Multi-unmanned aerial vehicle multi-task target air route guiding method

Abstract

The invention discloses a multi-unmanned aerial vehicle multi-task target air route guiding method, and belongs to the field of unmanned aerial vehicle intelligent optimization algorithms. Providing a feasible waypoint set generation method for a multi-target collaborative detection scene; meanwhile, a feasible waypoint set reduction method is designed based on requirements in the aspects of unmanned aerial vehicle performance, sensor performance and communication conditions; in addition, a set of complete air route guiding conflict detection and resolution mechanism is also constructed, so that the continuity of the air route guiding solution of the unmanned aerial vehicle is ensured. And finally, establishing an optimal model of the feasible waypoint for flight, and resolving by means of an improved memetic algorithm, thereby successfully implementing rapid and stable tracking detection on a plurality of targets on the premise of guaranteeing collaborative operation.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) intelligent optimization algorithms, and in particular to a multi-UAV multi-task target route guidance method. Background Art

[0002] In the military, multi-UAV multi-mission target routing technology is in urgent demand and has a strong development background. Modern warfare is undergoing profound changes, with battlefield environments becoming increasingly complex and dynamic. Traditional single UAVs, limited by their field of view, payload capacity, and flight time, struggle to fully and efficiently complete such complex missions. This has led to the emergence of multi-UAV collaborative combat systems. However, enabling these drones to effectively collaborate in complex air combat environments requires routing technology. The U.S. Defense Advanced Research Projects Agency (DARPA) has launched a series of projects aimed at enhancing the ability of multiple UAVs to coordinate missions in complex battlefield environments. By developing advanced algorithms and communication technologies, they are enabling multi-UAVs to stably track multiple dynamic targets and autonomously plan their routes. In future air combat, rationally planning each UAV's flight path to ensure they avoid interfering with each other while maintaining formation and efficiently executing their missions will become a critical factor in determining combat success. This is driving the continuous development and improvement of multi-UAV multi-mission target routing technology.

[0003] Foreign countries started early in the field of multi-UAV multi-target route guidance technology and have achieved numerous results. The United States, with its substantial investment in projects from the Defense Advanced Research Projects Agency (DARPA), is a leader in the research and development of technologies for the coordinated execution of multiple UAV missions in complex battlefield environments. Its advanced algorithms help UAVs achieve autonomous route planning, but in actual applications, when faced with high-intensity electromagnetic interference from the enemy, the stability of inter-UAV coordination and target tracking will be affected to a certain extent. European countries, such as the United Kingdom, are focusing on research into UAV communication protocols and coordination mechanisms, and France has achieved breakthroughs in sensor technology that can improve target tracking and route guidance to a certain extent. However, in scenarios where multiple targets are experiencing drastic dynamic changes and the battlefield environment is complex, it is difficult to fully achieve both coordination and stable target tracking.

[0004] China has seen rapid development in this field in recent years, with numerous universities and research institutions actively engaged in research. A distributed collaborative optimization route planning algorithm proposed by the Beijing University of Aeronautics and Astronautics enables multiple drones to quickly plan routes under complex constraints. However, stable tracking of multiple targets in complex battlefield environments still needs improvement. Northwestern Polytechnical University has made progress in drone navigation and positioning technology, providing support for route guidance. However, when collaboratively executing missions, balancing collaboration and stable target tracking presents challenges due to communication challenges in complex electromagnetic environments.

[0005] In air combat, possessing the technological advantage of ensuring coordination while stably tracking targets would significantly enhance drone combat capabilities. In modern warfare, battlefield environments are constantly changing, and multiple drones must perform reconnaissance, tracking, and strike missions against multiple dynamic targets under harsh conditions such as complex electromagnetic interference and enemy air defense threats. Only by ensuring efficient coordination between drones and stable target tracking can combatants gain a comprehensive, real-time understanding of the battlefield situation, providing a reliable basis for making precise combat decisions.

[0006] In the process of UAV counter-tracking and detection, in order to overcome the difficulty of achieving stable tracking of multiple targets during cooperative detection, the present invention innovatively proposes a feasible waypoint set generation method for multi-target cooperative detection scenarios. At the same time, based on multiple requirements such as UAV performance, sensor performance, and communication conditions, a feasible waypoint set simplification method is carefully designed. In addition, a complete set of route guidance conflict detection and resolution mechanisms is constructed to ensure the continuity of UAV route guidance solutions. Finally, a feasible waypoint optimization model for Yingfei is built, and it is solved with the help of an improved memetic algorithm, successfully achieving rapid and stable tracking and detection of multiple targets while ensuring cooperative operations. Summary of the Invention

[0007] To address the above technical issues, the present invention provides a multi-UAV multi-task target route guidance method, which belongs to the field of UAV intelligent optimization algorithms. Aiming at multi-target collaborative detection scenarios, a feasible waypoint set generation method is proposed. Simultaneously, a feasible waypoint set reduction method is designed based on the requirements of UAV performance, sensor performance, and communication conditions. Furthermore, a comprehensive route guidance conflict detection and resolution mechanism is constructed to ensure the continuity of UAV route guidance solutions. Finally, a feasible waypoint optimization model is constructed and solved using an improved memetic algorithm, successfully achieving rapid and stable tracking and detection of multiple targets while ensuring collaborative operations.

[0008] The present invention provides a multi-unmanned aerial vehicle (UAV) multi-task target route guidance method, which specifically includes the following steps:

[0009] Step 1: Calculate the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max ;

[0010] Step 2, based on the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max , build a set of feasible navigation directions

[0011] Step 3: Select a set of feasible navigation directions A direction of navigation Calculate sailing direction The feasible waypoint P i ψ，θ ;

[0012] Step 4: Determine the feasible waypoint P based on the current situation and the UAV's maneuverability. i ψ，θ Is it flyable: If the waypoint P is feasible i ψ，θ If you cannot fly, gather in a feasible navigation direction. Delete the unflyable direction And go to Step 8; if the feasible waypoint P i ψ，θ If it can fly, go to Step 5;

[0013] Step 5, when the feasible waypoint P i ψ，θ When it is flyable, determine the feasible waypoint P when the carrier aircraft provides communication support to the friendly aircraft i ψ，θ Communication distance D s and communication angle β c Whether the cooperative communication requirements are met: If yes, go to Step 6; if not, gather in the feasible navigation direction Delete navigation direction And go to Step 8;

