CN111401668A - Unmanned ship route planning method based on big data - Google Patents
Unmanned ship route planning method based on big data Download PDFInfo
- Publication number
- CN111401668A CN111401668A CN202010503287.5A CN202010503287A CN111401668A CN 111401668 A CN111401668 A CN 111401668A CN 202010503287 A CN202010503287 A CN 202010503287A CN 111401668 A CN111401668 A CN 111401668A
- Authority
- CN
- China
- Prior art keywords
- route
- data
- track
- point
- preset
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/231—Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Remote Sensing (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Radar, Positioning & Navigation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Automation & Control Theory (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Development Economics (AREA)
- Computing Systems (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Navigation (AREA)
Abstract
The invention relates to the field of big data processing and unmanned air route planning, in particular to an unmanned ship air route planning method based on big data. The method comprises the following steps: the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; and after data processing is carried out on the data in the database, the data are compressed and stored. Step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm. The method and the device aim at the problems that the air route track has large data volume, the data is compressed, and the problems that the data processing is slow, the data occupies large memory and the processing efficiency is low are solved.
Description
Technical Field
The invention relates to the field of big data processing and unmanned air route planning, in particular to an unmanned ship air route planning method based on big data.
Background
With the continuous progress of human science and technology, the technological development level of ships is continuously improved, and the ships are developed towards large-scale, specialization, high-speed and unmanned driving. Especially, the rapid development of high and new technologies such as artificial intelligence, internet of things, big data and the like and the combination of the technologies with ships and ocean engineering are highly emphasized by various shipbuilding shipping countries in the world in recent years, and the adoption of unmanned ships is becoming the development trend of the ship industry. Unmanned ships have wide application markets in the military field and the civil field, and the improvement of the automation level of the ships becomes urgent. The design of the unmanned ship relates to planning and navigation, and particularly, the automatic generation of the route of the unmanned ship becomes a factor restricting the rapid development of the unmanned ship industry. Therefore, an unmanned ship route planning method based on big data is needed.
Disclosure of Invention
1. The technical problem to be solved is as follows:
aiming at the technical problems, the invention provides a big data based unmanned ship route planning method, which comprises the steps of firstly establishing a track database by using a big data technology, and then obtaining the optimal route of a preset starting point and a preset end point by using a clustering algorithm.
2. The technical scheme is as follows:
an unmanned ship route planning method based on big data is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; and after data processing is carried out on the data in the database, the data are compressed and stored.
Step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm.
Further, the processing of the data in the database in the first step specifically includes cleaning abnormal data, converting the data from longitude and latitude coordinates into mercator coordinates, setting a compression threshold, and compressing the data according to the compression threshold.
Further, the trajectory data of the vessel voyage of the sea area of the predetermined range is obtained by acquiring data of the vessel AIS device and decoding the AIS data into a plain text.
Further, identifying track data between a predetermined starting point and a predetermined ending point of the unmanned ship to be sailed in a track database, specifically comprising the following steps:
step 21: and decompressing track data between the preset starting point and the preset end point, and extracting a turning point in the track.
Step 22: and obtaining the route turning points in the route based on clustering analysis on the turning points in the track.
Step 23: and (4) identifying the connectivity of the route turning points generated in the step (22), finding out communication paths among the route turning points to form a directed graph, and forming a route network.
Further, the step 21 specifically includes obtaining a heading of a constant direction line formed by a connection line of every two adjacent feature points in the trajectory data, obtaining an angle of an included angle formed by every 3 adjacent feature points in the trajectory, and if the angle of the included angle is greater than a preset threshold angle, determining that a point among the three feature points is a turning point in the trajectory.
Further, step 22 specifically includes: and identifying all similar turning points in the track data, and clustering the turning points based on a hierarchical DBSCAN algorithm to obtain all route turning points.
The method specifically comprises the steps of searching all route turning points by which the route turning points are clustered, extracting all route turning points containing a specified route by adopting an SQ L statement, storing the route turning points in a list or an array container, finding out another route turning point which is spatially closest to the route turning points from any one route turning point, and obtaining a directed graph G = (TN, <tttranslation = L & "" gTt/gTt) formed by the route turning points and the connectivity thereof after the connectivity identification between the route turning points is completed, wherein TN represents an isolated route turning point, and L represents the connectivity between the route turning points.
Further, in the second step, searching for the optimal route from the set starting point to the set end point based on the ant colony optimization algorithm specifically includes the following steps:
step 24: and combining the track data sets of the preset starting point and the preset end point into the to-be-selected air route track, and acquiring the initial cost value of each edge of the to-be-selected air route track.
