CN104965518A - Power inspection tour flying robot air route planning method based on three-dimensional digital map - Google Patents
Power inspection tour flying robot air route planning method based on three-dimensional digital map Download PDFInfo
- Publication number
- CN104965518A CN104965518A CN201510263381.7A CN201510263381A CN104965518A CN 104965518 A CN104965518 A CN 104965518A CN 201510263381 A CN201510263381 A CN 201510263381A CN 104965518 A CN104965518 A CN 104965518A
- Authority
- CN
- China
- Prior art keywords
- flying robot
- map
- inspection tour
- planning
- grid
- 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.)
- Granted
Links
Abstract
The invention discloses a power inspection tour flying robot air route planning method based on a three-dimensional digital map in the technical field of power transmission line inspection tour flying robot air route planning method research. The method comprises: acquiring three-dimensional data information of terrain and surface features near a power line by using laser scanning technology and constructing a corresponding three-dimensional digital map; generating a barrier map according to the three-dimensional digital map and establishing a safe flight path for the inspection tour flying robot; if a weather condition which is not beneficial to flying robot inspection tour happens, immediately making an emergency landing in order to enable the flying robot safely land as near as possible, and reclaiming the flying robot after the emergency situation finishes; if no special weather condition happens, enabling the flying robot to determine whether an inspection tour task is completed after performing inspection tour for a certain time; if the inspection tour task is completed, enabling the flying robot to return along the original safe flight path; and if the inspection tour task is not completed, enabling the flying robot to perform autonomous risk avoiding according to the established safe flight path.
Description
Technical field
The invention belongs to polling transmission line flying robot flight course planning technique study technical field, particularly a kind of electric inspection process flying robot flight course planning method based on Three-dimensional Numeric Map.
Background technology
Along with the progress of science and technology, the tour of transmission line of electricity is replaced by patrolling and examining slowly to be patrolled and examined by flying robot by initial people.Like this, the work risk of staff is just greatly reduced.But meanwhile, because the rugged surroundings factors such as line of electric force Terrain, atural object, meteorology and flying robot self run into the impact of emergency situations, causing patrolling and examining flying robot needs to carry out path planning and urgent danger prevention.
Current overhead transmission line is patrolled and examined flying robot and can be patrolled and examined transmission line of electricity preferably, but in the process of making an inspection tour, the safety set up is kept away and prominently when barrier flight path seldom considers flying robot's operation is aloft met rugged surroundings (storm etc.) and dynamic barrier (as occurred suddenly some birds etc.), does not also have a set of perfect flight course planning method to solve these emergency situations.
Summary of the invention
For above-mentioned the deficiencies in the prior art part, the present invention proposes a kind of electric inspection process flying robot flight course planning method based on Three-dimensional Numeric Map, and it is characterized in that, the method comprises:
Step 1: the three-dimensional data information being gathered line of electric force Terrain atural object by laser scanner technique, and build corresponding Three-dimensional Numeric Map;
Step 2: according to Three-dimensional Numeric Map dyspoiesis thing map, and utilize the high frequency probability 3D mapping framework based on Octree to generate global context map, automatically the certain semantic model by mission requirements is set up again by airborne comprehensive sensor, thus utilize local multiresolution to keep away the paths planning method hindering and combine with layering structure, set up the safe flight path patrolling and examining flying robot;
Step 3: meet if prominent and be unfavorable for the weather condition that flying robot patrols and examines, then take emergency landing immediately, make flying robot's safe landing nearby, again etc. to be recycled after emergency situations terminates;
Step 4: if without special weather situation, then crusing robot judges after patrolling and examining setting-up time whether patrol task completes; If patrol task completes, then make a return voyage by former safe flight route; If patrol task does not complete, then carry out autonomous hedging according to the safe flight path in step 2.
