CN114489068A - Routing method and device for routing inspection task path of inspection robot under complex path - Google Patents
Routing method and device for routing inspection task path of inspection robot under complex path Download PDFInfo
- Publication number
- CN114489068A CN114489068A CN202210077024.1A CN202210077024A CN114489068A CN 114489068 A CN114489068 A CN 114489068A CN 202210077024 A CN202210077024 A CN 202210077024A CN 114489068 A CN114489068 A CN 114489068A
- Authority
- CN
- China
- Prior art keywords
- path
- task
- sub
- points
- area
- 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
- 238000007689 inspection Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004590 computer program Methods 0.000 claims description 11
- 230000015654 memory Effects 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
Abstract
The invention discloses a routing method for a routing inspection task path of an inspection robot under a complex path, which comprises an acquisition module, a task input module and an output module, wherein the acquisition module and the task input module are connected with the output module, the acquisition module is used for acquiring an inspection scene map, the task input module is used for receiving a task path required by the inspection of the robot, and the output module is used for planning an optimal path for the task path in the inspection scene map. The method cuts the whole road network into the multilayer region blocks by utilizing the thought of the multilayer region blocks, changes a very complicated road network into simple path region blocks, cuts a complicated task path plan into path plans in small region blocks, and connects the paths of the region blocks in series by searching among sub-regions to obtain an optimal task path.
Description
Technical Field
The invention belongs to the technical field of inspection robots, and particularly relates to an inspection task path planning method and device for an inspection robot under a complex path.
Background
In the inspection robots at the present stage, the commonly used path planning method mainly includes manual planning and various conditional breadth searches.
The traditional manual planning is mainly based on manual connection of task points, tasks are executed according to a manual connection sequence, and a task path is approximately optimal, stable and reliable.
The task path planning based on the breadth search can intelligently plan a task path from the position of the robot, the path is flexible and changeable, manual planning is not needed, but a global optimal path is difficult to obtain based on the breadth search mode, and a local optimal path is extremely possibly obtained, so that how to automatically plan an optimal task path is a research hotspot in recent years, and in the existing task path planning, the common planning method is manual planning and various conditional breadth searches; manual planning requires manual sorting by manually selecting path points to obtain a fixed path, and different tasks require manual planning, so that the method is not convenient; the path plan is obtained based on a searching mode, an optimal path can be obtained under a simple path, but the obtained path is difficult to obtain under a complex path network in the whole power grid, and generally is a local optimal value.
Disclosure of Invention
The invention aims to solve the problem that the obtained path is difficult to obtain the optimal path under the existing complex path network, and generally has a local optimal value, and provides a routing inspection task path planning method for an inspection robot under the complex path based on the invention, wherein the whole path network is divided into a plurality of large areas, the large areas are further divided into thinner small areas, and the optimal planning based on multilayer areas is combined with the path planning in the areas, so that the optimal task path is automatically obtained;
in order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the invention provides a routing planning method for a routing inspection task of a routing inspection robot under a complex path, which comprises the following steps,
the system comprises an acquisition module, a task input module and an output module, wherein the acquisition module is connected with the task input module and the output module, the acquisition module is used for acquiring an inspection scene map, the task input module is used for receiving a task path required to be inspected by a robot, and the output module is used for planning an optimal path for the task path in the inspection scene map.
Optionally, the output module includes area division, area refinement, road network initialization, and task path planning, which are sequentially connected,
the area division is used for dividing the whole patrol scene map into several main areas,
the region refinement is used for dividing the region into a plurality of sub-regions based on path points which are adjacent in the same direction in a large main region range,
the road network initialization is used for connecting the path points in all the sub-regions into a road network based on a data structure of a directed graph, taking the cost value among all the path points as a weight, calculating and initializing the path points into a complete graph by using a Floyd algorithm, and finally calculating the minimum weight among all the path points by using the Floyd algorithm,
and the task path planning is used for identifying the position of the robot and carrying out optimal path planning on path points in each sub-area.
Optionally, the main area may be divided according to the south-east, the west-north area, or the main area may be divided according to the front, the back, the left, and the right areas.
Optionally, the task path planning includes task point subdivision, sub-region path planning, and inter-sub-region search, where the task point subdivision allocates the received task path to be inspected to each sub-region according to its own information, so as to facilitate path planning in a later period, and starts sub-region path planning after task point subdivision,
the initial starting point of the sub-area path planning is that a large area and a sub-area where the robot is located are located based on the position of the robot, a local optimal path is obtained in the sub-area in a deep search mode, after the optimal path is obtained, whether unplanned task points exist in the current large area or not is judged, if yes, inter-sub-area search is conducted, if not, whether unplanned task points exist in other large areas or not is judged, if yes, sub-area search is conducted on the sub-areas in the large areas, and if not, path planning of the task points in all the areas is completed;
and searching all task points of the sub-areas needing to be planned by taking the last point position of the currently planned path as a starting point among the sub-areas, searching the task point with the minimum cost value, and feeding back the task point to a sub-area path planning module by taking the task point as a new starting point to perform a new round of planning.
