CN103576692A - Method for achieving coordinated flight of multiple unmanned aerial vehicles - Google Patents

Abstract

The invention belongs to the technical field of multiple unmanned aerial vehicles, and particularly relates to a method for achieving coordinated flight of multiple unmanned aerial vehicles, wherein the method can be used for target tracking, flight path optimization, coordinated management, coordinated flight and task distribution of the multiple unmanned aerial vehicles. The method includes the steps of determining the number of flight task objects, determining operating parameters of the leading aerial vehicles and the following aerial vehicles in the multiple unmanned aerial vehicles, determining whether the following aerial vehicles in each task group should follow and fly to task objects or not by setting navigation marks, setting the flying states of the leading aerial vehicles, and enabling the leading aerial vehicles to fly to the task destinations respectively and to return to the starting places respectively after arriving at the destinations, wherein for the following aerial vehicles, 0 stands for that the leading aerial vehicles have not returned, 1 stands for that the leading aerial vehicles have returned and can start following flight, for the leading aerial vehicles, 0 stands for that the leading aerial vehicles are in the process of searching for targets, 1 stands for that the leading aerial vehicles are in the process of returning to the starting places, and 2 stands for that the leading aerial vehicles are in the process of leading the following aerial vehicles to fly to the task destinations. Information is shared, and therefore the multiple unmanned aerial vehicles is made to have higher autonomy, flexibility and safety in coordinated flight, and the efficiency is improved when the multiple unmanned aerial vehicles are used in cooperation for executing the task.

Description

A kind of multiple no-manned plane is worked in coordination with flying method

Technical field

The invention belongs to multiple no-manned plane technical field, be specifically related to a kind of collaborative flying method of multiple no-manned plane that can be used for multiple no-manned plane target following, flight path optimization, coordinated management, collaborative flight, task distribution.

Background technology

Under future Information Battle Environment complicated and changeable, single frame unmanned plane will be difficult to finish the work, and the multiple UAVs that must fly by Collaborative Control in a lot of situations just can complete; Each unmanned plane requires 1 to 3 people's crew to distribute, and consults and coordinate many mankind soldiers.Except the cost of human operator who, this method runs into indeterminable challenge, how to reach collaborative.Under the restriction of current science and technology, want unmanned machine to arrive pilot's powerful information processing capability and intelligence or difficult like that, if by the clustering phenomenon of natural imitation circle biology, the unmanned plane quantitatively having comparative advantage utilizes swarm intelligence just can reach even to surmount the people that has who quantitatively accounts for inferior position to drive machine.The Evolution of analysis of biological system and behavior rule, combine some principle of biotic population intelligence and behavior with multiple no-manned plane Collaborative Control theory, have wide future in engineering applications.Though the research of the collaborative flight of unmanned aerial vehicle group at present and trajectory planning aspect has obtained certain achievement in research at home and abroad, also there is no unified theory and effective method.

In the various biological groups of the Nature, as honeybee, ant, birds etc., he is not that some roles coordinate other autonomous individualities, but its integral body can show a kind of in order, coordinate and the state of intelligence.Ant colony algorithm as studied herein, just can, by self-organization, complete some task between honeybee.These colonies are all by mutual cooperation each other, the task of having gone single individuality to complete, although each individuality is done a kind of simple action behavior, by mutual, coordinate the behavior of the multiple intelligence such as finally but complete search, prevent, look for food.Also there is the self-organization behavior of many scholar's research biologies, as: the bionics algorithms such as ant group algorithm, boids algorithm, fish-swarm algorithm, ant colony algorithm, and they are widely used for to each research field, obtained many achievements.Such as, the unmanned plane cotasking that the people such as Sufi propose based on ant group algorithm distributes, referring to: Sufi, Chen Yan, Shen Lincheng. the UAV Cooperative Multi-task based on ant group algorithm distributes. aviation journal, 2008,29 (S1): 184-191 page.The people such as Duan Haibin have proposed the unmanned aerial vehicle flight path plan optimization algorithm based on chaos bee colony optimized algorithm, referring to Xu, and Chunfang, Duan, Haibin; Liu, Fang, Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning, Aerospace Science and Technology, 14 (8), p535-541,2010.Also there are many traditional mathematical methods for multiple no-manned plane Research on Interactive Problem.Such as sesbania proposes multiple no-manned plane coordinated investigation mission planning modeling technique, referring to: sesbania, the technical research of multiple no-manned plane coordinated investigation mission planning problem modeling and optimization, National University of Defense Technology's master thesis, 2007.Shen Yanhang proposes the many man-machine coordinations control method based on search theory, referring to: Shen Yanhang, Zhou Zhou, Zhu little Ping, the multiple no-manned plane cooperative control method research based on search theory, Northwestern Polytechnical University's journal, 2006 (24): 367～369.