Local multiresolution in described step 2 is kept away and is hindered the paths planning method that combines with layering structure and comprise following sub-step:
Sub-step 101: set top for the task incipient stage, accessible around, a series of destination of regulation in advance, patrols and examines flying robot in flight course and flies according to these destinations; Because global context map can not change at these destinations, therefore use Global motion planning when starting task;
In the global context map that the high frequency probability 3D mapping framework based on Octree produces, each node on behalf of Octree one is called the cubic volume of voxel; Wherein, leaf node n is z in sensor measurement distance
1:ttime probability be expressed as
This formula is by current distance z
t, P (n) Prior Probability and sensor measurement distance be z
1:t-1time probability P (n|z
1:t-1) determine; The method not only can take known spatial, but also can take arbitrarily unknown space;
Finally, Global motion planning enters lower one deck as input layer, local multiresolution path planning layer;
Described top for mission planning layer and global path planning layer, drawn by certain semantic model and global context map respectively;
Sub-step 102: at local multiresolution path planning layer, adopt the path planning based on grid, adopt multiple to patrol and examine the 3D grid that the size centered by flying robot is M × M × M, by the method for recurrence, these grids are embedded each other, obtain the uniform grid that size is N × N × N, in the grid of each M × M × M, all comprise a unit;
Described local multiresolution path planning layer is obtained by airborne sensor, global context map and partial barriers map;
Step 103: for the planning in grid, needs embedding non-directed graph, and grid inside connects all grids around; In the grid of patrolling and examining centered by flying robot, use a kind of graph search algorithm to carry out path planning, finally obtain the most effective the shortest path in grid;
Step 104: in lowermost layer, break the barriers map and motion model are set up a local based on self-adaptation obstacle region of rejection and are kept away barrier layer as safe floor; All barriers are regarded as the place of the highest repulsion degree, and robot always moves to the place of low repulsion degree from the place of height repulsion degree.
Described global context map utilizes the high frequency probability 3D mapping framework based on Octree to generate.
Described certain semantic model is by the automatic foundation by mission requirements of airborne comprehensive sensor.
Described partial barriers map is generated by Three-dimensional Numeric Map.
The design in described safe flight path to consider hot line job safe distance, helicopter avoid when flying electromagnetic field interference, ensure away from the sampled point nearest from fixed obstacle enough with the self-adaptation radius of turn ρ allowing flying robot one minimum simultaneously
min, wherein,
wherein, V is the constant travel speed of flying robot, Φ
maxthe maximum deflection angle of flying robot) convolution can avoiding obstacles, without any collision.
Accompanying drawing explanation
Fig. 1 be the present invention is based on Three-dimensional Numeric Map patrol and examine flying robot's flight course planning process flow diagram.
Fig. 2 keeps away barrier with layering structure in conjunction with path planning process figure for patrolling and examining flying robot local multiresolution.
Embodiment
Below in conjunction with drawings and Examples, the method that the present invention proposes is further described.
Be illustrated in figure 1 the present invention is based on Three-dimensional Numeric Map patrol and examine flying robot's flight course planning process flow diagram; The method concrete steps are:
1) gathered the three-dimensional data information of line of electric force Terrain atural object by laser scanner technique, and build corresponding Three-dimensional Numeric Map;
2) according to Three-dimensional Numeric Map dyspoiesis thing map, and utilize the high frequency probability 3D mapping framework based on Octree to generate global context map, in addition the certain semantic model by mission requirements is automatically set up by airborne comprehensive sensor, thus utilize local multiresolution to keep away the paths planning method hindering and combine with layering structure, set up the safe flight path patrolling and examining flying robot.
