CN104536454A - Space-time synchronization matching method used for double unmanned aerial vehicle cooperation - Google Patents

Abstract

The invention discloses a time-space synchronous matching method for dual-UAV collaboration, which calculates the coordination cost from the current node of each UAV to all scalable nodes according to the defined dual-UAV collaboration cost function, and selects the minimum coordination cost The expandable node of is used as the extended track node to obtain the current track segment of the dual UAV. The dual-UAV cooperative cost function of the present invention makes up for the deficiency that the cost function in the existing method only considers the constraints of a single UAV track planning, so that the synchronization of the dual UAVs is added to the track point generation process, which is closer to reality process. The present invention divides the respective tracks of the dual drones into track segments, and converts time synergy into track cost and embeds them into the cost function of single drone track segment planning, realizing the arrival track segment of the dual drones The time deviation of nodes and the resolution of track conflicts solve the problems in the prior art that track planning and task coordination are separated, and the influence of time-space synchronization on track planning is not considered.

Description

A Spatio-temporal Synchronous Matching Method for Dual-UAV Collaboration

technical field

The invention relates to the field of unmanned systems, and specifically describes a time-space synchronization matching method for dual-UAV cooperation.

Background technique

As an emerging means of reconnaissance, UAVs provide an important supplement to manned reconnaissance aircraft and reconnaissance satellites. In the process of performing reconnaissance missions, UAVs have unique advantages: (1) UAVs can stay in the sky over designated areas , to conduct long-term continuous circling reconnaissance. (2) The flight trajectory of UAV is changeable, it is not easy to be tracked, and it has strong survivability. (3) High-altitude flying drones are less affected by the natural environment, etc., with high detection accuracy and small information transmission delay. (4) The cost of UAV task execution is relatively low, and it can obtain high intelligence information, which has a high cost-effectiveness ratio. Therefore, there is an increasing demand for single or multiple UAVs carrying different mission loads to perform reconnaissance, surveillance, and earth observation tasks. Under the increasingly high mission requirements, the role of multi-UAV mission planning is also becoming more and more prominent.

The purpose of multi-UAV mission planning is to reasonably allocate UAVs according to UAV load performance and mission requirements, maximize the role of payloads, and ensure the optimal overall performance of missions. Aiming at the problem of multi-UAV mission planning, many related researches have been carried out at home and abroad. The US Defense Advanced Research Projects Agency (DARPA)'s autonomous formation hybrid active control project explores new surveillance and control methods in order to achieve relatively small number of operators to control large-scale unmanned combat platform formations, focusing on multi-task decomposition and distribution question. In the literature, Ryan et al. regarded the multi-UAV cooperative reconnaissance mission planning problem as a multi-traveling salesman problem with time windows, and gave the solution method of the tabu search algorithm. Researchers from National University of Defense Technology, Northwestern Polytechnical University and other universities have respectively established UAV collaborative mission planning models and proposed corresponding optimization methods. However, in the above research, the constraints of the actual flight of the UAV and the planning task objectives are all simplified, avoiding the complexity of the real problem, resulting in a decrease in the applicability and effectiveness of the method. Perform modeling and analysis, lacking analysis of uncertainty in collaborative tasks. In the actual task execution of UAVs, affected by factors such as meteorological conditions and UAV flight control conditions, there are uncertainties in the space-time coordination of multiple UAVs, that is, it is difficult to achieve the time and space coordination of multiple UAVs. The space is completely synchronized, so it brings great difficulty to the formation control of multiple UAVs and the execution of tightly coupled tasks, and it has also become a difficult problem for multi-UAV task coordination.

Contents of the invention

The technical problem to be solved by the present invention is to provide a space-time synchronous matching method for dual-UAV cooperation, which can resolve the time deviation and track conflict between the dual-UAVs arriving at the track segment node, and achieve the goal in the shortest possible time. Arriving at the target point at the same time within the interval provides a time-space synchronization means for dual-UAV formation control and dual-UAV tightly coupled mission execution with strong space constraints.

