1. A distributed unmanned aerial vehicle queue and dynamic obstacle avoidance control method is characterized in that a Transverse Feedback Linkage (TFL) method is adopted to conduct differential homoembryo mapping transformation on a dynamic model of a quad-rotor unmanned aerial vehicle, the dynamic model is decomposed into a tangential subsystem along the direction of an expected path and a Transverse subsystem intersected with the expected path, then nonsingular fast terminal sliding mode controllers are respectively designed in the Transverse subsystems to enable the motion of the unmanned aerial vehicle to be converged on the expected path, and a coordination and obstacle avoidance control algorithm combining consistency and a potential field method is designed in the tangential subsystems, so that the collaborative cruise tasks of multiple unmanned aerial vehicles and dynamic obstacle avoidance in the cruise process are achieved.

2. The distributed unmanned aerial vehicle queue and dynamic obstacle avoidance control method as claimed in claim 1, which is characterized by comprising the following steps:

defining a ground inertial coordinate system { I }, and a body coordinate system { B } of the ith quad-rotor unmanned aerial vehicle in the queue⁽ⁱ⁾And simultaneously defining the position p of the ith four-rotor unmanned plane under an inertial coordinate system (I)⁽ⁱ⁾＝[x⁽ⁱ⁾,y⁽ⁱ⁾,z⁽ⁱ⁾]^TAnd linear velocity vectorWith the above definition, a dynamics model of a quad-rotor drone can be established as follows:

m in the above formula⁽ⁱ⁾The mass of the ith unmanned aerial vehicle, g is the gravity acceleration,as magnitude of total lift, e_z＝[0,0,1]^TIs a unit vector in the positive direction of the Z axis, R⁽ⁱ⁾(t) is the body coordinate system { B⁽ⁱ⁾Considering the cooperative flight process of multiple unmanned aerial vehicles, the attitude change of each unmanned aerial vehicle is stable, and a centered dynamic model is simplified as follows to define virtual control input:

the dynamic model in (1) is simplified to

Wherein the state vectorI₃An identity matrix representing a 3 × 3 sense;

when a TFL method is used for researching a path tracking problem, a proper mapping is required to be selected according to an expected path, a motion system is decomposed into different subsystems to be respectively designed by a controller, and an expected cruise path equation delta is given as follows:

in the above formula, a smooth self-intersection-free closed path in a three-dimensional space is determined, wherein r,for the path parameters, the virtual output equation of the quad-rotor unmanned aerial vehicle system is selected as

Designing auxiliary functions of gamma (xi) and pi (xi) as

WhereinFor the number of turns, theta, of the unmanned aerial vehicle passing along the desired path_δ2 π r as closedThe perimeter of the path is such that,note the bookIs composed ofThe ith component of (a), r_iIs composed ofRelative steps of, respectively toR is obtained_iThe Lie derivative, which takes the form:

in the above formula, K (xi) and L (xi) are TFL input transformation matrix, when r is_iSatisfy the requirement ofThe time L (xi) is reversible, so that a decoupling control law v can be designed to realize decoupling control, and the decoupling control is realized according to virtual outputConstructing differential homoembryo map T⁽ⁱ⁾:The kinetic model was transformed into the following form:

whereinCoefficient matrices for the respective subsystems, said being tangential subsystems, their states η⁽ⁱ⁾(t) represents the state of motion of the drone on the desired cruise path; is a transverse subsystem, its state ζ⁽ⁱ⁾(t) deviation of the drone from the desired cruise path, ζ being set by the design controller⁽ⁱ⁾(t) converging to zero to achieve the control target that the position of the unmanned aerial vehicle converges to the expected path;

for a transverse subsystem, designing a nonsingular fast terminal sliding mode controller, and firstly defining a state error vector for the ith unmanned aerial vehicle in a queueWhereinDesign of slip form surface S⁽ⁱ⁾Is composed of

Wherein alpha and beta are normal numbers, p and q are positive odd numbers and satisfy 2 > q/p > 1, and a control law is designed