Be illustrated in figure 2 patrol and examine flying robot local multiresolution keep away barrier with layering structure in conjunction with path planning process figure, these paths planning method concrete steps are:
First, designing top is mission planning layer and global path planning layer, is drawn respectively by semantic model and global context map.This is two-layer is the task incipient stage, and accessible around, therefore artificially can specify a series of destination in advance, flying robot must fly according to these destinations in flight course.Because environmental map can not change at these destinations, therefore use Global motion planning when starting task.In the global context map that the high frequency probability 3D mapping framework based on Octree produces, each node on behalf of Octree one is called the cubic volume of voxel.Wherein, leaf node n is z in sensor measurement distance
1:ttime probability be expressed as
This formula is by current distance z
t, P (n) Prior Probability and sensor measurement distance be z
1:t-1time probability P (n|z
1:t-1) determine.The method not only can take known spatial, but also can take arbitrarily unknown space.Finally, Global motion planning enters lower one deck as input layer, local multiresolution path planning layer.
Secondly, design local multiresolution path planning layer, this layer is obtained by airborne sensor, global context map and partial barriers map.At local multiresolution path planning layer, adopt the path planning based on grid, and when distance increases, reduce the resolution of grid, when distance reduces, improve resolution accuracy.For this path planning layer, adopt the 3D grid that multiple size centered by flying robot is M × M × M, by the method for recurrence, these grids are embedded each other, obtain the uniform grid that size is N × N × N, in the grid of each M × M × M, all comprise (log
2(N/M)+1) M
3individual unit.For the planning in grid, need embedding non-directed graph, grid inside connects all grids around.In the grid centered by robot, a kind of graph search algorithm is used to carry out path planning.In this searching algorithm, to connect the start node of node as search of eight most inside cell grids, the cost on traversal border is that the boundary length two parts by being drawn by Euclidean distance between the obstacle cost of its institute's join domain and central area are tried to achieve, i.e. f (n)=g (n)+d (n).The length of this barrier cost g (n) each unit for edge score is multiplied by, and obtained to the Euclidean distance d (n) of object element by heuristic search, wherein,
finally obtain the most effective the shortest path in grid.
Finally, break the barriers map and motion model design a local based on self-adaptation obstacle region of rejection and keep away barrier layer as safe floor, and it is the bottom of this structure.All barriers are all regarded as the place of the highest repulsion degree, flying robot patrols and examines in process can repel these barriers automatically, impact point is regarded as the place of minimum repulsion degree, and therefore the movement locus of flying robot always moves to the place of low repulsion degree from the place of height repulsion degree.If the positional information of flying robot is q
robot=[x, y, z]
t, target position information is q
goal=[x
goal, y
goal, z
goal]
t, the position of i-th barrier is q
obstacle(i)=[x
obstacle, y
obstacle, z
obstacle]
t, then robot to the repulsion degree function of i-th barrier is
robot can be expressed as to the repulsion degree function of impact point
wherein, k
rep (i)represent the repulsion degree scale-up factor of robot to i-th barrier, q
robot-q
obstaclerepresent position q
robotto the distance of i-th barrier.By the descent direction of search synthesis repulsion degree, realize the collision-free Trajectory Planning of Welding of flying robot.When flying robot is prominent meet dynamic barrier time (birds as flight), this layer by the little barrier of the sensor senses on robot platform frequency and avoid the collision of these barriers.If flying robot from barrier too close to, this safe floor can also reduce the speed of flying robot or make its complete stop motion.The local avoidance obstacle instruction that the speed reducing crusing robot can be sent by airborne microcontroller acts on speeds control layer and is controlled, and airborne Hovering control device then can force it to stop at a certain position.