The countries such as the U.S. early pay attention to and start the collaborative research of multiple no-manned plane, at aspects such as architecture, collaborative path plannings, are studied.Referring to A.Ollero et a1.Architecture and perception issues in the comets multiuav project.IEEE Robotics and Automation Magazine.special issue on R & A in Europe:Projects funded by the Comm of the EU.2004 and Madhavan Shanmugavel, Antonios Tsourdos, Brian White, Rafa Zbikowski.Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs.Control Engineering Practice18 (2010) 1084 – 1092P.

The people such as Basturk have proposed the optimized algorithm based on bee colony principle the earliest, referring to Basturk B, Karaboga D.An Artificial Bee Colony (ABC) Algorithmfor Numeric function Optimization[C] .USA, Indiana IEEE Swarm Intelligence Symposium, 2006:3-4 and KarabogaD, BasturkB.Artificial BeeColony (ABC) Optimization Algorithm for Solving Constrained Optimization[J] .Foundations of Fuzzy Logicand Soft Computing, 2007.

Although above-mentioned classic method and comprise that novel intelligent optimization algorithm that bee colony optimizes is for unmanned aerial vehicle flight path planning problem, and obtained certain achievement in research, also do not utilize social insect's community superiority to realize the collaborative flight of unmanned plane cluster; Do not have really from the natural essence of colony of social insect, to go out to send to realize the control that unmanned plane cluster flies yet.All do not have to solve the key issues such as the aloft Collaborative Control of unmanned plane cluster, trajectory planning, collision prevention from the angle of the actual biological behaviour of simulation bee colony, only the angle from optimizing, obtains abstract solution, limited to practical problems effect.

Bee colony intelligent use is more novel in the research of unmanned plane cluster offline mode, there is very important researching value and meaning.

Summary of the invention

The object of the invention is to propose a kind of multiple no-manned plane that makes and can reach good collaborative flight effect, improve the collaborative flying method of multiple no-manned plane boat of the collaborative efficiency of executing the task of multiple no-manned plane and security, reliability, dirigibility and independence.

The object of the present invention is achieved like this:

The present invention includes following steps:

(1) determine aerial mission number of targets, determine the operational factor that leads machine in multiple no-manned plane and follow machine;

(2) by navigator, indicate to arrange to determine whether the machine of following of each task grouping should follow the task object that flies to, 0 represents to lead machine also not return, and within 1 o'clock, represents to return, and can start to follow flight;

(3) state of flight that leads machine is set, 0 represents to search in the process of the target of flying to; In 1 process that represents to return to one's starting point; 2 represent to lead the machine of following to fly in the process of task object;

(4) the task objective ground that leads machine to fly to respectively separately, after arrival, return to one's starting point, then according to the aircraft number of each required by task, random aircraft of specifying some is followed the corresponding machine residing position of task object of flying to that leads according to above-mentioned flithg rules, after arriving, aerial mission finishes, wherein lead machine at every turn after running into obstacle collision prevention, by the route that flown in destination in search again, redefine the direction of speed.

Lead machine in search target and return and lead in the process of the machine of following, following bee colony collision regulation with all other aircrafts; That the machine of following is followed in bee colony in flight course is poly-, alignment, collision prevention and random rule; Described operational factor comprises renewal speed weight w _cohere, w _avoid, w _align, w _random, peak acceleration a _max, conversion factor α, the visual field be apart from d _vis, minor increment d _min, maximal rate v _max, renewal speed weight w wherein _cohere=w _avoid=w _align=w _random=a _max=0.3, α=0.75, other parameter values w=0.8, v _max=1.55, d _vis=30, d _min=15, all machines that lead are with v _maxspeed flight.