[0014] Step 6: When the carrier aircraft provides communication support to the friendly aircraft, the feasible waypoint P i ψ，θ Communication distance D s and communication angle β c When the collaborative communication requirements are met, the feasible waypoint P is determined. i ψ，θ Does it meet the UAV formation requirements? If yes, go to Step 7; if not, gather in a feasible navigation direction. Delete navigation direction And go to Step 8;

[0015] Step 7, when the feasible waypoint P i ψ，θ When the UAV formation requirements are met, the feasible waypoint P is determined. i ψ，θ Whether all detection tasks can be performed: If the UAV can perform all detection tasks, it will gather in the feasible navigation direction. Delete navigation direction The feasible waypoint P i ψ，θ Store the feasible waypoint set H Pand go to Step 8; if the UAV cannot perform all the detection tasks, it will gather in the feasible navigation direction. Delete navigation direction And go to Step 8;

[0016] Step 8: Determine the set of feasible navigation directions Is it empty: If not, go to Step 3; if it is empty, output the feasible waypoint set H P , and go to Step 9;

[0017] Step 9, when the feasible navigation direction set When it is empty, determine the feasible waypoint set H P Is it empty: If the feasible waypoint set H P If it is empty, put the task with the lowest priority in the task list into the conflict list, delete the task with the lowest priority, and go to Step 7; if the feasible waypoint set H P If it is not empty, go to Step 10;

[0018] Step 10, when the feasible waypoint set H P When it is not empty, randomly select the feasible navigation direction set Select M feasible navigation directions and pass through each feasible navigation direction at the same time , get feasible waypoint information Each feasible waypoint information As the initial population, an initial population of M is formed; by Obtain all feasible waypoint information adjacent to each population individual feasible waypoint to form a feasible waypoint neighborhood S neighbor , replace the population individuals with the best performance feasible waypoint information Implement local update of the initial population, generate the initial elite population, and then go to Step 11;

[0019] Step 11: Build a Yingfei waypoint optimization model, calculate the fitness value of each individual in the initial elite population through the utility function in the Yingfei waypoint optimization model, and then go to Step 12;

[0020] Step 12: Retain the two individuals with the largest fitness values ​​in the initial population, and randomly pair the unretained individuals. Determine whether to crossover between the unretained individuals to generate the next generation: if not, retain the two parent populations in the next generation population. If crossover occurs, randomly generate a crossover segment, and the feasible waypoints of the offspring before and after the crossover segment are the feasible waypoints of parent 1, and after the crossover segment are the feasible waypoints of parent 2. After the crossover transformation, go to Step 13.

[0021] Step 13: After the unretained individuals in the initial population are crossed, a population is randomly selected to determine whether to mutate: if not, the feasible waypoints are retained in the offspring; if mutated, the azimuth or pitch corresponding to the feasible waypoint is randomly selected and the azimuth or pitch value is replaced with the feasible navigation direction set. Replace the population individual with the feasible waypoint information corresponding to the feasible navigation direction after mutation by an adjacent value in the , and go to Step 14 after mutation transformation;

[0022] Step 14, in the feasible waypoint information collection The fitness of the feasible waypoints in the neighborhood of the current feasible waypoint is calculated by the fitness function. If the neighborhood of the feasible waypoint S neighbor If there is a waypoint with a fitness value greater than the current feasible waypoint, the current feasible waypoint is replaced with a feasible waypoint with a larger fitness value, a descendant population is generated, and the process goes to Step 15;

[0023] Step 15, determine whether the stopping condition is met: the stopping condition is that the optimal feasible waypoint in the population remains unchanged for 10 consecutive iterations; if the stopping condition is not met, the newly generated feasible waypoint is used as the population and the process goes to Step 11; if the stopping condition is met, the route guidance ends and the feasible waypoint with the maximum fitness value in the population is output.

[0024] Furthermore, in Step 1, the maximum yaw angle ψ max The calculation method is as follows:

[0025] First, according to the maximum overload allowed during cruising and horizontal speed during cruising Calculate the maximum turning radius R when level level ;

[0026] Then, according to the horizontal speed during cruising and the maximum turning radius R when horizontal level Calculate the angular velocity ω of the horizontal plane level ;

[0027] Finally, when the flight trajectory of the carrier aircraft is a circular arc, the deflection angle of the carrier aircraft is obtained; according to the isosceles triangle formed by the center of the circle, the carrier aircraft position and the target position, the maximum yaw angle ψ is calculated. max ;

[0028]

[0029] ψ max =(π-ω level T) / 2 (3)

[0030] Where: T represents the period interval; g represents the acceleration due to gravity;

[0031] The maximum pitch angle θ max The calculation method is as follows:

[0032] First, the maximum overload allowed during cruising and vertical speed during cruising Calculate the maximum turning radius R on the vertical plane vertical ;

[0033] Then, according to the vertical speed during cruising and the maximum turning radius R in the vertical plane vertical Calculate the angular velocity ω in the vertical plane vertical ;

[0034] Finally, when the flight trajectory of the carrier aircraft is a circular arc, the deflection angle of the carrier aircraft is obtained; according to the isosceles triangle formed by the center of the circle, the carrier aircraft position and the target position, the maximum pitch angle θ is calculated. max :

[0035]

[0036] θ max =(π-ω vertical T) / 2 (6)

[0037] Furthermore, in Step 2, the set of feasible navigation directions The construction process is as follows:

[0038] Step 2.1, based on the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max , get the feasible navigation direction space h ψ,θ {ψ=[-ψ max ,ψ max ];θ=[-θ max ,θ max ]};

[0039] Step 2.2, based on the maximum yaw angle ψ of the drone max , yaw division number λ, maximum pitch angle θ max and the pitch division number k sets the yaw step amount δ ψ and pitch step δ θ ; δ ψ =2ψ max / λ,δ θ =2θ max / k;

[0040] Step 2.3, according to the yaw step amount δ ψ and the pitch step δ θThe feasible navigation directions are discretized and divided according to the feasible navigation direction space h ψ,θ {ψ∈[-ψ max ,ψ max ];θ∈[-θ max ,θ max ]}Get the set of feasible navigation directions

[0041] Further, in Step 3, the feasible waypoint P i ψ，θ The calculation method is as follows:

[0042] Select a set of feasible navigation directions A direction of navigation ψ i is a yaw in the set of feasible navigation directions, θ i is a pitch in the set of feasible navigation directions;

[0043] According to the current carrier speed V ac , in the velocity system, when the aircraft moves in a straight line at a constant speed, calculate the navigation direction The feasible waypoint P i ψ，θ , P i ψ，θ =(x i ,y i , z i , ψ i ,θ i );

[0044] x i =v acx t

[0045] y i =v acy t (7)

[0046] z i =v acz t

[0047] Where: x i Represents a feasible waypoint P i ψ，θ The x-axis coordinate of i Represents a feasible waypoint P i ψ，θ The y-axis coordinate of i Represents a feasible waypoint P i ψ，θ The z-axis coordinate of v acx Indicates the x-axis speed of the carrier; v acy Indicates the y-axis speed of the carrier; v acz represents the z-axis speed of the carrier aircraft; t represents time.

