CN105865457B - Path planning method under a kind of dynamic environment based on Cultural Algorithm - Google Patents
Path planning method under a kind of dynamic environment based on Cultural Algorithm Download PDFInfo
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- CN105865457B CN105865457B CN201610422062.0A CN201610422062A CN105865457B CN 105865457 B CN105865457 B CN 105865457B CN 201610422062 A CN201610422062 A CN 201610422062A CN 105865457 B CN105865457 B CN 105865457B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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Abstract
The present invention proposes path planning method under a kind of dynamic environment based on Cultural Algorithm, is related to online trajectory planning technology, this method feature is: (1) being cooked up using online path planning method up to track.(2) extracting up to characteristic node in track is knowledge.(3) planning space is determined using knowledge, plan optimal trajectory using off-line planning method in planning space.(4) change for environment, planning is re-started to part unreachable in track using online path planning method, and carry out instructing optimizing using knowledge.The invention has the advantages that in a dynamic environment, more existing online path planning method can more rapidly cook up flight track, gained track is optimal trajectory simultaneously, enhances aircraft to the respond of emergency case, and further reduce the probability that emergency case is encountered in flight course.
Description
Technical field
The invention belongs to air vehicle technique fields, are related to a kind of for solving the problems, such as the culture of unmanned vehicle trajectory planning
Algorithm, path planning method under specially a kind of dynamic environment based on Cultural Algorithm.
Background technique
Trajectory planning problem to be solved is to find the track that aircraft can be made quickly and safely to complete task.
Existing method may insure in the situation known to the influence factors such as environment, cook up a feasible track.But real world
Environment among continuous change, when unmanned vehicle flight can encounter the change of threat, target point it is mobile the problems such as, at this time
It needs accordingly to change track according to real-time condition.
It is timely responded to enable unmanned vehicle to make in flight course to environment change, existing online track rule
The method of drawing, such as D* algorithm, it is intended to rapidly cook up a feasible safe track, which is frequently not optimal trajectory.
Optimal trajectory can shorten the length of unmanned vehicle flight path, that is, shorten the used time of aircraft flight task, in this base
It further reduced the probability for encountering emergency event on plinth.The existing path planning method based on evolutionary computation is offline track
Planing method needs the optimizing in entire planning space, although optimal boat can be searched out because evolutionary computation is random search algorithm
Mark, but time-consuming, is not able to satisfy the requirement planned in real time.
One is proposed in conjunction with the characteristic of Cultural Algorithm for the real-time and dynamic adaptable for meeting trajectory planning problem simultaneously
The Cultural Algorithm for solving the problems, such as unmanned vehicle trajectory planning is planted, which has the knowledge for being different from traditional culture algorithm
Form, the different renewal of knowledge and influence.
Summary of the invention
The object of the invention provides path planning method under a kind of dynamic environment based on Cultural Algorithm, can navigate in real-time update
Optimal trajectory is searched out while mark；The speed that online trajectory planning can further be promoted, is more conducive to aircraft and changes to environment
Change, which is made, to be timely responded to.
In order to solve the above technical problems, the present invention proposes trajectory planning under a kind of dynamic environment based on Cultural Algorithm
Method comprising: obtain cartographic information and aerial mission information, unmanned vehicle trajectory planning comprising:
Generate initial feasible track；
Knowledge extraction is carried out to feasible track, extracts initial signature of flight path nodal information as knowledge；
Trajectory planning is influenced using knowledge, optimizing space is determined with knowledge, outstanding node group is searched out in optimizing space
At optimal trajectory, aircraft flies along this optimal trajectory；
When environment change influences current optimal trajectory, feasible sub- track only is regenerated to impacted part, it will be new
Sub- track replace into Reciprocal course, and to complete track carry out knowledge extraction, the update of knowledge is completed with this, uses new knowledge
It determines space and plans optimal trajectory, until aircraft reaches at target.
Characteristic node information is knowledge in the feasible track of extraction of the present invention, planning space is determined using knowledge, in ring
When border changes, with characteristic node more new knowledge in new feasible track.
Environment of the present invention changes when influencing flight track, only to part affected by environment plan again feasible track,
More new knowledge, then under new knowledge, complete optimal trajectory planning.
Beneficial effects of the present invention: comparing existing path planning method, by the present invention in that with the frame of Cultural Algorithm,
In conjunction with the advantage of Different Flight planing method respectively, proposes a kind of new online path planning method, this method is being advised
Requirement of real-time is taken into account while marking optimal trajectory.Compared to existing path planning method, the present invention plans that duration is shorter, plans
Gained track is more excellent.
Detailed description of the invention
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 used in optimizing in population space of the present invention.
Before a is deletes operator, b is after deleting operator, and c is before disturbing operator, d is after disturbing operator, e is insertion operator
Before, f is after insertion operator, g is before crossover operator, h is after crossover operator.
Fig. 5 is that D* algorithm of the present invention generates initial track.
Fig. 6 is the track that situational knowledge of the present invention retains.
Fig. 7 is flight path optimization obtained by optimizing of the present invention.
When Fig. 8 is target of the present invention mobile a distance, the optimal trajectory cooked up by current location.
Fig. 9 is the mobile front and back of target of the present invention, and the initially planned track of aircraft and aerocraft real flight track compare.