Wherein isThe gain is controlled by a positive constant, and,the following conclusions can be drawn from the lyapunov stability analysis method: under the action of a control law, the state error of the transverse subsystem is converged to zero, namely, each unmanned aerial vehicle can be converged to an expected cruise path;

for tangential subsystems, this can be expressed as follows:

considering obstacle threats in the unmanned aerial vehicle queue cruising process, regarding the obstacles as cylinder models, and designing the following repulsive field function according to the radius and the position of the cylinder models:

wherein K_obIs a positive gain coefficient of a repulsive field and represents the magnitude of the field intensity r_safeIs the safe zone radius of the obstacle, r⁽ⁱ⁾＝||Γ(p_ob)-Γ(p⁽ⁱ⁾) | l, take k at the same time_δ≡ 0 denotes the relative distance between the obstacle and the i-th drone in the desired path direction, d⁽ⁱ⁾For the spatial distance between the ith unmanned aerial vehicle and the obstacle, for the ith unmanned aerial vehicle in the queue, a distributed obstacle avoidance control law v is designed^||(i)(t) is as follows:

wherein the desired speed in the direction of the cruising pathα_obRho is the normal coefficient, phi_i(. is an odd function and satisfies phi_i′(·)≥0，sgn(Φ_i(x) Sgn (x), which is an element of the adjacency matrix corresponding to the multi-drone communication topology, representsThe relationship of the neighbours on the topological structure,for a desired distance between adjacent drones on a desired path,the sign of the repulsion force along the expected path direction generated by the obstacle to the ith unmanned aerial vehicle in the safety region range is positive, and the repulsion force indicates the positive direction of the unmanned aerial vehicle group.

3. The distributed unmanned aerial vehicle queue and dynamic obstacle avoidance control method according to claim 1, wherein as proved by a Lyapunov stability analysis method and a Lassel invariance principle, under the action of a control law (15), when the time tends to be infinite, the tangential subsystem can converge to a given expected distance and an expected speed, and no collision with an obstacle occurs.

Background

The cooperative flight of multiple unmanned aerial vehicles has the outstanding advantages of large detection range, high load capacity, strong environment adaptability and the like, can complete complex flight tasks and improve the overall working efficiency, and becomes a research hotspot in the field of unmanned aerial vehicles in recent years. The autonomous cruise is one of basic modes of a plurality of unmanned aerial vehicles for executing a collaborative flight task, and plays an important role in application scenes such as military drilling, traffic control, regional investigation and power inspection. Therefore, the research on the cooperative cruise control of the multiple unmanned aerial vehicles has important application value.

In the process of executing the cooperative flight mission by the multiple unmanned aerial vehicles, the establishment and the maintenance of the formation are the basis for completing the mission, and the generated cooperative control problem is not only a core but also a difficult point. At present, a Leader-Follower method, a behavior-based method, a virtual structure method, a consistency method and the like are mainly used as a formation control method of multiple unmanned aerial vehicles, and many scholars at home and abroad deeply research the formation control method and obtain abundant research results. However, in the above method, the unmanned aerial vehicle cluster mostly adopts a method of relative position vector or virtual structure to form and maintain the formation, the cooperative flight is mainly realized by a given time-varying formation central function, during the actual cruising process, a plurality of unmanned aerial vehicles need to complete the cooperative flight task along the same expected path, and the relative position and speed vector among the unmanned aerial vehicles change constantly. Meanwhile, the fact of the cruise task is considered to be a path tracking problem, namely, the cruise path is a given space path which is time-invariant, the given space path does not need to be expressed in a track form with time-variant parameters, and the unmanned aerial vehicle does not need to reach a specific expected position at a certain moment, so that the design of a cruise control strategy by directly adopting a traditional multi-unmanned aerial vehicle formation mode has certain difficulty. To solve the above problems, some scholars propose a TFL (Transverse Feedback Linearization) method, which maps the system state onto a space tangent and intersecting with the desired path to realize the tracking of the moving system to the fixed path. Researchers at Suli Federal science and Technology, Switzerland researched the multi-unmanned-robot Control problem with path restriction based on the TFL method, so as to realize inter-machine cooperation on the same expected path, and verify the effectiveness of the designed controller through physical experiments (Conference: IEEE Conference on Control technologies and Applications; Rev: Sun J W, Gill R; published New year and month: 2019; article title: Vehicle plant Control with virtual path controls; page number: 456-461).

