CN104142156A - Path navigation method - Google Patents
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- CN104142156A CN104142156A CN201410423802.3A CN201410423802A CN104142156A CN 104142156 A CN104142156 A CN 104142156A CN 201410423802 A CN201410423802 A CN 201410423802A CN 104142156 A CN104142156 A CN 104142156A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Abstract
The invention relates to a path navigation method which comprises the steps of (1) carrying out meshing, namely, carrying out meshing on navigation data according to longitude and latitude to generate 16384*16384 meshes; (2) loading the navigation data, namely, analyzing mesh coordinates of a starting point and an ending point, figuring a rectangle with a straight line of the starting point and the ending point as a diagonal line, and loading the meshes included in the rectangle; and (3) carrying out navigation analysis, namely, with time as a weight value, carrying out navigation analysis by adopting an A* algorithm, figuring out an optimal path between two points, if the optimal path can not be found in current data, enlarging a data range, if the optimal path can not be found after one-time data expanding, loading all data, and fining the optimal path. According to the path navigation method, the correctness of the path can be ensured, the internal storage is reduced, the efficiency is improved, a destination point can be rapidly and accurately detected so that a navigation algorithm is simple and rapid, and the extension of other navigation services is facilitated.
Description
Technical field
The invention belongs to Path Planning Technique field in transportation network, especially a kind of path navigation method.
Background technology
Path navigation is important content of intelligent transportation field, general navigation algorithm is that whole transportation network is traveled through to analysis, yet in actual application environment, the particularly restriction of mobile phone EMS memory, not only committed memory is large to make to travel through whole network, and analysis efficiency is low, a kind of conventional network optimized approach is according to road is divided by grade at present, sets up road level one by one, yet, for the navigation in some areas, this mode can not obtain good result.In addition, some navigation algorithms need to be done a large amount of pre-service to navigation data, expend the plenty of time, and are not suitable for modification and the maintenance of road data.
Summary of the invention
The object of the invention is, in order to overcome the deficiencies in the prior art, provides a kind of path navigation method.
The present invention solves its technical matters and takes following technical scheme to realize:
A path navigation method, comprises that step is as follows:
(1) grid division: navigation data is carried out to grid division according to longitude and latitude, and symbiosis becomes 16384 * 16384 grids;
(2) navigation data loads: analyze the mesh coordinate of starting point and terminal, calculate that to take the straight line of starting point and terminal be cornerwise rectangle, load and be contained in the grid in rectangle;
(3) navigation is analyzed: take the time as weights, adopt the analysis of navigating of A* algorithm, calculate the optimal path between 2, if can not find optimal path in current data, expand data area, if still can not find optimal path after a data extending, load total data, find optimal path.
And the concrete steps of described step (1) grid division are as follows:
1. load road node data, in order to distinguish the longitude dimension values identical with southern hemisphere and northern hemisphere that thing hemisphere is identical, south latitude and west longitude value are represented with negative, coordinate (117.0332,-39.4211) be expressed as west longitude 117.0332, south latitude 39.4211;
2. in order to guarantee that numerical value is all positive number, respectively the longitude lon of path coordinate is added to 180, dimension values lat adds 90 degree, then respectively divided by 360 and 180, then by the ratio value of obtaining and 16384, multiplies each other, and obtains mesh coordinate (x, y);
Formula is:
And the concrete steps that described step (2) navigation data loads are as follows:
1. read the mesh coordinate (x of starting point StartPoint and terminal EndPoint
s, y
s), (x
e, y
e), execution step is 2.;
2. obtain maximum X coordinate x
max=max (x
s, x
e), minimum X coordinate x
min=min (x
s, x
e), maximum Y coordinate y
max=max (y
s, y
e), minimum Y coordinate y
min=min (y
s, y
e), load X coordinate at range delta x=[x
min, x
max] between and Y coordinate at range delta y=[y
min, y
max] between grid data.