Step 25: initializing the visibility of adjacent route turning points and the intensity of bioinformatics hormones according to the initial cost value of each edge of the to-be-selected route track, and acquiring the node navigation probability of the to-be-selected route track, thereby obtaining the optimal route from a set starting point to a set terminal point.
3. Has the advantages that:
(1) the method and the device aim at the problems that the air route track has large data volume, the data is compressed, and the problems that the data processing is slow, the data occupies large memory and the processing efficiency is low are solved.
(2) The method and the device perform cluster analysis on the steering points in the identified track characteristic points to identify the airway steering points, have strong anti-noise capability and high processing efficiency, intelligently search the optimal air route in the directed graph formed by the airway steering points and the connectivity by utilizing the ant colony optimization algorithm, and can ensure the correctness of the depicted data.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart according to the ant colony optimization algorithm of the present invention.
Detailed Description
As shown in the attached figure 1, the unmanned ship route planning method based on big data is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; and after data processing is carried out on the data in the database, the data are compressed and stored.
Step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm.
Further, the processing of the data in the database in the first step specifically includes cleaning abnormal data, converting the data from longitude and latitude coordinates into mercator coordinates, setting a compression threshold, and compressing the data according to the compression threshold.
Further, the trajectory data of the vessel voyage of the sea area of the predetermined range is obtained by acquiring data of the vessel AIS device and decoding the AIS data into a plain text.
Further, identifying track data between a predetermined starting point and a predetermined ending point of the unmanned ship to be sailed in a track database, specifically comprising the following steps:
step 21: and decompressing track data between the preset starting point and the preset end point, and extracting a turning point in the track.
Step 22: and obtaining the route turning points in the route based on clustering analysis on the turning points in the track.
Step 23: and (4) identifying the connectivity of the route turning points generated in the step (22), finding out communication paths among the route turning points to form a directed graph, and forming a route network.
Further, the step 21 specifically includes obtaining a heading of a constant direction line formed by a connecting line of every two adjacent track points in the track data, obtaining an angle of a composition included angle of every 3 adjacent feature points in the track, and if the angle is larger than a preset threshold angle, determining that a point among the three feature points is a turning point in the track.
Further, step 22 specifically includes: and identifying all similar turning points in the track data, and clustering the turning points based on a hierarchical DBSCAN algorithm to obtain all route turning points.
The method specifically comprises the steps of searching all route turning points by which the route turning points are clustered, extracting all route turning points containing a specified route by adopting an SQ L statement, storing the route turning points in a list or an array container, finding out another route turning point which is spatially closest to the route turning points from any one route turning point, and obtaining a directed graph G = (TN, <tttranslation = L & "" gTt/gTt) formed by the route turning points and the connectivity thereof after the connectivity identification between the route turning points is completed, wherein TN represents an isolated route turning point, and L represents the connectivity between the route turning points.
Further, in the second step, searching for the optimal route from the set starting point to the set end point based on the ant colony optimization algorithm specifically includes the following steps:
step 24: and combining the track data sets of the preset starting point and the preset end point into the to-be-selected air route track, and acquiring the initial cost value of each edge of the to-be-selected air route track. The calculation formula is as follows:
C(r，s)=kCa+(1-k)Cb
wherein: c (r, s) represents the initial cost of the navigation of the ship from the turning point r to the turning point s along the route track; ca represents the cost value of the nearby flight in the flight path to the flight path; cb represents the length of the flight path; k represents a weight value, 0< k <1.
Step 25: initializing the visibility of adjacent route turning points and the intensity of bioinformatics hormones according to the initial cost value of each edge of the to-be-selected route track, and acquiring the node navigation probability of the to-be-selected route track, thereby obtaining the optimal route from a set starting point to a set terminal point. The flow chart is shown in fig. 2, and specifically includes assuming that m ships navigate by using different course routes according to an ant colony optimization algorithm to obtain the actual navigation cost of each specific course route, and repeating for multiple times until the shortest cost route is found, from a preset starting point to a preset terminal point. Where n represents the number of cycles.
The set rule is as follows: the probability of an artificial ant selecting a new feasible turning point is determined by the cost of two turning points and the strength of the bioinformatics hormone.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. An unmanned ship route planning method based on big data is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; after data processing is carried out on the data in the database, the data are compressed and stored;
step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm.
2. The big data based unmanned ship route planning method according to claim 1, wherein: the data processing in the database in the first step specifically comprises cleaning abnormal data, converting the cleaned data into the coordinates of the mercator system from the longitude and latitude coordinates, setting a compression threshold value, and compressing the data according to the compression threshold value.
