An unmanned aerial vehicle formation cooperative obstacle avoidance method based on improved fuzzy decision

Abstract

This invention discloses a UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making, comprising: continuously acquiring the positions of UAVs and the virtual lead UAV using onboard sensors; obtaining position and attitude control commands for the UAV formation through a formation control method based on virtual structure and consistency theory; setting the distance obstacle avoidance range and rotation angle obstacle avoidance range of the UAVs according to their performance, and constructing a fuzzy decision-maker; determining the threat level of the UAVs based on the fuzzy decision-maker and obstacles, and outputting corresponding formation obstacle avoidance decisions; for UAVs that have broken out of formation, adjusting the obstacle avoidance control quantity of the UAVs that have broken out of formation based on the speed obstacle method and the expected obstacle avoidance speed. This invention ensures that the maneuverability of most members is not affected during inter-UAV collision avoidance, improves the stability and safety of the formation, and is more conducive to the execution of formation tasks.

Description

Technical Field

[0001] This invention relates to the field of autonomous obstacle avoidance technology for unmanned aerial vehicles (UAVs), and more specifically, to a UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making. Background Technology

[0002] As drone missions become increasingly complex, the limited payload capacity of individual drones often results in unsatisfactory performance. Therefore, scientists have begun to study the collaborative operation of drone swarms, with obstacle avoidance in formation being a crucial component. Multi-drone swarm obstacle avoidance requires that each member of the swarm safely and without collision avoid obstacles detected by environmental monitoring systems during mission execution, while also preventing collisions between members within the swarm. If a collision occurs during mission execution, it can lead to the crashing of some drones, reducing the swarm's mission capability, or even the collapse of the entire swarm system, resulting in mission failure. Therefore, a sound drone swarm obstacle avoidance strategy is essential for the success of drone swarm missions.

[0003] Existing UAV obstacle avoidance methods can be mainly divided into the following two categories: (1) obstacle avoidance methods based on route planning. The main idea of ​​this method is to transform the obstacle avoidance problem into a route planning problem, such as genetic algorithms, artificial potential field methods, and A Algorithm; (2) Obstacle avoidance method based on geometric relationship. The main idea of ​​this method is to calculate the avoidance route based on key information such as the relative distance, speed, acceleration and angle between the UAV and the obstacle.

[0004] However, most current obstacle avoidance algorithms treat formation maintenance and obstacle avoidance as independent and contradictory. In those schemes, each member of the drone formation performs obstacle avoidance behavior as an individual, and then restores the formation after obstacle avoidance is completed. This formation change behavior itself does not help the task execution and will take a lot of time and affect work efficiency. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making. This invention can maintain the formation for obstacle avoidance as much as possible when the obstacle avoidance space allows, and can also ensure that the interval between each member is not less than the minimum safe distance. This not only ensures that the maneuverability of most members is not affected when avoiding collisions between UAVs, but also improves the stability and safety of the formation, which is more conducive to the execution of formation tasks.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution: a UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making, specifically including the following steps:

[0007] Step 1: Utilize the onboard sensors of the UAV to continuously acquire the position of the UAV and the virtual lead aircraft position. Then, through the formation control method based on virtual structure and consistency theory, obtain the position and attitude control commands for the UAV formation.

[0008] Step 2: Based on the drone's performance, set the drone's distance obstacle avoidance range and rotation angle obstacle avoidance range, and construct a fuzzy decision-maker;

[0009] Step 3: Determine the threat level of the UAV based on the fuzzy decision-maker and obstacles, and output the corresponding formation obstacle avoidance decision;

[0010] Step 4: For drones that have broken formation, adjust the obstacle avoidance control amount of the drones that have broken formation based on the speed obstacle method and the desired obstacle avoidance speed.