Technical scheme of the present invention is:

A kind of time-space synchronous matching method that is used for double unmanned aerial vehicle coordination, described double unmanned aerial vehicle comprises unmanned aerial vehicle UAV-A and unmanned aerial vehicle UAV-B, is characterized in that, defines unmanned aerial vehicle track segment (T _i ,T _i+1 ) corresponding dual-UAV cooperative cost function: cost(T _i ,T _i+1 ) is the collaborative cost, T _i is the current node, T _i+1 is the extended node; C _p ,p=1,2,...,7 is the current track segment (T _i ,T _i+1 ), the minimum track cost function, λ _p , p=1,2,...,7 are the cost coefficients corresponding to each minimum track cost function, L(T _i ,T _i+1 ) is the current track segment (T _i , T _i+1 ) cooperative voyage cost function, α is the cost coefficient of the cooperative voyage cost;

The specific steps of the space-time synchronization matching method for dual-UAV cooperation are as follows:

(1) Obtain the starting point A ₁ , the end point A K of the UAV-A track segment, and the constraints required for track planning, and obtain the starting point B ₁ , the end point B _K , and the flight path segment of the UAV-B UAV- _B . Constraints required for trajectory planning, let loop variable i=1;

(2) Calculate the scalable nodes (X ₁ , X ₂ ,…,X _n ) of UAV-A’s current track node A _i ; calculate the scalable nodes of UAV-B’s current track node B _i (Y ₁ ,Y ₂ ,…,Y _m );

( ₃ ) _{Calculate the} collaborative cost cost( _{A i} ,X ₁ ),cost(A _i ,X ₂ ),...,cost(A _i ,X _n ); select the scalable node with the smallest coordination cost as the current track node A of UAV-A The extended track node A _i+1 of _i ; the corresponding collaborative cost is cost(A _i ,A _i+1 ), and the current track segment of UAV-A is (A _i ,A _i+1 );

_According to _the defined dual UAV cooperative cost function _, calculate the cooperative cost cost( _{B i ,} Y ₁ ),cost(B _i ,Y ₂ ),...,cost(B _i ,Y _m ); select the scalable node with the smallest coordination cost as the UAV-B current track node B _i Extend the track node B _i+1 , the corresponding coordination cost is cost(B _i ,B _i+1 ), and the current track segment of UAV-B is (B _i ,B _i+1 );

(4) Judging whether the current track segment of unmanned aerial vehicle UAV-A and unmanned aerial vehicle UAV-B satisfies the safety distance and the non-intersection constraint condition, if the safety distance and the non-intersection constraint condition are not satisfied, go to step (5); if Satisfy the safety distance and non-intersection constraint conditions, go to step (6);

(5) Compare the collaborative cost of UAV-A's current track segment (A _i ,A _i+1 ) and UAV-B's current track segment (B _i ,B _i+1 );

If the coordination cost of UAV-B’s current track segment (B _i ,B _i+1 ) is high, keep the extended track node A _i+1 of the current track node A _i of UAV-A, and add Unmanned Aerial Vehicle UAV-A track list; re-select the scalable node of the unmanned aerial vehicle UAV-B with the second smallest coordination cost as the extended track node B _i+1 , and the current track segment of the unmanned aerial vehicle UAV-B is ( B _i ,B _i+1 ), then go to step (4);

If the coordination cost of UAV-A's current track (A _i ,A _i+1 ) is high, keep the extended track point B _i+1 of the current track node B _i of UAV-B, and add Man-machine UAV-B track list; re-select the scalable node of the unmanned aerial vehicle UAV-A with the second smallest coordination cost as the extended track node A _i+1 , and the current track segment of the unmanned aerial vehicle UAV-A is (A _i ,A _i+1 ), go to step (4);

(6) Keep UAV-A's current extended track node A _i+1 and add it to the UAV-A track list; keep UAV-B's current extended track node B _i+1 and add it to the UAV-B track list; The extended track node is updated to the current track node, that is, i is increased by 1;

(7) Judging whether the end condition of trajectory planning is satisfied, if not, go to step (2); if satisfied, end.

Described step (4) judges whether unmanned aerial vehicle UAV-A and unmanned aerial vehicle UAV-B current track section satisfy safety distance and the method for non-intersection constraint condition as follows:

Computing the track intersectionality parameter R,

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$

Calculate the track safety distance parameter Q,

$\mathrm{<span\; class="notranslate">\; Q</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$

If R·Q<0, it is considered that the safety distance and non-intersection constraint conditions are satisfied, and if R·Q≥0, it is considered that the safety distance and non-intersection constraint conditions are not satisfied.

The end condition of the trajectory planning is reaching the end point of the trajectory segment.