In the cooperative cruising process of multiple unmanned aerial vehicles, not only the mutual cooperation inside the queue needs to be considered, but also the inconvenience brought to the task execution by the complex surrounding environment and the emergency needs to be dealt with, wherein the threat of the external obstacle is an important factor influencing the smooth execution of the flight task. Therefore, effective anti-collision and obstacle avoidance strategies are the premise and key for guaranteeing flight safety. Some scholars of the university of Damascus in America study the clustering behavior of a multi-agent system (multi-agent system), design a nonlinear consistency protocol to realize clustering convergence and obstacle avoidance of the multi-agent system, and give out related theoretical proof that the effectiveness of the algorithm is verified through simulation experiments (journal: IEEE Transactions on Automatic Control; author: R.Olfati-Saber; published year and month: 2006; article title: packaging for multi-agent dynamic systems: algorithms and the order; page: 401-. The research team of the university of qing dynasty researches the multi-unmanned aerial vehicle formation anti-collision problem based on the artificial potential field method, adds an obstacle avoidance item into a consistency controller, proves the stability of a Control algorithm, and verifies the obstacle avoidance effect through numerical simulation, but adopts a strategy of performing inter-machine collision Control only in height without considering actions in other directions (Conference: American Control Conference; author: Kuriki Y, Namerikawa T; published year and month: 2014; article title: present-based cooperative Control with contract availability for a multi-UAV system; page number: 2077 and 2082). A research team of Stanford university in America adopts a vector field driving method to achieve avoidance of static obstacles in the flying process of a multi-unmanned aerial vehicle cluster, a multi-unmanned aerial vehicle cluster is regarded as a virtual rigid body structure, translation, rotation and structural transformation of the virtual rigid body are controlled by manipulating a handle connected with a ground station, and meanwhile, the fact that a single unmanned aerial vehicle in the movement of the virtual rigid body can avoid environmental obstacles is guaranteed (journal: IEEE Transactions on Robotics; author: Zhou DJ, Wang ZJ, Schwager M; published year: 2018 month; article title: Agile coordination and existence compatibility for rotor swinging virtual structure; page number: 916-. The document adopts a distributed control mode, the vertex unmanned aerial vehicles in the virtual rigid body do not cooperate with each other, formation is only formed by a position vector which keeps fixed with the center of the rigid body, and related theoretical proof is lacked. Some scholars also adopt a mathematical programming method to enable the unmanned aerial vehicle to autonomously decide a track to avoid the obstacle when encountering the obstacle. The method can guarantee flight safety when obstacles exist to a certain extent, but has high requirements on the tracking performance of the unmanned aerial vehicle, and has the problems of large calculation amount for solving an optimization equation, difficulty in guaranteeing real-time performance and the like when environmental and dynamic constraint conditions are complex, and the stability is difficult to prove.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide a multi-unmanned aerial vehicle cooperative control and obstacle avoidance algorithm based on a sliding mode and a consistency protocol, so that cooperative obstacle avoidance on an unmanned aerial vehicle cluster cruise task and a cruise path is realized. Therefore, the technical scheme adopted by the invention is that a distributed unmanned aerial vehicle queue and dynamic obstacle avoidance control method adopts a Transverse Feedback Linkage (TFL) method to carry out differential homomorphic mapping transformation on a dynamic model of a quad-rotor unmanned aerial vehicle so as to decompose the dynamic model into a tangential subsystem along an expected path direction and a Transverse subsystem intersected with the expected path, then nonsingular fast terminal sliding mode controllers are respectively designed in the Transverse subsystems so as to enable the motion of the unmanned aerial vehicle to be converged to the expected path, and a coordination and obstacle avoidance control algorithm combining consistency and a potential field method is designed in the tangential subsystems so as to realize the collaborative cruise tasks of multiple unmanned aerial vehicles and the dynamic obstacle avoidance in the cruise process.