Figure 10 is that the present invention threatens optimal trajectory when occurring mobile, cooked up by current location.
Figure 11 is that the present invention threatens mobile front and back, and the initially planned track of aircraft and aerocraft real flight track compare.
Specific embodiment
It elaborates below with reference to attached drawing to a specific embodiment of the invention:
The present invention proposes to solve the problems, such as unmanned vehicle trajectory planning under a kind of that the dynamic environment based on Cultural Algorithm
Method, the specific steps are as follows:
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 the digital equivalent map such as Fig. 2；Obtain flight
Device initial position corresponds to map reference and aiming spot corresponds to map reference；Determine aircraft maximum pitch angle and yaw
Angle, the information such as vehicle flight speeds, target point movement speed.
Step 2. generates initial feasible track；
A feasible track by position of aircraft to aiming spot is generated using online path planning method D* algorithm
Line1, as shown in Figure 5.
Step 3. knowledge is extracted；
Leave out the track line Line2 that redundant node in Line1 obtains being made of characteristic node, keeping characteristics node location letter
Breath and node can change range, and knowledge is extracted and optimizing space determines that signal is sought as shown in figure 3, being determined in example by Line2
Excellent space, as shown in Figure 6.
Step 4. optimization gained track；
In determining spatial dimension, based on Line2, using offline path planning method genetic algorithm to track into
Row optimization carries out track optimizing using crossover operator as shown in Figure 4, searches out optimal trajectory Line3, as shown in Figure 7.
The adjustment of step 5. space vehicle dynamic
Aircraft flies along Line3, when encounter environment change current track is had an impact when, make corresponding tune
It is whole:
Step 5.1 target is mobile
Target moves, and using target point home position as starting point, target point current location is terminating point, is calculated using D*
Method generates feasible sub- track, and sub- track is added in Line3, carries out optimizing generation to complete track by step 3, step 4
New feasible track Line4, aircraft is terminated the flight at target along this track flight until reaching, in flight course, if meeting
Change to environment, then repeatedly step 5；In-flight, target generation is mobile obtains track as shown in figure 8, meeting target movement, aircraft
Track comparison in front and back is as shown in Figure 9.
Step 5.2 threatens movement
It threatens moving influence to current track, then extracts impacted two end node of sub- track, be respectively with two nodes
Point and terminal, generate feasible sub- track using D* algorithm, and sub- track is replaced affected children track in Line3, by step 3,
Step 4 carries out optimizing to complete track and generates new feasible track Line4 ', and aircraft is along this track flight until reaching target
Place terminates the flight, and in flight course, changes if encountering environment, repeatedly step 5；In-flight, environment movement obtains track such as
Shown in Figure 10, environment movement is met, track comparison is as shown in figure 11 before and after aircraft.
Claims (6)
1. path planning method under a kind of dynamic environment based on Cultural Algorithm comprising: it obtains cartographic information and flight is appointed
Business information, unmanned vehicle trajectory planning, the unmanned vehicle trajectory planning include:
Generate initial feasible track；
Knowledge extraction is carried out to feasible track, extracts in initial track and biggish characteristic node information conduct is changed to track slope
Knowledge；
Trajectory planning is influenced using knowledge, optimizing space is determined with knowledge, outstanding node composition is searched out in optimizing space most
Excellent track, aircraft fly along this optimal trajectory；
When environment change influences current optimal trajectory, feasible sub- track only is regenerated to impacted part, by new son
Track is replaced into Reciprocal course, and carries out knowledge extraction to complete track, and the update of knowledge is completed with this, is determined using new knowledge
Optimal trajectory is simultaneously planned in space, until aircraft reaches at target；
Wherein, the aerial mission information specifically includes: aircraft maximum pitch angle and yaw angle, vehicle flight speeds,
Target point movement speed；
It is described to generate initial feasible track specifically: to generate one by position of aircraft using online path planning method D* algorithm
To the feasible track Line1 of aiming spot；
The knowledge is extracted specifically: is left out redundant node in the Line1 and is obtained the track line being made of the characteristic node
Line2 retains the alterable range of the characteristic node location information and the characteristic node.
2. path planning method under a kind of dynamic environment based on Cultural Algorithm according to claim 1, it is characterised in that:
It extracts in feasible track and apparent characteristic node coordinate information is changed for situational knowledge to track slope, characteristic node can change model
It encloses for normative knowledge.
3. path planning method under a kind of dynamic environment based on Cultural Algorithm according to claim 1, it is characterised in that:
, with using the determining region of normative knowledge as union, track will be determined using the point of situational knowledge two-by-two as region determined by diagonal line
Planning space.
4. path planning method under a kind of dynamic environment based on Cultural Algorithm according to claim 1, it is characterised in that:
To the node of track produced by initial track optimization, only generated in planning space.
5. path planning method under a kind of dynamic environment based on Cultural Algorithm according to claim 1, it is characterised in that:
When environment changes influence flight track, feasible track is only planned again to part affected by environment, and replace into Reciprocal course.
6. path planning method under a kind of dynamic environment based on Cultural Algorithm according to claim 1, it is characterised in that:
When environment changes influence flight, knowledge extraction is carried out to new planning gained track, and update optimizing space.
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Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN112711267B (en) * | 2020-04-24 | 2021-09-28 | 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 |
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