C ₁ is the minimum track segment length cost:

C ₁ =min(l _j )

l _j , j=1,..., N ₁ is the length of the jth optional track segment, N ₁ is the number of optional track segments; l _j ≥ l _min , l _min is the minimum track segment length;

C ₂ is the maximum turning angle cost:

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; arccos</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span>$

a _i ＝(Tx _i+1 -Txi _i ,Ty _i+1 -Ty _i ) ^T , (Txi _i ,Ty _i ) is the projected position coordinates of current node T _i , (Tx _i+1 ,Ty _i+1 ) is Extended node projection position coordinates, ||a _i || is the modulus of vector a _i ; and satisfy is the maximum allowable turning angle of the UAV;

C ₃ is the target entry direction cost:

C ₃ =min(η _j )

η _j , j=1,...,N ₃ is the jth optional entry direction angle, N ₃ is the total number of optional entry direction angles; η _j ≤Φ, Φ is the maximum allowable entry direction angle.

C ₄ is the maximum climb/dive angle cost:

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arctan</span>}<span\; class="notranslate">\; (</span>\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; Tz</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Tz</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span>$

Tz _i is the elevation of the current node T _i , Tz _i+1 is the elevation of the extended node T _i+1 ; and satisfy θ is the maximum allowable dive/climb angle of the UAV;

C ₅ is the longest voyage cost:

C ₅ =min(∑d _j )

d _j , j=1,..., N ₅ is the jth optional voyage from the starting point to the current node T _i , N ₅ is the total number of optional voyages; and it satisfies ∑d _j ≤ D _mzx , D _mzx is the longest voyage distance;

C ₆ is the flight altitude cost:

C ₆ =min(H _j )

H _j , j=1,..., N ₆ is the jth optional minimum ground clearance height, N ₆ is the total number of optional minimum ground clearance heights; and satisfy H _min ≤ H _j ≤ H _max , H _min is the minimum Flight height limit, H _max is the maximum flight height limit;

C ₇ is the distance threat area cost:

C ₇ =min(W _j )

_{W j} , j= ₁ , .

L(T _i ,T _i+1 )＝|(L _Ti +L _Ti+1 )-L _q |

L _Ti is the voyage of the current node T _i to the destination, L _Ti+1 is the voyage of the extended node T _i+1 to the destination, and L _q is the cooperative voyage.

The cooperative range L _q adopts different formulas according to the different dimensions of the flight path of the UAV. For one-dimensional cooperative flight, L _q =k ₁ .max{D ₁ ,D ₂ }, k ₁ is the straight-line distance coefficient, and max{D ₁ ,D ₂ } is the maximum straight-line distance between the dual UAVs and the destination. For two-dimensional cooperative range, L _q =k ₂ .max{D ₁ ,D ₂ }, k ₂ is the two-dimensional Euclidean distance coefficient; max{D ₁ ,D ₂ } is the maximum two-dimensional Euclidean distance between the two UAVs. distance. For the three-dimensional cooperative flight, L _q = k ₃ .max{D ₁ , D ₂ }, k ₃ is the three-dimensional Euclidean distance coefficient, and max{D ₁ , D ₂ } is the maximum three-dimensional Euclidean distance between the dual drones and the destination.

For one-dimensional cooperative flight, L _q =k ₁ .max{D ₁ ,D ₂ }, k ₁ is the straight-line distance coefficient, and max{D ₁ ,D ₂ } is the maximum straight-line distance between the dual UAVs and the destination.

For two-dimensional cooperative range, L _q =k ₂ .max{D ₁ ,D ₂ }, k ₂ is the two-dimensional Euclidean distance coefficient; max{D ₁ ,D ₂ } is the maximum two-dimensional Euclidean distance between the two UAVs. distance.

For the three-dimensional cooperative flight, L _q = k ₃ .max{D ₁ , D ₂ }, k ₃ is the three-dimensional Euclidean distance coefficient, and max{D ₁ , D ₂ } is the maximum three-dimensional Euclidean distance between the dual drones and the destination.

The constraints required for the UAV track planning include the UAV minimum track segment length l _min , the maximum allowable turning angle The maximum allowable dive/climb angle θ, the longest flight path distance D _mzx , the minimum flight altitude limit H _min , the maximum flight altitude limit H _max and the flight safety distance Ds.

The advantage of the present invention compared with prior art is:

(1) On the basis of the distance of the dual-UAV cooperative track, the present invention proposes a dual-UAV time-space synchronization cooperative cost function, which provides a basis for the expansion of the dual-UAV cooperative track node, and makes up for the existing methods. The cost function only considers the lack of constraints of a single UAV track planning, which makes the track point generation process join the synchronization of two UAVs, which is closer to the real process.

