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

Abstract

The invention belongs to the field of multi-UAV technology, and specifically relates to a multi-UAV cooperative flight method that can be used for multi-UAV target tracking, track optimization, cooperative management, cooperative flight, and task assignment. The present invention includes: determining the number of flight mission targets, determining the operating parameters of the lead aircraft and the follower aircraft in the multi-UAV; determining whether the follower aircraft of each task group should follow the mission target by setting the pilot flag, and 0 means that the lead aircraft is still flying to the mission target. No return, 1 indicates that it has returned and can start to follow the flight; set the flight status of the lead aircraft, 0 indicates that it is in the process of searching for the target; 1 indicates that it is in the process of returning to the starting point; 2 indicates that the lead aircraft is flying to the mission target During the process; the lead aircraft flew to their respective mission destinations, and returned to the starting point after arrival. The information sharing of the present invention makes multi-UAV cooperative flight more autonomous, flexible and safe, and improves the efficiency of multi-UAV cooperative mission execution.

Description

A multi-UAV cooperative flight method

technical field

The invention belongs to the field of multi-UAV technology, and specifically relates to a multi-UAV cooperative flight method that can be used for multi-UAV target tracking, track optimization, cooperative management, cooperative flight, and task assignment.

Background technique

In the complex and changeable information battlefield environment in the future, it will be difficult for a single UAV to complete the task. In many cases, it must be completed through cooperative control of multiple UAVs; each UAV requires a 1 A crew of up to 3 assigns, negotiates and coordinates many human fighters. In addition to the cost of human operators, this approach encounters unsolvable challenges in how to achieve coordination. Under the constraints of today's technology, it is still quite difficult for unmanned aerial vehicles to reach the powerful information processing capabilities and intelligence of pilots. Intelligence can match or even surpass the numerically inferior manned aircraft. Analyzing the evolutionary characteristics and behavioral laws of biological systems, combining some principles and behaviors of biological swarm intelligence with the theory of multi-UAV cooperative control, has broad engineering application prospects. At present, the research on UAV swarm cooperative flight and track planning has achieved certain research results at home and abroad, but there is no unified theory and effective method.

In various biological groups in nature, such as bees, ants, birds, etc., he is not a certain role to coordinate other autonomous individuals, but the whole can show a state of order, coordination and intelligence. Like the bee colony algorithm studied in this paper, bees can complete certain tasks through self-organization. These groups cooperate with each other to complete tasks that cannot be completed by a single individual. Although each individual performs a simple action behavior, through interaction and coordination, they finally complete the search, prevention, foraging, etc. Various intelligent behaviors. There are also many scholars studying the self-organization behavior of organisms, such as: ant colony algorithm, boids algorithm, fish swarm algorithm, bee colony algorithm and other bionic algorithms, and they are widely used in various research fields and have achieved many results. For example, Su Fei and others proposed the UAV collaborative task assignment based on ant colony algorithm, see: Su Fei, Chen Yan, Shen Lincheng. UAV cooperative multi-task assignment based on ant colony algorithm, Acta Aeronautica Sinica, 2008, 29( S1): pp. 184-191. Duan Haibin and others proposed an optimization algorithm for UAV path planning based on chaotic bee colony optimization algorithm, see Xu, 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. There are also many traditional mathematical methods for multi-UAV coordination problems. For example, Tian Jing proposed multi-UAV cooperative reconnaissance task planning modeling technology, see: Tian Jing, Multi-UAV cooperative reconnaissance mission planning problem modeling and optimization technology research, National University of Defense Technology Master's Degree Thesis, 2007. Shen Yanhang proposed a multi-UAV cooperative control method based on search theory, see: Shen Yanhang, Zhou Zhou, Zhu Xiaoping, Research on Multi-UAV Cooperative Control Method Based on Search Theory, Journal of Northwestern Polytechnical University, 2006(24):367～369.

The United States and other countries paid attention to and started multi-UAV collaborative research earlier, and conducted research on system structure and collaborative path planning. See A. Ollero et a1. Architecture and perception issues in the comests 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 Zhanmugavel, Wkifa, Antonios Ts Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs. Control Engineering Practice18(2010)1084–1092P.

Basturk and others first proposed an optimization algorithm based on the bee colony principle, see Basturk B, Karaboga D. An Artificial Bee Colony (ABC) Algorithm for Numeric function Optimization [C]. USA, Indiana IEEESwarm Intelligence Symposium, 2006: 3-4 and KarabogaD, BasturkB. Artificial BeeColony (ABC) Optimization Algorithm for Solving Constrained Optimization [J]. Foundations of Fuzzy Logic and Soft Computing, 2007.

Although the above-mentioned traditional methods and new intelligent optimization algorithms including bee colony optimization have been applied to the problem of UAV trajectory planning, and have achieved certain research results, they have not yet utilized the advantages of social insect groups to realize UAV cluster coordination. flight; also does not really proceed from the natural essence of social insect groups to realize the control of UAV swarm flight. None of them have solved key issues such as cooperative control, track planning, and collision avoidance in UAV swarm flight from the perspective of simulating the actual biological behavior of bee colonies. They only solve abstract problems from the perspective of optimization. Questions are limited.

