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

Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative obstacle avoidance method for static and dynamic obstacles, and the method comprises the steps: transmitting the motion state and obstacle information of each unmanned aerial vehicle to surrounding unmanned aerial vehicles; calculating an obstacle avoidance controller of each unmanned aerial vehicle according to the relative position information of the obstacle and the unmanned aerial vehicle in combination with the current speed information of the unmanned aerial vehicle; designing an inter-unmanned aerial vehicle collision avoidance controller and a formation keeping controller according to the relative position information and the relative speed information between the unmanned aerial vehicles; designing a driving controller for the unmanned aerial vehicle to fly to the target area according to the relative position information between the unmanned aerial vehicle and the target point; and a cooperative obstacle avoidance controller is calculated according to the controllers and a corresponding unmanned aerial vehicle is controlled. The invention provides a multi-unmanned aerial vehicle cooperative obstacle avoidance control scheme which meets actual requirements and is easy to implement, the safety and the integrity of a multi-unmanned aerial vehicle system can be well balanced, and obstacle avoidance and formation reconstruction tasks in static and dynamic obstacle environments can be effectively completed while the multi-unmanned aerial vehicle system flies to a target area.

Description

Technical Field

[0001] The present invention belongs to the technical field of multiple unmanned aerial vehicles, and in particular relates to a multiple unmanned aerial vehicle collaborative obstacle avoidance method for static and dynamic obstacles. Background Art

[0002] Quad-rotor drones are widely used in military and civilian fields due to their simple structure, strong maneuverability and high flexibility. In order to make up for the disadvantage of low efficiency of single-machine and expand the application field of drones, quad-rotor drone cluster systems came into being. Facing the needs of complex operating environments, multi-drone systems have important application value in typical scenarios such as target reconnaissance and round-up due to their advantages such as high work efficiency and strong fault tolerance. The stability and safety of multi-drone systems directly determine the execution effect of collaborative tasks. In order to give full play to the advantages of multiple drones in performing tasks in static and dynamic obstacle environments, it is necessary to propose an effective collaborative obstacle avoidance algorithm. The multi-drone collaborative obstacle avoidance method for static and dynamic obstacles has the following difficulties:

[0003] (1) In the environment of static and dynamic obstacles, it is necessary to take into account the avoidance of static obstacles and dynamic obstacles by multiple drones, as well as the avoidance of collisions with surrounding drones when avoiding external obstacles;

[0004] (2) Multiple UAVs are a complete system. When avoiding obstacles, it is also necessary to consider that the cluster formation should not be greatly dispersed or unable to return to the desired formation after avoiding obstacles.

[0005] The above difficulties make it difficult to effectively implement multi-UAV collaborative obstacle avoidance for static and dynamic obstacles. The current results have the following defects:

[0006] (1) Although the current model prediction-based methods can regard a series of constraints and obstacles as penalty items, the amount of calculation is relatively large, 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"; although the method based on artificial potential field has a lower amount of calculation, it is easy to cause the trajectory to be uneven and easy to fall into local extreme values, 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, the above methods mainly deal with obstacle avoidance problems in static obstacle environments and are not suitable for dealing with collision avoidance problems between drones because they only use relative position information;

[0007] (2) In order to meet the real-time computing requirements, the consistency-based multi-UAV formation maintenance control method is mostly used to achieve multi-UAV formation reconstruction. However, this method cannot handle the multi-UAV formation reconstruction in an obstacle environment. Simply combining it with the above method will make the multi-UAV system unable to balance the stability of the formation and the safety of the system.

[0008] In summary, the problem of multi-UAV collaborative obstacle avoidance in static and dynamic obstacle environments urgently requires the proposal of an algorithm that can simultaneously take into account static and dynamic obstacle avoidance, inter-UAV collision avoidance, and formation maintenance. Summary of the invention

[0009] The purpose of the present invention is to consider the problems of obstacle avoidance, collision avoidance between aircrafts and formation maintenance of static and dynamic obstacles, and propose a multi-UAV collaborative obstacle avoidance method for static and dynamic obstacles, aiming to enable multiple UAVs to safely and quickly avoid static and dynamic obstacles and complete the reconstruction of the formation after obstacle avoidance, overcoming the defects of the existing multi-UAV collaborative obstacle avoidance algorithm, and being able to take into account obstacle avoidance, collision avoidance between aircrafts and formation maintenance at the same time.

