether the next feasible node is positioned in the openlist, if so, updating the father node, the G value and the F value of the feasible node based on the path with the smaller G value of the feasible node, otherwise, adding the feasible node into the openlist, and marking the father node of the feasible node. Steps S5 to S7 are repeated.
In the embodiment, the most original a-star algorithm architecture is adopted, and is only used for explaining the application of the optimization method of the invention. If the optimization method is applied to a deep optimization A-star algorithm, Dijkstra path planning algorithm, skip point search algorithm and the like, higher operation efficiency can be obtained; this is because the fewer current nodes explored, the fewer target bounding boxes that are determined.
Simulation experiment
The optimization method is used in the A-star algorithm flight path planning method based on the grid map, and the effectiveness of the optimization method is verified from the aspects of the number of the expansion nodes and the simulation time.
Aiming at the traditional A star algorithm and the traditional A star algorithm applying the optimization method, a simulation experiment is carried out on a grid map, as shown in figure 3, the grid map applied in the simulation experiment has dark gray grids as obstacles and black grids as the starting point and the terminal point of the ship navigation.
The result of the flight path planning by adopting the traditional a-star algorithm is shown in fig. 4, wherein the light gray grid is the node explored by the a-star algorithm, in the simulation experiment, the number of the expansion nodes of the traditional a-star algorithm is 98, the simulation time is 2.8670ms, and the output optimal path length is 15.31.
The traditional a-star algorithm applying the optimization method of the present invention performs the flight path planning, as shown in fig. 5, the target bounding box of the current node is continuously reduced with the determination of the current node, wherein the light gray grid is the node explored by the a-star algorithm running, in the simulation experiment, the number of the extended nodes of the traditional a-star algorithm applying the optimization method of the present invention is 18, the simulation time is 1.0163ms, and the output optimal path length is 15.31.
Therefore, the target boundary box in the optimization method has the main function of node pruning, the expansion of the nodes to the wrong direction is avoided, and meanwhile, the expansion of the redundant invalid nodes is continuously reduced through the continuous reduction of the target boundary box, so that the operation efficiency of the algorithm is greatly improved. The optimization method provided by the invention has a better and outstanding effect when being applied to a complex environment in a wide sea area.
It should be understood that the above description of specific embodiments of the present invention is only for the purpose of illustrating the technical lines and features of the present invention, and is intended to enable those skilled in the art to understand the contents of the present invention and to implement the present invention, but the present invention is not limited to the above specific embodiments. It is intended that all such changes and modifications as fall within the scope of the appended claims be embraced therein.
Claims (6)
1. A method for optimizing a flight path planning algorithm is characterized by comprising the following steps:
s1, acquiring a node set O for judging a waypoint and a node set C for judging the waypoint in the course planning by adopting a course planning algorithm according to the current position coordinates of the ship and the position coordinates of the target point;
step S1 further includes:
judging whether the current position coordinate of the ship and the position coordinate of the target point are in the same connected region by adopting a width-first search algorithm; numbering grids of a feasible region communicated with a starting point of ship navigation and numbering grids of a feasible region communicated with an end point of the ship navigation according to a width-first search algorithm; if the numbers of the ship navigation start point and the ship navigation end point are consistent, the ship navigation start point and the ship navigation end point are in the same communication area; if the numbers of the ship navigation start point and the ship navigation end point are not consistent, the ship navigation start point and the ship navigation end point are not in the same communication area;
if so, acquiring a set O and a set C by adopting a flight path planning algorithm;
if not, resetting the current position coordinates and the target point coordinates of the ship;
s2, aiming at each node in the set C, obtaining a target boundary box of each node by adopting a Dijkstra Dijjack Tesla search method;
in step S2, obtaining a target bounding box of each node by using a Dijkstra search method includes: carrying out destination-free Dijkstra search on the nodes to obtain nodes in Dijkstra search mapping in each search direction of the nodes;
constructing a bounding box of each search direction according to the minimum circumscribed rectangle of the nodes in the Dijkstra search map in each search direction;
selecting a search direction boundary box containing a ship target point as a target boundary box of a current node;
s3, when the neighborhood of each node in the traversal set C is searched for the next feasible node, selecting the node which is in the non-obstacle area and the current node target boundary box in the current node neighborhood as the next feasible node; updating the set O to judge the waypoints according to the next feasible node;
s4, traversing the set C to obtain a final output track route;
selecting the node with the minimum cost in the set O as the current node according to the node cost calculation rule; adding the current node into the set C, and deleting the current node from the set O; and judging whether the current node is the terminal point of the ship navigation, if so, reversely tracking along the father node by taking the terminal point of the ship navigation as the starting point until the starting point of the ship navigation, and outputting by taking the reversely tracked path as the optimal path from the starting point to the terminal point of the ship navigation.