(2) The present invention divides the respective tracks of the dual drones into track segments, and converts the time synergy into the track cost and embeds it into the cost function of the single drone track segment planning, realizing the arrival of the dual drones The time deviation of track segment nodes and the resolution of track conflicts solve the problems in the prior art that track planning and task coordination are separated, and the influence of time-space synchronization on track planning is not considered. The invention provides a space-time synchronization means for the dual-UAV formation control and the execution of the dual-UAV tightly coupled tasks with strong space constraint requirements.

Description of drawings

Fig. 1 is a flow chart of a space-time synchronous matching method for dual-UAV collaboration in the present invention;

Fig. 2 is a schematic diagram of time-space synchronization of multi-track segments according to the present invention.

Detailed ways

The present invention proposes for the first time a collaborative cost function from the current node of the dual UAV to the extended node. The defined collaborative cost function not only needs to cover the constraint conditions of single UAV track planning, but also needs to meet the synchronization requirements of the dual UAV. Adding the judgment of the cost of dual-UAV collaboration in the process of expanding node selection can realize the time deviation of the dual-UAV arriving at the node of the track segment and the resolution of track conflicts, and solve the separation of track planning and task coordination in the existing technology. Issues such as the influence of space-time synchronization on trajectory planning are not considered.

Define the UAV track segment (T _i , T _i+1 ) corresponding to the dual-UAV cooperative cost function as

$\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 7</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&alpha;\; L</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

cost(T _i ,T _i+1 ) is the collaborative cost, T _i is the current node, and T _i+1 is the extended node;

The definition of the cooperative cost function in formula (1) includes two parts: the UAV minimum track cost and the cooperative voyage cost. The minimum trajectory cost mainly covers the constraints of single UAV trajectory planning, and the cooperative voyage cost mainly meets the synchronization requirements of dual UAVs. C _p (p=1,2,…,7) is the minimum track cost function of the current track (T _i ,T _i+1 ), λ _p ,p=1,2,…,7 is the cost of each cost Coefficient, L(T _i ,T _i+1 ) is the cooperative voyage cost function of the current track (T _i ,T _i+1 ), and α is the cost coefficient of the cooperative voyage cost. For example, for a specific UAV, each coefficient can take the following values, (λ ₁ , λ ₂ , . . . , λ ₇ , α)=(0.5, 0.7, 0.2, 0.5, 0.7, 0.3, 0.2, 0.6).

The specific cost function is as follows:

C ₁ is the minimum track segment length cost:

C ₁ =min(l _j ) (2)

In formula (2), l _j , j=1,...,N ₁ is the length of the jth optional track segment, N ₁ is the number of optional track segments; l _j ≥ l _min , l _min is the minimum track segment Trace length.

C ₂ is the maximum turning angle cost:

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; arccos</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In the formula (3), a _i =(Tx _i+1 -Txi _i ,Ty _i+1 -Ty _i ) ^T , (Tx _i ,Ty _i ) is the projected position coordinates of the current node T _i , (Tx _i+1 , Ty _i+1 ) is the projection position coordinates of the extended node, and ||a _i || is the modulus of the vector a _i . and satisfied is the maximum allowable turning angle of the UAV.

C ₃ is the target entry direction cost:

C ₃ =min(η _j ) (4)

In formula (4), η _j , j=1,..., N ₃ is the jth optional entry direction angle, N ₃ is the total number of optional entry direction angles; η _j ≤ Φ, Φ is the maximum allowable entry direction angle .

C ₄ is the maximum climb/dive angle cost:

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arctan</span>}<span\; class="notranslate">\; (</span>\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; Tz</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Tz</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

In the formula (5), Tz _i is the elevation of the current node T _i , and Tz _i+1 is the elevation of the extended node T _i+1 . and satisfied θ is the maximum allowable dive/climb angle of the UAV.

C ₅ is the longest voyage cost:

C ₅ =min(∑d _j ) (6)

In formula (6), d _j , j=1,..., N ₅ is the jth optional voyage from the starting point to the current node T _i , and N ₅ is the total number of optional voyages. And satisfy ∑d _j ≤ D _mzx , D _mzx is the longest voyage distance, which is determined by the safe flight fuel load calculation of UAV or the arrival time limit of task execution.