[0010] In order to solve the above technical problems, the present invention discloses a multi-UAV collaborative obstacle avoidance method for static and dynamic obstacles, comprising:

[0011] Step 1, internal information transmission between multiple drones, including: multiple drones detect the surrounding environment through visual sensors to determine whether to avoid obstacles and transmit their own motion state information and obstacle information to surrounding drones;

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

[0013] Step 3, designing a collision avoidance controller and a formation keeping controller between multiple UAVs, including designing a collision avoidance controller and a formation keeping controller between UAVs according to the relative position information and relative speed information between the UAVs;

[0014] Step 4, designing a driving controller for multiple UAVs to fly to the target area, including: designing a UAV driving controller according to the relative position information between the UAVs and the target point;

[0015] Step 5: Based on the obstacle avoidance controller designed in step 2, the collision avoidance controller between aircraft and the formation keeping controller in step 3, and the drive controller designed in step 4, a collaborative obstacle avoidance controller is calculated and the corresponding UAVs are controlled to achieve safe obstacle avoidance and rapid formation reconstruction while each UAV flies to the target area.

[0016] Furthermore, the internal information transmission between multiple drones described in step 1 specifically includes:

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

[0018] The multi-UAV system is in the same wireless LAN and can send its position, speed and detected obstacle information to neighboring UAVs;

[0019] The communication topology of multiple UAVs is set to a random undirected graph, that is, information can be exchanged between adjacent UAVs.

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

[0021] Step 2-1, define the multi-UAV to include N quad-rotor UAVs, N ≥ 1. Considering that the multi-UAV system mainly focuses on the consistency of position and speed, the i-th UAV model satisfies:

[0022]

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

[0024] Step 2-2, set the obstacle detection area of ​​drone i to a fan-shaped area, and its fixed point is the current horizontal position p of drone i i =[p xi ,p yi ] T , where p xi represents the x-axis position of the i-th drone, p yi Indicates the y-axis position of the i-th drone, and the detection radius is r d , the direction from the vertex of the detection area to the midpoint of the arc and the flight direction of the drone i The angle is ±θ p ;

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

[0026]

[0027] Then drone i needs to avoid obstacles, where K is the number of obstacles in the current field of view of drone i, o k =[o xk ,o yk ,o rk ] T is the position information vector of 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 Indicates the y-axis position of the kth obstacle.

[0028] Furthermore, the calculation of the obstacle avoidance potential field function and the rotation potential field function of each UAV in step 2 includes: according to the relative position information of the kth obstacle and the ith UAV, and combined with the current speed information of the ith UAV, the following obstacle avoidance potential field function Γ1(p i ,o k ):

[0029]

[0030] in, is the constant gain matrix, r a =r d +o rk , the above formula is about p i The gradient of can produce the following obstacle avoidance traction:

[0031]

[0032] in, is the gradient operator symbol,

[0033] When the other traction force of UAV i is equal to the obstacle avoidance control force, it will fall into a state of flutter and jam, making it unable to move. In order to avoid the problem of UAV falling into local minimum and flutter, a new type of rotating potential field function Γ2(p i ,o k )as follows:

[0034]

[0035] Along the rotation potential field function Γ2(p i ,o k ) can obtain the rotational traction for:

[0036]

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

[0038]

[0039] Among them, r s The safe distance of the drone means that there should be no obstacles within this range.

[0040] Furthermore, the calculation of the obstacle avoidance controller of each UAV in step 2 includes: the distributed obstacle avoidance controller of the i-th UAV is designed as:

[0041]

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

[0043] Further, the design of the UAV collision avoidance controller and formation keeping controller in step 3 includes:

[0044] According to the relative motion state between the UAVs, the collision avoidance control law of each UAV is designed based on the damped Hooke's law:

[0045]

[0046]

[0047] Among them, f c Indicates the collision avoidance control strength generated by the relative position, is the gain constant, p ji =p j -p i Table and v ji =v j -v i Respectively represent the relative position and relative speed between UAVs, r s is 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 desired formation E i (t) is:

[0051]

[0052] Among them, e pi (t),e vi (t) are the expected position and expected speed of the ith UAV respectively;

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

[0054]

[0055] in, and is the gain matrix, represents the set of real numbers; represents the differential of the desired formation speed of the i-th UAV; w ij is the communication topology weight between UAVs.

[0056] The communication topology weight w between drones ij , that is, when UAV i and UAV j are neighbors, w ij =w ji =1, otherwise w ij =0.