[0011] Furthermore, the position and attitude control commands for the drone formation are as follows:

[0012]

[0013] in, Indicates drone i Formation position control commands, , Indicates drone i position vector, , , , , All are time constants. Indicates drone Consistency control of formation positions , This represents the position vector of the virtual alpha aircraft. , , All represent the consistency coefficient. Indicates drone The position vector of the virtual lead aircraft, given that it is already in the desired position within the formation. , Indicates drone The expected relative positional relationship with the virtual primary. Indicates drone and The connection weights, Indicates drone j The position vector of the virtual lead aircraft, given that it is already in the desired position within the formation. n Indicates the number of drones; Indicates drone Formation attitude control commands. Indicates drone Yaw angle; , For drones Formation consistency control quantity Indicates drone The amount of control over formation attitude consistency. , This is the yaw angle of the virtual lead aircraft.

[0014] Furthermore, step 2 includes the following sub-steps:

[0015] Step 2.1: Set the drone's obstacle avoidance range according to the drone's performance. and the obstacle avoidance range by rotation angle ;

[0016] Step 2.2: Adjust the distance to the obstacle avoidance range. Transform the domain to [-3,3] and perform fuzzification to obtain the distance fuzzy set {AT, VB, LB, MT, LS, VS, NT}, representing {absolute threat, relatively large threat, slightly large threat, moderate threat, slightly large threat, etc.}, respectively.

[0017] Minor threat, lesser threat, no threat;

[0018] Step 2.3: Adjust the obstacle avoidance range by rotating the angle. Transform to the domain of [-3,3] and perform fuzzification to obtain the rotation angle fuzzy set {NT, VS, LS, MT, LB, MB, VB}, which represent {no threat, minor threat, slightly minor threat, medium threat, slightly major threat, medium major threat, and major threat}, respectively.

[0019] Step 2.4: Based on the distance fuzzy set and the rotation angle fuzzy set, establish a fuzzy decision-maker based on threat level, as shown in the table below:

[0020]

[0021] The fuzzy decision maker has ten levels of threat level {NT, VVS, VS, MS, LS, MT, LB, MB, VB, AT}, which represent {no threat, very low threat, relatively low threat, moderately low threat, slightly low threat, moderate threat, slightly high threat, moderately high threat, relatively high threat, and absolute threat}, respectively.

[0022] Furthermore, step 3 includes the following sub-steps:

[0023] Step 3.1: Determine the threat level of each UAV in the formation based on the fuzzy controller and obstacles, identify the UAV with the greatest threat, implement the obstacle avoidance strategy for the UAV with the greatest threat, and update the control commands of the virtual leader and the expected control variables for obstacle avoidance of the UAV; otherwise, proceed to step 3.2.

[0024] Step 3.2: Determine whether other drones in the formation are under a threat of greater than a significant threat. If so, proceed to step 3.3. Otherwise, determine whether the drones in the formation are maintaining the desired formation. If not, proceed to step 3.3. Otherwise, predict the threat level of the drones in the next moment through the central information control center. If the drones are under a threat of greater than a significant threat in the next moment, proceed to step 3.3. Otherwise, the drones will continue to execute the formation control strategy.

[0025] Step 3.3: De-form and update the drone obstacle avoidance control parameters.

[0026] Furthermore, the update process for the UAV obstacle avoidance control parameters is as follows:

[0027]

[0028] The control command update process for the virtual primary is as follows:

[0029]

[0030] in, For obstacle avoidance maneuvering strategy, For drones The quality; Indicates drone i Formation position obstacle avoidance control quantity, ; This represents the position vector of drone i. ; , All are time constants. This indicates the obstacle avoidance commands for the drones most threatened. Indicates drone The amount of formation attitude obstacle avoidance control. This indicates the control commands for updating the virtual primary. Indicates the three-dimensional spatial position of the virtual alpha aircraft. This indicates the formation position control command for the virtual lead aircraft.

[0031] Furthermore, if there are multiple most threatened drones in step 3.1, the drone with the smallest number is selected as the most threatened drone.