Lead machine and follow machine collision regulation, its step and being characterised in that:

(1) with in barrier whether the next position that checks aircraft bump

New position is set first temporarily, then judges that new position is whether in aircraft collision prevention distance;

(2) whether the determining positions of following a group of planes by judgement starts the flight of cut-through thing;

The machine of the following center calculating, if the collision prevention distance that the distance between them is less than 2 times starts the flight of cut-through thing.

(3) lead machine cut-through thing

According to flight sign with because which kind of motion bumps, determine the reposition that leads machine.Lead machine to change direction cut-through thing.

(4) follow machine cut-through thing (if bumping)

If follow machine, before cut-through thing, can bump, start the flight of the cut-through thing the same with leading machine, the direction of motion of flight with lead machine consistent.

Described introducing maximum acceleration value a _maxmaximum speed limit amplitude of variation v _newfor:

The described machine that leads is followed the collaborative flying speed renewal of machine employing speed weighted sum v ' _new=w _cohere.v _cohere+ w _avoid.v _avoid+ w _align.v _align+ w _random.v _randomrealize, the machine of following reduces flying speed employing inertia weight speed renewal v (t+1)=wv (t)+v after arriving target _newrealize.

Beneficial effect of the present invention is:

The present invention is because employing leads power traction neck investigation search, and the machine of following is followed flight, leads machine and the Information Sharing Strategy of following machine to make the collaborative flight of multiple no-manned plane more have independence, dirigibility, security, has improved the collaborative efficiency of executing the task of multiple no-manned plane; The present invention instructs the collaborative flight of multiple no-manned plane, collision prevention, without grasping other prioris, has splendid practicality and fabulous robustness, significant for the collaborative countermeasures of actual multiple no-manned plane.

Accompanying drawing explanation

Fig. 1 is the FB(flow block) of performing step of the present invention;

Fig. 2 leads the machine emulation emulation of setting out;

Fig. 3 is that the unmanned plane that bee colony is inspired is followed collaborative flight simulation figure;

Fig. 4 leads the power traction neck machine of following to arrive destination emulation.

Embodiment

The technical solution used in the present invention is bee colony flithg rules to be introduced to multiple no-manned plane work in coordination with in-flight to reach the collaborative flying quality of better multiple no-manned plane, and for many man-machine coordinations flight Design of Problems lead the search of power traction neck, the machine of following to follow flight, propose multiple no-manned plane bee colony flying method, obtained new multiple no-manned plane Synergistic method.

With reference to Fig. 1, specific implementation step of the present invention is as follows:

The basic thought of realizing be in simulating nature circle in bee colony the flight of every honeybee all to be subject to the impact of its neighbours honeybee flight condition.Four primitive rules of bee colony flight are as follows:

Assemble rule: by supposing that a honeybee trends towards moving to describe the trend that honeybee forms a population to the individual center of his around.

Alignment rule: description be honeybee using the flight of identical speed and it as contiguous individual guidance.

Collision regulation: honeybee avoids the custom of collision.

Random rule: change the individual decision making of the honeybee of motion.

Each honeybee individual behavior is subject to these four motions to be affected.

All honeybee set are made as N, and wherein the individual i of certain focus is counted as it at visual range d _visneighbours within >0 are individual.

Four rules are defined as respectively vector form v _cohere, v _align, v _avoid, v _random, they are that individual i speed is upgraded a vectorial part.Following various middle p _j(j ∈ N) is the position that leads other all honeybees outside honeybee, and p is the position that leads honeybee.

1. assemble

Assemble vector v _coherebee colony with respect to the mean value of institute's directed quantity of the current location of honeybee around

$v_{\mathrm{cohere}}=\frac{1}{d_{\mathrm{vis}}}\·\frac{1}{\left|N\right|}\·\underset{j\∈N}{\mathrm{\Σ}}{p}_j-p---\left(1\right)$

1/d in formula _visrestriction is assembled vector within [0,1].