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

[0058] Define the location information of the target area as g = [g x ,g y ] T According to the relative position information between the i-th UAV and the target area, design the driving controller for the i-th UAV to fly to the target area for:

[0059]

[0060] Designing a driving controller for multiple UAVs to fly to the target area can enable multiple UAVs to reach the target area with a shorter path. At the same time, during the formation reconstruction process, multiple UAVs will not fall into local extremes and fail to reach the desired target position.

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

[0062] According to the designed step 2 obstacle avoidance controller, step 3 inter-machine collision avoidance controller, formation keeping controller, and step 4 driving controller, the corresponding UAVs are controlled. The following variables are selected as the collaborative obstacle avoidance controller of the i-th UAV:

[0063]

[0064] Among them, U i (t) represents the control command of the position subsystem of the i-th UAV, and are the weights of obstacle avoidance control, driving control and inter-machine collision control, respectively. represents the distributed obstacle avoidance controller of the i-th UAV at time t, represents the formation holding 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.

[0065] Furthermore, the corresponding UAVs are controlled according to the designed collaborative obstacle avoidance controller, so that each UAV can safely avoid obstacles and quickly reconstruct its formation in static and dynamic obstacle environments.

[0066] Beneficial effects:

[0067] (1) The designed calculation amount is low, which meets the real-time requirements of online computing. In order to avoid the situation where the potential field force is too large and the trajectory is not smooth, the UAV's own speed is introduced to design the obstacle avoidance potential field function. At the same time, in order to avoid falling into the local extreme value situation, the rotation potential field function is introduced to accelerate the UAV to leave the undesirable area. In order to prevent the UAV from colliding with surrounding UAVs while avoiding obstacles, the inter-UAV collision avoidance controller is designed based on the relative speed information of the UAVs, so that multiple UAVs can avoid external static and dynamic obstacles while not colliding with surrounding neighboring UAVs.

[0068] (2) The designed obstacle avoidance controller and inter-machine collision avoidance control are well compatible with the formation maintenance control based on consistency theory, and the designed obstacle avoidance controller, inter-machine collision avoidance controller, formation controller and drive controller are coupled to the control layer of the UAV to improve the solution efficiency. Under the action of the four control items of the present invention, the safety of the system can be guaranteed for multiple UAVs in static and dynamic obstacle environments, and they will quickly return to the desired formation after completing obstacle avoidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings are only used to illustrate preferred implementation modes and are not to be considered as limitations of the present invention.

[0070] Figure 1 is a flow chart of the method disclosed in the embodiment of the present invention;

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

[0072] Figure 3 It is a simulation process diagram and a horizontal position trajectory diagram in the Gazebo simulator disclosed in an embodiment of the present invention;

[0073] Figure 4 is a horizontal velocity trajectory diagram of 8 UAVs disclosed in an embodiment of the present invention;

[0074] Figure 5 Schematic diagram of the mutual distances between eight drones disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The embodiment of the present invention aims at the problem that the existing system cannot effectively control multiple UAVs to stably and safely realize formation reconstruction in a multi-obstacle environment. By designing an obstacle avoidance and collision avoidance algorithm that is compatible with the consistency theory and can fully use the relative information between UAVs, the smoothness and safety of the trajectories of multiple UAVs are ensured. The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0076] The present invention provides a multi-UAV collaborative obstacle avoidance method for static and dynamic obstacles. Figure 1 As shown, the specific steps include:

[0077] Step 1, internal information transmission between multiple drones, including: multiple drones detect the surrounding environment through visual sensors to determine whether to avoid obstacles and transmit their own motion state information and obstacle information to surrounding drones; specifically including:

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

[0079] The multi-UAV system is in the same wireless LAN and can send its position, speed and detected obstacle information to neighboring UAVs;

[0080] The communication topology of multiple UAVs is set to a random undirected graph, that is, information can be exchanged between adjacent UAVs.

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

[0082] Step 2, the design of the UAV obstacle avoidance controller includes: determining whether the UAV needs to avoid obstacles, and when obstacle avoidance is needed, calculating the obstacle avoidance potential field function and rotation potential field function of each UAV according to the relative position information of the obstacle and the UAV and the current speed 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 to include N quad-rotor UAVs, N ≥ 1. Considering that the multi-UAV system mainly focuses on the consistency of position and speed, the i-th UAV model satisfies:

[0084]

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

[0086] Step 2-2, set the obstacle detection area of ​​drone i to a fan-shaped area, and its fixed point is the current horizontal position p of drone i i =[p xi ,p yi ] T , where p xi represents the x-axis position of the i-th drone, p yi Indicates the y-axis position of the i-th drone, and the detection radius is r d , the direction from the vertex of the detection area to the midpoint of the arc and the flight direction of the drone i The angle is ±θ p ;