C ₆ is the flight altitude cost:

C ₆ =min(H _j ) (7)

In formula (7), H _j , j=1,..., N ₆ is the jth optional minimum ground clearance height, and N ₆ is the total number of optional minimum ground clearance heights. And satisfy H _min ≤ H _j ≤ H _max , H _min is the minimum flight height limit, H _max is the maximum flight height limit.

C ₇ is the distance threat area cost:

C ₇ =min(W _j ) (8)

In the formula (8), W _j , j=1,...,N ₇ is the jth optional nearest threat zone distance, and N ₇ is the total number of optional nearest threat zone distances.

L(T _i ,T _i+1 ) is the cooperative voyage cost:

L(T _i ,T _i+1 )＝|(L _Ti +L _Ti+1 )-L _q | (9)

In formula (9), L _Ti is the voyage of the current node T _i to the destination, L _Ti+1 is the voyage of the extended node T _i+1 to the destination, and L _q is the cooperative voyage.

The cooperative range L _q adopts different formulas according to the different dimensions of the flight path of the UAV. For one-dimensional cooperative flight, L _q =k ₁ .max{D ₁ ,D ₂ }, k ₁ is the straight-line distance coefficient, and max{D ₁ ,D ₂ } is the maximum straight-line distance between the dual UAVs and the destination. For two-dimensional cooperative range, L _q =k ₂ .max{D ₁ ,D ₂ }, k ₂ is the two-dimensional Euclidean distance coefficient; max{D ₁ ,D ₂ } is the maximum two-dimensional Euclidean distance between the two UAVs. distance. For the three-dimensional cooperative flight, L _q = k ₃ .max{D ₁ , D ₂ }, k ₃ is the three-dimensional Euclidean distance coefficient, and max{D ₁ , D ₂ } is the maximum three-dimensional Euclidean distance between the dual drones and the destination.

Among them, the calculation speed of one-dimensional cooperative range is faster, but the accuracy is low; the calculation speed of two-dimensional cooperative range is slower than that of one-dimensional, but the accuracy is higher, which is suitable for the calculation of fixed-altitude cruise UAV; the calculation speed of three-dimensional cooperative range is slower than that of two-dimensional Dimensions, but with the highest accuracy, it is suitable for the calculation of variable altitude UAVs in complex air environments.

As shown in Fig. 1, a kind of space-time synchronous matching method for dual UAV collaboration of the present invention, the specific steps are as follows:

(1) Obtain the initialization parameters of the UAV.

The purpose of the initialization is to prepare for the implementation of the method before the dual drone takes off. Obtain the starting point A ₁ , the end point A _K of the track segment of UAV-A, and the constraints required for track planning; obtain the starting point B ₁ , the end point B _K of the track segment of UAV-B, and the path planning requirements Constraints are required.

The constraints required for the UAV track planning include the UAV minimum track segment length l _min , the maximum allowable turning angle The maximum allowable dive/climb angle θ, the longest flight path distance D _mzx , the minimum flight altitude limit H _min , the maximum flight altitude limit H _max , the flight safety distance Ds, etc.

(2) Determine the respective current track nodes of the dual UAVs, and calculate the scalable nodes of the current track nodes.

Determine the current track node A _i of UAV-A, calculate all the scalable nodes (X ₁ , X ₂ ,...,X _n ) of the current track node; determine the current track node B _i of UAV-B , to calculate the extensible nodes (Y ₁ ,Y ₂ ,…,Y _m ) of the current track node.

Without considering the impassable area, in the 8-neighborhood grid, n and m are both less than or equal to 8; in the 24-neighborhood grid, n and m are both less than or equal to 24.

Methods for determining scalable nodes include but are not limited to A* algorithm, sparse A* algorithm, Gaussian pseudospectral method, adaptive Gaussian pseudospectral method, etc., see the document "Unmanned Flight Control Technology and Engineering" (edited by Zeng Qinghua and Guo Zhenyun, Published by National Defense Industry Press, 2011.8).

(3) Calculate the collaborative cost from the current node of each UAV to all scalable nodes according to the defined dual-UAV cooperative cost function, and select the scalable node with the smallest cooperative cost as the extended track node to obtain the dual-UAV current track segment.

Calculate the _{collaborative} cost function _{cost(A i , X 1} ) _, ……,cost(A _i ,X _j ),...,cost(A _i ,X _n ), choose the scalable node with the smallest coordination cost as the extended track node A _i+1 ; the corresponding coordination cost is cost(A _i ,A _i+1 ) , and the current track segment of UAV-A is (A _i ,A _i+1 ) _.