[0048] Further, in Step 4, the navigation direction The method for determining whether a flight is possible is as follows:

[0049] Assume that at time t, the position of the carrier aircraft is The aircraft movement speed is Sailing direction A feasible waypoint P on i ψ，θ The coordinate position is The sailing direction A feasible waypoint P on i ψ，θ Distance from the carrier aircraft for:

[0050]

[0051] Feasible waypoint P i ψ，θ Corresponding sailing direction With OX hx The angle between the axes is Then the feasible waypoint P i ψ，θ The arc length in the direction of navigation for:

[0052]

[0053] According to the position of the carrier With feasible waypoint P i ψ，θ The geometric relationship of i ψ，θ Corresponding sailing direction Estimated turning radius r, required steering overload N z , demand climb overload N y and feasible waypoint velocity vector

[0054] In the direction of navigation On the feasible waypoint P i ψ，θ and the current aircraft position The arc length between At the same time, according to the vector relationship, the feasible waypoint relative to OX hx The deflection angle of the axis is The arc length is The arc radius is the navigation direction Estimated turning radius r on:

[0055]

[0056] At the same time, the aircraft moves to the feasible waypoint P i ψ，θ Required normal overload N during flight:

[0057]

[0058] When the aircraft is currently in level flight, the required normal overload direction γ is the roll angle:

[0059]

[0060] Demand shift to overload N z and demand ramp-up overload N y :

[0061]

[0062] Assume a feasible waypoint P i ψ，θ The upper required speed direction vector is The radius vector of the arc starting point is but The equation group (15) should be satisfied:

[0063]

[0064] Solve equation group (15) to get the required speed direction vector at the feasible waypoint

[0065]

[0066] When the aircraft moves to the feasible waypoint P i ψ，θ If the speed remains constant during flight, the aircraft will reach the feasible waypoint P i ψ，θ Velocity vector for:

[0067]

[0068] Will Rotate to the inertial system to get the feasible waypoint velocity vector in the inertial system

[0069]

[0070] Where, ω e is the Earth's rotation speed, r e is the radius of the Earth, P lat and P lon are the latitude and longitude of the observation point respectively;

[0071] Assemble in a feasible sailing direction In each feasible navigation direction, based on feasible waypoints, demand steering overload N z , demand climb overload N y , feasible waypoint velocity vector under geographic system And in the inertial system Get a set of feasible waypoint information A feasible navigation direction Corresponding feasible waypoint information,

[0072]

[0073] The required normal overload N is divided into the maximum overload n allowed under the current situation. max Compare:

[0074] If N>n max , then the feasible waypoint P i ψ，θ Unable to fly; if feasible waypoint P i ψ，θ Cannot fly, gather in a feasible navigation direction Delete the feasible waypoint P i ψ，θ Corresponding sailing direction And go to Step 3;

[0075] If N<=n max , then the feasible waypoint P i ψ，θ Can fly; if the waypoint P is feasible i ψ，θ Can fly, gather in a feasible navigation direction Keep the navigation direction Then go to Step 5.

[0076] Furthermore, in Step 5, the collaborative communication requirement satisfies the following formula:

[0077]

[0078] Where: X P Indicates the location of a feasible waypoint; X f Indicates friendly position; V ac represents the velocity vector of the carrier aircraft; Indicates the maximum communication distance of the data link, Indicates the maximum communication angle;

[0079] If D s and β c If the coordination is satisfied, go to Step 6; if not, gather in the feasible navigation direction Delete navigation direction And go to Step 8.

[0080] Furthermore, in Step 6, the UAV formation is required to satisfy formula (21):

[0081] Considering the formation requirements, the carrier aircraft and the friendly aircraft must remain within the baseline distance, and the friendly aircraft must be within the pitch and yaw angle range of the carrier aircraft; the formation baseline distance is D twice The friendly aircraft is located at a yaw angle of ψ twice The pitch angle of the friendly aircraft to the carrier aircraft is θ twice ;

[0082]

[0083] Where: Indicates the maximum baseline distance of the formation; Indicates the minimum baseline distance of the formation; Indicates the maximum pitch angle of the friendly aircraft relative to the carrier aircraft; δ indicates the maximum range of the yaw angle of the friendly aircraft relative to the carrier aircraft;

[0084] Determine the feasible waypoint P i ψ，θ Whether the formation requirements are met, if yes, go to Step 7; if not, gather in the feasible sailing direction Delete navigation direction And go to Step 8.

[0085] Furthermore, in Step 7, the method for determining whether the drone can perform all detection tasks is as follows:

[0086] When the aircraft is at a feasible waypoint P i ψ，θ When the detectable target meets the conditions of formula (22), the UAV can perform all detection tasks:

[0087]

[0088] Where: D los Indicates the distance between the carrier and the target; X tgt Indicates the target location; Indicates the maximum detection range of the sensor used by the carrier aircraft to detect targets; Indicates the deviation angle of the target relative to the sensor center; ψ los Indicates the target direction; ψ P Indicates the feasible waypoint heading; Indicates the maximum detection deflection angle of the sensor used by the carrier aircraft to detect this mission.

[0089] Further, in Step 10, the feasible waypoint information

[0090] The feasible waypoint neighborhood j represents the number of feasible waypoint information, K represents the number of all adjacent feasible waypoints, and the optimal feasible waypoint result is calculated by formula (23) in the neighborhood of feasible waypoints, and the population individual is replaced by the optimal feasible waypoint information Realize local update of the initial population and generate the initial elite population After generating the initial elite population, go to Step 11;

[0091]

[0092] Where, Indicates another feasible waypoint information in the neighborhood; g cost (·) represents the effectiveness function, as shown in formula (24).