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

[0088]

[0089] Then drone i needs to avoid obstacles, where K is the number of obstacles in the current field of view of drone i, o k =[o xk ,o yk ,o rk ] T is the position information vector of 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 Indicates the y-axis position of the kth obstacle;

[0090] Step 2-3: When obstacle avoidance is required, the following obstacle avoidance potential field function Γ1(p i ,o k ):

[0091]

[0092] in, is the constant gain matrix, r a =r d +ork , the above formula is about p i The gradient of can produce the following obstacle avoidance traction

[0093]

[0094] in, is the gradient operator symbol,

[0095] When the other traction force of UAV i is equal to the obstacle avoidance control force, it will fall into a state of flutter and jam, making it unable to move. In order to avoid the problem of UAV falling into local minimum and flutter, a new type of rotating potential field function Γ2(p i ,o k )as follows:

[0096]

[0097] Along the rotation potential field function Γ2(p i ,o k ) can obtain the rotational traction for:

[0098]

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

[0100]

[0101] Among them, r s is the safe distance of the UAV, indicating that no obstacles can appear within this range. Then the distributed obstacle avoidance controller of the i-th UAV is Designed for:

[0102]

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

[0104] Step 3, designing a collision avoidance controller and a formation keeping controller between multiple UAVs, including designing a collision avoidance controller and a formation keeping controller between UAVs according to the relative position information and relative speed information between the UAVs;

[0105] According to the relative motion state between UAVs, the collision avoidance controller of each UAV is designed based on Hooke's law with damping.

[0106]

[0107] Among them, f c Indicates the collision avoidance control strength generated by the relative position, is the gain constant, p ji =p j -p i and v ji =v j -v i Respectively represent the relative position and relative speed between UAVs, r s is 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 desired formation E i (t) is:

[0111]

[0112] Among them, e pi (t),e vi (t) are the expected position and expected speed of the ith UAV respectively;

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

[0114]

[0115] in, and is the gain matrix, represents the set of real numbers; represents the differential of the desired formation speed of the i-th UAV; w ij is the communication topology weight between UAVs.

[0116] The communication topology weight w between drones ij , that is, when UAV i and UAV j are neighbors, w ij =w ji =1, otherwise w ij =0.

[0117] Step 4, designing a driving controller for multiple UAVs to fly to the target area, including: designing a UAV driving controller based on the relative position information between the UAVs and the target point; specifically including:

[0118] The location information of the target area is defined as g = [g x ,g y ] T According to the relative position information between the i-th UAV and the target area, design the driving 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 collision avoidance controller between drones and the formation keeping controller in step 3, and the drive controller designed in step 4, a collaborative obstacle avoidance controller is calculated and the corresponding drones are controlled to achieve safe obstacle avoidance and rapid formation reconstruction while each drone flies to the target area. Specifically, it includes:

[0121] According to the designed step 2 obstacle avoidance controller, step 3 inter-machine collision avoidance controller, formation keeping controller, and step 4 driving controller, the corresponding UAVs are controlled. The following variables are selected as the collaborative obstacle avoidance controller of the i-th UAV:

[0122]

[0123] Among them, U i (t) represents the control command of the position subsystem of the i-th UAV, and are the weights of obstacle avoidance control, driving control and inter-machine collision control, respectively. represents the distributed obstacle avoidance controller of the i-th UAV at time t, represents the formation holding 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.

[0124] The designed collaborative obstacle avoidance controller controls the corresponding UAVs to achieve safe obstacle avoidance and rapid formation reconstruction of each UAV in static and dynamic obstacle environments.

[0125] like Figure 2 As shown, the cooperative obstacle avoidance controller of the i-th UAV is used to control the UAV, so that each UAV can safely avoid obstacles and quickly reconstruct its formation in static and dynamic obstacle environments.

[0126] In summary, the embodiment of the present invention considers a cluster composed of four-rotor drones, and accordingly designs a collaborative obstacle avoidance controller for multiple drones through the method of this embodiment, so that multiple drones can safely and smoothly fly to the target point in static and dynamic environments and quickly return to the desired formation after avoiding obstacles. Therefore, the embodiment of the present invention provides a solution that meets practical needs and is easy to implement for the "unmanned cluster collaborative obstacle avoidance problem".