3) by information such as device measuring wind speed, humidity, Ground Meteorological such as anemoscope, humidity sensor and weather satellite.Meet meteorology change (as storm etc.) being unfavorable for that flying robot patrols and examines if prominent, take emergency landing immediately, make flying robot's safe landing nearby fast, again etc. to be recycled after emergency situations terminates;
4) if change without special weather, flying robot judges after patrolling and examining a period of time whether patrol task completes, if complete, make a return voyage by former safe flight route, if do not complete, then always according to step 2) in best safety flight path carry out autonomous hedging.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (6)
1., based on an electric inspection process flying robot flight course planning method for Three-dimensional Numeric Map, it is characterized in that described method comprises:
Step 1: the three-dimensional data information being gathered line of electric force Terrain atural object by laser scanner technique, and build corresponding Three-dimensional Numeric Map;
Step 2: according to Three-dimensional Numeric Map dyspoiesis thing map, and utilize the high frequency probability 3D mapping framework based on Octree to generate global context map, automatically the certain semantic model by mission requirements is set up again by airborne comprehensive sensor, thus utilize local multiresolution to keep away the paths planning method hindering and combine with layering structure, set up the safe flight path patrolling and examining flying robot;
Step 3: meet if prominent and be unfavorable for the weather condition that flying robot patrols and examines, then take emergency landing immediately, make flying robot's safe landing nearby, again etc. to be recycled after emergency situations terminates;
Step 4: if without special weather situation, then crusing robot judges after patrolling and examining setting-up time whether patrol task completes; If patrol task completes, then make a return voyage by former safe flight route; If patrol task does not complete, then carry out autonomous hedging according to the safe flight path in step 2.
2. method according to claim 1, the local multiresolution that it is characterized in that in described step 2 is kept away and is hindered the paths planning method combined with layering structure and comprise following sub-step:
Sub-step 101: set top for the task incipient stage, accessible around, a series of destination of regulation in advance, patrols and examines flying robot in flight course and flies according to these destinations; Because global context map can not change at these destinations, therefore use Global motion planning when starting task;
In the global context map that the high frequency probability 3D mapping framework based on Octree produces, each node on behalf of Octree one is called the cubic volume of voxel; Wherein, leaf node n is z in sensor measurement distance
1:ttime probability be expressed as
This formula is by current distance z
t, P (n) Prior Probability and sensor measurement distance be z
1:t-1time probability P (n|z
1:t-1) determine; The method not only can take known spatial, but also can take arbitrarily unknown space;
Finally, Global motion planning enters lower one deck as input layer, local multiresolution path planning layer;
Described top for mission planning layer and global path planning layer, drawn by certain semantic model and global context map respectively;
Sub-step 102: at local multiresolution path planning layer, adopt the path planning based on grid, adopt multiple to patrol and examine the 3D grid that the size centered by flying robot is M × M × M, by the method for recurrence, these grids are embedded each other, obtain the uniform grid that size is N × N × N, in the grid of each M × M × M, all comprise a unit;
Described local multiresolution path planning layer is obtained by airborne sensor, global context map and partial barriers map;
Step 103: for the planning in grid, needs embedding non-directed graph, and grid inside connects all grids around; In the grid of patrolling and examining centered by flying robot, use a kind of graph search algorithm to carry out path planning, finally obtain the most effective the shortest path in grid;
Step 104: in lowermost layer, break the barriers map and motion model are set up a local based on self-adaptation obstacle region of rejection and are kept away barrier layer as safe floor; All barriers are regarded as the place of the highest repulsion degree, and robot always moves to the place of low repulsion degree from the place of height repulsion degree.
3. method according to claim 1, is characterized in that described global context map utilizes the high frequency probability 3D mapping framework based on Octree to generate.
4. method according to claim 1, is characterized in that described certain semantic model is by the automatic foundation by mission requirements of airborne comprehensive sensor.
5. method according to claim 1, is characterized in that described partial barriers map is generated by Three-dimensional Numeric Map.