Calculate the collaborative cost function _{cost(B i , Y 1 ) ,} ……,cost(B _i ,Y _k ),...,cost(B _i ,Y _m ), select the scalable node with the smallest coordination cost as the extended track node B _i+1 , and the corresponding coordination cost is cost(B _i ,B _i+1 ) , and the current track segment of UAV-B is (B _i ,B _i+1 ).

(4) Judging whether the current track of the dual-UAV meets the safety distance and non-intersection constraint conditions, if the constraint conditions are not satisfied, go to step (5), otherwise go to step (6);

As shown in FIG. 2 , it is a schematic diagram of multi-track segment time-space synchronization in the present invention. Judging the shortest safe distance and track intersection between UAV-A's current track (A _i , A _i+1 ) and UAV-B's current track (B _i , B _i+1 ); Ds is the flight safety distance.

Calculate the track intersection parameter R, then

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</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><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

Calculate the track safety distance parameter Q, then

$\mathrm{<span\; class="notranslate">\; Q</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; )</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">\; 12</span>}<span\; class="notranslate">\; )</span>$

In the formulas (11) and (12), → is the vector from the start point to the end point of the track segment. If R·Q<0, the two tracks satisfy the safety distance and non-intersection constraints, otherwise they do not satisfy the constraints.

(5) Keep the extended track nodes with small coordination cost, delete the extended track nodes with large coordinated cost, reselect the corresponding expanded track nodes as extended track nodes, and turn to step (4).

Compare the collaborative cost of UAV-A current track segment (A _i ,A _i+1 ) and UAV-B current track segment (B _i ,B _i+1 );

If the coordination cost of UAV-B's current track segment (B _i , B _i+1 ) is relatively large, keep the extended track node A _i+1 of the current track node A _i of UAV-A; Select the extensible node of the unmanned aerial vehicle UAV-B with the second smallest coordination cost as the extended track node B _i+1 , and obtain the current track segment of the unmanned aerial vehicle UAV-B as (B _i ,B _i+1 ), and then turn to Enter step (4);

If the coordination cost of UAV-A's current track (A _i ,A _i+1 ) is relatively high, keep the extended track point B _i+1 of UAV-B's current track node B _i ; reselect The extensible node of UAV-A with the second lowest coordination cost is used as the extended track node A _i+1 , and the current track segment of UAV-A is obtained as (A _i , A _i+1 ), and the step (4);

(6) Save the current track segments of the two UAVs, and update the extended node to the current node.

Reserve the current extended track node A _i+1 of UAV-A and add it to the UAV-A track list; keep the current extended track node B _i+1 of UAV-B and add it to the UAV-B track list; add the extended track The node is updated to the current track node, that is, i is increased by 1;

(7) Judging whether the end condition of trajectory planning is satisfied, if not, go to step (2), otherwise end the synchronization matching process.

Judging whether UAV-A and UAV-B meet the path planning end condition, the condition is usually to reach the end of the path segment or given separately by the UAV collaborative task, if the path planning end condition is met, output UAV-A and The coordinated track of UAV-B, the synchronization matching process is over.

The above description is only the best specific implementation mode of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of changes or modifications within the technical scope disclosed in the present invention. Replacement should be covered within the protection scope of the present invention.

The content that is not described in detail in the description of the present invention belongs to the well-known technology of those skilled in the art.

Claims (9)