And the concrete grammar step that described step (3) navigation is analyzed is:
1. load navigation data, comprise road node data and roadside, road data, for node, to increase attribute as follows:
A, weight: 36
B, real time cost: RealTimeCost, the product of average used time (path is divided by average velocity) and weight on current section;
C, heuristic time cost: HeuristicTimeCost, current road circuit node is to the product except value and weight of the air line distance of terminal and the average velocity in current section;
D, final time cost: FinalTimeCost, real time cost+heuristic time cost;
E, detected state: CheckState;
F, forerunner's node: PrioiNode;
Execution step 2.;
2. create Priority Queues PriorityQueue, the final time cost FinalTimeCost of take is weights, and the FinalTimeCost that starting point is set is 0, and is added in queue;
3. judge that whether queue is empty, the node of weights minimum in export queue if not empty, and be set as present node CurrentNode, execution step is 4.; If it is empty, perform step 8.;
4. judge whether CurrentNode has adjacent node, if do not have, execution step 3.; If have, from data, obtain the direct connected node NextNode that present node can reach, and judge whether NextNode is terminal, if execution step 5.; If not execution step 6.;
5. according to forerunner's node PriorNode of terminal EndPoint, and forerunner's node PriorNode of forerunner's node PriorNode, until starting point, recurrence is obtained whole path, exits;
6. judge the detected state CheckState of connected node NextNode, if detected state Unchecked not, forerunner's node PriorNode that connected node NextNode is set is present node CurrentNode, counter real time cost RealTimeCost, inspire time cost HeuristicTimeCost and final time cost FinalTimeCost and joined in queue, tamper detection state is Checking; If just at detected state Checking, whether than former final time cost FinalTimeCost little in judgement if through present node, arriving the new final time cost FinalTimeCost of adjacent node, if revise former final time cost FinalTimeCost for new final time cost FinalTimeCost, if not, execution step 7., if 7. detected state Checked, perform step;
7. the detected state CheckState that revises present node CurrentNode is detected state Checked, continues execution step 3.
8. judge whether current data area is total data, if so, there is not path in explanation, exits; If not, load grid X coordinate range at [x
min-Δ x, x
min] [x
max, x
max+ Δ x] and Y coordinate range at [y
min-Δ y, y
max+ Δ y] and grid X coordinate range at [x
min, x
max] and Y coordinate at [y
max, y
max+ Δ y], [y
min-Δ y, y
min] grid data, if still can not find optimal path after a data extending, load total data, find optimal path.
Advantage of the present invention and good effect are:
1, the present invention is according at least there being this feature of feasible path in road net between any two points, and by road node division, is different grids in conjunction with geographic coordinate.Optimal path in network between 2 overwhelming majority situations are present in the region between 2, so load the correctness that data in limited grid can not only guarantee the path in most situations, have also reduced internal memory, have improved efficiency.
2, navigation weights of the present invention are not based on path length, neither be based on category of roads, but adopt the product of time cost and the factor, the factor is here to obtain by great many of experiments, can detect more fast and accurately point of destination.Make navigation algorithm simple and fast more, be beneficial to the expansion of other navigation Service.
3, owing to having lowered EMS memory occupation, the present invention can be applicable to the navigation under the off-line mode on mobile device, and the division of grid makes it can be applicable to other services above, such as location-based service.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the inventive method;
Fig. 2 is the method flow diagram that loads mesh coordinate data in the inventive method;
Fig. 3 is grid dividing condition instance graph;
Fig. 4 is that grid data is chosen instance graph;
Fig. 5 navigation results instance graph.
Embodiment
Below in conjunction with accompanying drawing, the invention process is further described, following examples are descriptive, are not determinate, can not limit protection scope of the present invention with this.