6. method according to claim 1, it is characterized in that the design in described safe flight path to consider hot line job safe distance, helicopter avoid when flying electromagnetic field interference, ensure away from the sampled point nearest from fixed obstacle enough with the self-adaptation radius of turn ρ allowing flying robot one minimum simultaneously
min, wherein,
wherein, V is the constant travel speed of flying robot, Φ
maxthe maximum deflection angle of flying robot) convolution can avoiding obstacles, without any collision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510263381.7A CN104965518B (en) | 2015-05-21 | 2015-05-21 | Electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510263381.7A CN104965518B (en) | 2015-05-21 | 2015-05-21 | Electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104965518A true CN104965518A (en) | 2015-10-07 |
| CN104965518B CN104965518B (en) | 2017-11-28 |
Family
ID=54219557
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510263381.7A Expired - Fee Related CN104965518B (en) | 2015-05-21 | 2015-05-21 | Electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104965518B (en) |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105843253A (en) * | 2016-04-08 | 2016-08-10 | 北京博瑞空间科技发展有限公司 | Design method and system for unmanned aerial vehicle's travel path |
| CN105892489A (en) * | 2016-05-24 | 2016-08-24 | 国网山东省电力公司电力科学研究院 | Multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and control method |
| CN106647804A (en) * | 2016-12-01 | 2017-05-10 | 深圳创动科技有限公司 | Automatic routing inspection method and system |
| CN106774392A (en) * | 2016-12-13 | 2017-05-31 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The dynamic programming method of flight path during a kind of power circuit polling |
| CN107228673A (en) * | 2017-05-19 | 2017-10-03 | 北京旋极伏羲大数据技术有限公司 | Route planner and device |
| CN107831773A (en) * | 2017-11-30 | 2018-03-23 | 深圳市沃特沃德股份有限公司 | The method and sweeping robot that sweeping robot all standing cleans |
| CN108318040A (en) * | 2018-02-06 | 2018-07-24 | 贵州电网有限责任公司 | A kind of more rotor trajectory planning system and method for transmission line-oriented inspection |
| CN108582073A (en) * | 2018-05-02 | 2018-09-28 | 北京邮电大学 | A kind of quick barrier-avoiding method of mechanical arm based on improved random road sign Map Method |
| CN108733755A (en) * | 2018-04-11 | 2018-11-02 | 国网山东省电力公司信息通信公司 | A kind of intelligent polling method and system based on transmission line of electricity three-dimensional information |
| CN109765930A (en) * | 2019-01-29 | 2019-05-17 | 理光软件研究所（北京）有限公司 | A kind of unmanned plane vision navigation system |
| CN110146085A (en) * | 2019-05-30 | 2019-08-20 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
| CN110162081A (en) * | 2018-02-14 | 2019-08-23 | 广州极飞科技有限公司 | Mobile device control method and device, mobile terminal and mobile device |
| CN110362117A (en) * | 2019-08-19 | 2019-10-22 | 广东电网有限责任公司 | A kind of unmanned plane paths planning method, equipment, unmanned aerial vehicle (UAV) control device and storage medium |
| CN110377026A (en) * | 2018-04-13 | 2019-10-25 | 株式会社东芝 | Information processing unit, storage medium and information processing method |
| CN111399506A (en) * | 2020-03-13 | 2020-07-10 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraints |
| CN111443730A (en) * | 2020-05-12 | 2020-07-24 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle track automatic generation method and device for power transmission line inspection |
| CN112083440A (en) * | 2020-08-08 | 2020-12-15 | 浙江科聪控制技术有限公司 | Laser obstacle avoidance system for anti-explosion one-zone mobile robot |
| CN112269380A (en) * | 2020-10-15 | 2021-01-26 | 许继电源有限公司 | Obstacle meeting control method and system for substation inspection robot |