1. a kind of time-space synchronous matching method that is used for double unmanned aerial vehicle collaboration, described double unmanned aerial vehicle comprises unmanned aerial vehicle UAV-A and unmanned aerial vehicle UAV-B, is characterized in that, defines unmanned aerial vehicle track segment ( T _i , T _i+1 ) corresponding to the dual-UAV collaborative cost function: cost(T _i ,T _i+1 ) is the collaborative cost, T _i is the current node, T _i+1 is the extended node; C _p ,p=1,2,...,7 is the current track segment (T _i ,T _i+1 ), the minimum track cost function, λ _p , p=1,2,...,7 are the cost coefficients corresponding to each minimum track cost function, L(T _i ,T _i+1 ) is the current track segment (T _i , T _i+1 ) cooperative voyage cost function, α is the cost coefficient of the cooperative voyage cost; The specific steps of the space-time synchronization matching method for dual-UAV cooperation are as follows: (1) Obtain the starting point A ₁ , the end point A K of the UAV-A track segment, and the constraints required for track planning, and obtain the starting point B ₁ , the end point B _K , and the flight path segment of the UAV-B UAV- _B . Constraints required for trajectory planning, let loop variable i=1; (2) Calculate the scalable nodes (X ₁ , X ₂ ,…,X _n ) of UAV-A’s current track node A _i ; calculate the scalable nodes of UAV-B’s current track node B _i (Y ₁ ,Y ₂ ,…,Y _m ); ( ₃ ) _{Calculate the} collaborative cost cost( _{A i} ,X ₁ ),cost(A _i ,X ₂ ),……,cost(A _i ,X _n ); select the extensible node with the smallest coordination cost as the extended flight path of UAV-A’s current track node A _i track node A _i+1 ; the corresponding collaborative cost is cost(A _i ,A _i+1 ), and the current track segment of UAV-A is (A _i ,A _i+1 ); _According to _the defined dual UAV cooperative cost function _, calculate the cooperative cost cost( _{B i ,} Y ₁ ), cost(B _i ,Y ₂ ),……,cost(B _i ,Y _m ); select the scalable node with the smallest collaborative cost as the extended track node of UAV-B’s current track node B _i B _i+1 , the corresponding coordination cost is cost(B _i ,B _i+1 ), and the current track segment of UAV-B is (B _i ,B _i+1 ); (4) Judging whether the current track segment of unmanned aerial vehicle UAV-A and unmanned aerial vehicle UAV-B satisfies the safety distance and the non-intersection constraint condition, if the safety distance and the non-intersection constraint condition are not satisfied, go to step (5); if Satisfy the safety distance and non-intersection constraint conditions, go to step (6); (5) Compare the collaborative cost of UAV-A's current track segment (A _i ,A _i+1 ) and UAV-B's current track segment (B _i ,B _i+1 ); If the coordination cost of UAV-B’s current track segment (B _i ,B _i+1 ) is high, keep the extended track node A _i+1 of the current track node A _i of UAV-A, and add Unmanned Aerial Vehicle UAV-A track list; re-select the scalable node of the unmanned aerial vehicle UAV-B with the second smallest coordination cost as the extended track node B _i+1 , and the current track segment of the unmanned aerial vehicle UAV-B is ( B _i ,B _i+1 ), then go to step (4); If the coordination cost of UAV-A's current track (A _i ,A _i+1 ) is high, keep the extended track point B _i+1 of the current track node B _i of UAV-B, and add Man-machine UAV-B track list; re-select the scalable node of the unmanned aerial vehicle UAV-A with the second smallest coordination cost as the extended track node A _i+1 , and the current track segment of the unmanned aerial vehicle UAV-A is (A _i ,A _i+1 ), go to step (4); (6) Keep UAV-A's current extended track node A _i+1 and add it to the UAV-A track list; keep UAV-B's current extended track node B _i+1 and add it to the UAV-B track list; The extended track node is updated to the current track node, that is, i is increased by 1; (7) Judging whether the end condition of trajectory planning is satisfied, if not, go to step (2); if satisfied, then end. 2. a kind of time-space synchronous matching method that is used for double unmanned aerial vehicle collaboration according to claim 1, is characterized in that, described step (4) judges unmanned aerial vehicle UAV-A and unmanned aerial vehicle UAV-B current navigation The method of whether the trace segment satisfies the safety distance and non-crossing constraints is as follows: Computing the track intersection parameter R, $\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$ Calculate the track safety distance parameter Q, $\mathrm{<span\; class="notranslate">\; Q</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span>\stackrel{<span\; class="notranslate">\; \&Right\; Arrow;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Ds</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>$ If R·Q<0, it is considered that the safety distance and non-intersection constraint conditions are satisfied, and if R·Q≥0, it is considered that the safety distance and non-intersection constraint conditions are not satisfied. 