2. alignment

Alignment vector v _alignas all honeybee speed v around it _jmean value:

$v_{\mathrm{align}}=\frac{1}{v_{\max }}\·\frac{1}{\left|N\right|}\·\underset{j\∈N}{\mathrm{\Σ}}{v}_j---\left(2\right)$

V in formula _max>0 is when following flying speed maximal value (length of the velocity vector) restriction of honeybee while not affected by search bee.1/v _maxunder restriction, alignment vector is between [0,1].

3. collision prevention

As collision prevention minor increment d _min≤ d _vistime, collision prevention vector v _avoidthe physical location vector that depends on current honeybee.

$v^{\′}=\frac{1}{d_{\min }}\·\frac{1}{\left|N_{\min }\right|}\·\underset{j\∈N_{\min }}{\mathrm{\Σ}}(p-p_i)\·(\frac{d_{\min }}{|p-p_j|}-1)---\left(3\right)$

$v_{\mathrm{avoid}}=\frac{v^{\′}}{{\left|v^{\′}\right|}^{\mathrm{\α}}}---\left(4\right)$

N in formula _minbe the subset of contiguous bee colony, α is collision prevention conversion factor, and α ∈ [0,1] makes v _avoidlength remain in [0,1].Each vector v ' in [0,1] scope.Collision prevention principle has guaranteed that bee colony collision prevention distance is much smaller than d _min.

4. random

Random vector v _randombe defined as:

$v_{\mathrm{random}}=\mathrm{\β}\·\frac{v^{\′\′}}{\left|v^{\′\′}\right|}---\left(5\right)$

In formula, v ' ' selects at random from [1,1], and zoom factor β is limited in [0,1], is according to index F (x)=1-e when parameter lambda=2 ^{-λ x}random select of distribution function.Randomly assigne also can be for the situation of simulation bee colony collision prevention the place ahead obstacle.

Four vectorial weighted sums are upgraded by following formula:

v′ _new=w _cohere.v _cohere+w _avoid.v _avoid+w _align.v _align+w _random.v _random (6)

Individual gathering weight w wherein _cohere, collision prevention weight w _avoid, assemble weight w _align, random weight w _randomit is positive number.

The real honeybee of occurring in nature promotes its speed a peak acceleration.

For simplified model, introduce maximum acceleration value a _maxmaximum speed limit amplitude of variation v _newfor:

In model, hypothesis is followed the less trend of the nearlyer speed of honeybee distance objective, introduces an inertia weight w ∈ (0,1), makes raw velocity v (t) along with speed renewal constantly reduces.In each iteration, speed is upgraded v (t+1) and is completed by following formula:

v(t+1)=w·v(t)+v _new (8)

Each employs the reposition p (t+1) of honeybee to be determined by following formula:

p(t+1)=p(t)+v(t+1) （9）

Effect of the present invention can further illustrate by following emulation:

1. simulated conditions and emulation content:

Emulation is divided into three phases to be carried out, first stage: the simulation of bee colony rule of conduct; Second stage: leading unmanned plane sets out and leads other unmanned planes to execute the task; Phase III: avoiding barrier on subordinate phase basis.In emulation, acquired behavior based on bee colony, emulation at first each unmanned plane in same position, search bee is before activating, be only to assemble, collision prevention and randomly assigne are applicable to obtain within a certain period of time a more real arrangement, get rid of the spinoff of any initial placement, (if used alignment principle, the honeybee in bee colony is by the adjacent honeybee alignment with other)

When search bee is activated, weigh the course length of bee colony distance objective position, this course length and initial position are to the distance versus (the shortest potential route) of target location.In order to measure bee colony to the accuracy of target location, when arriving maximal rate, bee colony measures the distance at bee colony center.

Parameter value is selected as follows: set identical speed weight w _cohere=w _avoid=w _align=w _random=a _max=0.3.At this moment four peak accelerations that sports rule may reach.Parameter alpha=0.75, this value mainly by virtue of experience gets, so that distance and adjacent honeybee that honeybee can keep enough with contiguous individuality are not too large.Other parameter values w=0.8, v _max=1.55, d _vis=30, d _min=15.All search bees are with v _maxspeed flight.