[0093] Furthermore, in Step 11, the construction process of the flight waypoint optimization model is as follows:

[0094] Construct a flight point optimization model, calculate the fitness value of each individual in the initial elite population or the offspring population through the utility function; then go to Step 12;

[0095] Feasible waypoint information collection All feasible waypoints in are considered as the waypoints that the carrier aircraft should fly, because all waypoints meet the maneuverability constraints, communication distance constraints and detection constraints. In order to make the UAV's detection efficiency of the target reach the optimal state, the optimization model of the waypoints that should fly is constructed, as shown in (24):

[0096]

[0097] Where g1 is the target relative azimuth deviation cost factor; g2 is the target relative pitch deviation cost factor; g1 and g2 correspond to the cost weight factors ω1 and ω2 respectively; g cost () represents the UAV detection effectiveness function;

[0098] The target relative orientation deviation cost factor g1 is as shown in formula (25):

[0099] The azimuth component of the target track point deviating from the sensor center is used as a cost factor in the optimization. When the target is closer to the edge of the sensor field of view, a greater cost will be obtained, as shown in Equation (25):

[0100]

[0101] In the formula, the position of the carrier is The target position is Xf =(x f ,y f ,z f ), velocity vector in geographic system is the azimuth deviation of the target relative to the center of the field of view of the aircraft sensor;

[0102] The target relative pitch deviation cost factor g2 is as shown in formula (26):

[0103] The pitch component of the target track point that deviates from the sensor center is used as a cost factor in the optimization. When the target is closer to the edge of the sensor field of view, a greater cost will be obtained, as shown in Equation (26):

[0104]

[0105] In the formula, the position of the carrier is The target position is X f =(x f ,y f ,z f ), is the pitch deviation of the target relative to the center of the field of view of the aircraft sensor;

[0106] Formula (24) is used as the fitness function of the meme algorithm, and the fitness function G is shown in formula (27):

[0107]

[0108] Where, Represents an elite population individual, i.e., feasible waypoint information; g cost (·) represents the effectiveness function, and m represents the number of individuals in the population.

[0109] The beneficial effects of the present invention are as follows: differentiated route guidance strategies are constructed for a variety of situations, and waypoints to be flown can be quickly given in different situations; in the scenario of multi-target collaborative detection, the feasible flight directions of the UAV are discretized based on the maneuverability of the UAV, and an innovative method for generating a feasible waypoint set is proposed; and a feasible waypoint set simplification method is designed based on multiple requirements such as UAV performance, sensor performance, and communication conditions; at the same time, a complete set of route guidance conflict detection and resolution mechanisms is constructed to ensure the continuity of UAV route guidance solutions; an optimization model for feasible waypoints to be flown is built, and solved with the help of an improved meme algorithm, successfully achieving rapid and stable tracking and detection of multiple targets under the premise of ensuring collaborative operations; multi-target tracking and detection through the route guidance method can effectively ensure the collaborative formation formation, improve detection efficiency, enhance safety, improve communication quality, and facilitate command and coordination. The ability to quickly generate waypoints in real time allows for immediate responses to dynamic changes on the battlefield and better grasp instantaneous windows of opportunity. The ability to stably track multiple targets enables a more comprehensive grasp of environmental information in complex scenarios. By simultaneously acquiring key data such as the position, speed, and motion trajectory of multiple targets, operators are able to construct a complete situational image, analyze the relationships and motion patterns between targets, and accurately predict their behavioral trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 Generate a flow chart for the set of feasible waypoints;

[0111] Figure 2 It is the geometric relationship of feasible waypoints in the specified feasible direction. DETAILED DESCRIPTION

[0112] The present invention is further described below with reference to the accompanying drawings and examples. The present invention includes but is not limited to the following examples.

[0113] The present invention provides a multi-unmanned aerial vehicle multi-task target route guidance method, such as Figure 1 As shown, the specific steps include:

[0114] Step 1: Calculate the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max ;

[0115] The maximum yaw angle ψ max The calculation method is as follows:

[0116] First, according to the maximum overload allowed during cruising and horizontal speed during cruising Calculate the maximum turning radius R when level level ;

[0117] Then, according to the horizontal speed during cruising and the maximum turning radius R when horizontal level Calculate the angular velocity ω of the horizontal plane level ;

[0118] Finally, when the flight trajectory of the carrier aircraft is a circular arc, the deflection angle of the carrier aircraft is obtained; according to the isosceles triangle formed by the center of the circle, the carrier aircraft position and the target position, the maximum yaw angle ψ is calculated. max ;

[0119]

[0120]

[0121] ψ max =(π-ω level T) / 2 (3)

[0122] Where: T represents the period interval; g represents the acceleration due to gravity;

[0123] The maximum pitch angle θ max The calculation method is as follows:

[0124] First, the maximum overload allowed during cruising and vertical speed during cruising Calculate the maximum turning radius R on the vertical plane vertical ;

[0125] Then, according to the vertical speed during cruising and the maximum turning radius R in the vertical plane vertical Calculate the angular velocity ω in the vertical plane vertical ;

[0126] Finally, when the flight trajectory of the carrier aircraft is a circular arc, the deflection angle of the carrier aircraft is obtained; according to the isosceles triangle formed by the center of the circle, the carrier aircraft position and the target position, the maximum pitch angle θ is calculated. max :

[0127]

[0128] θ max =(π-ω vertical T) / 2 (6)

[0129] Step 2, based on the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max , build a set of feasible navigation directions

[0130] The set of feasible navigation directions The construction process is as follows:

[0131] Step 2.1, based on the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max , get the feasible navigation direction space h ψ,θ {ψ=[-ψ max ,ψ max ];θ=[-θ max ,θ max ]};

[0132] Step 2.2, based on the maximum yaw angle ψ of the drone max , yaw division number λ, maximum pitch angle θ max and the pitch division number k sets the yaw step amount δ ψ and pitch step δ θ ; δ ψ =2ψ max / λ,δ θ =2θ max / k;

[0133] Step 2.3, according to the yaw step amount δ ψ and the pitch step δ θ The feasible navigation directions are discretized and divided according to the feasible navigation direction space h ψ,θ {ψ∈[-ψ max ,ψ max ];θ∈[-θ max ,θ max ]}Get the set of feasible navigation directions

[0134] Step 3: Select a set of feasible navigation directions A direction of navigation Calculate sailing direction The feasible waypoint P i ψ，θ ;

[0135] The feasible waypoint P i ψ，θ The calculation method is as follows:

[0136] Select a set of feasible navigation directions A direction of navigation ψ i is a yaw in the set of feasible navigation directions, θ i is a pitch in the set of feasible navigation directions;

[0137] According to the current carrier speed V ac , in the velocity system, when the aircraft moves in a straight line at a constant speed, calculate the navigation direction The feasible waypoint Pi ψ，θ , P i ψ，θ =(x i ,y i , z i , ψ i ,θ i );

[0138] x i =v acx t

[0139] y i =v acy t (7)

[0140] z i =v acz t

[0141] Where: x i Represents a feasible waypoint P i ψ，θ The x-axis coordinate of i Represents a feasible waypoint P i ψ，θ The y-axis coordinate of i Represents a feasible waypoint P i ψ，θ The z-axis coordinate of v acx Indicates the x-axis speed of the carrier; v acy Indicates the y-axis speed of the carrier; v acz represents the z-axis speed of the carrier aircraft; t represents time.