The method comprises the following specific steps:

defining a ground inertial coordinate system { I }, and a body coordinate system { B } of the ith quad-rotor unmanned aerial vehicle in the queue⁽ⁱ⁾And simultaneously defining the position p of the ith four-rotor unmanned plane under an inertial coordinate system (I)⁽ⁱ⁾＝[x⁽ⁱ⁾,y⁽ⁱ⁾,z⁽ⁱ⁾]And linear velocity vectorWith the above definition, a dynamics model of a quad-rotor drone can be established as follows:

m in the above formula⁽ⁱ⁾The mass of the ith unmanned aerial vehicle, g is the gravity acceleration,as magnitude of total lift, e_z＝[0,0,1]^TIs a unit vector in the positive direction of the Z axis, R⁽ⁱ⁾(t) is the body coordinate system { B⁽ⁱ⁾Considering the cooperative flight process of multiple unmanned aerial vehicles, the attitude change of each unmanned aerial vehicle is stable, and a centered dynamic model is simplified as follows to define virtual control input:

the dynamic model in (1) is simplified to

Wherein the state vectorI₃An identity matrix representing a 3 × 3 sense;

when a TFL method is used for researching a path tracking problem, a proper mapping is required to be selected according to an expected path, a motion system is decomposed into different subsystems to be respectively designed by a controller, and an expected cruise path equation delta is given as follows:

the above formula defines a smooth self-free closed path in three-dimensional space, whereinFor the path parameters, the virtual output equation of the quad-rotor unmanned aerial vehicle system is selected as

Designing auxiliary functions of gamma (xi) and pi (xi) as

WhereinFor the number of turns, theta, of the unmanned aerial vehicle passing along the desired path_δ2 rr is the perimeter of the closed path,note the bookIs composed ofThe ith component of (a), r_iIs composed ofRelative steps of, respectively toR is obtained_iThe Lie derivative, which takes the form:

in the above formula, K (xi) and L (xi) are TFL input transformation matrix, when r is_iSatisfy the requirement ofThe time L (xi) is reversible, so that a decoupling control law v can be designed to realize decoupling control, and the decoupling control is realized according to virtual outputConstructing differential homoembryo mapsThe kinetic model was transformed into the following form:

whereinCoefficient matrices for the respective subsystems, said being tangential subsystems, their states η⁽ⁱ⁾(t) represents the state of motion of the drone on the desired cruise path; is a transverse subsystem, its state ζ⁽ⁱ⁾(t) deviation of the drone from the desired cruise path, ζ being set by the design controller⁽ⁱ⁾(t) converging to zero to achieve the control target that the position of the unmanned aerial vehicle converges to the expected path;

for a transverse subsystem, designing a nonsingular fast terminal sliding mode controller, and firstly defining a state error vector for the ith unmanned aerial vehicle in a queueWhereinDesign of slip form surface S⁽ⁱ⁾Is composed of

Wherein alpha and beta are normal numbers, p and q are positive odd numbers and satisfy 2 > q/p > 1, and a control law is designed

Wherein isThe gain is controlled by a positive constant, and,the following conclusions can be drawn from the lyapunov stability analysis method: under the action of a control law, the state error of the transverse subsystem is converged to zero, namely, each unmanned aerial vehicle can be converged to an expected cruise path;

for tangential subsystems, this can be expressed as follows:

considering obstacle threats in the unmanned aerial vehicle queue cruising process, regarding the obstacles as cylinder models, and designing the following repulsive field function according to the radius and the position of the cylinder models:

wherein K_obIs a positive gain coefficient of a repulsive field and represents the magnitude of the field intensity r_safeIs the safe zone radius of the obstacle, r⁽ⁱ⁾＝||Γ(p_ob)-Γ(p⁽ⁱ⁾) | l, take k at the same time_δ≡ 0 denotes the relative distance between the obstacle and the i-th drone in the desired path direction, d⁽ⁱ⁾For the spatial distance between the ith unmanned aerial vehicle and the obstacle, for the ith unmanned aerial vehicle in the queue, a distributed obstacle avoidance control law v is designed⁽ⁱ⁾(t) is as follows:

wherein the desired speed in the direction of the cruising pathα_obRho is the normal coefficient, phi_i(. is an odd function and satisfies phi_i′(·)≥0，sgn(Φ_i(x) Sgn (x), which is an element of the multi-drone communication topology corresponding to the adjacency matrix, representing the adjacency relation on the topology,for a desired distance between adjacent drones on a desired path,the sign of the repulsion force along the expected path direction generated by the obstacle to the ith unmanned aerial vehicle in the safety region range is positive, and the repulsion force indicates the positive direction of the unmanned aerial vehicle group.

According to the Lyapunov stability analysis method and the Lassel invariance principle, the tangential subsystem can be converged to a given expected distance and expected speed when the time tends to be infinite under the action of the control law (15), and collision with an obstacle cannot occur.

The invention has the characteristics and beneficial effects that:

1. the invention transforms the unmanned aerial vehicle dynamic model based on the TFL method, and realizes the tracking of the expected path through decoupling control;

2. the invention combines the consistency protocol with the potential field method, so that the unmanned aerial vehicle cluster can effectively avoid dynamic obstacles while achieving synergy on an expected cruising path, and the convergence and the safety of a formation system are proved by a Lyapunov stability system analysis method and a LaSalle invariance principle;

3. the method successfully applies the algorithm to the multi-unmanned aerial vehicle cluster physical platform, designs and carries out flight experiments, and verifies the effectiveness and reliability of the proposed algorithm.

Description of the drawings:

FIG. 1 is a control system architecture for use with the present invention;

FIG. 2 is an experimental platform employed in the present invention;

FIG. 3 is a graph of spatial position of the unmanned aerial vehicle fleet operation;

fig. 4 is a graph of distance traveled by a queue of drones on a desired path;

fig. 5 is a graph of the speed at which a queue of drones travels on a desired path;

FIG. 6 is an effect diagram of an unmanned aerial vehicle queue collaborative obstacle avoidance experiment;

FIG. 7 is a graph of distance from an obstacle during a queue collaborative obstacle avoidance process for an unmanned aerial vehicle;

fig. 8 is a graph of distance traveled on a desired path during a fleet collaborative obstacle avoidance process for a drone.

Detailed Description

In order to overcome the defects of the prior art, the invention aims to design a multi-unmanned aerial vehicle cooperative control and obstacle avoidance algorithm based on a sliding mode and a consistency protocol, so as to realize cooperative obstacle avoidance on the unmanned aerial vehicle cluster cruise task and the cruise path. The technical scheme includes that a Transverse Feedback Linkage (TFL) method is adopted to conduct differential homomorphic mapping transformation on a dynamic model of the quad-rotor unmanned aerial vehicle, the dynamic model is decomposed into a tangential subsystem and a Transverse subsystem, the tangential subsystem and the Transverse subsystem are intersected with an expected path, the non-singular rapid terminal sliding mode controllers are respectively designed in the Transverse subsystems to enable the motion of the unmanned aerial vehicle to be converged on the expected path, and a coordination and obstacle avoidance control algorithm combining consistency and a potential field method is designed in the tangential subsystems, so that the multi-unmanned aerial vehicle coordinated cruise task and dynamic obstacle avoidance in the cruise process are achieved.

The method comprises the following specific steps:

defining a ground inertial coordinate system { I }, and a body coordinate system { B } of the ith quad-rotor unmanned aerial vehicle in the queue⁽ⁱ⁾And simultaneously defining the position p of the ith four-rotor unmanned plane under an inertial coordinate system (I)⁽ⁱ⁾＝[x⁽ⁱ⁾,y⁽ⁱ⁾,z^(i）]And linear velocity directionMeasurement ofWith the above definition, a dynamics model of a quad-rotor drone can be established as follows:

m in the above formula⁽ⁱ⁾The mass of the ith unmanned aerial vehicle, g is the gravity acceleration,as magnitude of total lift, e_z＝[0,0,1]^TIs a unit vector in the positive direction of the Z axis, R⁽ⁱ⁾(t) is the body coordinate system { B⁽ⁱ⁾Rotating matrix from the unmanned aerial vehicle to an inertial coordinate system { I }, considering the cooperative flight process of the unmanned aerial vehicles, enabling the attitude change of each unmanned aerial vehicle to be stable, simplifying a centered dynamic model as follows, and defining virtual control input