Optionally, the method comprises the following steps:
s1: the method comprises the steps that an acquisition module acquires a patrol scene map in the power grid;
s2: the regional division divides the whole patrol scene map into a plurality of main regions;
s3: the area refinement is used for dividing the path points which are adjacent in the same direction into a plurality of sub-areas in a large main area range;
s4: the method comprises the steps that road network initialization is used for connecting path points in all sub-regions into a road network based on a data structure of a directed graph, cost values among the path points are used as weights, a Floyd algorithm is used for calculating and initializing the path points into a complete graph, and finally the Floyd algorithm is used for calculating the minimum weight among the path points;
s5: the task point subdivision allocates the received task path needing to be inspected to respective sub-regions according to the information of the task path, so that path planning at the later stage is facilitated, and sub-region path planning is started after the task point subdivision;
s6: the initial starting point of the regional path planning is to position a large region and a sub-region where the robot is located based on the position of the robot, and obtain a local optimal path in the sub-region by using a deep search mode;
s7: after the optimal path is obtained, whether unplanned task points exist in the current large area or not is judged, if yes, inter-sub-area search is carried out, if not, whether unplanned task points exist in other large areas or not is judged, if yes, sub-area search is carried out on sub-areas in the large areas, and if not, path planning of the task points in all the areas is finished.
In a second aspect, an embodiment of the present application provides a routing inspection task path planning device for an inspection robot under a complex path, including: one or more processors; one or more memories; a plurality of application programs;
and one or more programs, wherein the one or more programs are stored in the memory, which when executed by the processor, cause the apparatus to perform the method of any of the first aspects above.
In a third aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method of any one of the above first aspects.
The invention has the beneficial effects
The method only needs to manually divide the region blocks once for the whole road network, can meet the optimal task path planning of the whole road network, does not need to manually plan a certain task, greatly reduces manual operation, reduces manual excessive operation, and can also obtain the optimal task path which is not a local optimal path.
Drawings
FIG. 1 is a schematic diagram of the work flow of the output module of the present invention.
Fig. 2 is a schematic diagram of a task path planning workflow according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1-2, the present invention provides a routing planning method for a routing inspection task of a routing inspection robot under a complex path, including,
the system comprises an acquisition module, a task input module and an output module, wherein the acquisition module and the task input module are connected with the output module, the acquisition module is used for acquiring a patrol scene map, the task input module is used for receiving a task path required to be patrolled by a robot, the output module is used for planning an optimal path for the task path in the patrol scene map, the output module comprises region division, region refinement, road network initialization and task path planning, and the region division, the region refinement, the road network initialization and the task path planning are sequentially connected,
the area division is used for dividing the whole routing inspection scene map into a plurality of main areas, the main areas can be divided according to the areas of the south, the east, the west and the north, or the main areas are divided according to the areas of the front, the back, the left and the right,
the region refinement is based on dividing the path points which are already adjacent in the same direction, which are oriented in the same direction, for example, the east direction of two or more path points, i.e. the points are divided into a sub-region,
the road network initialization is used for connecting the path points in all the sub-regions into a road network based on a data structure of a directed graph, taking the cost value among all the path points as a weight, calculating and initializing the path points into a complete graph by using a Floyd algorithm, and finally calculating the minimum weight among all the path points by using the Floyd algorithm,
the mission path planning is used for identifying the position of the robot and carrying out optimal path planning on path points in each sub-area,
the task path planning comprises task point subdivision, sub-region path planning and inter-sub-region searching, the task point subdivision distributes the received task path required to be inspected into respective sub-regions according to the information of the task path, so that the path planning of the sub-regions is started after the task point subdivision,
the initial starting point of the sub-area path planning is that a large area and a sub-area where the robot is located are located based on the position of the robot, a local optimal path is obtained in the sub-area in a deep search mode, after the optimal path is obtained, whether unplanned task points exist in the current large area or not is judged, if yes, inter-sub-area search is conducted, if not, whether unplanned task points exist in other large areas or not is judged, if yes, sub-area search is conducted on the sub-areas in the large areas, and if not, path planning of the task points in all the areas is completed;
searching among the sub-regions by taking the last point position of the currently planned path as a starting point, searching the task points of all the sub-regions to be planned, searching the task point with the minimum cost value, and feeding back the task point as a new starting point to the sub-region path planning module for a new round of planning;