[0142] Step 4: Determine the feasible waypoint P based on the current situation and the UAV's maneuverability. i ψ，θ Is it flyable: If the waypoint P is feasible i ψ，θ If you cannot fly, gather in a feasible navigation direction. Delete the unflyable direction And go to Step 8; if the feasible waypoint P i ψ，θ If it can fly, go to Step 5;

[0143] Sailing direction The method for determining whether a flight is possible is as follows:

[0144] Assume that at time t, the position of the carrier aircraft is The aircraft movement speed is Sailing direction A feasible waypoint P on i ψ，θ The coordinate position is The sailing direction A feasible waypoint P on i ψ，θ Distance from the carrier aircraft for:

[0145]

[0146] Feasible waypoint P i ψ，θ Corresponding sailing direction With OX hx The angle between the axes is Then the feasible waypoint P i ψ，θ The arc length in the direction of navigation for:

[0147]

[0148] According to the position of the carrier With feasible waypoint P i ψ，θ The geometric relationship of i ψ，θ Corresponding sailing direction Estimated turning radius r, required steering overload N z , demand climb overload N y and feasible waypoint velocity vector Geometric relationships such as Figure 2 As shown;

[0149] In the direction of navigation On the feasible waypoint P i ψ，θ and the current aircraft position The arc length between At the same time, according to the vector relationship, the feasible waypoint relative to OX hx The deflection angle of the axis is The arc length is The arc radius is the navigation direction Estimated turning radius r on:

[0150]

[0151] At the same time, the aircraft moves to the feasible waypoint P i ψ，θ Required normal overload N during flight:

[0152]

[0153] When the aircraft is currently in level flight, the required normal overload direction γ is the roll angle:

[0154]

[0155] Demand shift to overload N z and demand ramp-up overload N y :

[0156]

[0157] Assume a feasible waypoint P i ψ，θ The upper required speed direction vector is The radius vector of the arc starting point is but The equation group (15) should be satisfied:

[0158]

[0159] Solve equation group (15) to get the required speed direction vector at the feasible waypoint

[0160]

[0161] When the aircraft moves to the feasible waypoint P i ψ，θ If the speed remains constant during flight, the aircraft will reach the feasible waypoint P i ψ，θ Velocity vector for:

[0162]

[0163] Will Rotate to the inertial system to get the feasible waypoint velocity vector in the inertial system

[0164]

[0165] Where, ω e is the Earth's rotation speed, r e is the radius of the Earth, P lat and P lon are the latitude and longitude of the observation point respectively;

[0166] Assemble in a feasible sailing direction In each feasible navigation direction, based on feasible waypoints, demand steering overload N z , demand climb overload N y , feasible waypoint velocity vector under geographic system And in the inertial system Get a set of feasible waypoint information A feasible navigation direction Corresponding feasible waypoint information,

[0167]

[0168] The required normal overload N is divided into the maximum overload n allowed under the current situation. max Compare:

[0169] If N>n max , then the feasible waypoint P i ψ，θ Unable to fly; if feasible waypoint P i ψ，θ Cannot fly, gather in a feasible navigation direction Delete the feasible waypoint P i ψ，θ Corresponding sailing direction And go to Step 3;

[0170] If N<=n max , then the feasible waypoint P i ψ，θ Can fly; if the waypoint P is feasible i ψ，θ Can fly, gather in a feasible navigation direction Keep the navigation direction Then go to Step 5.

[0171] Step 5: Based on the communication distance and angle requirements when the carrier aircraft provides communication support to the friendly aircraft: Maximum communication distance of the data link The maximum communication angle is 180km is 60°; the basis for satisfying the communication condition is shown in the following formula.

[0172]

[0173] Where:

[0174] D s - The distance between the carrier aircraft and the friendly forces requiring communication support;

[0175] X P ——Positions of feasible waypoints;

[0176] X f - Friendly positions;

[0177] b c - The angle between the carrier aircraft and the friendly force requiring communication support and the carrier aircraft's speed;

[0178] V ac ——Velocity vector of the carrier aircraft.

[0179] Determine whether the feasible waypoint meets the coordination requirements. If so, go to Step 6. If not, delete the navigation direction from the set of feasible navigation directions. Go to Step 8.

[0180] Step 6, considering the formation requirements, the carrier aircraft and the friendly aircraft must maintain a certain baseline distance, and the friendly aircraft must be within a certain pitch and yaw angle range of the carrier aircraft. The formation baseline distance is D twice , the friendly aircraft is located at the yaw direction of the carrier aircraft ψ twice , the pitch direction of the friendly aircraft is θ twice The basis for the formation conditions to be met is shown in the following formula.

[0181]

[0182] Where:

[0183] ——The maximum baseline distance of the formation (this example considers is 1000m);

[0184] ——Minimum baseline distance of formation (this example considers 100m);

[0185] ——The maximum pitch angle of the friendly aircraft in the carrier aircraft (this example considers is 30°);

[0186] δ——2δ represents the maximum range of the friendly aircraft in the yaw direction of the carrier aircraft (in this example, δ is considered to be 30°).

[0187] Determine whether the feasible waypoint meets the formation requirements. If so, go to Step 7; if not, delete the navigation direction from the feasible navigation direction set. Go to Step 8.

[0188] Step 7: Read all the mission target information of the UAV and determine whether the UAV can perform all the detection tasks at the waypoint. If it can perform all the detection tasks, delete the navigation direction from the feasible navigation direction set. Store the waypoint in the feasible waypoint set H P Go to Step 8; if all detection tasks cannot be performed, delete the navigation direction from the set of feasible navigation directions. Go to Step 8.

[0189] When the aircraft is at this feasible waypoint, the detectable target should meet the following conditions

[0190]

[0191] Where:

[0192] D los - the distance between the carrier aircraft and the target;

[0193] X tgt - target location;

[0194] - The maximum detection range of the sensor used by the aircraft to detect the target (in this example, 200km);

[0195] —The target’s deflection angle relative to the sensor center;

[0196] ψ los - target direction;

[0197] ψ P —— feasible waypoint heading;

[0198] ——The maximum detection angle of the sensor used by the aircraft to detect the mission (in this example, is 60°).