3. A method for time-space synchronous matching of dual UAVs according to claim 1, characterized in that the path planning end condition is reaching the end of the path segment. 4. a kind of time-space synchronous matching method that is used for double unmanned aerial vehicle cooperation according to claim 1, is characterized in that, C ₁ is the minimum track segment length cost: C ₁ =min(l _j ) l _j , j=1,..., N ₁ is the length of the jth optional track segment, N ₁ is the number of optional track segments; l _j ≥ l _min , l _min is the minimum track segment length; C ₂ is the maximum turning angle cost: ${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; arccos</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span>$ a _i ＝(Tx _i+1 -Txi _i ,Ty _i+1 -Ty _i ) ^T , (Txi _i ,Ty _i ) is the projected position coordinates of current node T _i , (Tx _i+1 ,Ty _i+1 ) is Extended node projection position coordinates, ||a _i || is the modulus of vector a _i ; and satisfy is the maximum allowable turning angle of the UAV; C ₃ is the target entry direction cost: C ₃ =min(η _j ) η _j , j=1,...,N ₃ is the jth optional entry direction angle, N ₃ is the total number of optional entry direction angles; η _j ≤Φ, Φ is the maximum allowable entry direction angle. C ₄ is the maximum climb/dive angle cost: ${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arctan</span>}<span\; class="notranslate">\; (</span>\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; Tz</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Tz</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span>$ Tz _i is the elevation of the current node T _i , Tz _i+1 is the elevation of the extended node T _i+1 ; and satisfy θ is the maximum allowable dive/climb angle of the UAV; C ₅ is the longest voyage cost: C ₅ =min(∑d _j ) d _j , j=1,..., N ₅ is the jth optional voyage from the starting point to the current node T _i , N ₅ is the total number of optional voyages; and satisfy D _mzx is the longest voyage distance; C ₆ is the flight altitude cost: C ₆ =min(H _j ) H _j , j=1,..., N ₆ is the jth optional minimum ground clearance, N ₆ is the total number of optional minimum ground clearance; and satisfy H _min ≤ H _j ≤ H _max , H _min is the minimum Flight height limit, H _max is the maximum flight height limit; C ₇ is the distance threat area cost: C ₇ =min(W _j ) _{W j} , j= ₁ , . 5. A kind of time-space synchronous matching method that is used for double UAV collaboration according to claim 1, is characterized in that, L(T _i ,T _i+1 )＝|(L _Ti +L _Ti+1 )-L _q | L _Ti is the voyage of the current node T _i to the destination, L _Ti+1 is the voyage of the extended node T _i+1 to the destination, and L _q is the cooperative voyage. The cooperative range L _q adopts different formulas according to the different dimensions of the flight path of the UAV. For one-dimensional cooperative flight, L _q =k ₁ .max{D ₁ ,D ₂ }, k ₁ is the straight-line distance coefficient, and max{D ₁ ,D ₂ } is the maximum straight-line distance between the dual UAVs and the destination. For two-dimensional cooperative voyage, L _q =k ₂ .max{D ₁ ,D ₂ }, k ₂ is the two-dimensional Euclidean distance coefficient; max{D ₁ ,D ₂ } is the maximum two-dimensional Euclidean distance between the two UAVs. distance. For the three-dimensional cooperative flight, L _q = k ₃ .max{D ₁ , D ₂ }, k ₃ is the three-dimensional Euclidean distance coefficient, and max{D ₁ , D ₂ } is the maximum three-dimensional Euclidean distance between the dual UAVs and the destination. 6. A space-time synchronous matching method for dual-UAV collaboration according to claim 5, characterized in that, for a one-dimensional collaborative range, L _q = k ₁ .max{D ₁ , D ₂ }, k ₁ is the straight-line distance coefficient, and max{D ₁ , D ₂ } is the maximum straight-line distance between the dual UAVs and the end point. 7. A space-time synchronous matching method for dual-UAV collaboration according to claim 5, characterized in that, for a two-dimensional cooperative range, L _q =k ₂ .max{D ₁ , D ₂ }, k ₂ is the two-dimensional Euclidean distance coefficient; max{D ₁ , D ₂ } is the maximum two-dimensional Euclidean distance between the two UAVs and the end point. 8. A space-time synchronous matching method for dual-UAV collaboration according to claim 5, characterized in that, for the three-dimensional collaborative range, L _q = k ₃ .max{D ₁ , D ₂ }, k ₃ is the three-dimensional Euclidean distance coefficient, and max{D ₁ , D ₂ } is the maximum three-dimensional Euclidean distance between the two UAVs and the end point. 9. A kind of time-space synchronous matching method that is used for double unmanned aerial vehicle collaboration according to claim 5, is characterized in that, the required constraint condition of described unmanned aerial vehicle track planning comprises unmanned aerial vehicle minimum track segment length l _min , the maximum allowable turning angle , the maximum allowable dive/climb angle θ, the longest flight path distance D _mzx , the minimum flight altitude limit H _min , the maximum flight altitude limit H _max and the flight safety distance Ds.