A path navigation method, as shown in Figure 1, comprises that step is as follows:
(1) grid division: navigation data is carried out to grid division according to longitude and latitude, and symbiosis becomes 16384 * 16384 grids, and each grid area is 3000 square kilometres; Concrete steps are as follows:
1. load road node data, in order to distinguish the longitude dimension values identical with southern hemisphere and northern hemisphere that thing hemisphere is identical, south latitude and west longitude value are represented with negative, coordinate (117.0332,-39.4211) be expressed as west longitude 117.0332, south latitude 39.4211;
2. in order to guarantee that numerical value is all positive number, respectively the longitude lon of path coordinate is added to 180, dimension values lat adds 90 degree, then respectively divided by 360 and 180, then by the ratio value of obtaining and 16384, multiplies each other, and obtains mesh coordinate (x, y);
Formula is:
(2) navigation data loads: analyze the mesh coordinate of starting point and terminal, calculate that to take the straight line of starting point and terminal be cornerwise rectangle, load and be contained in the grid in rectangle; As Fig. 2, concrete steps are as follows:
1. read the mesh coordinate (x of starting point StartPoint and terminal EndPoint
s, y
s), (x
e, y
e), execution step is 2.;
2. obtain maximum X coordinate x
max=max (x
s, x
e), minimum X coordinate x
min=min (x
s, x
e), maximum Y coordinate y
max=max (y
s, y
e), minimum Y coordinate y
min=min (y
s, y
e), load X coordinate at range delta x=[x
min, x
max] between and Y coordinate at range delta y=[y
min, y
max] between grid data;
(3) navigation is analyzed: take the time as weights, adopt the analysis of navigating of A* algorithm, calculate the optimal path between 2, if can not find optimal path in current data, expand data area, if still can not find optimal path after a data extending, load total data, find optimal path.Concrete steps are as follows:
1. load navigation data, comprise road node data and roadside, road data, for node, to increase attribute as follows:
A, weight: 36
B, real time cost: RealTimeCost, the product of average used time (path is divided by average velocity) and weight on current section;
C, heuristic time cost: HeuristicTimeCost, current road circuit node is to the product except value and weight of the air line distance of terminal and the average velocity in current section;
D, final time cost: FinalTimeCost, real time cost+heuristic time cost;
E, detected state: CheckState;
F, forerunner's node: PrioiNode;
Execution step 2.;
2. create Priority Queues PriorityQueue, the final time cost FinalTimeCost of take is weights; The FinalTimeCost that starting point is set is 0, and is added in queue;
3. judge that whether queue is empty, the node of weights minimum in export queue if not empty, and be set as present node CurrentNode, execution step is 4.; If it is empty, perform step 8.;
4. judge whether CurrentNode has adjacent node, if do not have, execution step 3.; If have, from data, obtain the direct connected node NextNode that present node can reach, and judge whether NextNode is terminal, if execution step 5.; If not execution step 6.;
5. according to forerunner's node PriorNode of terminal EndPoint, and forerunner's node PriorNode of forerunner's node PriorNode, until starting point, recurrence is obtained whole path, exits.
6. judge the detected state CheckState of connected node NextNode, if detected state Unchecked not, forerunner's node PriorNode that connected node NextNode is set is present node CurrentNode, counter real time cost RealTimeCost, inspire time cost HeuristicTimeCost and final time cost FinalTimeCost and joined in queue, tamper detection state is Checking; If just at detected state Checking, whether than former final time cost FinalTimeCost little in judgement if through present node, arriving the new final time cost FinalTimeCost of adjacent node, if revise former final time cost FinalTimeCost for new final time cost FinalTimeCost, if not, execution step 7., if 7. detected state Checked, perform step;
7. the detected state CheckState that revises present node CurrentNode is detected state Checked, continues execution step 3.
8. judge whether current data area is total data, if so, there is not path in explanation, exits; If not, load grid X coordinate range at [x
min-Δ x, x
min] [x
max, x
max+ Δ x] and Y coordinate range at [y
min-Δ y, y
max+ Δ y] and grid X coordinate range at [x
min, x
max] and Y coordinate at [y
max, y
max+ Δ y], [y
min-Δ y, y
min] grid data, if still can not find optimal path after a data extending, load total data, find optimal path.