| CN112650218A (en) * | 2020-12-04 | 2021-04-13 | 国网湖北省电力有限公司检修公司 | Transformer substation inspection route planning method and device based on collision detection |
| CN112748733A (en) * | 2020-12-16 | 2021-05-04 | 广东电网有限责任公司 | Cable drum carriage path planning method, device, equipment and storage medium |
| CN112747736A (en) * | 2020-12-22 | 2021-05-04 | 西北工业大学 | Indoor unmanned aerial vehicle path planning method based on vision |
| CN112947460A (en) * | 2021-03-01 | 2021-06-11 | 北京玄马知能科技有限公司 | Automatic route presetting planning method for inspection robot based on laser point cloud model |
| CN113504791A (en) * | 2021-07-08 | 2021-10-15 | 中国南方电网有限责任公司超高压输电公司大理局 | Method and device for determining flight route of unmanned aerial vehicle |
| CN113741490A (en) * | 2020-05-29 | 2021-12-03 | 广州极飞科技股份有限公司 | Inspection method, inspection device, aircraft and storage medium |
| CN113988482A (en) * | 2021-12-23 | 2022-01-28 | 深圳市领志光机电自动化系统有限公司 | Three-dimensional space track path automatic matching generation system based on internet data chain |
| CN115048479A (en) * | 2022-08-16 | 2022-09-13 | 赛维航电科技有限公司 | Method, device, system and medium for processing navigation chart of airborne mobile terminal |
| CN115097868A (en) * | 2022-08-24 | 2022-09-23 | 深圳市瓴鹰智能科技有限公司 | Flight control method and related device |
| CN116952249A (en) * | 2023-09-18 | 2023-10-27 | 中煤科工机器人科技有限公司 | Autonomous navigation method for wheel type inspection robot |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003240517A (en) * | 2002-02-13 | 2003-08-27 | Kawasaki Heavy Ind Ltd | Method, system and apparatus for detection of earth surface state |
| CN102622653A (en) * | 2012-02-27 | 2012-08-01 | 北京航空航天大学 | Multi-resolution path planning method for micro unmanned aerial vehicle under influence of wind field |
| US20130138292A1 (en) * | 2005-02-23 | 2013-05-30 | Aviation Safety Technologies, Llc | Method And Device Of Calculating Aircraft Braking Friction And Other Relating Landing Performance Parameters Based On The Data Received From Aircraft's On Board Flight Data Management System |
-
2015
- 2015-05-21 CN CN201510263381.7A patent/CN104965518B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003240517A (en) * | 2002-02-13 | 2003-08-27 | Kawasaki Heavy Ind Ltd | Method, system and apparatus for detection of earth surface state |
| US20130138292A1 (en) * | 2005-02-23 | 2013-05-30 | Aviation Safety Technologies, Llc | Method And Device Of Calculating Aircraft Braking Friction And Other Relating Landing Performance Parameters Based On The Data Received From Aircraft's On Board Flight Data Management System |
| CN102622653A (en) * | 2012-02-27 | 2012-08-01 | 北京航空航天大学 | Multi-resolution path planning method for micro unmanned aerial vehicle under influence of wind field |
Non-Patent Citations (3)
| Title |
|---|
| 张志亮 等: ""浙江电网直升机巡线的探讨"", 《湖州师范学院学报》 * |
| 柳长安: ""电力线路巡检飞行机器人三维轨迹生成方法"", 《同济大学学报（自然科学版）》 * |
| 邓荣军 等: ""基于遗传算法的输电线路无人机巡检路径规划"", 《计算机测量与控制》 * |
Cited By (40)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105843253A (en) * | 2016-04-08 | 2016-08-10 | 北京博瑞空间科技发展有限公司 | Design method and system for unmanned aerial vehicle's travel path |
| CN105892489A (en) * | 2016-05-24 | 2016-08-24 | 国网山东省电力公司电力科学研究院 | Multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and control method |
| CN105892489B (en) * | 2016-05-24 | 2019-09-10 | 国网山东省电力公司电力科学研究院 | A kind of automatic obstacle avoiding UAV system and control method based on Multi-sensor Fusion |
| CN106647804A (en) * | 2016-12-01 | 2017-05-10 | 深圳创动科技有限公司 | Automatic routing inspection method and system |
| CN106647804B (en) * | 2016-12-01 | 2019-09-20 | 深圳创动科技有限公司 | A kind of automatic detecting method and system |