The dynamic model in (1) can be simplified to

Wherein the state vectorI₃An identity matrix representing a 3 × 3 sense;

when a TFL method is used for researching a path tracking problem, a proper mapping is required to be selected according to an expected path, a motion system is decomposed into different subsystems to be respectively designed by a controller, and an expected cruise path equation delta is given as follows:

the above formula defines a smooth self-free closed path in three-dimensional space, whereinFor the path parameters, the virtual output equation of the quad-rotor unmanned aerial vehicle system is selected as

Designing auxiliary functions of gamma (xi) and pi (xi) as

WhereinFor the number of turns, theta, of the unmanned aerial vehicle passing along the desired path_δ2 rr is the perimeter of the closed path,note the bookIs composed ofThe ith component of (a), r_iIs composed ofRelative steps of, respectively toR is obtained_iThe Lie derivative, which takes the form:

in the above formula, K (xi) and L (xi) are TFL input transformation matrix, when r is_iSatisfy the requirement ofThe time L (xi) is reversible, so that a decoupling control law v can be designed to realize decoupling control, and the decoupling control is realized according to virtual outputConstructing differential homoembryo mapsThe kinetic model was transformed into the following form:

whereinThe above is the tangential subsystem, the state η of which⁽ⁱ⁾(t) represents the state of motion of the drone on the desired cruise path; is a transverse subsystem, its state ζ⁽ⁱ⁾(t) deviation of the drone from the desired cruise path, ζ being set by the design controller⁽ⁱ⁾(t) converging to zero to achieve the control target that the position of the unmanned aerial vehicle converges to the expected path;

for a transverse subsystem, designing a nonsingular fast terminal sliding mode controller, and firstly defining a state error vector for the ith unmanned aerial vehicle in a queueWhereinDesign of slip form surface S⁽ⁱ⁾Is composed of

Wherein alpha and beta are normal numbers, p and q are positive odd numbers and satisfy 2 > q/p > 1, and a control law is designed

Wherein isThe gain is controlled by a positive constant, and,the following conclusions can be drawn from the Lyapunov stability analysis method: under the action of a control law, the state error of the transverse subsystem is converged to zero, namely, each unmanned aerial vehicle can be converged to an expected cruise path;

for tangential subsystems, this can be expressed as follows:

considering obstacle threats in the unmanned aerial vehicle queue cruising process, regarding the obstacles as cylinder models, and designing the following repulsive field function according to the radius and the position of the cylinder models:

wherein K_obIs a positive gain coefficient of a repulsive field and represents the magnitude of the field intensity r_safeIs the safe zone radius of the obstacle, r⁽ⁱ⁾＝||Γ(p_ob)-Γ(p⁽ⁱ⁾) | l, take k at the same time_δ≡ 0 denotes the relative distance between the obstacle and the i-th drone in the desired path direction, d⁽ⁱ⁾For the spatial distance between the ith unmanned aerial vehicle and the obstacle, for the ith unmanned aerial vehicle in the queue, a distributed obstacle avoidance control law v is designed⁽ⁱ⁾(t) is as follows:

wherein the desired speed in the direction of the cruising pathα_obRho is the normal coefficient, phi_i(. is an odd function and satisfies phi_i′(·)≥0，sgn(Φ_i(x) Sgn (x), which is an element of the multi-drone communication topology corresponding to the adjacency matrix, representing the adjacency relation on the topology,for a desired distance between adjacent drones on a desired path,the symbol of repulsion force generated by the obstacle to the ith unmanned aerial vehicle in the safe area range along the expected path direction is positive, the sign of the repulsion force indicates the positive direction of the unmanned aerial vehicle group, and the Lyapunov stability analysis method and the LaSalle invariance principle prove that under the action of the control law, when the time tends to be infinite, the tangential subsystem can converge to a given expected distance and a given expected speed, and the collision with the obstacle does not occur.