[0032] Furthermore, the process for determining whether the drones within the formation maintain the desired formation in step 3.2 is as follows: If The most threatened drones With drones The expected formation relationship between them was lost, and the drones They will break out of formation; if The most threatened drones With drones The expected formation is still maintained; among them, Indicates drone and drones The relative formation quantities between them Indicates drone and drones The expected formation quantity between them.

[0033] Furthermore, in step 3.2, the drone The expected control quantity for obstacle avoidance of UAVs that still execute formation control strategy is:

[0034]

[0035] in, , Indicates drone Position vector and yaw angle; , This represents the desired obstacle avoidance control quantity for the drone k, given its position and yaw angle. For drones Consistency control quantity; Indicates the main obstacle avoidance drone a The three-dimensional spatial position; For drones With the main obstacle avoidance drone a The expected formation distance between them Main obstacle avoidance drone a The relative distance from the expected virtual primary position. Indicates drone The relative distance from the expected virtual primary position; , The virtual lead aircraft's position vector and yaw angle; , , Represents the consistency coefficient; , All are time constants.

[0036] Furthermore, the desired obstacle avoidance speed is:

[0037]

[0038] in: For drones determined solely by the external speed obstacle method of drone formationj Obstacle avoidance speed; Is this considering collision avoidance between drones? j The expected obstacle avoidance speed; For drones j With the formation member drone m The relative distance; The critical distance at which drones need to employ obstacle avoidance strategies; This indicates consideration of inter-machine collision avoidance. The acceptable range.

[0039] Compared with existing technologies, the present invention has the following advantages: The UAV formation cooperative obstacle avoidance method of the present invention adopts a formation control method combining virtual structure and consistency theory, utilizing a virtual leader to achieve formation control, which has advantages such as good formation stability and high control accuracy; the present invention determines the threat level of each UAV based on obstacle distance and obstacle avoidance angle, and enables formation members to take necessary response decisions according to the actual situation through fuzzy rules, avoiding overreaction during obstacle avoidance; the closer the UAV is to the obstacle and the smaller the obstacle avoidance angle, the greater its collision risk and the greater the corresponding threat level. An improved fuzzy decision algorithm is used to construct a fuzzy decision-maker, which can change the control decision in real time according to the risk level faced by the UAV, avoiding excessive obstacle avoidance behavior and making formation obstacle avoidance more intelligent and cooperative; simultaneously, the present invention implements inter-UAV collision avoidance function based on speed obstacle method for formation members that have broken out of formation, ensuring that the maneuverability of most members is not affected during inter-UAV collision avoidance, and improving the stability and safety of the formation. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in this invention, the accompanying drawings used in the specific embodiments will be briefly introduced below. Obviously, the drawings described below are only a part of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0041] Figure 1 This is a flowchart illustrating the UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to the present invention.

[0042] Figure 2 This is a schematic diagram of fuzzy sets in this invention, wherein, Figure 2 (a) in the diagram is a schematic diagram of a distance fuzzy set. Figure 2 (b) in the diagram is a schematic diagram of the fuzzy set of rotation angles;

[0043] Figure 3 This is a schematic diagram of the formation structure in this invention;

[0044] Figure 4 This is a flowchart illustrating the external collaborative obstacle avoidance method for UAV formations in this invention.

[0045] Figure 5 This is a schematic diagram of the collision avoidance analysis for obstacles on one side in this invention;

[0046] Figure 6 This is a schematic diagram of the collision avoidance analysis in this invention, where there are obstacles on both sides.