| CN106774392A (en) * | 2016-12-13 | 2017-05-31 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The dynamic programming method of flight path during a kind of power circuit polling |
| CN107228673A (en) * | 2017-05-19 | 2017-10-03 | 北京旋极伏羲大数据技术有限公司 | Route planner and device |
| CN107228673B (en) * | 2017-05-19 | 2020-02-18 | 北京旋极伏羲大数据技术有限公司 | Route planning method and device |
| CN107831773A (en) * | 2017-11-30 | 2018-03-23 | 深圳市沃特沃德股份有限公司 | The method and sweeping robot that sweeping robot all standing cleans |
| CN108318040A (en) * | 2018-02-06 | 2018-07-24 | 贵州电网有限责任公司 | A kind of more rotor trajectory planning system and method for transmission line-oriented inspection |
| CN110162081A (en) * | 2018-02-14 | 2019-08-23 | 广州极飞科技有限公司 | Mobile device control method and device, mobile terminal and mobile device |
| CN108733755A (en) * | 2018-04-11 | 2018-11-02 | 国网山东省电力公司信息通信公司 | A kind of intelligent polling method and system based on transmission line of electricity three-dimensional information |
| CN108733755B (en) * | 2018-04-11 | 2021-01-26 | 国网智能科技股份有限公司 | Intelligent inspection method and system based on three-dimensional information of power transmission line |
| CN110377026B (en) * | 2018-04-13 | 2022-06-14 | 株式会社东芝 | Information processing apparatus, storage medium, and information processing method |
| CN110377026A (en) * | 2018-04-13 | 2019-10-25 | 株式会社东芝 | Information processing unit, storage medium and information processing method |
| CN108582073A (en) * | 2018-05-02 | 2018-09-28 | 北京邮电大学 | A kind of quick barrier-avoiding method of mechanical arm based on improved random road sign Map Method |
| CN109765930A (en) * | 2019-01-29 | 2019-05-17 | 理光软件研究所（北京）有限公司 | A kind of unmanned plane vision navigation system |
| CN110146085A (en) * | 2019-05-30 | 2019-08-20 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
| CN110146085B (en) * | 2019-05-30 | 2022-04-19 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle real-time avoidance rescheduling method based on graph building and rapid random tree exploration |
| CN110362117A (en) * | 2019-08-19 | 2019-10-22 | 广东电网有限责任公司 | A kind of unmanned plane paths planning method, equipment, unmanned aerial vehicle (UAV) control device and storage medium |
| CN111399506A (en) * | 2020-03-13 | 2020-07-10 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraints |
| CN111399506B (en) * | 2020-03-13 | 2023-04-25 | 大连海事大学 | Global-local hybrid unmanned ship path planning method based on dynamic constraint |
| CN111443730B (en) * | 2020-05-12 | 2021-02-12 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle track automatic generation method and device for power transmission line inspection |
| CN111443730A (en) * | 2020-05-12 | 2020-07-24 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle track automatic generation method and device for power transmission line inspection |
| CN113741490A (en) * | 2020-05-29 | 2021-12-03 | 广州极飞科技股份有限公司 | Inspection method, inspection device, aircraft and storage medium |
| CN112083440A (en) * | 2020-08-08 | 2020-12-15 | 浙江科聪控制技术有限公司 | Laser obstacle avoidance system for anti-explosion one-zone mobile robot |
| CN112269380A (en) * | 2020-10-15 | 2021-01-26 | 许继电源有限公司 | Obstacle meeting control method and system for substation inspection robot |
| CN112650218A (en) * | 2020-12-04 | 2021-04-13 | 国网湖北省电力有限公司检修公司 | Transformer substation inspection route planning method and device based on collision detection |
| CN112748733A (en) * | 2020-12-16 | 2021-05-04 | 广东电网有限责任公司 | Cable drum carriage path planning method, device, equipment and storage medium |
| CN112747736A (en) * | 2020-12-22 | 2021-05-04 | 西北工业大学 | Indoor unmanned aerial vehicle path planning method based on vision |
| CN112947460A (en) * | 2021-03-01 | 2021-06-11 | 北京玄马知能科技有限公司 | Automatic route presetting planning method for inspection robot based on laser point cloud model |