Example
For clear description content of the present invention, choose a simple road net and describe execution step in detail, as shown in Figure 5, total 14Ge road circuit node, is set as 6 * 6 sizes by grid.
(1) grid division, the dividing condition of grid is as Fig. 3;
1. read the coordinate information of circuit node, and according to Formula of Coordinate System Transformation
Latitude and longitude coordinates is converted to mesh coordinate, and the result obtaining is as following table:
Table 1 road nodal information NodeInfoTable table
| Node | LAT | LON | X | Y |
| N4 | -26.75 | -45 | 2 | 2 |
| N5 | -18.75 | -45 | 2 | 2 |
| N6 | -18.75 | -15 | 2 | 2 |
| N7 | -7.5 | -15 | 2 | 2 |
| N8 | -7.5 | 30 | 3 | 2 |
| N10 | 22.5 | -45 | 2 | 3 |
| N11 | 22.5 | 15 | 3 | 3 |
| N12 | 22.5 | 30 | 3 | 3 |
Table 2 road section information SegmentInfoTable table
| Segment | Length100km | AveSpeedkm/s | FNode | TNode | Weight100s |
| S1 | 8 | 10 | N4 | N5 | 28.8 |
| S2 | 33 | 10 | N5 | N6 | 118.8 |
| S3 | 44 | 30 | N5 | N10 | 54 |
| S4 | 95 | 40 | N5 | N12 | 79.2 |
| S5 | 12 | 10 | N6 | N7 | 43.2 |
| S6 | 50 | 10 | N7 | N8 | 180 |
| S7 | 32 | 10 | N8 | N12 | 115.2 |
| S8 | 66 | 20 | N10 | N11 | 108 |
| S9 | 16 | 20 | N11 | N12 | 28.8 |
(2) navigation data loads; Loading grid data is in black overstriking frame as shown in the figure, as Fig. 4.
1. suppose that starting point is N4, terminal is N12, and its mesh coordinate is (2,2), (3,3);
2. calculate minimum X coordinate x
min=min (2,3)=2, maximum X coordinate x
max=max (2,3)=3, maximum Y coordinate y
max=max (2,3)=3, minimum Y coordinate y
min=min (2,3)=2, the grid data X coordinate loading at (2,3) and Y coordinate between (2,3), i.e. grid (2,2), (2,3), (3,2), (3,3);
(3) navigation is analyzed, and according to NodeInfoTable and SegmentInfoTable table information, in starting point, is made as N4, and terminal is made as in the situation of N12, and obtaining final route is the thick line in black overstriking square frame, and as shown in Figure 5, concrete steps are as follows:
1. the FinalTimeCost=0 of N4 is set, and is joined in PriotityQueue;
2. PriotityQueue is not empty, the node of weights minimum in export queue, and be set as present node CurrentNode, execution step is 3.;
3. CurrentNode has adjacent node, obtains the direct connected node N5 that present node can reach from data, and N5 is not terminal N14, and execution step 4.;
4. the detected state CheckState that judges connected node N5 is Unchecked, the PriorNode that N5 is set is N4, counter real time cost RealTimeCost=28.8, inspire time cost HeuristicTimeCost=94 * 36/10=338.4 and final time cost FinalTimeCost=28.8+338.4=367.2 and joined in queue, tamper detection state is Checking;
5. now, N4 does not have other connected node, goes to step 2., and CurrentNode is made as to N5, obtains the adjacent node N6 of N5, N12, and N10, and calculate respectively FinalTimeCost, computing formula is:
FinalTimeCostN10＝54+(66+16)×36/30＝152.4
FinalTimeCostN12＝79.2+0＝79.2
FinalTimeCostN6＝118.8+Len(N6,N12)/10＝118.8+66×36/10＝356.4
Wherein Len (N6, N12) is according to the actual range of the N6->N12 of calculation of longitude & latitude.
N6, N12, N10 is Unchecked state, is revised as Checking, modification PriorNode is N5, joins in queue.