[0047] Figure 7 This is a schematic diagram of the inter-machine relative velocity obstacle method in this invention;

[0048] Figure 8 This is a schematic diagram illustrating the solution of the inter-machine collision avoidance speed in this invention;

[0049] Figure 9 This is a schematic diagram illustrating the formation obstacle avoidance maneuver effect of the UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making in this invention. Figure 9 (a) in the diagram is a schematic of the obstacle avoidance result with sufficient obstacle avoidance space. Figure 9 (b) in the diagram shows the obstacle avoidance result with a smaller obstacle avoidance space. Figure 9 (c) in the diagram is a schematic of the obstacle avoidance result with a smaller obstacle avoidance space. Detailed Implementation

[0050] The technical solution of the present invention will be further described below with reference to the accompanying drawings. The following specific embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0051] like Figure 1 This is a flowchart of the UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to the present invention. The UAV formation cooperative obstacle avoidance method specifically includes the following steps:

[0052] Step 1: Utilizing the onboard sensors of the UAVs, continuously acquire the positions of the UAVs and the virtual lead UAV. Through formation control methods based on virtual structure and consistency theory, obtain position and attitude control commands for the UAV formation, thereby maintaining stable position and velocity relationships between the UAVs and achieving formation control. Specifically,

[0053] Using onboard sensors, the drone continuously acquires its own position during flight and calculates the virtual lead drone's position, such as... Figure 3 As shown, , , Representing drones , , The three-dimensional spatial position; , , These are the pre-set formation drones in the virtual structure. , , The relative distance to the virtual alpha. For ease of description, let... , , They represent drones , , Given that the virtual lead aircraft C is already in its desired position within the formation, the position vector it should occupy is as follows: (Obviously, when not in formation...) After forming a formation, it should meet the following requirements. Therefore, as long as the position and speed are consistent, it means that the spatial formation is consistent. That is, when the formation is stable, the following should be satisfied:

[0054]

[0055] in, In contrast to drones Virtual primary The position vector it should be in; For drones The velocity vector, including linear velocity and angular velocity;

[0056] To achieve the aforementioned progressively consistent state, each individual drone should satisfy the following:

[0057]

[0058] The position and attitude control commands for the drone formation were obtained as follows:

[0059]

[0060] in, Indicates drone i Formation position control commands, , Indicates drone i position vector, , , , , All are time constants. Indicates drone Consistency control of formation positions , This represents the position vector of the virtual alpha aircraft. , , All represent the consistency coefficient. Indicates drone The position vector of the virtual lead aircraft, given that it is already in the desired position within the formation. , Indicates drone The expected relative positional relationship with the virtual primary. Indicates drone and The connection weights, Indicates drone j The position vector of the virtual lead aircraft, given that it is already in the desired position within the formation. n Indicates the number of drones; Indicates drone Formation attitude control commands. Indicates drone Yaw angle; , For drones Formation consistency control quantity Indicates drone The amount of control over formation attitude consistency. , This is the yaw angle of the virtual lead aircraft.

[0061] Step 2: Based on the drone's performance, set the drone's distance obstacle avoidance range and rotation angle obstacle avoidance range, and construct a fuzzy decision-maker; specifically including the following sub-steps:

[0062] This invention determines the threat level by considering both distance and rotation angle; that is, the input to the fuzzy decision-maker is the distance from the UAV to the obstacle surface. and the relative angle between the drone's speed and direction and the obstacle. ,in, The radius of the drone. Let the radius of the obstacle be . This represents the relative distance between the drone and the obstacle.

[0063] Step 2.1: Set the drone's obstacle avoidance range according to the drone's performance. and the obstacle avoidance range by rotation angle ;

[0064] Step 2.2, as follows Figure 2 (a) in the text refers to the distance from the obstacle avoidance range. Transform to the domain of [-3,3], perform fuzzification, and obtain the distance fuzzy set {AT, VB, LB, MT, LS, VS, NT}, which represent {absolute threat, greater threat, slightly greater threat, medium threat, slightly less threat, lesser threat, no threat}, respectively;

[0065] Step 2.3, as follows Figure 2 (b) in the diagram refers to the obstacle avoidance range of the rotation angle. Transform to the domain of [-3,3] and perform fuzzification to obtain the rotation angle fuzzy set {NT, VS, LS, MT, LB, MB, VB}, which represent {no threat, minor threat, slightly minor threat, medium threat, slightly major threat, medium major threat, and major threat}, respectively.

