An obstacle avoidance flight control method for a full-drive variable configuration unmanned aerial vehicle

Abstract

The application provides an obstacle avoidance flight control method for a full-drive variable configuration unmanned aerial vehicle, and belongs to the technical field of flight robot control. The method comprises the following steps: designing a configuration of the full-drive variable configuration unmanned aerial vehicle and analyzing the motion ability of the full-drive variable configuration unmanned aerial vehicle; installing each rotor and a corresponding motor on the end of an arm through a tiltable steering engine, so that the rotor has a controllable tilting degree of freedom relative to the rack; performing dynamic modeling and control distribution model optimization on the full-drive variable configuration unmanned aerial vehicle; designing a PID controller for position and attitude decoupling and integrating a position loop disturbance observer and an attitude loop disturbance observer; establishing a quadratic programming problem based on a control barrier function, optimizing an obstacle avoidance calculation strategy, and avoiding collision behaviors. The application has small calculation amount, strong obstacle avoidance autonomy, can effectively guarantee collision-free safe flight of the unmanned aerial vehicle in a complex and narrow environment, and is suitable for post-disaster search, cave exploration and inclined shaft inspection and the like.

Description

Technical Field

[0001] This invention belongs to the field of flight robot control technology, specifically relating to an obstacle avoidance flight control method for a fully driven variable configuration unmanned aerial vehicle. Background Technology

[0002] In recent years, small unmanned aerial vehicles (UAVs) using multi-rotor propulsion mechanisms have been widely used in disaster early warning, geological exploration, and other fields. However, when performing tasks in complex and confined environments, conventional rotary-wing UAVs are limited by their own structure and control methods, and face a significant risk of collision with surrounding obstacles during flight. This not only affects the efficiency of mission execution but may also lead to safety accidents such as crashes.

[0003] To address the aforementioned issues, existing technologies typically employ offline or online trajectory planning methods. This involves generating obstacle-avoidance flight trajectories in advance or in real-time, and then controlling the drone to follow these trajectories to achieve obstacle avoidance. In environments with spatial scales close to or even smaller than the drone's own size (such as narrow frames), existing solutions utilize special trajectory planning methods to enable lateral displacement of the drone during flight. By leveraging the coupling relationship between lateral displacement and attitude, the drone is induced into a tilted attitude, thereby achieving passage through narrow frames.

[0004] However, such methods are still based on the underactuated structure of traditional quadcopter UAVs, and their attitude adjustment depends on position changes, making it impossible to achieve independent control of position and attitude. This structural and control limitation significantly restricts the maneuverability and flight safety of UAVs when facing complex environments with higher requirements for coordinated attitude and position control, such as narrow passages and continuous obstacles.

[0005] With the development of fully-driven variable-configuration UAVs, increasing the actuation degrees of freedom has provided new possibilities for independent control of position and attitude. However, most existing obstacle avoidance schemes for fully-driven variable-configuration UAVs still use a control framework that combines trajectory planning and trajectory tracking. Under this framework, the unavoidable tracking error during trajectory tracking directly affects the obstacle avoidance performance, thereby reducing flight safety. To reduce this type of error, some schemes adopt online real-time trajectory planning, but this not only significantly increases the computational load and places stringent requirements on onboard computing resources and real-time performance, but even so, tracking errors are still difficult to completely eliminate, and their impact on obstacle avoidance performance still exists.

[0006] Furthermore, when flying in complex environments, drones are inevitably affected by external disturbances, and existing control methods still have shortcomings in terms of disturbance rejection performance and control accuracy. At the same time, most obstacle avoidance schemes focus on trajectory planning and tracking, lacking a mechanism for real-time safety constraints on the flight state itself. When the environment changes or control errors accumulate, the risk of collision still exists.

[0007] Therefore, existing technologies still lack an effective solution that can fully leverage the structural advantages of fully driven UAVs, while balancing control precision and anti-interference performance, and imposing safety constraints on the flight process, thereby enabling UAVs to fly safely and flexibly in complex and confined environments. Summary of the Invention

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] A method for obstacle avoidance flight control of a fully driven variable-configuration unmanned aerial vehicle, comprising:

[0010] Step 1: Design the configuration of the fully driven variable configuration UAV and analyze its motion capabilities; install each rotor and its corresponding motor at the end of the arm via tiltable servos, so that the rotor has a controllable tilting degree of freedom relative to the frame.

[0011] Step 2: Perform dynamic modeling and control allocation model optimization for the fully driven variable configuration UAV;

[0012] Step 3: Design a PID controller that decouples position and attitude and integrate a position loop disturbance observer and an attitude loop disturbance observer;

[0013] Step 4: Based on the obstacle control function, establish a quadratic programming problem, optimize the obstacle avoidance calculation strategy, and avoid collision behavior.