The application of swarm intelligence to the study of UAV swarm flight mode is relatively new and has very important research value and significance.

Contents of the invention

The purpose of the present invention is to propose a multi-UAV cooperative flight method that enables multi-UAVs to achieve a good cooperative flight effect and improves the efficiency, safety, reliability, flexibility and autonomy of multi-UAV cooperative missions .

The purpose of the present invention is achieved like this:

The present invention comprises the steps:

(1) Determine the number of flight mission targets, and determine the operating parameters of the lead aircraft and follower aircraft in the multi-UAV;

(2) Determine whether the follower aircraft of each task group should follow and fly to the mission target through the setting of the pilot flag. 0 indicates that the lead aircraft has not returned, and 1 indicates that it has returned and can start to follow the flight;

(3) Set the flight status of the lead aircraft, 0 means searching for the target; 1 means returning to the starting point; 2 means leading the follower to the mission target;

(4) The lead planes fly to their respective mission destinations, return to the departure point after arrival, and then randomly designate a certain number of planes according to the number of planes required for each mission to follow the corresponding lead planes to the mission target according to the above flight rules. After arriving at the position at , the flight mission ends, and the lead aircraft will re-search the route to the destination after encountering obstacles and avoiding collisions, that is, re-determine the direction of speed.

The lead aircraft follows the rules of swarm collision avoidance with all other aircraft during the process of searching for targets and returning and leading the follower aircraft; the follower aircraft follows the rules of swarm cohesion, alignment, collision avoidance and randomness during flight; the described operation Parameters include update speed weights w _cohere , w _avoid , w _align , w _random , maximum acceleration a _max , conversion factor α, field of view d _vis , minimum distance d _min , maximum speed v _max , where update speed weight w _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 leading planes fly at v _max speed.

The steps and characteristics of the collision avoidance rules for the lead aircraft and the follower aircraft are as follows:

(1) Check whether the next position of the aircraft collides with the obstacle

First temporarily set a new position, and then judge whether the new position is within the collision avoidance distance of the aircraft;

(2) Determine whether to start flying around obstacles by judging the position of the following aircraft;

If the calculated center position of the follower is less than 2 times the collision avoidance distance, it will start flying around obstacles.

(3) Lead the machine around obstacles

Based on the flight signature and due to which motion the collision occurred, determine the new position of the lead aircraft. The lead machine changes direction around the obstacle.

(4) Follow the aircraft around obstacles (if a collision occurs)

If the follower aircraft will collide before bypassing the obstacle, it will start to fly around the obstacle the same as the lead aircraft, and the direction of flight is consistent with that of the lead aircraft.

The introduction of the maximum acceleration value a _max to limit the speed change range v _new is:

The update of _the coordinating flight speed _of the leading aircraft following _the aircraft is realized by speed _{weighted sum} v _{′ new =} w _cohere . The flight speed is realized by updating the inertia weight speed v(t+1)=w·v(t)+v _new .

The beneficial effects of the present invention are:

Since the present invention adopts the lead plane to lead the investigation and search, the follower plane to follow the flight, the information sharing strategy of the lead plane and the follower plane makes the multi-UAV cooperative flight more autonomous, flexible and safe, and improves the multi-UAV cooperative execution. Efficiency of tasks; the present invention guides multi-UAV cooperative flight and collision avoidance without having to master other prior knowledge, has excellent practicability and excellent robustness, and is of great significance to the actual multi-UAV cooperative flight strategy .

Description of drawings

Fig. 1 is the block flow diagram of the realization step of the present invention;

Fig. 2 is the emulation emulation that lead machine starts;

Figure 3 is a simulation diagram of UAV following cooperative flight inspired by bee swarms;

Figure 4 is the simulation of the lead aircraft leading the follower to the destination.

Detailed ways

The technical solution adopted by the present invention is to introduce the bee colony flight rules into multi-UAV cooperative flight to achieve better multi-UAV cooperative flight performance, and design the leading aircraft to lead the search and follow the aircraft to solve the problem of multi-person cooperative flight. Following the flight, a multi-UAV swarm flight method is proposed, and a new multi-UAV collaborative method is obtained.

With reference to Fig. 1, the concrete realization steps of the present invention are as follows:

The basic idea of the realization is to simulate the flight of each bee in the bee colony in nature will be affected by the flight status of its neighbor bees. The four basic rules of swarm flight are as follows:

Aggregation rule: Describes the tendency of bees to form a population by assuming that a bee tends to move towards the center of the individuals around it.

Alignment rule: Describes that bees fly at the same speed and use this as a guide for neighboring individuals.

Collision avoidance rules: the habits of bees to avoid collisions.

Stochastic rules: Individual decision-making in bees that alter movement.

The individual behavior of each bee is affected by these four movements.

The set of all bees is set to N, and a focal individual i is regarded as its neighbor individual within the visual distance d _vis >0.