[0127] Example:

[0128] In this embodiment, a multi-UAV system is composed of 8 quad-rotors, and the UAVs are equipped with dual-light micro-pod visual sensors and wireless image transmission self-organizing network modules. The desired formation is set to a fixed cone formation. There are 10 static obstacles and 2 dynamic obstacles in the flight environment. The weights of the controllers are and The weight of the controller can be increased or decreased according to the needs. i ,o k ) is set to the constant gain matrix The x-axis and y-axis can be increased or decreased according to the obstacle avoidance requirements. Obstacle Avoidance Controller The constant gain in Set to 1, 1 respectively, collision avoidance controller The gain constant in Set to 50, 4 respectively, formation holding controller The gain matrix in Set to The results of the collaborative obstacle avoidance process of multiple drones in the Gazebo simulator are as follows: Figure 3-5 As shown, Figure 3 The horizontal position trajectories of 8 UAVs and 2 dynamic obstacles are given, where the solid sphere is the starting position of the mission UAV, and the five-pointed star is the starting position of the 2 dynamic obstacles. When no obstacles are encountered, the 8 UAVs maintain cone formation flight. Figure 3 The circles in the upper part of the partial figure at t=22s are dynamic obstacles. Combined with the horizontal trajectory, it can be seen that the 8 drones effectively avoided the static and moving obstacles in front; Figure 4 The horizontal velocity trajectories of 8 UAVs and 2 dynamic obstacles in this process are given. is the horizontal velocity of the dynamic obstacle, v x ,v yis the average of the horizontal speeds of the eight drones. The upper and lower bounds of the shaded part represent the maximum and minimum speeds of the multiple drones. The figure reflects the speed fluctuations of the multiple drones during the obstacle avoidance process. It can be seen that the speed fluctuations of the multiple drones during the obstacle avoidance process are small, that is, the method of the present invention is conducive to maintaining the integrity of the system. In addition, it can be seen that the time for the speed to stabilize after obstacle avoidance is about 4 seconds, and the formation of the original task can be completed; Figure 5 The distances between multiple UAVs are given, and it can be seen that the multiple UAVs have made a good balance between obstacle avoidance, collision avoidance, and formation maintenance, and effectively achieved safe flight and formation reconstruction of multiple UAVs under static and dynamic obstacles.

[0129] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium can store a computer program, and when the computer program is executed by the data processing unit, the invention content of a multi-UAV cooperative obstacle avoidance method for static and dynamic obstacles provided by the present invention and some or all steps in each embodiment can be run. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0130] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of computer programs and their corresponding general hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention are essentially or partly contributed to the prior art can be embodied in the form of a computer program, i.e., a software product, which can be stored in a storage medium and includes several instructions for enabling a device including a data processing unit (which can be a personal computer, a server, a single-chip microcomputer, a MUU or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0131] The present invention provides a multi-UAV collaborative obstacle avoidance method for static and dynamic obstacles. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A multi-UAV collaborative obstacle avoidance method for static and dynamic obstacles, characterized in that: include: Step 1, internal information transmission between multiple drones, including: multiple drones detect the surrounding environment through visual sensors to determine whether to avoid obstacles and transmit their own motion state information and obstacle information to surrounding drones; Step 2, design of the obstacle avoidance controller of the UAV, including: judging whether the UAV needs to avoid obstacles according to the relative position information of the obstacle and the UAV and the current speed information of the UAV, calculating the obstacle avoidance potential field function and rotation potential field function of each UAV when obstacle avoidance is needed, and calculating the obstacle avoidance controller of each UAV along the position gradient direction; Step 3, designing a collision avoidance controller and a formation keeping controller between multiple UAVs, including: designing a collision avoidance controller and a formation keeping controller between UAVs according to the relative position information and relative speed information between the UAVs; Step 4, designing a driving controller for multiple UAVs to fly to the target area, including: designing a UAV driving controller according to the relative position information between the UAVs and the target point; Step 5: Based on the obstacle avoidance controller designed in step 2, the collision avoidance controller between aircraft and the formation keeping controller in step 3, and the drive controller designed in step 4, a collaborative obstacle avoidance controller is calculated and the corresponding UAVs are controlled to achieve safe obstacle avoidance and rapid formation reconstruction while each UAV flies to the target area.

2. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 1, characterized in that: The internal information transmission between multiple drones described in step 1 specifically includes: each drone is equipped with a self-organizing network module; the multi-drone system is in the same wireless local area network and sends its position, speed and detected obstacle information to neighboring drones; the communication topology of the multiple drones is set to a random undirected graph, that is, adjacent drones can exchange information.

3. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 2, characterized in that: In step 2, the drone obstacle avoidance controller is designed to determine whether the drone needs to avoid obstacles, including: Step 2-1, define the multi-UAV to include N quad-rotor UAVs, N ≥ 1. Considering that the multi-UAV system focuses on the consistency of position and speed, the i-th UAV model satisfies: Among them, 1≤i≤N, represents the differential signal of the position of the i-th UAV at time t, v i (t) represents the speed of the i-th UAV at time t; represents the differential speed signal of the ith UAV at time t, U i (t) represents the control command of the position subsystem of the i-th UAV at time t; Step 2-2, set the obstacle detection area of ​​drone i to a fan-shaped area, and its fixed point is the current horizontal position p of drone i i =[p xi ,p yi ] T , where p xi represents the x-axis position of the i-th drone, p yi Indicates the y-axis position of the i-th drone, and the detection radius is r d , the direction from the vertex of the detection area to the midpoint of the arc and the flight direction of the drone i The angle is ±θ p ; When there is an obstacle o within the detection range of drone i k ,k=1,2,...,K, so that Then drone i needs to avoid obstacles, where K is the number of obstacles in the current field of view of drone i, o k =[o xk ,o yk ,o rk ] T is the position information vector of 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 Indicates the y-axis position of the kth obstacle.

4. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 3, characterized in that: The calculation of the obstacle avoidance potential field function and the rotation potential field function of each UAV in step 2 includes: according to the relative position information of the kth obstacle and the ith UAV, and combined with the current speed information of the ith UAV, the following obstacle avoidance potential field function Γ1(p i ,o k ): in, is the constant gain matrix, r a =r d +o rk , the above formula is about p i The gradient of produces the following obstacle avoidance traction in, is the gradient operator symbol, When the other traction force of UAV i is equal to the obstacle avoidance control force, it will fall into a state of buffeting and jamming, making it unable to move. In order to avoid the UAV from falling into local minima and buffeting, a new type of rotating potential field function Γ2(p i ,o k )as follows: Along the rotation potential field function Γ2(p i ,o k ) to obtain the rotational traction for: Among them, T r is a rotation matrix and The rotation angle α is defined as: Among them, r s It is the safe distance of the drone, which means that no obstacles can appear within this range.

5. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 4, characterized in that: The obstacle avoidance controllers for each UAV calculated in step 2 include: the distributed obstacle avoidance controller of the i-th UAV Designed for: in, is the constant gain, v i is the current speed of drone i.

6. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 5, characterized in that: The design of multi-UAV collision avoidance controller in step 3 includes: According to the relative motion state between UAVs, the collision avoidance controller of each UAV is designed based on Hooke's law with damping. Among them, f c Indicates the collision avoidance control strength generated by the relative position, is the gain constant, p ji =p j -p i and v ji =v j -v i Respectively represent the relative position and relative speed between UAVs, r s It is the safe distance for drones.

7. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 6, characterized in that: The design of the multi-UAV formation holding controller in step 3 specifically includes: Define the horizontal motion state vector Z of the i-th UAV at time t i (t) is: Horizontal desired formation E i (t) is: Among them, e pi (t), e vi (t) are the expected position and expected speed of the ith UAV respectively; According to the desired formation E i (t) and the relative state information between the UAVs, the formation keeping controller of each UAV at time t is calculated based on the consistency theory for: in, and is the gain matrix, represents the set of real numbers; represents the differential of the desired formation speed of the i-th UAV; w ij is the communication topology weight between UAVs.

8. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 7, characterized in that: The communication topology weight w between UAVs ij , that is, when UAV i and UAV j are neighbors, w ij =w ji =1, otherwise w ij =0.

9. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 8, characterized in that: The multi-drone drive controller design in step 4 includes: Define the location information of the target area as g = [g x ,gy] T According to the relative position information between the ith UAV and the target area, a driving controller is designed to fly the ith UAV to the target area at time t. for:

10. The method for multi-UAV collaborative obstacle avoidance facing static and dynamic obstacles according to claim 9, characterized in that: Step 5 includes: According to the designed distributed obstacle avoidance controller, inter-machine collision avoidance controller, formation keeping controller and driving controller flying to the target area, the following variables are selected as the collaborative obstacle avoidance controller of the i-th UAV: Among them, U i (t) represents the control command of the position subsystem of the i-th UAV, and are the weights of obstacle avoidance control, driving control and inter-machine collision control, respectively. represents the distributed obstacle avoidance controller of the i-th UAV at time t, represents the formation holding 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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