| CN113504791A (en) * | 2021-07-08 | 2021-10-15 | 中国南方电网有限责任公司超高压输电公司大理局 | Method and device for determining flight route of unmanned aerial vehicle |
| CN113504791B (en) * | 2021-07-08 | 2022-06-14 | 中国南方电网有限责任公司超高压输电公司大理局 | Method and device for determining flight route of unmanned aerial vehicle |
| CN113988482A (en) * | 2021-12-23 | 2022-01-28 | 深圳市领志光机电自动化系统有限公司 | Three-dimensional space track path automatic matching generation system based on internet data chain |
| CN113988482B (en) * | 2021-12-23 | 2022-03-15 | 深圳市领志光机电自动化系统有限公司 | Three-dimensional space track path automatic matching generation system based on internet data chain |
| CN115048479A (en) * | 2022-08-16 | 2022-09-13 | 赛维航电科技有限公司 | Method, device, system and medium for processing navigation chart of airborne mobile terminal |
| CN115048479B (en) * | 2022-08-16 | 2022-11-11 | 赛维航电科技有限公司 | Method, device, system and medium for processing aerograph of airborne mobile terminal |
| CN115097868A (en) * | 2022-08-24 | 2022-09-23 | 深圳市瓴鹰智能科技有限公司 | Flight control method and related device |
| CN116952249A (en) * | 2023-09-18 | 2023-10-27 | 中煤科工机器人科技有限公司 | Autonomous navigation method for wheel type inspection robot |
| CN116952249B (en) * | 2023-09-18 | 2023-12-08 | 中煤科工机器人科技有限公司 | Autonomous navigation method for wheel type inspection robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104965518B (en) | 2017-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104965518A (en) | Power inspection tour flying robot air route planning method based on three-dimensional digital map | |
| Watkins et al. | Ten questions concerning the use of drones in urban environments | |
| Ahmed et al. | Recent advances in unmanned aerial vehicles: a review | |
| CN110062919B (en) | Drop-off location planning for delivery vehicles | |
| CN109923492B (en) | Flight path determination | |
| Chen et al. | State of technology review of civilian UAVs | |
| CN105892489B (en) | A kind of automatic obstacle avoiding UAV system and control method based on Multi-sensor Fusion | |
| CN101477169B (en) | Electric power circuit detection method by polling flying robot | |
| KR102072863B1 (en) | Aerial forest inventory system | |
| Kendoul | Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems | |
| CN109388150B (en) | Multi-sensor environment mapping | |
| Guerrero et al. | UAV path planning for structure inspection in windy environments | |
| DE102014117526B4 (en) | Manage flight routes of a sailing aircraft | |
| US11619953B2 (en) | Three dimensional aircraft autonomous navigation under constraints | |
| CN105045274B (en) | A kind of intelligent shaft tower connected graph construction method for unmanned plane inspection trajectory planning | |
| JP2019537140A (en) | Vehicle collision avoidance | |
| Kantor et al. | Collection of environmental data from an airship platform | |
| WO2022044533A1 (en) | Information processing device and information processing method, computer program, and moving body device | |
| Cobano et al. | Thermal detection and generation of collision-free trajectories for cooperative soaring UAVs | |
| US11640180B2 (en) | Systems and methods for flight path optimization | |
| WO2023080954A1 (en) | Improving performance of autonomous vehicle operation in varying conditions by using imagery generated with machine learning for simulations | |
| US20220308598A1 (en) | Learning device, information processing device, and learned control model | |
| Gao | Autonomous soaring and surveillance in wind fields with an unmanned aerial vehicle | |
| Marta et al. | Drones Safe Flight: On Addressing Operational Safety For Low Cost Small Drones | |
| Duan et al. | Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171128 |