[0014] The present invention has the following beneficial effects:

[0015] (1) By selecting an appropriate fully driven variable configuration unmanned vehicle configuration, the present invention effectively breaks through the limitations of the underactuated structure of traditional quadcopter UAVs and solves the problem of insufficient maneuverability of traditional quadcopter UAVs in narrow and complex environments.

[0016] (2) This invention establishes a dynamic model of a fully driven variable configuration UAV and optimizes the control allocation model to construct a control allocation optimization model, thereby realizing the optimal allocation of control quantities under the condition of multiple actuator redundancy and solving the problem of real-time optimization of control allocation under low computing power conditions;

[0017] (3) This invention significantly improves the control accuracy and anti-interference capability of UAVs during flight by designing a PID controller with decoupled position loop and attitude loop and introducing an interference observer to compensate for external disturbances, thus solving the problem of insufficient flight stability and anti-interference performance in complex environments.

[0018] (4) This invention reduces the risk of collision caused by tracking error by introducing a control obstacle function to perform safety filtering on the control quantity output by the PID controller, realizes real-time safety constraints on the flight state of the fully driven variable configuration UAV under low computing power conditions, and solves the problem of high collision risk of UAV in complex and narrow environments. Attached Figure Description

[0019] Figure 1 This is a flowchart of the obstacle avoidance flight control method for the fully driven variable configuration UAV of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0021] This invention provides an obstacle avoidance flight control method for a fully driven variable-configuration unmanned aerial vehicle (UAV). In terms of structural hardware, the algorithm is based on a fully driven variable-configuration UAV with six degrees of freedom of motion. By adding rotor joints, the rotor can generate lift that is not perpendicular to the frame plane, thus overcoming the underactuated characteristics of traditional rotorcraft UAVs. Regarding the control algorithm, this invention employs an optimized control allocation scheme, ensuring a minimum energy allocation from virtual control quantities of force and torque to actual control quantities of motor speed and servo angle. In terms of the obstacle avoidance algorithm, this invention uses obstacle control functions as its theoretical basis, divides the safe zone into regions using a plane, and merges constraints into single constraints using intersection and union methods, thus forming a lightweight and autonomous obstacle avoidance flight control method.

[0022] like Figure 1 As shown, the obstacle avoidance flight control method for the fully driven variable configuration UAV of the present invention specifically includes the following steps:

[0023] Step 1: Design the configuration of the fully driven variable-configuration UAV and analyze its motion capabilities. This includes mounting each rotor and its corresponding motor to the end of the arm via tiltable servos, giving the rotors a controllable tilt degree of freedom relative to the frame. Through this configuration design, the rotors generate a lift component perpendicular to the frame plane, as well as a horizontal component parallel to the frame plane, thus providing the fully driven variable-configuration UAV with additional control degrees of freedom beyond those of traditional quadcopter UAVs.

[0024] The rotor tilt direction of the described fully driven variable configuration UAV adopts an X-shaped diagonally symmetrical distribution, meaning that after each rotor tilts around the arm axis, its horizontal thrust component exhibits an X-shaped diagonally symmetrical distribution in the body coordinate system. Based on this configuration, by rationally distributing the tilt angle of each servo and the rotor speed, the fully driven variable configuration UAV can independently adjust the three-axis forces and torques without relying on attitude changes, achieving maneuverable flight modes that traditional quadcopter UAVs cannot accomplish.

[0025] Based on the above configuration design, the motion capabilities of the fully driven variable configuration UAV are analyzed. Due to the rotor tilt direction configuration design, compared with traditional quadcopter UAVs, it adds thrust parallel to the frame plane, i.e., it has vector thrust, enabling six degrees of freedom omnidirectional motion. Therefore, this fully driven variable configuration UAV can not only maintain stable hovering under non-zero tilt angle conditions, but also fly in any direction while maintaining a specific tilt attitude. For example, in narrow passage scenarios with left-right or up-down tilt, traditional quadcopter UAVs have difficulty passing through due to the strong coupling between attitude and displacement. The fully driven variable configuration UAV of this invention can achieve stable forward movement while maintaining the tilt attitude of the fuselage, thus successfully completing the passage mission under the same external dimensions as traditional quadcopter UAVs.