The present invention will be described in detail with reference to the following examples and drawings.

Introduction of one or more unmanned aerial vehicles in cooperation with flight experimental platform

The independently-built multi-unmanned aerial vehicle cooperative control experiment platform is shown in fig. 2, and comprises three four-rotor unmanned aerial vehicles with the wheelbase of 0.25m, wherein each unmanned aerial vehicle carries a Pixhawk flight controller and an ARM (advanced RISC machines) computing board, positioning information is obtained through a motion capture system, a designed control method is operated on the ARM computing board, a posture control instruction is sent to the Pixhawk through a serial port to complete flight control, and a ground station instruction is received through WiFi. In keeping away the barrier experiment, there is an unmanned aerial vehicle close with above-mentioned structure as dynamic barrier in addition, its motion of accessible remote controller control.

Two, many unmanned aerial vehicle flight experiments that cruise in coordination

When the experiment is started, all unmanned aerial vehicles are placed on the ground, take off to a certain height from respective initial positions, send instructions through a ground station when about 4.5 seconds, switch to a designed queue control algorithm and last for about 150 seconds. According to the field scale, the radius of the expected path is selected to be 1.5m, the height is selected to be 1.0m, the distance of the expected path between adjacent unmanned aerial vehicles in the queue is 3.14m, the queue advancing speed is set to be 0.4m/s, and the selection of control parameters is thatα ═ 3.5, β ═ 1.0, p ═ 3, q ═ 5, ρ ═ 0.1, and the nonlinear function taken as Φ₁(x)＝5.5tanh(1.5x),Φ₂(x) 3tanh (x), the multi-drone communication topology matrix isThe experimental results are shown in FIGS. 3 to 5.

As can be seen from fig. 3, the queue of drones can always converge on the set desired cruise path, the distance error of the drones along the direction of the desired path shown in fig. 4 indicates that the drones can complete the cooperative cruise mission according to the set relative path distance, and the travel speed of the drones along the desired path shown in fig. 5 indicates that the drones can travel at the set desired speed.

Three, many unmanned aerial vehicle developments are kept away and are hindered experiment

The unmanned aerial vehicle is operated in a queue mode, after the system state is stable, the obstacle unmanned aerial vehicle is manually controlled through the remote controller to penetrate through an expected path of the queue for many times, the unmanned aerial vehicle in the queue is prevented from advancing, and the experiment is carried outThe process is shown in fig. 6, where the circular arc curve is the desired path for coordinated cruising of multiple drones, and the arrow indicates the direction of motion of the drone. The barrier unmanned aerial vehicle that this group's experiment was used can be regarded as cylinder barrier type, according to actual conditions, sets for barrier radius r in the controller parameter_ob0.25m, safety radius r of the obstacle_safe1.0m, repulsive field gain factor K_ob3.5, proportionality coefficient α_obGiven the desired path and desired relative distance and speed, and the selection of the remaining controller parameters, 0.05, are the same as in the previous set of experiments, with the results shown in fig. 6 and 7. When t is 0 seconds the queue has reached a steady state, i.e. operating according to the given conditions.

Fig. 7 shows the spatial distance between the unmanned aerial vehicle and the obstacle unmanned aerial vehicle during the queue traveling process of the unmanned aerial vehicle, and it can be seen that when the relative distance is smaller than the safe radius r_obDuring the time, the unmanned aerial vehicle in the queue can keep away from the barrier, and the distance between each unmanned aerial vehicle of whole experimentation and the barrier all is greater than the barrier radius of setting for, does not bump. Fig. 8 is the distance traveled by the queue of drones along the desired path, and it can be seen that when one drone in the queue receives an obstacle obstruction, the remaining drones can cooperate to some extent with it, maintaining the given desired distance.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.