CN105865457A - Culture algorithm-based route planning method under dynamic environment - Google Patents
Culture algorithm-based route planning method under dynamic environment Download PDFInfo
- Publication number
- CN105865457A CN105865457A CN201610422062.0A CN201610422062A CN105865457A CN 105865457 A CN105865457 A CN 105865457A CN 201610422062 A CN201610422062 A CN 201610422062A CN 105865457 A CN105865457 A CN 105865457A
- Authority
- CN
- China
- Prior art keywords
- knowledge
- flight path
- planning
- path
- planning method
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a culture algorithm-based route planning method under a dynamic environment and relates to an on-line route planning technology. The method is characterized in that firstly, reachable routes are planned through an on-line route planning method; secondly, feature nodes in the reachable routes are extracted to serve as knowledge; thirdly, a planning space is determined through the knowledge, and an optimal route is planned in the planning space through an off-line planning method; fourthly, for the environment change, the on-line route planning method is used for planning the unreachable part in the route again, and the knowledge is used for guiding optimization. The method has the advantages that under the dynamic environment, a flying route can be planned more quickly compared with an existing on-line route planning method, the obtained route is an optimal route, the reaction capacity of an aircraft to emergencies is enhanced, and the possibility of encountering emergencies in the flying process is further reduced.
Description
Technical field
The invention belongs to unmanned air vehicle technique field, relate to a kind of Cultural Algorithm for solving unmanned vehicle trajectory planning problem, path planning method under a kind of dynamic environment based on Cultural Algorithm.
Background technology
Trajectory planning needs the problem solved to be to find a flight path that aircraft can be made to complete task quickly and safely.Existing method may insure that in the case of known to the influence factors such as environment, cooks up a feasible flight path.But the environment of real world is among constantly changing, the problem such as mobile of the change of threat, impact point can be run into during unmanned vehicle flight, now need according to real-time condition, flight path to be changed accordingly.
Timely responding to make unmanned vehicle to change to make to environment in flight course, existing online path planning method, such as D* algorithm etc., it is intended to cooking up a feasible safe flight path rapidly, this flight path is frequently not optimal trajectory.Optimal trajectory can shorten the length of unmanned vehicle flight path, i.e. shortens the used time of aircraft flight task, reduce further the probability running into accident on this basis.Existing path planning method based on evolutionary computation is off-line path planning method, because evolutionary computation is random search algorithm, needs optimizing in whole planning space, although can search out optimal trajectory, but the longest, it is impossible to meet the requirement of planning in real time.
For meeting real-time and the dynamic adaptable of trajectory planning problem simultaneously, characteristic in conjunction with Cultural Algorithm, proposing a kind of Cultural Algorithm for solving unmanned vehicle trajectory planning problem, this algorithm has and is different from the knowledge form of traditional culture algorithm, the different renewals of knowledge and impact.
Summary of the invention
The object of the invention provides path planning method under a kind of dynamic environment based on Cultural Algorithm, can search out optimal trajectory while real-time update flight path；The speed of online trajectory planning can be promoted further, be more conducive to aircraft and environment is changed make and timely responding to.
In order to solve above-mentioned technical problem, the present invention proposes path planning method under a kind of dynamic environment based on Cultural Algorithm, comprising: obtain cartographic information and aerial mission information, and unmanned vehicle trajectory planning, comprising:
Generate initial feasible flight path；
Feasible flight path is carried out knowledge extraction, extracts initial signature of flight path nodal information as knowledge；
Using knowledge to affect trajectory planning, determine optimizing space with knowledge, search out outstanding node composition optimal trajectory in optimizing space, aircraft flies along this optimal trajectory；
When environment changes and has influence on current optimal trajectory, only impacted part is regenerated feasible sub-flight path, new sub-flight path is replaced to Reciprocal course, and complete flight path is carried out knowledge extraction, the renewal of knowledge is completed with this, new knowledge is used to determine space and plan optimal trajectory, until aircraft arrives at target.
In the feasible flight path of extraction of the present invention, characteristic node information is knowledge, uses knowledge to determine planning space, when environment changes, with characteristic node more new knowledge in new feasible flight path.
When environment of the present invention change affects flight track, only part affected by environment is planned again feasible flight path, more new knowledge, then under new knowledge, complete optimal trajectory planning.