[0199] Step 8: Determine whether the set of feasible navigation directions is empty. If not, go to Step 3; if it is empty, output the feasible waypoint set H. P , go to Step 9.

[0200] Step 9: If the set of feasible waypoints is empty, place the lowest priority task in the task list into the conflict list, delete the lowest priority task, and go to Step 7; if the set of feasible waypoints is not empty, go to Step 10.

[0201] Step 10: Initial elite population generation.

[0202] When the feasible navigation direction set When it is not empty, randomly select the feasible navigation direction set Select M feasible navigation directions, and each feasible navigation direction can be Get the feasible waypoint information calculated based on it Each feasible waypoint information si As the initial population, an initial population of M is formed:

[0203] By obtaining all feasible waypoint information adjacent to each population individual feasible waypoint in the feasible waypoint information set, a feasible waypoint neighborhood is formed. K is the number of all adjacent feasible waypoints. The optimal feasible waypoint result is calculated in the neighborhood, and the population individuals are replaced with the optimal feasible waypoint information. Realize local update of the initial population and generate the initial elite population After generating the initial elite population, go to Step 11;

[0204]

[0205] Where, Indicates a feasible waypoint information in the neighborhood; g cost (·) represents the efficiency function, as shown in Equation (24); j represents the number of feasible waypoint information, and K represents the number of all adjacent feasible waypoints.

[0206] Step 11, constructing a waypoint optimization model for flying, calculating the fitness value of each individual in the initial elite population or the offspring population through the utility function, and constructing a waypoint optimization model for flying, as shown in formula (33); then go to Step 12;

[0207]

[0208] Where g1 is the target relative azimuth deviation cost factor; g2 is the target relative pitch deviation cost factor; g1 and g2 correspond to the cost weight factors ω1 and ω2 respectively; g cost Represents the UAV detection effectiveness function.

[0209] a) Target orientation cost factor

[0210] The azimuth component of the target track point that deviates from the sensor center is taken into consideration as a cost factor in the optimization. When the target is closer to the sensor field of view boundary, a larger cost will be obtained, as shown in (34).

[0211]

[0212] Where, is the azimuth deviation of the target relative to the center of the field of view of the carrier sensor.

[0213] b) Target pitch cost factor

[0214] The pitch component of the target track point that deviates from the sensor center is used as a cost factor in the optimization. When the target is closer to the boundary of the sensor field of view, a larger cost will be obtained, as shown in Equation (35).

[0215]

[0216] In the formula, the position of the carrier is The target position is X f =(x f ,y f ,z f ), is the pitch deviation of the target relative to the center of the field of view of the carrier sensor.

[0217] Formula (33) is used as the fitness function of the meme algorithm, and the fitness function G is shown in formula (36):

[0218]

[0219] Where, Represents an elite population individual, i.e., feasible waypoint information; g cost (·) represents the effectiveness function, and m represents the number of individuals in the population.

[0220] In Step 12, the two individuals with the best fitness are retained. The remaining individuals are randomly paired and the crossover probability is used to determine whether to cross over to generate the next generation. If crossover is not performed, the two parent populations are retained in the next generation population. If crossover is performed, a crossover segment is randomly generated. The feasible waypoints of the offspring before and before the crossover segment are the feasible waypoints of parent 1, and after the crossover segment are the feasible waypoints of parent 2. After the crossover transformation, proceed to Step 13.

[0221] Step 13: For each population, first randomly select whether to mutate based on the mutation probability. If no mutation is performed, the feasible waypoint is retained to the offspring; if mutation is performed, individuals are randomly selected. One direction or pitch The direction or pitch Change to an adjacent value in the set of feasible navigation directions or Then obtain the feasible navigation direction after transformation or Corresponding feasible waypoint information or The feasible waypoint information or As the updated population, go to Step 14 after mutation transformation.

[0222] In Step 14, within the set of feasible waypoints, a greedy algorithm is used to search the neighborhood of the current feasible route guide point. If a feasible waypoint with a better fitness exists in the neighborhood, the feasible waypoint is replaced with the one with the better fitness, generating a descendant population. Then, proceed to Step 15.

[0223] Step 15, determine whether the stopping condition is met. The stopping condition is that the optimal feasible waypoint in the population remains unchanged for 10 consecutive iterations. If the stopping condition is not met, use the newly generated feasible waypoint as the population and go to Step 11; if the stopping condition is met, end and output the optimal feasible waypoint in the population.

[0224] This invention addresses the problem of multiple drones rapidly and stably tracking multiple targets in collaborative detection scenarios. Based on aircraft performance, formation requirements, and mission requirements, this method constructs a feasible waypoint set generation method. Using this feasible waypoint set as the solution space, a drone route guidance algorithm based on an improved memetic algorithm is designed. Using this method, drones can rapidly and stably track multiple targets while ensuring coordination with friendly aircraft, improving the efficiency of multi-drone collaborative detection and enabling the team to better understand the battlefield situation during air combat.