the method utilizes the thought of multilayer region blocks, cuts the whole road network into the multilayer region blocks, changes a very complicated road network into simple path region blocks, cuts a complicated task path plan into path plans inside small region blocks, and connects the paths of the region blocks in series through retrieval among sub-regions to obtain an optimal task path;
the method comprises the following steps:
s1: the method comprises the steps that an acquisition module acquires a patrol scene map in the power grid;
s2: the region division divides the whole patrol scene map into a plurality of main regions;
s3: the area refinement is used for dividing the path points which are adjacent in the same direction into a plurality of sub-areas in a large main area range;
s4: the method comprises the steps that road network initialization is used for connecting path points in all sub-regions into a road network based on a data structure of a directed graph, cost values among the path points are used as weights, a Floyd algorithm is used for calculating and initializing the path points into a complete graph, and finally the Floyd algorithm is used for calculating the minimum weight among the path points;
s5: the task point subdivision allocates the received task path needing to be inspected to respective sub-regions according to the information of the task path, so that path planning at the later stage is facilitated, and sub-region path planning is started after the task point subdivision;
s6: the initial starting point of the regional path planning is to position a large region and a sub-region where the robot is located based on the position of the robot, and obtain a local optimal path in the sub-region by using a deep search mode;
s7: after the optimal path is obtained, whether unplanned task points exist in the current large area or not is judged, if yes, inter-sub-area search is carried out, if not, whether unplanned task points exist in other large areas or not is judged, if yes, sub-area search is carried out on sub-areas in the large areas, and if not, path planning of the task points in all the areas is finished.
The embodiment of the application provides a task path planning device is patrolled and examined to robot patrols and examines under complicated route, includes: one or more processors; one or more memories; a plurality of application programs;
and one or more programs, wherein the one or more programs are stored in the memory, which when executed by the processor, cause the apparatus to perform the method of any of the first aspects above.
An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the method of any one of the above first aspects;
the integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (7)
1. A routing inspection task path planning method for an inspection robot under a complex path is characterized by comprising the following steps of,
the system comprises an acquisition module, a task input module and an output module, wherein the acquisition module is connected with the task input module and the output module, the acquisition module is used for acquiring an inspection scene map, the task input module is used for receiving a task path required to be inspected by a robot, and the output module is used for planning an optimal path for the task path in the inspection scene map.
2. The routing inspection task path planning method for the inspection robot under the complex path according to claim 1,
the output module comprises region division, region refinement, road network initialization and task path planning which are sequentially connected,
the area division is used for dividing the whole patrol scene map into several main areas,
the region refinement is used for dividing the region into a plurality of sub-regions based on path points which are adjacent in the same direction in a large main region range,
the road network initialization is used for connecting the path points in all the sub-regions into a road network based on a data structure of a directed graph, taking the cost value among all the path points as a weight, calculating and initializing the path points into a complete graph by using a Floyd algorithm, and finally calculating the minimum weight among all the path points by using the Floyd algorithm,
and the task path planning is used for identifying the position of the robot and carrying out optimal path planning on path points in each sub-area.
3. The routing inspection task path planning method for the inspection robot under the complex path according to claim 2,
the main area can be divided according to the regions of southeast, northwest, or the main area is divided according to the regions of front, back, left and right.
4. The inspection robot inspection task path planning method under the complex path according to claim 3,
the task path planning comprises task point subdivision, sub-region path planning and inter-sub-region searching, the task point subdivision distributes the received task path required to be inspected into respective sub-regions according to the information of the task path, so that the path planning of the sub-regions is started after the task point subdivision,
the initial starting point of the sub-area path planning is that a large area and a sub-area where the robot is located are located based on the position of the robot, a local optimal path is obtained in the sub-area in a deep search mode, after the optimal path is obtained, whether unplanned task points exist in the current large area or not is judged, if yes, inter-sub-area search is conducted, if not, whether unplanned task points exist in other large areas or not is judged, if yes, sub-area search is conducted on the sub-areas in the large areas, and if not, path planning of the task points in all the areas is completed;
and searching among the sub-regions by taking the last point position of the currently planned path as a starting point, searching the task points of all the sub-regions needing to be planned, searching the task point with the minimum cost value, and feeding back the task point as a new starting point to the sub-region path planning module for a new round of planning.