6. the CheckState that revises present node N5 is detected state Checked, continues to obtain the node of weights minimum in current queue, is now N12; N12 is just in time terminal, exits circulation, according to PriorNode acquisition approach, is N4->N5->N12.
Claims (4)
1. a path navigation method, is characterized in that comprising that step is as follows:
(1) grid division: navigation data is carried out to grid division according to longitude and latitude, and symbiosis becomes 16384 * 16384 grids;
(2) navigation data loads: analyze the mesh coordinate of starting point and terminal, calculate that to take the straight line of starting point and terminal be cornerwise rectangle, load and be contained in the grid in rectangle;
(3) navigation is analyzed: take the time as weights, adopt the analysis of navigating of A* algorithm, calculate the optimal path between 2, if can not find optimal path in current data, expand data area, if still can not find optimal path after a data extending, load total data, find optimal path.
2. path navigation method according to claim 1, is characterized in that: the concrete steps of described step (1) grid division are as follows:
1. load road node data, in order to distinguish the longitude dimension values identical with southern hemisphere and northern hemisphere that thing hemisphere is identical, south latitude and west longitude value are represented with negative, coordinate (117.0332,-39.4211) be expressed as west longitude 117.0332, south latitude 39.4211;
2. in order to guarantee that numerical value is all positive number, respectively the longitude lon of path coordinate is added to 180, dimension values lat adds 90 degree, then respectively divided by 360 and 180, then by the ratio value of obtaining and 16384, multiplies each other, and obtains mesh coordinate (x, y);
Formula is:
3. path navigation method according to claim 1, is characterized in that: the concrete steps that described step (2) navigation data loads are as follows:
1. read the mesh coordinate (x of starting point StartPoint and terminal EndPoint
s, y
s), (x
e, y
e), execution step is 2.;
2. obtain maximum X coordinate x
max=max (x
s, x
e), minimum X coordinate x
min=min (x
s, x
e), maximum Y coordinate y
max=max (y
s, y
e), minimum Y coordinate y
min=min (y
s, y
e), load X coordinate at range delta x=[x
min, x
max] between and Y coordinate at range delta y=[y
min, y
max] between grid data.
4. path navigation method according to claim 1, is characterized in that: the concrete grammar step that described step (3) navigation is analyzed is:
1. load navigation data, comprise road node data and roadside, road data, for node, to increase attribute as follows:
A, weight: 36
B, real time cost: RealTimeCost, the product of average used time (path is divided by average velocity) and weight on current section;
C, heuristic time cost: HeuristicTimeCost, current road circuit node is to the product except value and weight of the air line distance of terminal and the average velocity in current section;
D, final time cost: FinalTimeCost, real time cost+heuristic time cost;
E, detected state: CheckState;
F, forerunner's node: PrioiNode;
Execution step 2.;
2. create Priority Queues PriorityQueue, the final time cost FinalTimeCost of take is weights, and the FinalTimeCost that starting point is set is 0, and is added in queue;
3. judge that whether queue is empty, the node of weights minimum in export queue if not empty, and be set as present node CurrentNode, execution step is 4.; If it is empty, perform step 8.;
4. judge whether CurrentNode has adjacent node, if do not have, execution step 3.; If have, from data, obtain the direct connected node NextNode that present node can reach, and judge whether NextNode is terminal, if execution step 5.; If not execution step 6.;
5. according to forerunner's node PriorNode of terminal EndPoint, and forerunner's node PriorNode of forerunner's node PriorNode, until starting point, recurrence is obtained whole path, exits;
6. judge the detected state CheckState of connected node NextNode, if detected state Unchecked not, forerunner's node PriorNode that connected node NextNode is set is present node CurrentNode, counter real time cost RealTimeCost, inspire time cost HeuristicTimeCost and final time cost FinalTimeCost and joined in queue, tamper detection state is Checking; If just at detected state Checking, whether than former final time cost FinalTimeCost little in judgement if through present node, arriving the new final time cost FinalTimeCost of adjacent node, if revise former final time cost FinalTimeCost for new final time cost FinalTimeCost, if not, execution step 7., if 7. detected state Checked, perform step;
7. the detected state CheckState that revises present node CurrentNode is detected state Checked, continues execution step 3.