2. The method for optimizing a flight path planning algorithm according to claim 1, wherein in step S1, acquiring a node set O for determining waypoints and a node set C for determining waypoints in performing flight path planning by using a flight path planning algorithm comprises:
and selecting the node with the minimum cost in the set O to add into the node set C of the judged waypoint according to the node set O for judging the waypoint and the node cost calculation rule.
3. The optimization method of the trajectory planning algorithm according to claim 2,
the node cost calculation rule is as follows: f is the total cost of the path from the node, G is the cost of the path from the starting point of the ship to the node, and H is the cost of the path from the node to the end point of the ship.
4. The method for optimizing a flight path planning algorithm according to claim 3, wherein in step S3, the step of updating the set O to determine the waypoints according to the next feasible node comprises:
and judging whether the next feasible node is positioned in the set O, if so, updating the father node, the G value and the F value of the feasible node based on the path with the smaller G value of the feasible node, otherwise, adding the feasible node into the set O, and marking the father node of the feasible node.
5. The optimization method of the flight path planning algorithm according to claim 4, wherein the step S4 includes:
traversing the set C;
starting from the terminal point of the ship navigation, and reversely tracking along the father node until reaching the starting point of the ship navigation;
and outputting the reverse tracking path as an optimal path from the starting point to the end point of the ship navigation.
6. The optimization method of the track planning algorithm according to claim 1, wherein the track planning algorithm comprises a path planning algorithm based on a graph search space point finding, such as an a-star algorithm, a Dijkstra path planning algorithm, an optimal priority search algorithm, a depth priority search algorithm, a breadth priority search algorithm, and a skip point search algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910230859.4A CN110006430B (en) | 2019-03-26 | 2019-03-26 | Optimization method of track planning algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910230859.4A CN110006430B (en) | 2019-03-26 | 2019-03-26 | Optimization method of track planning algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110006430A CN110006430A (en) | 2019-07-12 |
| CN110006430B true CN110006430B (en) | 2021-05-04 |
Family
ID=67168162
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910230859.4A Active CN110006430B (en) | 2019-03-26 | 2019-03-26 | Optimization method of track planning algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110006430B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110455295B (en) * | 2019-09-16 | 2023-05-30 | 广州电加软件有限责任公司 | Automatic planning method for river channel shipping route |
| CN110975291B (en) * | 2019-11-20 | 2023-11-10 | 中国人民解放军国防科技大学 | Path extraction method and system |
| CN111024080B (en) * | 2019-12-01 | 2020-08-21 | 中国人民解放军军事科学院评估论证研究中心 | Unmanned aerial vehicle group-to-multi-mobile time-sensitive target reconnaissance path planning method |
| CN111397624A (en) * | 2020-03-27 | 2020-07-10 | 湖南大学 | Global path planning method based on JPS and Hybrid A |
| CN112711267B (en) * | 2020-04-24 | 2021-09-28 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle autonomous inspection method based on RTK high-precision positioning and machine vision fusion |
| CN115398272A (en) * | 2020-04-30 | 2022-11-25 | 华为技术有限公司 | Method and device for detecting passable area of vehicle |
| CN111693049B (en) * | 2020-05-20 | 2022-02-11 | 五邑大学 | Dynamic path planning method and device for coverage feeding of unmanned ship |
| CN111879324A (en) * | 2020-06-29 | 2020-11-03 | 智慧航海(青岛)智能系统工程有限公司 | Path planning method and device based on ship angular speed limitation |
| CN111857143A (en) * | 2020-07-23 | 2020-10-30 | 北京以萨技术股份有限公司 | Robot path planning method, system, terminal and medium based on machine vision |
| CN112731961A (en) * | 2020-12-08 | 2021-04-30 | 深圳供电局有限公司 | Path planning method, device, equipment and storage medium |
| CN113720344A (en) * | 2021-08-30 | 2021-11-30 | 深圳市银星智能科技股份有限公司 | Path searching method and device, intelligent device and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102879006A (en) * | 2011-07-13 | 2013-01-16 | 爱信艾达株式会社 | Path searching system, path searching method and path searching program |