5. The inspection robot inspection task path planning method under the complex path according to claim 4,
the method comprises the following steps:
s1: the method comprises the steps that an acquisition module acquires a patrol scene map in the power grid;
s2: the region division divides the whole patrol scene map into a plurality of main regions;
s3: the area refinement is used for dividing the path points which are adjacent in the same direction into a plurality of sub-areas within a large main area range;
s4: the method comprises the steps that road network initialization is used for connecting path points in all sub-regions into a road network based on a data structure of a directed graph, cost values among the path points are used as weights, a Floyd algorithm is used for calculating and initializing the path points into a complete graph, and finally the Floyd algorithm is used for calculating the minimum weight among the path points;
s5: the task point subdivision allocates the received task path needing to be inspected to respective sub-regions according to the information of the task path, so that path planning at the later stage is facilitated, and sub-region path planning is started after the task point subdivision;
s6: the initial starting point of the regional path planning is to position a large region and a sub-region where the robot is located based on the position of the robot, and obtain a local optimal path in the sub-region by using a deep search mode;
s7: after the optimal path is obtained, whether unplanned task points exist in the current large area or not is judged, if yes, inter-sub-area search is carried out, if not, whether unplanned task points exist in other large areas or not is judged, if yes, sub-area search is carried out on sub-areas in the large areas, and if not, path planning of the task points in all the areas is finished.
6. The utility model provides a patrol and examine robot and patrol and examine task path planning device under complicated route which characterized in that includes: one or more processors;
one or more memories;
a plurality of application programs;
and one or more programs, wherein the one or more programs are stored in the memory, which when executed by the processor, cause the apparatus to perform the method of any of claims 1-5.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210077024.1A CN114489068A (en) | 2022-01-24 | 2022-01-24 | Routing method and device for routing inspection task path of inspection robot under complex path |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210077024.1A CN114489068A (en) | 2022-01-24 | 2022-01-24 | Routing method and device for routing inspection task path of inspection robot under complex path |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114489068A true CN114489068A (en) | 2022-05-13 |
Family
ID=81472394
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210077024.1A Pending CN114489068A (en) | 2022-01-24 | 2022-01-24 | Routing method and device for routing inspection task path of inspection robot under complex path |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114489068A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05324603A (en) * | 1992-05-21 | 1993-12-07 | Mitsubishi Electric Corp | Operation supporting device |
| JP2007240281A (en) * | 2006-03-07 | 2007-09-20 | Navitime Japan Co Ltd | Area division round route search system, route search server, and area division round route search method |
| KR20130101913A (en) * | 2012-03-06 | 2013-09-16 | 고려대학교 산학협력단 | Path planning system and method |
| CN104615138A (en) * | 2015-01-14 | 2015-05-13 | 上海物景智能科技有限公司 | Dynamic indoor region coverage division method and device for mobile robot |
| JP2015125060A (en) * | 2013-12-26 | 2015-07-06 | 株式会社トヨタマップマスター | Route search device, method thereof, computer program for route search, and recording medium with computer program recorded thereon |
| CN110502006A (en) * | 2019-07-22 | 2019-11-26 | 中国矿业大学 | A kind of Mine Abandoned Land mobile robot complete coverage path planning method |
| CN110595478A (en) * | 2019-09-16 | 2019-12-20 | 北京华捷艾米科技有限公司 | Robot full-coverage path planning method, device and equipment based on off-line map |
| CN112462783A (en) * | 2020-12-02 | 2021-03-09 | 中国人民解放军国防科技大学 | Task planning method of heterogeneous multi-robot system under full-coverage application |
| CN112612273A (en) * | 2020-12-21 | 2021-04-06 | 南方电网电力科技股份有限公司 | Routing inspection robot obstacle avoidance path planning method, system, equipment and medium |
| CN112902970A (en) * | 2021-02-25 | 2021-06-04 | 深圳市朗驰欣创科技股份有限公司 | Routing inspection path planning method and routing inspection robot |
| CN112965485A (en) * | 2021-02-03 | 2021-06-15 | 武汉科技大学 | Robot full-coverage path planning method based on secondary region division |
| CN113110457A (en) * | 2021-04-19 | 2021-07-13 | 杭州视熵科技有限公司 | Autonomous coverage inspection method for intelligent robot in indoor complex dynamic environment |