Claims

1. A multi-UAV multi-task target route guidance method, characterized in that: The specific steps include: Step 1: Calculate the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max ; Step 2, based on the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max , build a set of feasible navigation directions Step 3: Select a set of feasible navigation directions A direction of navigation Calculate sailing direction The feasible waypoint P i ψ，θ ; Step 4: Determine the feasible waypoint P based on the current situation and the UAV's maneuverability. i ψ，θ Is it flyable: If the waypoint P is feasible i ψ，θ If you cannot fly, gather in a feasible navigation direction. Delete the unflyable direction And go to Step 8; if the feasible waypoint P i ψ，θ If it can fly, go to Step 5; Step 5, when the feasible waypoint P i ψ，θ When it is flyable, determine the feasible waypoint P when the carrier aircraft provides communication support to the friendly aircraft i ψ，θ Communication distance D s and communication angle β c Whether the cooperative communication requirements are met: If yes, go to Step 6; if not, gather in the feasible navigation direction Delete navigation direction And go to Step 8; Step 6: When the carrier aircraft provides communication support to the friendly aircraft, the feasible waypoint P i ψ，θ Communication distance D s and communication angle β c When the collaborative communication requirements are met, the feasible waypoint P is determined. i ψ，θ Does it meet the UAV formation requirements? If yes, go to Step 7; if not, gather in a feasible navigation direction. Delete navigation direction And go to Step 8; Step 7, when the feasible waypoint P i ψ，θ When the UAV formation requirements are met, the feasible waypoint P is determined. i ψ，θ Whether all detection tasks can be performed: If the UAV can perform all detection tasks, it will gather in the feasible navigation direction. Delete navigation direction The feasible waypoint P i ψ，θ Store the feasible waypoint set H P and go to Step 8; if the UAV cannot perform all the detection tasks, it will gather in the feasible navigation direction. Delete navigation direction And go to Step 8; Step 8: Determine the set of feasible navigation directions Is it empty: If not, go to Step 3; if it is empty, output the feasible waypoint set H P , and go to Step 9; Step 9, when the feasible navigation direction set When it is empty, determine the feasible waypoint set H P Is it empty: If the feasible waypoint set H P If it is empty, put the task with the lowest priority in the task list into the conflict list, delete the task with the lowest priority, and go to Step 7; if the feasible waypoint set H P If it is not empty, go to Step 10; Step 10, when the feasible waypoint set H P When it is not empty, randomly select the feasible navigation direction set Select M feasible navigation directions and pass through each feasible navigation direction at the same time Get feasible waypoint information Each feasible waypoint information As the initial population, an initial population of M is formed; by Obtain all feasible waypoint information adjacent to each population individual feasible waypoint to form a feasible waypoint neighborhood S neighbor , replace the population individuals with the best performance feasible waypoint information Implement local update of the initial population, generate the initial elite population, and then go to Step 11; Step 11: Build the Yingfei waypoint optimization model, calculate the fitness value of each individual in the initial elite population through the efficiency function in the Yingfei waypoint optimization model, and then go to Step 12: Step 12: Retain the two individuals with the largest fitness values ​​in the initial population, and randomly pair the unretained individuals. Determine whether to crossover between the unretained individuals to generate the next generation: if not, retain the two parent populations in the next generation population. If crossover occurs, randomly generate a crossover segment, and the feasible waypoints of the offspring before and after the crossover segment are the feasible waypoints of parent 1, and after the crossover segment are the feasible waypoints of parent 2. After the crossover transformation, go to Step 13. Step 13: After the unretained individuals in the initial population are crossed, a population is randomly selected to determine whether to mutate: if not, the feasible waypoints are retained in the offspring; if mutated, the azimuth or pitch corresponding to the feasible waypoint is randomly selected and the azimuth or pitch value is replaced with the feasible navigation direction set. Replace the population individual with the feasible waypoint information corresponding to the feasible navigation direction after mutation by an adjacent value in the , and go to Step 14 after mutation transformation; Step 14, in the feasible waypoint information collection The fitness of the feasible waypoints in the neighborhood of the current feasible waypoint is calculated by the fitness function. If the neighborhood of the feasible waypoint S neighbor If there is a waypoint with a fitness value greater than the current feasible waypoint, the current feasible waypoint is replaced with a feasible waypoint with a larger fitness value, a descendant population is generated, and the process goes to Step 15; Step 15, determine whether the stopping condition is met: the stopping condition is that the feasible waypoint with the maximum fitness value in the population remains unchanged for 10 consecutive iterations; if the stopping condition is not met, the newly generated feasible waypoint is used as the population and the process goes to Step 11; if the stopping condition is met, the route guidance ends and the feasible waypoint with the maximum fitness value in the population is output.

2. A multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 1, the maximum yaw angle ψ max The calculation method is as follows: First, according to the maximum overload allowed during cruising and horizontal speed during cruising Calculate the maximum turning radius R when level level ; Then, according to the horizontal speed during cruising and the maximum turning radius R when horizontal level Calculate the angular velocity ω of the horizontal plane level ; Finally, when the flight trajectory of the carrier aircraft is a circular arc, the deflection angle of the carrier aircraft is obtained; according to the isosceles triangle formed by the center of the circle, the carrier aircraft position and the target position, the maximum yaw angle ψ is calculated. max ; ψ max =(π-ω) level T) / 2 (3) Where: T represents the period interval; g represents the acceleration due to gravity; The maximum pitch angle θ max The calculation method is as follows: First, the maximum overload allowed during cruising and vertical speed during cruising Calculate the maximum turning radius R on the vertical plane vertical ; Then, according to the vertical speed during cruising and the maximum turning radius R in the vertical plane vertical Calculate the angular velocity ω in the vertical plane vertical ; Finally, when the flight trajectory of the carrier aircraft is a circular arc, the deflection angle of the carrier aircraft is obtained; according to the isosceles triangle formed by the center of the circle, the carrier aircraft position and the target position, the maximum pitch angle θ is calculated. max : i max =(π-ω) vertical T) / 2 (6).

3. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 2, the set of feasible navigation directions The construction process is as follows: Step 2.1, based on the maximum yaw angle ψ of the drone max and the maximum pitch angle θ max , get the feasible navigation direction space h ψ,θ {ψ=[-ψ max ,ψ max ];θ=[-θ max ,θ max ]}; Step 2.2, based on the maximum yaw angle ψ of the drone max , yaw division number λ, maximum pitch angle θ max and the pitch division number k sets the yaw step amount δ ψ and pitch step δ θ ; δ ψ =2ψ max / λ,δ θ =2θ max / k; Step 2.3, according to the yaw step amount δ ψ and the pitch step δ θ The feasible navigation directions are discretized and divided according to the feasible navigation direction space h ψ,θ {ψ∈[-ψ max ,ψ max ];θ∈[-θ max ,θ max ]}Get the set of feasible navigation directions 4. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 3, the feasible waypoint P i ψ,θ The calculation method is as follows: Select a set of feasible navigation directions A direction of navigation ψ i is a yaw in the set of feasible navigation directions, θ i is a pitch in the set of feasible navigation directions; According to the current carrier speed V ac , in the velocity system, when the aircraft moves in a straight line at a uniform speed, calculate the navigation direction h Pi The feasible waypoint P i ψ,θ , P i ψ,θ =(x i ,y i ,z i ,ψ i ,θ i ); Where: x i Represents a feasible waypoint P i ψ,θ The x-axis coordinate of i Represents a feasible waypoint P i ψ,θ The y-axis coordinate of i Represents a feasible waypoint P i ψ,θ The z-axis coordinate of v acx Indicates the x-axis speed of the carrier; v acy Indicates the y-axis speed of the carrier; v acz represents the z-axis speed of the carrier aircraft; t represents time.

5. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 4, the navigation direction The method for determining whether a flight is possible is as follows: Assume that at time t, the position of the carrier aircraft is The aircraft movement speed is Sailing direction A feasible waypoint P on i ψ,θ The coordinate position is The sailing direction A feasible waypoint P on i ψ,θ Distance from the carrier aircraft for: Feasible waypoint P i ψ,θ Corresponding sailing direction With OX hx The angle between the axes is Then the feasible waypoint P i ψ,θ The arc length in the direction of navigation for: According to the position of the carrier With feasible waypoint P i ψ，θ The geometric relationship of i ψ,θ Corresponding sailing direction Estimated turning radius r, required steering overload N z , demand climb overload N y and feasible waypoint velocity vector In the direction of navigation On the feasible waypoint P i ψ,θ and the current aircraft position The arc length between At the same time, according to the vector relationship, the feasible waypoint relative to OX hx The deflection angle of the axis is The arc length is The arc radius is the navigation direction Estimated turning radius r on: At the same time, the aircraft moves to the feasible waypoint P i ψ，θ Required normal overload N during flight: When the aircraft is currently in level flight, the required normal overload direction γ is the roll angle: Demand shift to overload N z and demand ramp-up overload N y : Assume a feasible waypoint P i ψ,θ The upper required speed direction vector is The radius vector of the arc starting point is but The equation group (15) should be satisfied: Solve equation group (15) to get the required speed direction vector at the feasible waypoint When the aircraft moves to the feasible waypoint P i ψ,θ If the speed remains constant during flight, the aircraft will reach the feasible waypoint P i ψ,θ Velocity vector for: Will Rotate to the inertial system to get the feasible waypoint velocity vector in the inertial system Where, ω e is the Earth's rotation speed, r e is the radius of the Earth, P lat and P lon are the latitude and longitude of the observation point respectively; Assemble in a feasible sailing direction In each feasible navigation direction, based on feasible waypoints, demand steering overload N z , demand climb overload N y , feasible waypoint velocity vector under geographic system And in the inertial system Get a set of feasible waypoint information A feasible navigation direction Corresponding feasible waypoint information, The required normal overload N is divided into the maximum overload n allowed under the current situation. max Compare: If N>n max , then the feasible waypoint P i ψ,θ Cannot fly; If the feasible waypoint P i ψ,θ Cannot fly, gather in a feasible navigation direction Delete the feasible waypoint P i ψ,θ Corresponding sailing direction And go to Step 3; If N<=n max , then the feasible waypoint P i ψ,θ Can fly; if the waypoint P is feasible i ψ,θ Can fly, gather in a feasible navigation direction Keep the navigation direction Then go to Step 5.

6. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 5, the collaborative communication requirement satisfies the following formula: Where: X P Indicates the location of a feasible waypoint; X f Indicates friendly position; V ac represents the velocity vector of the carrier aircraft; Indicates the maximum communication distance of the data link, Indicates the maximum communication angle; If D s and β c If the coordination is satisfied, go to Step 6; if not, gather in the feasible navigation direction Delete navigation direction And go to Step 8.

7. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 6, the UAV formation is required to satisfy the following formula (21): Considering the formation requirements, the carrier aircraft and the friendly aircraft must remain within the baseline distance, and the friendly aircraft must be within the pitch and yaw angle range of the carrier aircraft; the formation baseline distance is D twice The friendly aircraft is located at a yaw angle of ψ twice The pitch angle of the friendly aircraft to the carrier aircraft is θ twice ; Where: Indicates the maximum baseline distance of the formation; Indicates the minimum baseline distance of the formation; Indicates the maximum pitch angle of the friendly aircraft relative to the carrier aircraft; δ indicates the maximum range of the yaw angle of the friendly aircraft relative to the carrier aircraft; Determine the feasible waypoint P i ψ,θ Whether the formation requirements are met, if yes, go to Step 7; if not, gather in the feasible sailing direction Delete navigation direction And go to Step 8.

8. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 7, the method for determining whether the drone can perform all detection tasks is as follows: When the aircraft is at a feasible waypoint P i ψ,θ When the detectable target meets the conditions of formula (22), the UAV can perform all detection tasks: Where: D los Indicates the distance between the carrier and the target; X tgt Indicates the target location; Indicates the maximum detection range of the sensor used by the carrier aircraft to detect targets; Indicates the deviation angle of the target relative to the sensor center; ψ los Indicates the target direction; ψ P Indicates the feasible waypoint heading; Indicates the maximum detection deflection angle of the sensor used by the carrier aircraft to detect this mission.

9. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 10, the feasible waypoint information The feasible waypoint neighborhood j represents the number of feasible waypoint information, K represents the number of all adjacent feasible waypoints, and the optimal feasible waypoint result is calculated by formula (23) in the neighborhood of feasible waypoints, and the population individual is replaced by the optimal feasible waypoint information Realize local update of the initial population and generate the initial elite population After generating the initial elite population, go to Step 11; Where, Indicates another feasible waypoint information in the neighborhood; g cost (·) represents the effectiveness function, as shown in formula (24).

10. The multi-UAV multi-task target route guidance method according to claim 1, characterized in that: In Step 11, the construction process of the flight waypoint optimization model is as follows: Construct a flight point optimization model, calculate the fitness value of each individual in the initial elite population or the offspring population through the utility function; then go to Step 12; Feasible waypoint information collection All feasible waypoints in are considered as the waypoints that the carrier aircraft should fly, because all waypoints meet the maneuverability constraints, communication distance constraints and detection constraints. In order to make the UAV's detection efficiency of the target reach the optimal state, the optimization model of the waypoints that should fly is constructed, as shown in (24): Where g1 is the target relative azimuth deviation cost factor; g2 is the target relative pitch deviation cost factor; g1 and g2 correspond to the cost weight factors ω1 and ω2 respectively; g cost () represents the UAV detection effectiveness function; The target relative orientation deviation cost factor g1 is as shown in formula (25): The azimuth component of the target track point deviating from the sensor center is used as a cost factor in the optimization. When the target is closer to the edge of the sensor field of view, a greater cost will be obtained, as shown in Equation (25): In the formula, the position of the carrier is The target position is X f =(x f ,y f ,z f ), velocity vector in geographic system is the azimuth deviation of the target relative to the center of the field of view of the aircraft sensor; The target relative pitch deviation cost factor g2 is as shown in formula (26): The pitch component of the target track point that deviates from the sensor center is used as a cost factor in the optimization. When the target is closer to the edge of the sensor field of view, a greater cost will be obtained, as shown in Equation (26): In the formula, the position of the carrier is The target position is X f =(x f ,y f ,z f ), is the pitch deviation of the target relative to the center of the field of view of the aircraft sensor; Formula (24) is used as the fitness function G of the meme algorithm. The fitness function G is shown in formula (27): Where, Represents an elite population individual, i.e., feasible waypoint information; g cost (·) represents the effectiveness function, and m represents the number of individuals in the population.

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