[0066] Step 2.4: Based on the distance fuzzy set and the rotation angle fuzzy set, establish a fuzzy decision-maker based on threat level, as shown in the table below:

[0067]

[0068] The fuzzy decision maker has ten levels of threat level {NT, VVS, VS, MS, LS, MT, LB, MB, VB, AT}, which represent {no threat, very low threat, relatively low threat, moderately low threat, slightly low threat, moderate threat, slightly high threat, moderately high threat, relatively high threat, and absolute threat}, respectively.

[0069] The closer a drone is to an obstacle and the smaller its obstacle avoidance angle, the greater its collision risk and the greater the threat it faces. By using an improved fuzzy decision-making algorithm, control decisions can be changed in real time according to the level of risk faced by the drone, which can avoid excessive obstacle avoidance behavior and make formation obstacle avoidance more intelligent.

[0070] Step 3: Determine the threat level of the UAV based on the fuzzy decision-maker and obstacles, and output the corresponding formation obstacle avoidance decision to achieve external obstacle avoidance for the formation; for example... Figure 4 Specifically, it includes the following sub-steps:

[0071] Step 3.1: Determine the threat level of each UAV in the formation based on the fuzzy controller and obstacles, and identify the UAV with the greatest threat. If there are multiple UAVs with the greatest threat, select the UAV with the smallest number as the UAV with the greatest threat, execute the obstacle avoidance strategy for the UAV with the greatest threat, update the control command of the virtual leader and the expected control quantity of the UAV obstacle avoidance, so that the maneuver strategy of controlling the virtual leader is consistent with the maneuver strategy of the UAV; otherwise, proceed to step 3.2.

[0072] The update process for the drone obstacle avoidance control parameters is as follows:

[0073]

[0074] The control command update process for the virtual primary is as follows:

[0075]

[0076] in, For obstacle avoidance maneuvering strategy, For drones The quality; Indicates drone i Formation position obstacle avoidance control quantity, ; This represents the position vector of drone i. ; , All are time constants. This indicates the obstacle avoidance commands for the drones most threatened. Indicates drone The amount of formation attitude obstacle avoidance control. This indicates the control commands for updating the virtual primary. Indicates the three-dimensional spatial position of the virtual alpha aircraft. This indicates the formation position control command for the virtual lead aircraft.

[0077] Step 3.2: Determine whether other drones in the formation are under a significant or greater threat. If so, proceed to step 3.3; otherwise, determine whether the drones in the formation maintain the desired formation. The most threatened drones With drones The expected formation relationship between them was lost, and the drones It will break out of formation and proceed to step 3.3; if The most threatened drones With drones Maintaining the desired formation, to compensate for the drone's maneuverability limitations and ensure obstacle avoidance success, the central information control center needs to predict the threat level of the drone in the next moment. If the drone faces a threat level of "significant" or higher in the next moment, it indicates that the drone... Maintaining formation poses a significant risk of collision; therefore, proceed to step 3.3. Otherwise, the drones will continue to execute the formation control strategy. Indicates drone and drones The relative formation quantities between them Indicates drone and drones The expected formation quantity between them.

[0078] In step 3.2, the drone The expected control quantity for obstacle avoidance of UAVs that still execute formation control strategy is:

[0079]

[0080] in, , Indicates drone Position vector and yaw angle; , This represents the desired obstacle avoidance control quantity for the drone k, given its position and yaw angle. For drones Consistency control quantity; Indicates the main obstacle avoidance drone a The three-dimensional spatial position; For drones With the main obstacle avoidance drone a The expected formation distance between them Main obstacle avoidance drone a The relative distance from the expected virtual primary position. Indicates drone The relative distance from the expected virtual primary position; , The virtual lead aircraft's position vector and yaw angle; , , Represents the consistency coefficient; , All are time constants.