| CN113381331A (en) * | 2021-06-23 | 2021-09-10 | 国网山东省电力公司济宁市任城区供电公司 | Intelligent inspection system for transformer substation |
-
2022
- 2022-01-24 CN CN202210077024.1A patent/CN114489068A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05324603A (en) * | 1992-05-21 | 1993-12-07 | Mitsubishi Electric Corp | Operation supporting device |
| JP2007240281A (en) * | 2006-03-07 | 2007-09-20 | Navitime Japan Co Ltd | Area division round route search system, route search server, and area division round route search method |
| KR20130101913A (en) * | 2012-03-06 | 2013-09-16 | 고려대학교 산학협력단 | Path planning system and method |
| JP2015125060A (en) * | 2013-12-26 | 2015-07-06 | 株式会社トヨタマップマスター | Route search device, method thereof, computer program for route search, and recording medium with computer program recorded thereon |
| CN104615138A (en) * | 2015-01-14 | 2015-05-13 | 上海物景智能科技有限公司 | Dynamic indoor region coverage division method and device for mobile robot |
| CN110502006A (en) * | 2019-07-22 | 2019-11-26 | 中国矿业大学 | A kind of Mine Abandoned Land mobile robot complete coverage path planning method |
| CN110595478A (en) * | 2019-09-16 | 2019-12-20 | 北京华捷艾米科技有限公司 | Robot full-coverage path planning method, device and equipment based on off-line map |
| CN112462783A (en) * | 2020-12-02 | 2021-03-09 | 中国人民解放军国防科技大学 | Task planning method of heterogeneous multi-robot system under full-coverage application |
| CN112612273A (en) * | 2020-12-21 | 2021-04-06 | 南方电网电力科技股份有限公司 | Routing inspection robot obstacle avoidance path planning method, system, equipment and medium |
| CN112965485A (en) * | 2021-02-03 | 2021-06-15 | 武汉科技大学 | Robot full-coverage path planning method based on secondary region division |
| CN112902970A (en) * | 2021-02-25 | 2021-06-04 | 深圳市朗驰欣创科技股份有限公司 | Routing inspection path planning method and routing inspection robot |
| CN113110457A (en) * | 2021-04-19 | 2021-07-13 | 杭州视熵科技有限公司 | Autonomous coverage inspection method for intelligent robot in indoor complex dynamic environment |
| CN113381331A (en) * | 2021-06-23 | 2021-09-10 | 国网山东省电力公司济宁市任城区供电公司 | Intelligent inspection system for transformer substation |
Non-Patent Citations (1)
| Title |
|---|
| 张启飞 等: "基于多阶段决策的机器人全局路径规划算法", 计算机工程, vol. 42, no. 10, 31 October 2016 (2016-10-31), pages 296 - 302 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fowler et al. | Automatic extraction of irregular network digital terrain models | |
| CN108763287B (en) | Construction method of large-scale passable regional driving map and unmanned application method thereof | |
| CN109635052B (en) | Point cloud data processing method and device and storage medium | |
| US9489716B1 (en) | Street-level imagery acquisition and selection | |
| CN102496187B (en) | Method for tracking contour line to boundary and fault based on triangular mesh | |
| CN109947881B (en) | POI weight judging method and device, mobile terminal and computer readable storage medium | |
| CN112233056B (en) | Point cloud fusion method and device for multi-source data and storage medium | |
| CN106296488A (en) | A kind of intelligent tourism system based on mass-rent pattern and method | |
| CN109739943A (en) | Change statistical processing methods towards natural resources vector ground mulching | |
| CN115185303B (en) | Unmanned aerial vehicle patrol path planning method for national parks and natural protected areas | |
| CN113656477A (en) | Method for verifying and fusing multi-source heterogeneous data of homeland space | |
| CN114255270A (en) | Method and platform for automatically calculating roadbed earthwork based on oblique photography technology | |
| CN109345450A (en) | A kind of image mosaic method using geographical entity element information | |
| CN114489068A (en) | Routing method and device for routing inspection task path of inspection robot under complex path | |
| CN115661426B (en) | Model modification method, device, equipment and medium based on three-dimensional engine | |
| CN116563968A (en) | Intelligent inspection method and device based on risk thermodynamic map and electronic equipment | |
| CN111241228A (en) | Comprehensive drawing method based on vector data and graphical processing technology | |
| CN113074735B (en) | Processing method of map data structure | |
| CN111323026A (en) | Ground filtering method based on high-precision point cloud map | |
| CN115062940A (en) | Intelligent visual inspection method and system for launching pad based on digital twins | |
| CN109902139B (en) | R-tree-based track data compression method | |
| Stefanakis et al. | Incorporating fuzzy logic methodologies into GIS operations | |
| CN113593244A (en) | Flow determination method and device, storage medium and electronic device | |
| CN111382165A (en) | Mobile homeland management system | |
| CN114637305B (en) | Unmanned aerial vehicle shortest path planning method and device |
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 |