Beneficial effects of the present invention: compare existing path planning method, by the present invention in that with the framework of Cultural Algorithm, in conjunction with the respective advantage of Different Flight planing method, a kind of new online path planning method is proposed so that the method can take into account requirement of real-time while cooking up optimal trajectory.Comparing existing path planning method, the present invention plans that duration is shorter, planning gained flight path is more excellent.
Accompanying drawing explanation
Fig. 1 is threat probabilities map of the present invention.
Fig. 2 is digital equivalent map of the present invention.
Fig. 3 is that knowledge of the present invention determines optimizing space.
Fig. 4 is each operator that optimizing is used in population space of the present invention.
Before a is for deleting operator, after b is for deleting operator, before c is disturbing operator, after d is disturbing operator, before e is insertion operator, after f is insertion operator, before g is crossover operator, after h is crossover operator.
Fig. 5 is that D* algorithm of the present invention produces initial flight path.
Fig. 6 is the flight path that situational knowledge of the present invention retains.
Fig. 7 is optimizing gained flight path optimization of the present invention.
Fig. 8 is target of the present invention when moving a segment distance, current location the optimal trajectory cooked up.
Fig. 9 is before and after target of the present invention moves, and the initially planned flight path of aircraft contrasts with aerocraft real flight track.
Figure 10 is that the present invention threatens when being moved, current location the optimal trajectory cooked up.
Figure 11 is that before and after the present invention threatens movement, the initially planned flight path of aircraft contrasts with aerocraft real flight track.
Detailed description of the invention
Below with reference to accompanying drawing, the detailed description of the invention of the present invention is elaborated:
The present invention proposes a kind of based on the method solving unmanned vehicle trajectory planning problem under that dynamic environment of Cultural Algorithm, specifically comprises the following steps that
Step 1. obtains cartographic information and aerial mission information
It is loaded into environmental map and the threat probabilities map such as Fig. 1, generates such as the digital equivalent map of Fig. 2；Obtain aircraft original position correspondence map reference and aiming spot correspondence map reference；Determine the aircraft maximum angle of pitch and yaw angle, the information such as vehicle flight speeds, impact point translational speed.
Step 2. generates initial feasible flight path；
Use online path planning method D* algorithm generate one by the feasible flight path Line1 of position of aircraft to aiming spot, as shown in Figure 5.
Step 3. knowledge is extracted；
Leave out the track line Line2 that redundant node in Line1 obtains being made up of characteristic node, keeping characteristics node location information and node alterable scope, knowledge is extracted and optimizing space determines that signal is as it is shown on figure 3, determined optimizing space by Line2 in example, as shown in Figure 6.
Step 4. optimizes gained flight path；
In the spatial dimension determined, based on Line2, use off-line path planning method genetic algorithm that flight path is optimized, use crossover operator as shown in Figure 4 to carry out flight path optimizing, search out optimal trajectory Line3, as shown in Figure 7.
Step 5. space vehicle dynamic adjusts
Aircraft flies along Line3, when running into environment change and affecting the generation of current flight path, makes and adjusting accordingly:
Step 5.1 target moves
Target is moved, with impact point home position as starting point, impact point current location is terminating point, use D* algorithm to produce feasible sub-flight path, sub-flight path is added to Line3, by step 3, step 4, complete flight path is carried out optimizing and produce new feasible flight path Line4, aircraft is along this track flight until arriving and terminating the flight at target, in flight course, change if running into environment, then repeat step 5；In-flight, target is moved and obtains flight path as shown in Figure 8, meets target and moves, and before and after aircraft, flight path contrasts as shown in Figure 9.
Step 5.2 threatens mobile
Threaten moving influence to current flight path, then extract affected sub-flight path two end node, it is respectively beginning and end with two nodes, use D* algorithm to produce feasible sub-flight path, sub-flight path is replaced affected children flight path in Line3, by step 3, step 4, complete flight path is carried out optimizing and produce new feasible flight path Line4 ', aircraft is along this track flight until arriving and terminating the flight at target, in flight course, change if running into environment, then repeat step 5；In-flight, environment moves and obtains flight path as shown in Figure 10, meets environment and moves, and before and after aircraft, flight path contrasts as shown in figure 11.