The four rules are respectively defined as vector forms v _cohere , v _align , v _avoid , v _random , which are part of the velocity update vector of individual i. In the following formulas, p _j (j∈N) is the position of all other bees except the leading bee, and p is the position of the leading bee.

1. gather

The aggregation vector v _cohere is the average of all vectors of the hive relative to the current position of surrounding bees

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; cohere</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; vis</span>}}}<span\; class="notranslate">\; \&CenterDot;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&CenterDot;</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

where 1/d _vis limits the aggregation vector to be within [0,1].

2. Alignment

Align vector v _align as the average of all bee velocities v _j around it:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; align</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}<span\; class="notranslate">\; \&CenterDot;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

In the formula, v _max >0 is the limit of the maximum flight speed (the length of the velocity vector) when the follower bees are not affected by the scout bees. Under the 1/v _max limit, the alignment vector is between [0,1].

3. Collision avoidance

When the minimum collision avoidance distance d _min ≤ d _vis , the collision avoidance vector v _avoid depends on the actual position vector of the current bee.

${\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}<span\; class="notranslate">\; \&CenterDot;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; \&CenterDot;</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; avoid</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

In the formula, N _min is a subset of adjacent bee colonies, α is a conversion factor for collision avoidance, and α∈[0,1] keeps the length of v _avoid within [0,1]. Each vector v' is in the range [0,1]. The principle of collision avoidance ensures that the collision avoidance distance of the bee colony is much smaller than d _min .

4. random

The random vector v _random is defined as:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; random</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\frac{{\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

In the formula, v'' is randomly selected from [-1,1], and the scaling factor β is limited to [0,1], which is based on the distribution function of the index F(x)=1-e ^-λx when the parameter λ=2 chosen at random. The principle of randomness can also be used to simulate the situation in which bee colonies avoid collisions with obstacles ahead.

The weighted sum of the four vectors is updated by:

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

The aggregation weight w _cohere , the collision avoidance weight w _avoid , the aggregation weight w _align , and the random weight w _random of the individual are positive numbers.

In nature, real bees have a maximum acceleration to increase their speed.

In order to simplify the model, the maximum acceleration value a _max is introduced to limit the speed change range v _new as:

In the model, it is assumed that the closer the bee is to the target, the smaller the speed is, and an inertia weight w∈(0,1) is introduced to make the original speed v(t) decrease continuously with the speed update. At each iteration, the velocity update v(t+1) is done by:

v(t+1)=w v(t)+v _new (8)

The new position p(t+1) of each hired bee is determined by the following formula:

p(t+1)=p(t)+v(t+1) (9)

Effect of the present invention can be further illustrated by following simulation:

1. Simulation conditions and simulation content:

The simulation is divided into three stages, the first stage: the simulation of the behavior rules of the bee colony; the second stage: the leading UAV starts to lead other UAVs to perform tasks; the third stage: avoiding obstacles on the basis of the second stage thing. In the simulation, based on the habitual behavior of the bee colony, each UAV is in the same position at the beginning of the simulation, and the scout bees are only gathered before being activated, and the principles of collision avoidance and randomness are applied to obtain a more realistic arrangement within a certain period of time , excluding any side effects of the initial arrangement, (if the alignment principle is used, the bees in the colony will be aligned with other adjacent bees)

When the scout bees are activated, the track length of the bee colony from the target position is measured, and the track length is compared with the distance from the initial position to the target position (the shortest possible route). To measure the accuracy of the swarm to the target position, the distance to the center of the swarm was measured when the swarm reached its maximum velocity.

The parameter values are selected as follows: set the same speed weight w _cohere =w _avoid =w _align =w _random =a _max =0.3. This is the maximum acceleration possible for the four motion rules. The parameter α=0.75, this value is mainly obtained by experience, so that the bees can keep a sufficient distance from the adjacent individuals and the adjacent bees will not be too large. Other parameter values w=0.8, v _max =1.55, d _vis =30, d _min =15. All scout bees fly at v _max speed.

2. Simulation experiment content

In the simulation, it is assumed that there are 5 targets, and the number of followers required to select five leading aircraft is 3, 1, 3, 1, and 2 respectively. Changing the corresponding group can modify the number of aircraft that needs to be added.

The initial state is shown in Figure 2. The flight state of guiding the UAV to lead the corresponding number of follower aircraft to the target is shown in Figure 3. The end state of the coordinated flight is shown in Figure 4.

During the entire collaborative flight simulation process, the lead UAV simulates the lead bee to play the role of leading reconnaissance. The reconnaissance obtains the number of UAVs required for each target point, and then informs the follower UAVs to reach the target together to complete the task. The reason why the following aircraft in the picture is wandering around the destination is the result of the random principle in flight.

3. Simulation results

The simulation of the swarm flight mode applied to the cooperative flight of UAVs shows that the swarm flight mode can well complete the multi-person cooperative flight, and achieves the effect of using the swarm flight mode to control the multi-person 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.

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