8. judge whether current data area is total data, if so, there is not path in explanation, exits; If not, load grid X coordinate range at [x
min-Δ x, x
min] [x
max, x
max+ Δ x] and Y coordinate range at [y
min-Δ y, y
max+ Δ y] and grid X coordinate range at [x
min, x
max] and Y coordinate at [y
max, y
max+ Δ y], [y
min-Δ y, y
min] grid data, if still can not find optimal path after a data extending, load total data, find optimal path.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105606088A (en) * | 2016-02-01 | 2016-05-25 | 北京理工大学 | Route planning method based on dynamic environment |
| CN105758410A (en) * | 2015-11-14 | 2016-07-13 | 大连东软信息学院 | Method for quickly planning and mixing paths on basis of A-star algorithms |
| CN106649450A (en) * | 2016-09-23 | 2017-05-10 | 厦门嵘拓物联科技有限公司 | Method for identifying critical path in location service |
| CN108910355A (en) * | 2018-08-23 | 2018-11-30 | 西南大学 | A kind of intelligent garbage recyclable device and control method |
| CN110940337A (en) * | 2019-07-31 | 2020-03-31 | 中国第一汽车股份有限公司 | Path identification method, device, equipment and storage medium |
| CN111380526A (en) * | 2018-12-27 | 2020-07-07 | 北京航迹科技有限公司 | System and method for determining path |
| CN113607183A (en) * | 2021-08-05 | 2021-11-05 | 北京中交兴路信息科技有限公司 | Transportation route planning method and device for vehicle, storage medium and terminal |
| CN113607182A (en) * | 2021-08-05 | 2021-11-05 | 北京中交兴路信息科技有限公司 | Vehicle driving route navigation method and device, storage medium and terminal |
| CN113720342A (en) * | 2021-08-05 | 2021-11-30 | 杭州易现先进科技有限公司 | Navigation path planning method and device |
| CN115752493A (en) * | 2022-11-07 | 2023-03-07 | 国网天津市电力公司 | Path planning method for electric power fault first-aid repair |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010151539A (en) * | 2008-12-24 | 2010-07-08 | Mic Ware:Kk | Map information processing device, method for processing map information, and program |
| CN101777253A (en) * | 2009-12-24 | 2010-07-14 | 戴磊 | Real-time road condition acquiring, analyzing and back-feeding and intelligent transportation integrated service system |
| CN102298640A (en) * | 2011-09-14 | 2011-12-28 | 清华大学 | Method for preprocessing map display data |
| CN102445205A (en) * | 2011-10-12 | 2012-05-09 | 北京世纪高通科技有限公司 | Massive map data matching method and device |
| CN102542550A (en) * | 2010-12-08 | 2012-07-04 | 江南大学 | Interactive image segmentation method for reducing manual intervention |
| US20120253669A1 (en) * | 2011-03-28 | 2012-10-04 | Raytheon Company | Maritime Path Determination |
| CN103065472A (en) * | 2012-12-24 | 2013-04-24 | 中国科学院深圳先进技术研究院 | Real-time traffic status analysis method and real-time traffic status analysis system |
| CN103226581A (en) * | 2013-04-02 | 2013-07-31 | 浙江大学 | Heuristic shortest path search method based on direction optimization |
| CN103413209A (en) * | 2013-07-17 | 2013-11-27 | 西南交通大学 | Method for selecting multi-user and multi-warehouse logistics distribution path |
-
2014
- 2014-08-26 CN CN201410423802.3A patent/CN104142156A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010151539A (en) * | 2008-12-24 | 2010-07-08 | Mic Ware:Kk | Map information processing device, method for processing map information, and program |