Claims (6)
1. a path planning method under dynamic environment based on Cultural Algorithm, comprising: obtain cartographic information and aerial mission information, unmanned vehicle trajectory planning, described unmanned vehicle trajectory planning includes:
Generate initial feasible flight path；
Feasible flight path is carried out knowledge extraction, extracts in initial flight path and flight path slope is changed bigger characteristic node information as knowledge；
Using knowledge to affect trajectory planning, determine optimizing space with knowledge, search out outstanding node composition optimal trajectory in optimizing space, aircraft flies along this optimal trajectory；
When environment changes and has influence on current optimal trajectory, only impacted part is regenerated feasible sub-flight path, new sub-flight path is replaced to Reciprocal course, and complete flight path is carried out knowledge extraction, the renewal of knowledge is completed with this, new knowledge is used to determine space and plan optimal trajectory, until aircraft arrives at target.
Path planning method under a kind of dynamic environment based on Cultural Algorithm the most according to claim 1, it is characterized in that: extract in feasible flight path to flight path slope change obvious characteristic node coordinate information be situational knowledge, characteristic node alterable scope is normative knowledge.
Path planning method under a kind of dynamic environment based on Cultural Algorithm the most according to claim 1, it is characterised in that: will region determined by with the point of situational knowledge two-by-two as diagonal, with the region determined with normative knowledge as union, determine trajectory planning space.
Path planning method under a kind of dynamic environment based on Cultural Algorithm the most according to claim 1, it is characterised in that: the node of flight path produced to initial track optimization, only generate in planning space.
Path planning method under a kind of dynamic environment based on Cultural Algorithm the most according to claim 1, it is characterised in that: when environment change affects flight track, only part affected by environment is planned feasible flight path again, and replace to Reciprocal course.
Path planning method under a kind of dynamic environment based on Cultural Algorithm the most according to claim 1, it is characterised in that: when environment change affects flight, new planning gained flight path is carried out knowledge extraction, and updates optimizing space.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610422062.0A CN105865457B (en) | 2016-06-16 | 2016-06-16 | Path planning method under a kind of dynamic environment based on Cultural Algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610422062.0A CN105865457B (en) | 2016-06-16 | 2016-06-16 | Path planning method under a kind of dynamic environment based on Cultural Algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105865457A true CN105865457A (en) | 2016-08-17 |
| CN105865457B CN105865457B (en) | 2018-12-21 |
Family
ID=56650438
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610422062.0A Expired - Fee Related CN105865457B (en) | 2016-06-16 | 2016-06-16 | Path planning method under a kind of dynamic environment based on Cultural Algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105865457B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109683630A (en) * | 2019-01-25 | 2019-04-26 | 南京邮电大学 | Unmanned aerial vehicle flight path planing method based on population and PRM algorithm |
| CN111596683A (en) * | 2020-04-19 | 2020-08-28 | 西北工业大学 | Cultural algorithm framework-based multi-unmanned aerial vehicle collaborative track double-layer optimization method |
| CN112711267A (en) * | 2020-04-24 | 2021-04-27 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle autonomous inspection method based on RTK high-precision positioning and machine vision fusion |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101101614A (en) * | 2007-07-02 | 2008-01-09 | 北京理工大学 | Remote aerocraft real low altitude penetration route bumping ground probability resolution evaluation method and correction method |
| CN101286071A (en) * | 2008-04-24 | 2008-10-15 | 北京航空航天大学 | Multiple no-manned plane three-dimensional formation reconfiguration method based on particle swarm optimization and genetic algorithm |
| CN102854880A (en) * | 2012-10-08 | 2013-01-02 | 中国矿业大学 | Robot whole-situation path planning method facing uncertain environment of mixed terrain and region |
| CN103648139A (en) * | 2013-12-09 | 2014-03-19 | 天津工业大学 | Cultural ant colony algorithm-based wireless sensor network node deployment design method |
| CN104991895A (en) * | 2015-05-15 | 2015-10-21 | 南京航空航天大学 | Low-altitude rescue aircraft route planning method based on three dimensional airspace grids |