[0081] Step 3.3: De-form and update the drone obstacle avoidance control parameters.

[0082] Step 4: For drones that have broken formation, based on the speed obstacle method, adjust the obstacle avoidance control of the drones that have broken formation by the expected obstacle avoidance speed. Analyze the possibility of collisions between drones that have broken formation using the speed obstacle method, and adjust the speed of the drones by using the speed obstacle area boundary and critical relative speed. This ensures that the obstacle avoidance path does not change, thus avoiding collisions between drones. This not only ensures that the maneuverability of most members is not affected during collision avoidance, but also improves the stability and safety of the formation.

[0083] like Figure 5 In the case of an obstacle on only one side, even if A performs obstacle avoidance maneuvers for a period of time and B becomes the primary obstacle avoidance drone, due to the obstacle avoidance maneuver angle... Therefore, the obstacle avoidance maneuvers primarily performed by drone B are not contradictory to the obstacle avoidance behavior of drone A; however, if such an obstacle avoidance maneuver occurs... Figure 6 When there are obstacles on both sides, the drone j Generate information about obstacles Obstacle avoidance maneuver angle Obviously, compared with the above , Contradiction, drones j After breaking away from formation j The F direction is the desired flight direction for obstacle avoidance behavior, and obviously... j F direction and , There is a possibility of conflicting directions, so collision avoidance between drones needs to be considered for drones that break out of formation.

[0084] Analysis of UAVs Based on the Speed ​​Barrier Method j The trajectory of movement, such as Figure 7 As shown, assuming a drone j Generate information about obstacles The speed is drones m For a member within the formation, its speed is The relative speed between the two The condition for two drones not to collide is the shortest distance between them. Not less than the specified minimum safety distance ,Right now This can be expressed as a velocity-angle relationship. The relative velocity obstacle region is defined as follows:

[0085]

[0086] in, Indicates drone j Obstacle avoidance speed; Represents the relative position vector between drones With relative velocity The angle between them; Relative position vector between UAVs The angle between the speed obstacle zone and the boundary of the speed obstacle zone.

[0087] Through analysis based on the velocity barrier method, we can obtain Figure 8 According to the speed obstacle zone boundary and Obtain the critical relative velocity Then through Get drone j Collision-free speed range That is, in the picture and The solution process is as follows:

[0088]

[0089] When drones j Once the obstacle avoidance distance is reached, the speed closest to the original desired external obstacle avoidance speed within this range is taken as the final desired obstacle avoidance speed.

[0090]

[0091] in: For drones determined solely by the external speed obstacle method of drone formation j Obstacle avoidance speed; Is this considering collision avoidance between drones? j The expected obstacle avoidance speed; For drones j With the formation member drone m The relative distance; The critical distance at which drones need to employ obstacle avoidance strategies; This indicates consideration of inter-machine collision avoidance. The acceptable range.

[0092] The feasibility of the UAV formation cooperative obstacle avoidance method of the present invention will be verified through a dual-obstacle scenario.

[0093] Using a quadcopter with six degrees of freedom as the model for the UAV, the UAV flies along a preset flight path. The parameters of the UAV and the parameters related to the obstacle avoidance maneuver strategy are: flight speed Pitch angle Yaw angle obstacle radius drone radius Obstacle avoidance zone set as Additional obstacle avoidance distance was added to account for interference, communication delays, and other factors. Set the distance repulsion variation coefficient in the obstacle avoidance item. Speed ​​consistency coefficient Set the state regression consistency coefficient , .

[0094] The overall formation parameter settings are as follows:

[0095]

[0096] Figure 9 These are obstacle avoidance effect diagrams of the UAV obtained under the same simulation environment according to the present invention. Figure 9 As shown in (a), the obstacle avoidance space between the two obstacles is greater than the maximum width of the formation, and the obstacle avoidance space is relatively sufficient. When the UAV formation passes over the obstacle, the members inside the formation maintain a constant relative distance, that is, the formation remains unchanged during the obstacle avoidance process. Figure 9 In step (b), the distance between the two obstacles is reduced, making the space between them insufficient to meet the requirements of the desired formation. Clearly, this is insufficient to accommodate the desired formation, requiring a trade-off between maintaining formation and rapid obstacle avoidance. In the method of this invention, initially, the UAV6 in the formation is subjected to obstacles... The greatest threat is to maintain formation and avoid obstacles until the UAV4 is obstructed. As the threat grew, evasive action had to be taken, and UAV4 eventually broke formation, while the remaining crew maintained a relatively stable formation throughout the process. Figure 9 (c) further reduces the distance between the two obstacles. The smaller the space, the more drones will break formation during obstacle avoidance. However, some drones will continue to maintain relative formation, which to a certain extent ensures the execution of the formation mission. As can be seen from the drone formation cooperative obstacle avoidance method of the present invention, UAV1, UAV3 and UAV6 always maintain the desired relative distance, while the other drones have to break formation first and then return to the desired position during obstacle avoidance.

[0097] The UAV formation cooperative obstacle avoidance method of the present invention can greatly improve the stability and safety of the formation, and is more conducive to the execution of formation tasks. When the obstacle avoidance range is wide, it can maintain the formation well; when the obstacle avoidance range is narrow, it can effectively avoid internal collisions between UAVs, and restore the formation after the obstacle avoidance is completed.

[0098] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for cooperative obstacle avoidance in UAV formations based on improved fuzzy decision-making, characterized in that, Specifically, the steps include the following: Step 1: Utilize the onboard sensors of the UAV to continuously acquire the position of the UAV and the virtual lead aircraft position. Then, through the formation control method based on virtual structure and consistency theory, obtain the position and attitude control commands for the UAV formation. Step 2: Based on the drone's performance, set the drone's distance obstacle avoidance range and rotation angle obstacle avoidance range, and construct a fuzzy decision-maker; including the following sub-steps: Step 2.1: Set the drone's obstacle avoidance range according to the drone's performance. and rotation angle obstacle avoidance range ; Step 2.2: Adjust the distance to the obstacle avoidance range. Transform to the domain of [-3,3], perform fuzzification, and obtain the distance fuzzy set {AT, VB, LB, MT, LS, VS, NT}, which represent {absolute threat, greater threat, slightly greater threat, medium threat, slightly less threat, lesser threat, no threat}, respectively; Step 2.3: Adjust the obstacle avoidance range by rotating the angle. Transform to the domain of [-3,3] and perform fuzzification to obtain the rotation angle fuzzy set {NT, VS, LS, MT, LB, MB, VB}, which represent {no threat, minor threat, slightly minor threat, medium threat, slightly major threat, medium major threat, and major threat}, respectively. Step 2.4: Based on the distance fuzzy set and the rotation angle fuzzy set, establish a fuzzy decision-maker based on threat level, as shown in the table below: The fuzzy decision maker has ten levels of threat level {NT, VVS, VS, MS, LS, MT, LB, MB, VB, AT}, which represent {no threat, very low threat, relatively low threat, moderately low threat, slightly low threat, moderate threat, slightly high threat, moderately high threat, relatively high threat, and absolute threat}, respectively. Step 3: Determine the threat level of the UAV based on the fuzzy decision-maker and obstacles, and output the corresponding formation obstacle avoidance decision; including the following sub-steps: Step 3.1: Determine the threat level of each UAV in the formation based on the fuzzy controller and obstacles, identify the UAV with the greatest threat, implement the obstacle avoidance strategy for the UAV with the greatest threat, and update the control commands of the virtual leader and the expected control variables for obstacle avoidance of the UAV; otherwise, proceed to step 3.

2. Step 3.2: Determine whether other drones in the formation are under a threat of greater than a significant threat. If so, proceed to step 3.

3. Otherwise, determine whether the drones in the formation are maintaining the desired formation. If not, proceed to step 3.

3. Otherwise, predict the threat level of the drones in the next moment through the central information control center. If the drones are under a threat of greater than a significant threat in the next moment, proceed to step 3.

3. Otherwise, the drones will continue to execute the formation control strategy. Step 3.3: Depart from formation and update the drone obstacle avoidance control parameters; Step 4: For drones that have broken formation, adjust the obstacle avoidance control amount of the drones that have broken formation based on the speed obstacle method and the desired obstacle avoidance speed.

2. The UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to claim 1, characterized in that, The position and attitude control commands for the drone formation are as follows: in, Indicates drone i Formation position control commands, , Indicates drone i position vector, , , , , All are time constants. Indicates drone Consistency control of formation positions , This represents the position vector of the virtual alpha aircraft. , , All represent the consistency coefficient. Indicates drone The position vector of the virtual lead aircraft, given that it is already in the desired position within the formation. , Indicates drone The expected relative positional relationship with the virtual primary. Indicates drone and The connection weights, Indicates drone j The position vector of the virtual lead aircraft, given that it is already in the desired position within the formation. n Indicates the number of drones; Indicates drone Formation attitude control commands. Indicates drone Yaw angle; Indicates drone The amount of control over formation attitude consistency. , This is the yaw angle of the virtual lead aircraft.

3. The UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to claim 1, characterized in that, The update process for the drone obstacle avoidance control parameters is as follows: The control command update process for the virtual primary is as follows: in, For obstacle avoidance maneuvering strategy, For drones The quality; Indicates drone i Formation position obstacle avoidance control quantity, ; This represents the position vector of drone i. ; , All are time constants. This indicates the obstacle avoidance commands for the drones most threatened. Indicates drone The amount of formation attitude obstacle avoidance control. This indicates the control commands for updating the virtual primary. Indicates the three-dimensional spatial position of the virtual alpha aircraft. This indicates the formation position control command for the virtual lead aircraft.

4. The UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to claim 1, characterized in that, If there are multiple most threatened drones in step 3.1, the drone with the smallest number will be considered the most threatened drone.

5. The UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to claim 1, characterized in that, The process for determining whether the drones within the formation maintain the desired formation in step 3.2 is as follows: If The most threatened drones With drones The expected formation relationship between them was lost, and the drones They will break out of formation; if The most threatened drones With drones The expected formation is still maintained; among them, Indicates drone and drones The relative formation quantities between them Indicates drone and drones The expected formation quantity between them.

6. The UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to claim 1, characterized in that, In step 3.2, the drone The expected control quantity for obstacle avoidance of UAVs that still execute formation control strategy is: in, , Indicates drone Position vector and yaw angle; , This represents the desired obstacle avoidance control quantity for the drone k, given its position and yaw angle. For drones Consistency control quantity; Indicates the main obstacle avoidance drone a The three-dimensional spatial position; For drones With the main obstacle avoidance drone a The expected formation distance between them Main obstacle avoidance drone a The relative distance from the expected virtual primary position. Indicates drone The relative distance from the expected virtual primary position; , The virtual lead aircraft's position vector and yaw angle; , , Represents the consistency coefficient; , All are time constants.

7. The UAV formation cooperative obstacle avoidance method based on improved fuzzy decision-making according to claim 1, characterized in that, The desired obstacle avoidance speed is: in: For drones determined solely by the external speed obstacle method of drone formation j Obstacle avoidance speed; Is it considering inter-drone collision avoidance for drones? j The expected obstacle avoidance speed; For drones j With the formation member drone m The relative distance; The critical distance at which drones need to employ obstacle avoidance strategies; This indicates consideration of inter-machine collision avoidance. The acceptable range.

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