2. emulation experiment content

In emulation, supposition has 5 targets, chooses five and leads the required random number of following of machine to be respectively 3,1,3,1,2.Change corresponding group and can revise the aircraft number that needs increase.

When initial, state as shown in Figure 2.Guiding unmanned plane leads state of flight that the machine of following of respective number arrives at target as shown in Figure 3.Collaborative flight done state as shown in Figure 4.

In whole collaborative flight simulation process, lead unmanned plane simulation to lead and come in great numbers to leading scouting effect, scout and draw the needed unmanned plane number of each impact point, then notify the unmanned plane of following below, arrive together target and finish the work.The reason that the machine of following in figure is around hovered in destination is the result that aloft randomly assigne plays a role.

3. the simulation experiment result

Bee colony offline mode is applied to the collaborative flight of unmanned plane and carries out emulation, result shows that bee colony offline mode can complete many man-machine coordination flight well, has reached the effect of utilizing bee colony offline mode to control how man-machine flight.

Claims (4)

1. the collaborative flying method of multiple no-manned plane, is characterized in that: comprise the steps:

(1) determine aerial mission number of targets, determine the operational factor that leads machine in multiple no-manned plane and follow machine;

(2) by navigator, indicate to arrange to determine whether the machine of following of each task grouping should follow the task object that flies to, 0 represents to lead machine also not return, and within 1 o'clock, represents to return, and can start to follow flight;

(3) state of flight that leads machine is set, 0 represents to search in the process of the target of flying to; In 1 process that represents to return to one's starting point; 2 represent to lead the machine of following to fly in the process of task object;

(4) the task objective ground that leads machine to fly to respectively separately, after arrival, return to one's starting point, then according to the aircraft number of each required by task, random aircraft of specifying some is followed the corresponding machine residing position of task object of flying to that leads according to above-mentioned flithg rules, after arriving, aerial mission finishes, wherein lead machine at every turn after running into obstacle collision prevention, by the route that flown in destination in search again, redefine the direction of speed.

2. the collaborative flying method of a kind of multiple no-manned plane according to claim 1, is characterized in that: described leads machine in search target and return and lead in the process of the machine of following, and follows bee colony collision regulation with all other aircrafts; That the machine of following is followed in bee colony in flight course is poly-, alignment, collision prevention and random rule; Described operational factor comprises renewal speed weight w _cohere, w _avoid, w _align, w _random, peak acceleration a _max, conversion factor α, the visual field be apart from d _vis, minor increment d _min, maximal rate v _max, renewal speed weight w wherein _cohere=w _avoid=w _align=w _random=a _max=0.3, α=0.75, other parameter values w=0.8, v _max=1.55, d _vis=30, d _min=15, all machines that lead are with v _maxspeed flight.

3. the collaborative flying method of a kind of multiple no-manned plane according to claim 1, is characterized in that: described leads machine and follow machine collision regulation, its step and being characterised in that:

(1) with in barrier whether the next position that checks aircraft bump

New position is set first temporarily, then judges that new position is whether in aircraft collision prevention distance;

(2) whether the determining positions of following a group of planes by judgement starts the flight of cut-through thing;

The machine of the following center calculating, if the collision prevention distance that the distance between them is less than 2 times starts the flight of cut-through thing;

(3) lead machine cut-through thing

According to flight sign with because which kind of motion bumps, determine the reposition that leads machine, lead machine to change direction cut-through thing;

(4) follow machine cut-through thing (if bumping)

If follow machine, before cut-through thing, can bump, start the flight of the cut-through thing the same with leading machine, the direction of motion of flight with lead machine consistent.

4. the collaborative flying method of a kind of multiple no-manned plane according to claim 1, is characterized in that: described introducing maximum acceleration value a _maxmaximum speed limit amplitude of variation v _newfor:

The described machine that leads is followed the collaborative flying speed renewal of machine employing speed weighted sum v ' _new=w _cohere.v _cohere+ w _avoid.v _avoid+ w _align.v _align+ w _random.v _randomrealize, the machine of following reduces flying speed employing inertia weight speed renewal v (t+1)=wv (t)+v after arriving target _newrealize.

Images