3. The big data based unmanned ship route planning method according to claim 1, wherein: the trajectory data of the vessel's voyage in the sea area of the predetermined range is obtained by acquiring the data of the vessel AIS device and decoding the AIS data into plain text.
4. The big data based unmanned ship route planning method according to claim 1, wherein: identifying track data between a preset starting point and a preset terminal point of an unmanned ship to be sailed in a track database, and specifically comprising the following steps:
step 21: decompressing track data between a preset starting point and a preset end point, and extracting a turning point in the track;
step 22: obtaining an airway turning point in the airway based on clustering analysis on the turning point in the track;
step 23: and (4) identifying the connectivity of the route turning points generated in the step (22), finding out communication paths among the route turning points to form a directed graph, and forming a route network.
5. The big data based unmanned ship route planning method according to claim 4, wherein: step 21 specifically includes obtaining a heading of a constant direction line formed by a connecting line of every two adjacent feature points in the trajectory data, obtaining an angle of an included angle formed by every 3 adjacent feature points in the trajectory, and if the angle of the included angle is larger than a preset threshold angle, determining that a point among the three feature points is a turning point in the trajectory.
6. The big data based unmanned ship route planning method according to claim 4, wherein: step 22 specifically includes: and identifying all similar turning points in the track data, and clustering the turning points based on a hierarchical DBSCAN algorithm to obtain all route turning points.
7. The big data based unmanned ship route planning method as claimed in claim 4, wherein the step 23 comprises searching all the route turning points by which the route turning points are clustered, using SQ L sentence, extracting all the route turning points including a specific route and storing them in a list or array container, then starting from any one of the route turning points, finding out another route turning point which is spatially nearest to the route turning point to recognize connectivity, and after completing the connectivity recognition between the route turning points, obtaining a directed graph G = (TN, <tttransition = L &/lTt/gTt), where TN represents isolated route turning points and L represents connectivity between the route turning points.
8. The big data based unmanned ship route planning method according to claim 4, wherein: in the second step, searching the optimal route from the set starting point to the set end point based on the ant colony optimization algorithm specifically comprises the following steps:
step 24: the track data set of the preset starting point and the preset end point is a to-be-selected route track, and the initial cost value of each side of the to-be-selected route track is obtained;
step 25: initializing the visibility of adjacent route turning points and the intensity of bioinformatics hormones according to the initial cost value of each edge of the to-be-selected route track, and acquiring the node navigation probability of the to-be-selected route track, thereby obtaining the optimal route from a set starting point to a set terminal point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010503287.5A CN111401668A (en) | 2020-06-05 | 2020-06-05 | Unmanned ship route planning method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010503287.5A CN111401668A (en) | 2020-06-05 | 2020-06-05 | Unmanned ship route planning method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111401668A true CN111401668A (en) | 2020-07-10 |
Family
ID=71431917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010503287.5A Pending CN111401668A (en) | 2020-06-05 | 2020-06-05 | Unmanned ship route planning method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111401668A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN111861045A (en) * | 2020-08-06 | 2020-10-30 | 中国科学院地理科学与资源研究所 | Method for rapidly generating marine shortest route oriented to massive digital water depth model data body |
| CN112164247A (en) * | 2020-09-03 | 2021-01-01 | 重庆大学 | Ship route prediction method based on ship track clustering |
| CN112862156A (en) * | 2021-01-08 | 2021-05-28 | 北京工业大学 | Ship path planning method based on ship track and ant colony algorithm |
| CN113435639A (en) * | 2021-06-23 | 2021-09-24 | 交通运输部规划研究院 | Port water area planning method and system integrating AIS data mining |
| CN113505836A (en) * | 2021-07-14 | 2021-10-15 | 金睛兽数字科技(重庆)有限公司 | Ship route automatic generation method based on big data |
| CN114063126A (en) * | 2021-11-15 | 2022-02-18 | 广州一链通互联网科技有限公司 | Electronic fence system and method for establishing electronic fence accurate track model |
| CN114088097A (en) * | 2021-11-12 | 2022-02-25 | 天津大学 | Method for extracting marine vessel navigable manifold frame based on AIS big data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109375625A (en) * | 2018-11-12 | 2019-02-22 | 智慧航海（青岛）科技有限公司 | A kind of intelligent ship paths planning method based on fast search genetic algorithm |