-
2016
- 2016-06-16 CN CN201610422062.0A patent/CN105865457B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101101614A (en) * | 2007-07-02 | 2008-01-09 | 北京理工大学 | Remote aerocraft real low altitude penetration route bumping ground probability resolution evaluation method and correction method |
| CN101286071A (en) * | 2008-04-24 | 2008-10-15 | 北京航空航天大学 | Multiple no-manned plane three-dimensional formation reconfiguration method based on particle swarm optimization and genetic algorithm |
| CN102854880A (en) * | 2012-10-08 | 2013-01-02 | 中国矿业大学 | Robot whole-situation path planning method facing uncertain environment of mixed terrain and region |
| CN103648139A (en) * | 2013-12-09 | 2014-03-19 | 天津工业大学 | Cultural ant colony algorithm-based wireless sensor network node deployment design method |
| CN104991895A (en) * | 2015-05-15 | 2015-10-21 | 南京航空航天大学 | Low-altitude rescue aircraft route planning method based on three dimensional airspace grids |
Non-Patent Citations (1)
| Title |
|---|
| 刘群芳等: "结合进化算法的稀疏A*算法对动态目标的无人机航迹规划研究", 《计算机时代》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109683630A (en) * | 2019-01-25 | 2019-04-26 | 南京邮电大学 | Unmanned aerial vehicle flight path planing method based on population and PRM algorithm |
| CN109683630B (en) * | 2019-01-25 | 2021-11-09 | 南京邮电大学 | Unmanned aerial vehicle flight path planning method based on particle swarm optimization and PRM algorithm |
| CN111596683A (en) * | 2020-04-19 | 2020-08-28 | 西北工业大学 | Cultural algorithm framework-based multi-unmanned aerial vehicle collaborative track double-layer optimization method |
| CN112711267A (en) * | 2020-04-24 | 2021-04-27 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle autonomous inspection method based on RTK high-precision positioning and machine vision fusion |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105865457B (en) | 2018-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110609552B (en) | Method for planning formation plane flight path of underwater unmanned aircraft | |
| CN111024080B (en) | Unmanned aerial vehicle group-to-multi-mobile time-sensitive target reconnaissance path planning method | |
| CN107607120B (en) | Unmanned aerial vehicle dynamic track planning method based on improved restoration type Anytime sparse A algorithm | |
| CN112068588A (en) | Unmanned aerial vehicle trajectory generation method based on flight corridor and Bezier curve | |
| CN103267528A (en) | Multi-unmanned aerial vehicle cooperative area search method under non-flight zone limitation | |
| Xia et al. | Cooperative task assignment and track planning for multi-UAV attack mobile targets | |
| CN112985408B (en) | Path planning optimization method and system | |
| CN109520507A (en) | A kind of unmanned plane real-time route planing method based on improvement RRT | |
| CN110262548A (en) | A kind of unmanned aerial vehicle flight path planing method considering arrival time constraint | |
| CN105865457A (en) | Culture algorithm-based route planning method under dynamic environment | |
| CN104850009A (en) | Coordination control method for multi-unmanned aerial vehicle team based on predation escape pigeon optimization | |
| CN106372766A (en) | UAV (Unmanned Aerial Vehicle) path planning method for electromagnetic interference environment | |
| CN106595663A (en) | Aircraft auto-route planning method with combination of searching and optimization | |
| CN110084414B (en) | Empty pipe anti-collision method based on K-time control deep reinforcement learning | |
| Wang et al. | UAV online path planning based on improved genetic algorithm | |
| CN109211242B (en) | Three-dimensional space multi-target path planning method integrating RRT and ant colony algorithm | |
| CN116954233A (en) | Automatic matching method for inspection task and route | |
| CN110162104A (en) | The RRT unmanned plane paths planning method of imitative Driver Vision perception | |
| CN108759841B (en) | Rapid route planning method under complex environment | |
| CN111338375A (en) | Control method and system for four-rotor unmanned aerial vehicle to move and land based on hybrid strategy | |
| CN111045445A (en) | Aircraft intelligent collision avoidance method, equipment and medium based on reinforcement learning | |
| CN114089776A (en) | Unmanned aerial vehicle obstacle avoidance method based on deep reinforcement learning | |
| CN114237302B (en) | Three-dimensional real-time RRT route planning method based on rolling time domain | |
| CN114442664A (en) | Power transmission line unmanned aerial vehicle formation cooperative inspection task allocation method considering airspace conflicts | |
| CN116817947B (en) | Random time path planning method based on variable step length mechanism |
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: 20181221 Termination date: 20190616 |