| CN104008666A (en) * | 2014-05-08 | 2014-08-27 | 中山大学 | Direction sign laying method oriented to interest points |
| CN109115226A (en) * | 2018-09-01 | 2019-01-01 | 哈尔滨工程大学 | The paths planning method of multirobot conflict avoidance based on jump point search |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09178500A (en) * | 1995-12-26 | 1997-07-11 | Pioneer Electron Corp | Car navigation device |
| CN102880186B (en) * | 2012-08-03 | 2014-10-15 | 北京理工大学 | flight path planning method based on sparse A* algorithm and genetic algorithm |
| CN103837154B (en) * | 2014-03-14 | 2017-01-04 | 北京工商大学 | The method and system of path planning |
| CN107449426B (en) * | 2017-07-14 | 2020-05-05 | 厦门市礼小签电子科技有限公司 | Navigation logic method and indoor AR navigation system thereof |
| CN107607120B (en) * | 2017-09-06 | 2020-07-07 | 北京理工大学 | Unmanned aerial vehicle dynamic track planning method based on improved restoration type Anytime sparse A algorithm |
| CN107860386B (en) * | 2017-10-17 | 2020-09-04 | 洛阳中科龙网创新科技有限公司 | Dijkstra algorithm-based agricultural machine shortest path planning method |
| CN108680163B (en) * | 2018-04-25 | 2022-03-01 | 武汉理工大学 | Unmanned ship path searching system and method based on topological map |
-
2019
- 2019-03-26 CN CN201910230859.4A patent/CN110006430B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102879006A (en) * | 2011-07-13 | 2013-01-16 | 爱信艾达株式会社 | Path searching system, path searching method and path searching program |
| CN104008666A (en) * | 2014-05-08 | 2014-08-27 | 中山大学 | Direction sign laying method oriented to interest points |
| CN109115226A (en) * | 2018-09-01 | 2019-01-01 | 哈尔滨工程大学 | The paths planning method of multirobot conflict avoidance based on jump point search |
Non-Patent Citations (1)
| Title |
|---|
| "An improved shortest path algorithm based on orientation rectangle for restricted searching area";Wenyan Zhou 等;《Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design》;20130831;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110006430A (en) | 2019-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110006430B (en) | Optimization method of track planning algorithm | |
| CN111811514B (en) | Path planning method based on regular hexagon grid jump point search algorithm | |
| CN106647769B (en) | Based on A*Extract AGV path trace and the avoidance coordination approach of pilot point | |
| CN106371445B (en) | A kind of unmanned vehicle planning control method based on topological map | |
| CN109541634A (en) | A kind of paths planning method, device and mobile device | |
| CN111679692A (en) | Unmanned aerial vehicle path planning method based on improved A-star algorithm | |
| CN102435200B (en) | Rapid path planning method | |
| CN112229419B (en) | Dynamic path planning navigation method and system | |
| CN102496187B (en) | Method for tracking contour line to boundary and fault based on triangular mesh | |
| CN110006429A (en) | A kind of unmanned boat path planning method based on depth optimization | |
| CN109655063B (en) | Marine search route planning method for large amphibious aircraft | |
| CN110908386B (en) | Layered path planning method for unmanned vehicle | |
| CN112683275B (en) | Path planning method for grid map | |
| CN113485369A (en) | Indoor mobile robot path planning and path optimization method for improving A-x algorithm | |
| Chen et al. | Research on ship meteorological route based on A-star algorithm | |
| CN114440916A (en) | Navigation method, device, equipment and storage medium | |
| CN112444263A (en) | Global path planning method and device | |
| CN109470249B (en) | Optimal path planning and obstacle avoidance design method for underwater vehicle | |
| CN115167398A (en) | Unmanned ship path planning method based on improved A star algorithm | |
| CN116414139B (en) | Mobile robot complex path planning method based on A-Star algorithm | |
| Jia et al. | An improved JPS algorithm in symmetric graph | |
| CN115097824A (en) | Vehicle path planning method in complex environment | |
| CN112432652B (en) | Route planning system and route planning method | |
| Zha et al. | Wind farm water area path planning algorithm based on A* and reinforcement learning | |
| He et al. | Path Planning of Mobile Robot Based on Improved A-Star Bidirectional Search Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |