A multi-unmanned aerial vehicle cooperative obstacle avoidance method for static and dynamic obstacles

Abstract

The application discloses a kind of multi-unmanned aerial vehicle cooperative obstacle avoidance methods for static and dynamic obstacles, comprising, the motion state and obstacle information of itself are delivered to surrounding unmanned aerial vehicle;According to the relative position information of obstacle and unmanned aerial vehicle and the current speed information of unmanned aerial vehicle, the obstacle avoidance controller of each unmanned aerial vehicle is calculated;According to the relative position information and relative speed information between unmanned aerial vehicles, the inter-unmanned aerial vehicle collision avoidance controller and formation keeping controller are designed;According to the relative position information between unmanned aerial vehicle and target point, the drive controller of unmanned aerial vehicle flying to target area is designed;According to the above-mentioned controller, the cooperative obstacle avoidance controller is calculated and the corresponding unmanned aerial vehicle is controlled.The application proposes a kind of multi-unmanned aerial vehicle cooperative obstacle avoidance control scheme that meets actual demand and is easy to realize, can well weigh the safety and integrity of multi-unmanned aerial vehicle system and fly to target area while effectively completing obstacle avoidance and formation reconstruction tasks in static and dynamic obstacle environment.

Description

Technical Field

[0001] This invention belongs to the field of multi-UAV technology, and particularly relates to a multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles. Background Technology

[0002] Quadcopter drones are widely used in military and civilian fields due to their simple structure, high maneuverability, and flexibility. To overcome the low efficiency of single-unit drones and expand their application areas, quadcopter drone swarm systems have emerged. Facing the demands of complex operating environments, multi-drone systems, with their high efficiency and strong fault tolerance, have significant application value in typical scenarios such as target reconnaissance and encirclement. The stability and security of multi-drone systems directly determine the effectiveness of collaborative tasks. To fully leverage the advantages of multi-drone systems in performing tasks in both static and dynamic obstacle environments, effective cooperative obstacle avoidance algorithms are needed. However, multi-drone cooperative obstacle avoidance methods for static and dynamic obstacles present the following challenges:

[0003] (1) In static and dynamic obstacle environments, it is necessary to simultaneously take multiple drones away from static obstacles, dynamic obstacles, and avoid collisions with surrounding drones when avoiding external obstacles.

[0004] (2) Multiple UAVs are a complete system. While avoiding obstacles, it is also necessary to consider that the cluster formation should not be greatly dispersed due to obstacle avoidance or that it should not be able to return to the expected formation after obstacle avoidance.

[0005] The aforementioned difficulties make it challenging to effectively achieve multi-UAV cooperative obstacle avoidance for both static and dynamic obstacles. Current solutions have the following shortcomings:

[0006] (1) While current model-based prediction methods can treat a series of constraints and obstacles as penalty terms, they involve relatively large computational costs, such as the Chinese patent application number 202010408340.3, "UAV Formation Reconstruction System and Method Based on Ant Colony Algorithm and Artificial Potential Field Method"; the artificial potential field method, although computationally less complex, is prone to causing uneven trajectories and getting trapped in local extrema, such as the Chinese patent application number 202010408340.3, "UAV Formation Reconstruction System and Method Based on Ant Colony Algorithm and Artificial Potential Field Method". In addition, these methods mainly deal with obstacle avoidance problems in static obstacle environments and are not suitable for dealing with collision avoidance problems between UAVs because they only use relative position information;

[0007] (2) In order to meet the real-time computing requirements, most of the consistency-based multi-UAV formation maintenance control methods are used to realize the formation reconstruction of multi-UAVs. However, this method cannot handle the formation reconstruction of multi-UAVs in obstacle environments. Simply combining it with the above methods will make it impossible for the multi-UAV system to balance the stability of the formation and the security of the system.

[0008] In summary, for the problem of multi-UAV cooperative obstacle avoidance in static and dynamic obstacle environments, there is an urgent need to propose an algorithm that can simultaneously take into account obstacle avoidance, inter-UAV collision avoidance, and formation maintenance for both static and dynamic obstacles. Summary of the Invention

[0009] The purpose of this invention is to address the issues of obstacle avoidance, inter-drone collision avoidance, and formation maintenance for both static and dynamic obstacles. It proposes a multi-drone cooperative obstacle avoidance method for both static and dynamic obstacles, aiming to enable multiple drones to safely and quickly avoid static and dynamic obstacles and reconstruct their formation after obstacle avoidance. This method overcomes the shortcomings of existing multi-drone cooperative obstacle avoidance algorithms and can simultaneously address obstacle avoidance, inter-drone collision avoidance, and formation maintenance.

[0010] To address the aforementioned technical problems, this invention discloses a multi-UAV cooperative obstacle avoidance method for both static and dynamic obstacles, comprising:

[0011] Step 1, Inter-UAV Information Transmission, including: multiple UAVs use visual sensors to detect the surrounding environment to determine whether to avoid obstacles and transmit their own motion status information and obstacle information to surrounding UAVs.

[0012] Step 2, UAV obstacle avoidance controller design, including: determining whether the UAV needs to avoid obstacles based on the relative position information of the obstacle and the UAV and the current speed information of the UAV; when obstacle avoidance is required, calculating the obstacle avoidance potential field function and rotation potential field function of each UAV, and calculating the obstacle avoidance controller of each UAV along the position gradient direction.

[0013] Step 3, design of multi-UAV collision avoidance controller and formation keeping controller, including designing UAV collision avoidance controller and formation keeping controller based on the relative position information and relative speed information between UAVs;

[0014] Step 4, design of the drive controller for multiple UAVs flying to the target area, including: designing the UAV drive controller based on the relative position information of the UAVs and the target point;

[0015] Step 5: Based on the obstacle avoidance controller designed in Step 2, the inter-drone collision avoidance controller and formation holding controller designed in Step 3, and the drive controller designed in Step 4, calculate the cooperative obstacle avoidance controller and control the corresponding UAVs to achieve safe obstacle avoidance and rapid formation reconfiguration of each UAV while flying to the target area.

[0016] Furthermore, the internal information transmission between multiple unmanned aerial vehicles mentioned in step 1 specifically includes:

[0017] Each drone is equipped with a self-organizing network module;

[0018] Multiple drone systems operating within the same Wi-Fi network can send their location, speed, and obstacle detection information to neighboring drones.

[0019] The communication topology of multiple drones is set as a random undirected graph, meaning that adjacent drones can exchange information.

[0020] Furthermore, in step 2, the design of the obstacle avoidance controller between drones determines whether the drone needs obstacle avoidance, specifically including:

[0021] Step 2-1: Define the multi-UAV system as consisting of N quadcopter UAVs, where N≥1. Considering that the multi-UAV system mainly focuses on the consistency of position and velocity, the i-th UAV model satisfies:

[0022]

[0023] Where 1≤i≤N, v represents the differential signal of the i-th UAV position at time t. i (t) represents the velocity of the i-th UAV at time t; U represents the differential velocity signal of the i-th UAV at time t. i (t) represents the control command of the position subsystem of the i-th UAV at time t.

[0024] Step 2-2: The obstacle detection area for UAV i is set as a fan-shaped area, with its fixed point being the current horizontal position p of UAV i. i =[p xi ,p yi ] T , where p xi p represents the x-axis position of the i-th drone. yi This represents the y-axis position of the i-th drone, with a detection radius of r. d The direction from the vertex of the detection area to the midpoint of the arc is related to the flight direction of UAV i. The included angle is ±θ p ;

[0025] When there is an obstacle within the detection range of drone i k ,k=1,2,...,K, such that

[0026]

[0027] Then drone i needs to avoid obstacles, where K is the number of obstacles in drone i's current field of view, o k =[o xk ,o yk ,o rk ] T It is the position information vector of obstacle k, o rk It is the radius or width of the obstacle, o xk This represents the x-axis position of the k-th obstacle, o yk This indicates the y-axis position of the k-th obstacle.

[0028] Further, step 2, calculating the obstacle avoidance potential field function and rotation potential field function for each UAV, includes: based on the relative position information of the k-th obstacle and the i-th UAV, and combined with the current velocity information of the i-th UAV, designing the following obstacle avoidance potential field function Γ1(p i ,o k ):

[0029]

[0030] in, It is a constant gain matrix, r a =r d +o rk The above formula with respect to p i The gradient can generate the following obstacle avoidance traction force:

[0031]

[0032] in, It is the gradient operator.

[0033] When the other traction forces on drone i equal the obstacle avoidance control force, it will fall into a state of chattering and jamming, rendering it unable to move. To avoid the drone falling into local minima and chattering, a novel rotating potential field function Γ2(p) is designed based on potential field theory. i ,o k )as follows:

[0034]

[0035] Along the rotating potential function Γ2(p i ,o k The negative gradient of ) can be used to obtain the rotational traction force. for:

[0036]

[0037] Among them, T r It is a rotation matrix and The rotation angle α is defined as

[0038]

[0039] Where, r s The safe distance for drones indicates that there should be no obstacles within that range.

[0040] Furthermore, step 2 involves calculating the obstacle avoidance controller for each UAV, including: the distributed obstacle avoidance controller for the i-th UAV is designed as follows:

[0041]

[0042] in, It is a constant gain, v i This is the current speed of drone i.

[0043] Furthermore, the design of the inter-UAV collision avoidance controller and formation keeping controller described in step 3 includes:

[0044] Based on the relative motion between the drones, a collision avoidance control law is designed for each drone using Hooke's law with damping:

[0045]

[0046]

[0047] Among them, f c Indicates the strength of collision avoidance control resulting from relative position. It is the gain constant, p ji =p j -p i Table and v ji =v j -v i r represents the relative position and relative velocity between the drones, respectively. s This refers to the safe distance for drones;

[0048] Define the horizontal motion state vector Z of the i-th UAV at time t. i (t) is:

[0049]

[0050] Horizontal expected formation E i (t) is:

[0051]

[0052] Among them, e pi (t),e vi (t) represents the desired position and desired velocity of the i-th drone, respectively;

[0053] According to the desired formation E i (t) and the relative state information between UAVs, based on the consistency theory, calculate the formation-keeping controller for each UAV at time t. for:

[0054]

[0055] in, and It is the gain matrix. Represents the set of real numbers; w represents the derivative of the expected formation speed of the i-th drone; ij It is the communication topology weight between drones.

[0056] Communication topology weight w between drones ij That is, when drone i and drone j are neighbors, w ij =w ji =1, otherwise w ij =0.

[0057] Furthermore, the design of the UAV drive controller described in step 4 specifically includes:

[0058] Define the location information of the target region as g = [g x ,g y ] T Based on the relative position information of the i-th UAV and the target area, design the drive controller for the i-th UAV to fly to the target area. for:

[0059]

[0060] Designing a drive controller for multiple drones flying to a target area can enable multiple drones to reach the target area via a shorter path, while preventing multiple drones from getting stuck in local extremes and failing to reach the desired target position during formation reconfiguration.

[0061] Furthermore, the design of the UAV cooperative obstacle avoidance controller described in step 5 specifically includes:

[0062] Based on the designed obstacle avoidance controller (step 2), inter-drone collision avoidance controller (step 3), formation maintenance controller, and drive controller (step 4), the corresponding UAVs are controlled. The following variable is selected as the cooperative obstacle avoidance controller for the i-th UAV:

[0063]

[0064] Among them, U i (t) represents the control command for the position subsystem of the i-th UAV. and These are the weights of obstacle avoidance control, drive control, and inter-machine collision control, respectively. This represents the distributed obstacle avoidance controller for the i-th UAV at time t. This represents the formation-keeping controller for the i-th UAV at time t. This represents the drive controller of the i-th UAV at time t. Let represent the collision avoidance controller between the i-th and j-th drones at time t.

[0065] Furthermore, the designed collaborative obstacle avoidance controller controls the corresponding UAVs to achieve safe obstacle avoidance and rapid formation reconfiguration in both static and dynamic obstacle environments.

[0066] Beneficial effects:

[0067] (1) The computational load is low, which meets the requirements of online real-time calculation. In order to avoid the situation that the trajectory is not smooth due to the large potential force, the speed of the UAV itself is introduced to design the obstacle avoidance potential field function. At the same time, in order to avoid getting trapped in the local extreme value, a rotating potential field function is introduced to accelerate the UAV away from the undesirable area. In order to avoid collisions with surrounding UAVs while avoiding obstacles, an inter-UAV collision avoidance controller is designed in combination with the relative speed information of the UAVs, so that multiple UAVs can avoid external static and dynamic obstacles without colliding with neighboring UAVs.

[0068] (2) The designed obstacle avoidance controller and inter-machine collision avoidance control can be well compatible with the formation holding control based on consistency theory. The designed obstacle avoidance controller, inter-machine collision avoidance controller, formation controller and drive controller are coupled together into the control layer of the UAV to improve the solution efficiency. Under the action of the four control terms of the present invention, the safety of the system can be guaranteed in both static and dynamic obstacle environments, and the UAV will quickly return to the desired formation after completing obstacle avoidance. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The drawings are only used to illustrate preferred embodiments and are not intended to limit the present invention.

[0070] Figure 1 This is a flowchart of the method disclosed in an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the method control framework disclosed in the embodiments of the present invention;

[0072] Figure 3 These are simulation process diagrams and horizontal position trajectory diagrams in the Gazebo simulator disclosed in embodiments of the present invention;

[0073] Figure 4 This is a horizontal velocity trajectory diagram of eight drones disclosed in an embodiment of the present invention;

[0074] Figure 5 This is a schematic diagram showing the distances between the eight drones disclosed in an embodiment of the present invention. Detailed Implementation

[0075] This invention addresses the problem of existing methods failing to effectively control the stable and safe formation reconfiguration of multiple UAVs in multi-obstacle environments. It designs an obstacle avoidance and collision avoidance algorithm that is compatible with consistency theory and fully utilizes the relative information between UAVs, thereby ensuring the smoothness and safety of multi-UAV trajectories. The invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0076] This invention provides a real-time example of a multi-UAV cooperative obstacle avoidance method for both static and dynamic obstacles, such as... Figure 1 As shown, the specific steps include the following:

[0077] Step 1, Inter-UAV Information Transmission, includes: Multiple UAVs using visual sensors to detect their surroundings, determine whether to avoid obstacles, and transmit their own motion status information and obstacle information to surrounding UAVs; specifically including:

[0078] Each drone is equipped with a self-organizing network module;

[0079] Multiple drone systems operating within the same Wi-Fi network can send their location, speed, and obstacle detection information to neighboring drones.

[0080] The communication topology of multiple drones is set as a random undirected graph, meaning that adjacent drones can exchange information.

[0081] The visual sensor and the self-organizing network module are existing technologies, and the embodiments of the present invention are not limited thereto.

[0082] Step 2, UAV obstacle avoidance controller design, includes: determining whether the UAV needs obstacle avoidance; when obstacle avoidance is required, calculating the obstacle avoidance potential field function and rotational potential field function of each UAV based on the relative position information of the obstacle and the UAV, combined with the current velocity information of the UAV; and calculating the obstacle avoidance controller of each UAV along the position gradient direction; specifically including:

[0083] Step 2-1: Define the multi-UAV system as consisting of N quadcopter UAVs, where N≥1. Considering that the multi-UAV system mainly focuses on the consistency of position and velocity, the i-th UAV model satisfies:

[0084]

[0085] Where 1≤i≤N, v represents the differential signal of the i-th UAV position at time t. i (t) represents the velocity of the i-th UAV at time t; U represents the differential velocity signal of the i-th UAV at time t. i (t) represents the control command of the position subsystem of the i-th UAV at time t.

[0086] Step 2-2: The obstacle detection area for UAV i is set as a fan-shaped area, with its fixed point being the current horizontal position p of UAV i. i =[p xi ,p yi ] T , where p xi p represents the x-axis position of the i-th drone. yi This represents the y-axis position of the i-th drone, with a detection radius of r. d The direction from the vertex of the detection area to the midpoint of the arc is related to the flight direction of UAV i. The included angle is ±θ p ;

[0087] When there is an obstacle within the detection range of drone i k ,k=1,2,...,K, such that

[0088]

[0089] Then drone i needs to avoid obstacles, where K is the number of obstacles in drone i's current field of view, o k =[o xk ,o yk ,o rk ] T It is the position information vector of obstacle k, o rk It is the radius or width of the obstacle, o xk This represents the x-axis position of the k-th obstacle, o yk Indicates the y-axis position of the k-th obstacle;

[0090] Steps 2-3: When obstacle avoidance is required, based on the relative position information of the k-th obstacle and the i-th drone, and combined with the current velocity information of the i-th drone, the following obstacle avoidance potential field function Γ1(p) is designed. i ,o k ):

[0091]

[0092] in, It is a constant gain matrix, r a =r d +ork The above formula with respect to p i The gradient can generate the following obstacle avoidance traction force.

[0093]

[0094] in, It is the gradient operator.

[0095] When the other traction forces on drone i equal the obstacle avoidance control force, it will fall into a state of chattering and jamming, rendering it unable to move. To avoid the drone falling into local minima and chattering, a novel rotating potential field function Γ2(p) is designed based on potential field theory. i ,o k )as follows:

[0096]

[0097] Along the rotating potential function Γ2(p i ,o k The negative gradient of ) can be used to obtain the rotational traction force. for:

[0098]

[0099] Among them, T r It is a rotation matrix and The rotation angle α is defined as

[0100]

[0101] Where, r s The safe distance for a drone represents the range within which no obstacles can appear. Therefore, the distributed obstacle avoidance controller for the i-th drone... Designed as follows:

[0102]

[0103] in, It is a constant gain, v i This is the current speed of drone i.

[0104] Step 3, design of multi-UAV collision avoidance controller and formation keeping controller, including designing a UAV collision avoidance controller and formation keeping controller based on the relative position information and relative speed information between UAVs;

[0105] Based on the relative motion states between the drones, a collision avoidance controller for each drone is designed using Hooke's law with damping.

[0106]

[0107] Among them, f c Indicates the strength of collision avoidance control resulting from relative position. It is the gain constant, p ji =p j -p i and v ji =v j -v i r represents the relative position and relative velocity between the drones, respectively. s This refers to the safe distance for drones;

[0108] Define the horizontal motion state vector Z of the i-th UAV at time t. i (t) is:

[0109]

[0110] Horizontal expected formation E i (t) is:

[0111]

[0112] Among them, e pi (t),e vi (t) represents the desired position and desired velocity of the i-th drone, respectively;

[0113] According to the desired formation E i (t) and the relative state information between UAVs, based on the consistency theory, calculate the formation-keeping controller for each UAV at time t. for:

[0114]

[0115] in, and It is the gain matrix. Represents the set of real numbers; w represents the differential of the expected formation speed of the i-th drone; ij It is the communication topology weight between drones.

[0116] Communication topology weight w between drones ij That is, when drone i and drone j are neighbors, w ij =w ji =1, otherwise w ij =0.

[0117] Step 4, design of the drive controller for multiple UAVs flying to the target area, including: designing the UAV drive controller based on the relative position information of the UAVs and the target point; specifically including:

[0118] Define the location information of the target region as g = [g x ,g y ] T Based on the relative position information of the i-th UAV and the target area, design the drive controller for the i-th UAV to fly to the target area. for:

[0119]

[0120] Step 5: Based on the obstacle avoidance controller designed in Step 2, the inter-drone collision avoidance controller and formation holding controller in Step 3, and the drive controller designed in Step 4, calculate the cooperative obstacle avoidance controller and control the corresponding UAVs to achieve safe obstacle avoidance and rapid formation reconfiguration for each UAV while flying towards the target area. Specifically, this includes:

[0121] Based on the designed obstacle avoidance controller (step 2), inter-drone collision avoidance controller (step 3), formation maintenance controller, and drive controller (step 4), the corresponding UAVs are controlled. The following variable is selected as the cooperative obstacle avoidance controller for the i-th UAV:

[0122]

[0123] Among them, U i (t) represents the control command for the position subsystem of the i-th UAV. and These are the weights of obstacle avoidance control, drive control, and inter-machine collision control, respectively. This represents the distributed obstacle avoidance controller for the i-th UAV at time t. This represents the formation-keeping controller for the i-th UAV at time t. This represents the drive controller of the i-th UAV at time t. Let represent the collision avoidance controller between the i-th and j-th drones at time t.

[0124] The designed collaborative obstacle avoidance controller controls the corresponding UAVs, enabling each UAV to safely avoid obstacles and quickly reconfigure its formation in static and dynamic obstacle environments.

[0125] like Figure 2 As shown, the i-th UAV is finally controlled by the cooperative obstacle avoidance controller, which enables each UAV to safely avoid obstacles and quickly reconstruct its formation in static and dynamic obstacle environments.

[0126] In summary, this invention considers a swarm of quadcopter drones. Based on this, the method described in this embodiment designs a cooperative obstacle avoidance controller for multiple drones, enabling them to safely and smoothly fly to a target point in both static and dynamic environments and quickly return to the desired formation after obstacle avoidance. Therefore, this invention provides a practical and easily implemented solution to the "cooperative obstacle avoidance problem of drone swarms."

[0127] Example:

[0128] This embodiment consists of a multi-UAV system composed of eight quadcopters. Each UAV is equipped with a dual-light micro-pod visual sensor and a wireless image transmission self-organizing network module. The desired formation is a fixed cone formation, and the flight environment includes 10 static obstacles and 2 dynamic obstacles (acted as UAVs). The controller weights are as follows: and The weights of the controller can be increased or decreased according to requirements. Obstacle avoidance potential field function Γ1(p i ,o k The constant gain matrix in ) is set as The number of obstacles can be increased or decreased according to the obstacle avoidance requirements on the x-axis and y-axis respectively. Obstacle avoidance controller constant gain in Set to 1, 1 respectively, for the collision avoidance controller. Gain constant in Set to 50 and 4 respectively, for the formation holding controller. Gain matrix in Set as The results of the collaborative obstacle avoidance process of the multiple drones in the Gazebo simulator are as follows: Figure 3-5 As shown, where Figure 3 The horizontal position trajectories of 8 drones and 2 dynamic obstacles are given, where the solid sphere represents the starting position of the mission drones and the pentagram represents the starting position of the 2 dynamic obstacles. When no obstacles are encountered, the 8 drones maintain a conical formation flight. Figure 3 In the upper part of the local graph at t=22s, the circles mark the dynamic obstacles. Combined with the horizontal trajectory, it can be seen that the 8 drones effectively avoided both static and moving obstacles in front of them. Figure 4 The horizontal velocity trajectories of 8 drones and 2 dynamic obstacles during this process are given, among which... It is the horizontal velocity of the dynamic obstacle, v x ,v yThe figure represents the average horizontal speed of the eight drones. The upper and lower bounds of the shaded area represent the maximum and minimum speeds of the drones. This figure reflects the speed fluctuations of the drones during obstacle avoidance. It can be seen that the speed fluctuations of the drones during obstacle avoidance are relatively small, indicating that the method of this invention helps maintain the overall system integrity. In addition, it can be seen that the speed stabilizes in about 4 seconds after obstacle avoidance, which is sufficient to complete the formation of the original task. Figure 5 The distances between multiple UAVs are given, showing that the multiple UAVs have effectively balanced obstacle avoidance, inter-UAV collision avoidance, and formation maintenance, and have effectively achieved safe flight and formation reconfiguration of multiple UAVs under static and dynamic obstacles.

[0129] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0130] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0131] This invention provides a multi-UAV cooperative obstacle avoidance method for both static and dynamic obstacles. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for multi-UAV cooperative obstacle avoidance in the presence of static and dynamic obstacles, the method comprising: The method comprises the following steps: Step 1, internal information transmission among multiple unmanned aerial vehicles, comprising: multiple unmanned aerial vehicles detecting surrounding environment through visual sensors to determine whether to avoid obstacles and transmitting motion state information and obstacle information of the unmanned aerial vehicles to surrounding unmanned aerial vehicles; Step 2, unmanned aerial vehicle obstacle avoidance controller design, comprising: judging whether the unmanned aerial vehicle needs to avoid obstacles according to relative position information of the obstacle and the unmanned aerial vehicle and combining current speed information of the unmanned aerial vehicle, calculating obstacle potential field function and rotation potential field function of each unmanned aerial vehicle when the unmanned aerial vehicle needs to avoid obstacles, and calculating an obstacle avoidance controller of each unmanned aerial vehicle in a position gradient direction; Step 3, inter-unmanned aerial vehicle collision avoidance controller design and formation keeping controller design, comprising: designing an inter-unmanned aerial vehicle collision avoidance controller and a formation keeping controller according to relative position information and relative speed information among the unmanned aerial vehicles; Step 4, drive controller design for multiple unmanned aerial vehicles flying to a target area, comprising: designing a drive controller of the unmanned aerial vehicle according to relative position information of the unmanned aerial vehicle and the target point; Step 5, calculating a cooperative obstacle avoidance controller according to the obstacle avoidance controller designed in step 2, the inter-unmanned aerial vehicle collision avoidance controller and the formation keeping controller controlled in step 3, and the drive controller designed in step 4, and controlling the corresponding unmanned aerial vehicle, so as to realize safe obstacle avoidance and rapid formation reconstruction of each unmanned aerial vehicle flying to the target area.

2. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 1, wherein, The internal information transmission among multiple unmanned aerial vehicles in step 1 specifically comprises: each unmanned aerial vehicle is equipped with a self-organizing network module; the multiple unmanned aerial vehicle system is in the same wireless local area network, and transmits position, speed and detected obstacle information to neighbor unmanned aerial vehicles; the communication topology of the multiple unmanned aerial vehicles is set as a random undirected graph, that is, adjacent unmanned aerial vehicles can interact information.

3. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 2, wherein, The unmanned aerial vehicle obstacle avoidance controller design in step 2 specifically comprises: Step 2-1, defining that the multiple unmanned aerial vehicles comprise N quad-rotor unmanned aerial vehicles, N≥1, considering consistency of position and speed of the multiple unmanned aerial vehicle system, the i-th unmanned aerial vehicle model satisfies: wherein, 1≤i≤N, denotes the i-th UAV position differential signal at time t, v i denotes the i-th UAV velocity at time t; denotes the i-th UAV velocity differential signal at time t, U i denotes the i-th UAV position subsystem control command at time t; Step 2-2: The obstacle detection area for UAV i is set as a fan-shaped area, with its fixed point being the current horizontal position p of UAV i. i =[p xi ,p yi ] T , where p xi p represents the x-axis position of the i-th drone. yi This represents the y-axis position of the i-th drone, with a detection radius of r. d The direction from the vertex of the detection area to the midpoint of the arc is related to the flight direction of UAV i. The included angle is ±θ p ; When there is an obstacle o in the detection range of the drone i k k = 1, 2,..., K, such that Then the UAV i needs to avoid the obstacle, wherein K is the number of obstacles in the current field of view of the UAV i, o k = [o xk , o yk , o rk ] T is the position information vector of the obstacle k, o rk is the radius or width of the obstacle, o xk represents the x-axis position of the kth obstacle, o yk represents the y-axis position of the kth obstacle.

4. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 3, wherein, The calculation of the obstacle avoidance potential field function and the rotation potential field function of each UAV in step 2 comprises: according to the relative position information of the kth obstacle and the ith UAV, and in combination with the current speed information of the ith UAV, the following obstacle avoidance potential field function Γ1(p i k ) is designed:​ wherein is a constant gain matrix, r a = r d + o rk , the gradient of the above equation with respect to p i results in the following obstacle avoidance traction force wherein is a gradient operator, When the other traction of the UAV i is equal to the obstacle avoidance control force, it will be in the state of chattering and stuck, resulting in its inability to move; in order to avoid the UAV falling into local minimum and chattering, a new type of rotating potential field function Γ2(p i ,o k ) is designed based on the potential field theory as follows: The rotational traction force along the negative gradient of the rotational potential field function Γ2(p i k is:​​ where T r is a rotation matrix and The rotation angle a is defined as: wherein r s is the safety distance of the UAV, indicating that no obstacles can appear within this range.

5. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 4, wherein, The obstacle avoidance controller of each UAV calculated in step 2 includes: a distributed obstacle avoidance controller of the i-th UAV is designed as: wherein, is a constant gain, v i is the current speed of the drone i.

6. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 5, wherein, The inter-unmanned aerial vehicle collision avoidance controller design in step 3 comprises: According to the relative motion state between the unmanned aerial vehicles, a collision avoidance controller of each unmanned aerial vehicle is designed based on a damping-based Hook's law wherein f c represents the collision avoidance control strength generated by the relative position, is a gain constant, p ji = p j -p i and v ji = v j -v i respectively represent the relative position and the relative velocity between the unmanned aerial vehicles, r s is the safety distance of the unmanned aerial vehicle.

7. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 6, wherein, The multiple unmanned aerial vehicle formation keeping controller design in step 3 specifically comprises: defines the horizontal motion state vector Z of the ith drone at time t i (t) is: Horizontal desired platoon E i (t) is: wherein e pi (t), e vi (t) are the desired position and desired velocity of the i-th drone, respectively; According to the desired formation E i (t) and relative state information between UAVs, the formation keeping controller of each UAV at time t is calculated based on consensus theory is: wherein, and is a gain matrix, denotes a set of real numbers; denotes the differential of the desired formation velocity of the i-th UAV; ij is the communication topology weight between UAVs.

8. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 7, wherein, Communication topology weights w between drones ij i.e. when drone i and drone j are neighbors, w ij = w ji = 1, otherwise w ij = 0.

9. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 8, wherein, The multiple unmanned aerial vehicle drive controller design in step 4 comprises: Define the location information of the target region as g = [g x ,gy] T Based on the relative position information of the i-th UAV and the target area, design the drive controller for the i-th UAV to fly to the target area at time t. for:

10. The multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles according to claim 9, wherein, Step 5 comprises: According to the designed distributed obstacle avoidance controller, the inter-unmanned aerial vehicle collision avoidance controller, the formation keeping controller and the drive controller for flying to the target area, the following variable is selected as the cooperative obstacle avoidance controller of the i-th unmanned aerial vehicle: wherein U i (t) represents the control instruction of the position subsystem of the i-th UAV, and are the weights of the obstacle avoidance control term, the driving control term and the inter-UAV collision control term, respectively, represents the distributed obstacle avoidance controller of the i-th UAV at time t, represents the formation keeping controller of the i-th UAV at time t, represents the driving controller of the i-th UAV at time t, represents the collision avoidance controller between the i-th UAV and the j-th UAV at time t.

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