| CN101777253A (en) * | 2009-12-24 | 2010-07-14 | 戴磊 | Real-time road condition acquiring, analyzing and back-feeding and intelligent transportation integrated service system |
| CN102542550A (en) * | 2010-12-08 | 2012-07-04 | 江南大学 | Interactive image segmentation method for reducing manual intervention |
| US20120253669A1 (en) * | 2011-03-28 | 2012-10-04 | Raytheon Company | Maritime Path Determination |
| CN102298640A (en) * | 2011-09-14 | 2011-12-28 | 清华大学 | Method for preprocessing map display data |
| CN102445205A (en) * | 2011-10-12 | 2012-05-09 | 北京世纪高通科技有限公司 | Massive map data matching method and device |
| CN103065472A (en) * | 2012-12-24 | 2013-04-24 | 中国科学院深圳先进技术研究院 | Real-time traffic status analysis method and real-time traffic status analysis system |
| CN103226581A (en) * | 2013-04-02 | 2013-07-31 | 浙江大学 | Heuristic shortest path search method based on direction optimization |
| CN103413209A (en) * | 2013-07-17 | 2013-11-27 | 西南交通大学 | Method for selecting multi-user and multi-warehouse logistics distribution path |
Non-Patent Citations (3)
| Title |
|---|
| 王亚文 等: "一种动态限制搜索区域的最短路径规划算法", 《计算机应用研究》 * |
| 王玥 等: "《微小型无人飞行器协同控制技术》", 31 January 2014, 国防工业出版社 * |
| 赵伟华 等: "车辆导航系统最优路径规划的研究与实现", 《杭州电子工业学院学报》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105758410A (en) * | 2015-11-14 | 2016-07-13 | 大连东软信息学院 | Method for quickly planning and mixing paths on basis of A-star algorithms |
| CN105758410B (en) * | 2015-11-14 | 2018-12-25 | 大连东软信息学院 | Fast path based on A-Star algorithm plans mixed method |
| CN105606088A (en) * | 2016-02-01 | 2016-05-25 | 北京理工大学 | Route planning method based on dynamic environment |
| CN105606088B (en) * | 2016-02-01 | 2019-05-28 | 北京理工大学 | A kind of paths planning method based on dynamic environment |
| CN106649450B (en) * | 2016-09-23 | 2019-07-23 | 厦门嵘拓物联科技有限公司 | The method of critical path is identified in a kind of location-based service |
| CN106649450A (en) * | 2016-09-23 | 2017-05-10 | 厦门嵘拓物联科技有限公司 | Method for identifying critical path in location service |
| CN108910355A (en) * | 2018-08-23 | 2018-11-30 | 西南大学 | A kind of intelligent garbage recyclable device and control method |
| CN111380526A (en) * | 2018-12-27 | 2020-07-07 | 北京航迹科技有限公司 | System and method for determining path |
| CN111380526B (en) * | 2018-12-27 | 2022-04-05 | 北京航迹科技有限公司 | System and method for determining path |
| CN110940337A (en) * | 2019-07-31 | 2020-03-31 | 中国第一汽车股份有限公司 | Path identification method, device, equipment and storage medium |
| CN113607183A (en) * | 2021-08-05 | 2021-11-05 | 北京中交兴路信息科技有限公司 | Transportation route planning method and device for vehicle, storage medium and terminal |
| CN113607182A (en) * | 2021-08-05 | 2021-11-05 | 北京中交兴路信息科技有限公司 | Vehicle driving route navigation method and device, storage medium and terminal |
| CN113720342A (en) * | 2021-08-05 | 2021-11-30 | 杭州易现先进科技有限公司 | Navigation path planning method and device |
| CN113720342B (en) * | 2021-08-05 | 2024-03-26 | 杭州易现先进科技有限公司 | Navigation path planning method and device |
| CN115752493A (en) * | 2022-11-07 | 2023-03-07 | 国网天津市电力公司 | Path planning method for electric power fault first-aid repair |
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