| CN109374004A (en) * | 2018-11-12 | 2019-02-22 | 智慧航海（青岛）科技有限公司 | A kind of Intelligent unattended ship paths planning method based on IA* algorithm |
-
2020
- 2020-06-05 CN CN202010503287.5A patent/CN111401668A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109375625A (en) * | 2018-11-12 | 2019-02-22 | 智慧航海（青岛）科技有限公司 | A kind of intelligent ship paths planning method based on fast search genetic algorithm |
| CN109374004A (en) * | 2018-11-12 | 2019-02-22 | 智慧航海（青岛）科技有限公司 | A kind of Intelligent unattended ship paths planning method based on IA* algorithm |
Non-Patent Citations (2)
| Title |
|---|
| SHU-KAI ZHANG等: "Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity", 《OCEAN ENGINEERING》 * |
| 姚肖肖: "基于蚁群算法与海量AIS数据的船舶航线规划", 《交通信息安全》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN111708369B (en) * | 2020-07-17 | 2021-07-23 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN111861045A (en) * | 2020-08-06 | 2020-10-30 | 中国科学院地理科学与资源研究所 | Method for rapidly generating marine shortest route oriented to massive digital water depth model data body |
| CN111861045B (en) * | 2020-08-06 | 2024-04-09 | 中国科学院地理科学与资源研究所 | Rapid generation method of offshore shortest route for massive digital water depth model data body |
| CN112164247A (en) * | 2020-09-03 | 2021-01-01 | 重庆大学 | Ship route prediction method based on ship track clustering |
| CN112862156A (en) * | 2021-01-08 | 2021-05-28 | 北京工业大学 | Ship path planning method based on ship track and ant colony algorithm |
| CN113435639A (en) * | 2021-06-23 | 2021-09-24 | 交通运输部规划研究院 | Port water area planning method and system integrating AIS data mining |
| CN113505836A (en) * | 2021-07-14 | 2021-10-15 | 金睛兽数字科技(重庆)有限公司 | Ship route automatic generation method based on big data |
| CN113505836B (en) * | 2021-07-14 | 2022-11-15 | 金睛兽数字科技(重庆)有限公司 | Ship route automatic generation method based on big data |
| CN114088097A (en) * | 2021-11-12 | 2022-02-25 | 天津大学 | Method for extracting marine vessel navigable manifold frame based on AIS big data |
| CN114063126A (en) * | 2021-11-15 | 2022-02-18 | 广州一链通互联网科技有限公司 | Electronic fence system and method for establishing electronic fence accurate track model |
| CN114063126B (en) * | 2021-11-15 | 2022-07-29 | 广州一链通互联网科技有限公司 | Electronic fence system and method for establishing electronic fence accurate track model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111401668A (en) | Unmanned ship route planning method based on big data | |
| Niu et al. | Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles | |
| CN110196598B (en) | Dynamic collision avoidance method for unmanned ship | |
| KR101850866B1 (en) | See path calculation method of vessel | |
| CN113450596A (en) | Space-time DP method based on ship track feature point extraction | |
| Tsou | Integration of a geographic information system and evolutionary computation for automatic routing in coastal navigation | |
| CN115060264A (en) | PRM route planning method based on AIS data | |
| CN111721296B (en) | Data driving path planning method for underwater unmanned vehicle | |
| CN113283653B (en) | Ship track prediction method based on machine learning and AIS data | |
| CN108959348B (en) | Method for automatically generating navigation area by fusing multiple chart data | |
| CN111240325A (en) | Unmanned ship scene understanding method based on navigation situation ontology modeling | |
| Shah et al. | Speeding up A* search on visibility graphs defined over quadtrees to enable long distance path planning for unmanned surface vehicles | |
| CN109858951A (en) | New-energy automobile is powered on or changes the prediction of electricity demanding | |
| CN115147745A (en) | Small target detection method based on urban unmanned aerial vehicle image | |
| CN113587944A (en) | Quasi-real-time vehicle driving route generation method, system and equipment | |
| CN112069371A (en) | Marine ship effective rescue searching method based on gridding neighborhood | |
| CN111428612B (en) | Pedestrian re-identification method, terminal, device and storage medium | |
| CN116952239A (en) | Unmanned ship path planning method based on fusion of improved A and DWA | |
| CN113311843B (en) | Unmanned ship path planning method based on safe distance constraint and LOS sight judgment | |
| CN114723977A (en) | Stable feature point identification method for visual SLAM system | |
| CN113703463B (en) | Ship collision avoidance path planning method based on elite multi-population evolution algorithm | |
| CN115718623A (en) | Heterogeneous radar point trace fusion parallel processing method in dense clutter environment | |
| CN113776535A (en) | Unmanned ship route planning method based on rasterized electronic chart | |
| CN112925317B (en) | AUV path planning method based on improved brainstorming optimization algorithm | |
| CN114359493A (en) | Method and system for generating three-dimensional semantic map for unmanned ship |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200710 |
|
| WD01 | Invention patent application deemed withdrawn after publication |