[0026] Step 2 involves dynamic modeling and control assignment model optimization for the fully driven variable configuration UAV, including:

[0027] Step 2.1, Perform dynamic modeling for the fully driven variable configuration UAV:

[0028] The dynamic model of the fully driven variable configuration UAV constructed using Newton's and Euler's equations is as follows:

[0029] ;

[0030] in, For the mass of fully driven variable configuration UAVs; Acceleration in geographic coordinate system; This is the rotation matrix for converting coordinates from the body coordinate system to geographic coordinate system. The coordinates are in the body coordinate system; Geographic coordinates; For the control force of fully driven variable configuration UAVs, , , These are the components of the control force along the three axes of the body coordinate system; The gravitational force experienced by a fully driven variable-configuration UAV. It is the acceleration due to gravity. These are the unit vectors along the x, y, and z axes of the geographic coordinate system, respectively. The moment of inertia of the fully driven variable configuration UAV; The rotational angular velocity of the fully driven variable configuration UAV is given by, where, , , These are the components of the angular velocity of the fully driven variable configuration UAV along the three axes of the body coordinate system, with superscripts indicating the components. Denotes the first derivative, i.e. for The first derivative; For the control torque of the fully driven variable configuration UAV, , , These are the components of the control torque along the three axes of the body coordinate system; The cross product antisymmetric matrix of the body's angular velocity is as follows:

[0031] ;

[0032] Step 2.2, optimize the control allocation model for the fully driven variable configuration UAV:

[0033] Traditional quadcopter drones select control variables when performing control allocation. ,in, The subscript represents the thrust of each of the four rotors. Number the rotors: the upper right rotor is numbered 1, the lower left rotor is numbered 2, the upper left rotor is numbered 3, and the lower right rotor is numbered 4. Establish the control efficiency equation:

[0034] ;

[0035] in, For the total thrust expected of a fully driven variable configuration UAV, The expected total torque for a fully driven variable configuration UAV. Let be the tilt angle of the servo motor. In this control efficiency equation, the coefficient matrix of the control quantity... Is with The non-value function matrix is ​​difficult to obtain an analytical inverse solution offline, and needs to be inverted in real time according to the real-time state, which significantly reduces the solution efficiency.

[0036] This invention performs mechanical analysis based on the thrust vectors of each rotor of a fully driven variable-configuration UAV, and selects the component of the four-axis thrust as the virtual control variable. ,in, For the first Each servo motor tilts at an angle, thus avoiding the computational burden of online inversion. , (The definition is the same as above).

[0037] The new control efficiency equation is:

[0038] ;

[0039] in, For an allocation matrix that is constant, find the pseudo-inverse of that matrix. It can be obtained offline in advance, thus avoiding the need to perform matrix inverse operations in real time during the process of calculating control variables.

[0040] Based on the X-shaped symmetrical rotor tilt direction designed in step 1, the above-mentioned new control efficiency equation is as follows:

[0041] ;

[0042] in, The torque coefficient, The length of the arm, , , , The definition is the same as before. for abbreviation of for abbreviation of The definition is the same as above.

[0043] The established control efficiency equation can then be solved directly:

[0044] ;

[0045] for The identity matrix; For the required control quantity, ; for Free vectors, different The value selection corresponds to different allocation objectives:

[0046] If selected , for The zero vector, the allocation target corresponds to the minimum thrust sum:

[0047] ;

[0048] in, To find the minimum value function, It is the square of the 2-norm function.

[0049] Then, through relational transformation, we obtain and The expression is as follows:

[0050] ;

[0051] Among them, the function It is the arctangent function in the fourth quadrant, with a range of . .

[0052] Thus, the dynamic modeling and control allocation optimization scheme for the fully driven variable configuration UAV was completed.

[0053] Step 3: Design a position and attitude decoupled PID (proportional-integral-derivative) controller and integrate a position loop disturbance observer and an attitude loop, including:

[0054] Step 3.1: Design a PID controller that decouples position and attitude;

[0055] For fully driven variable configuration UAVs, since they have six degrees of freedom and can independently control three-dimensional position and three-axis attitude, the desired pitch angle and roll angle can be input independently, and it is no longer necessary to calculate the desired value from the position loop. Therefore, the PID controller of the fully driven variable configuration UAV is changed from a series PID controller to a parallel PID controller.

[0056] Unlike the control structure of traditional quadcopter drones, the PID controller of the fully driven variable configuration drone decouples its position loop and attitude loop, generating control inputs independently. The position loop provides the desired three-axis force signal, unlike the single vertical thrust signal of traditional quadcopter drones. The attitude loop can accept independently input desired attitude angle signals to control the attitude. Therefore, the control output of this parallel PID controller enables the drone to perform maneuvers that traditional quadcopter drones cannot perform, such as hovering at a non-0-degree attitude angle, inverted flight, and forward flight at a tilted attitude angle.

[0057] Step 3.2, design the position loop interference observer;

[0058] For the position loop channel, assuming the disturbance is concentrated in the control force input stage, the position control equation considering the disturbance is established as follows:

[0059] ;

[0060] in, For the speed of fully driven variable configuration drones, Speed ​​of fully driven variable configuration UAV The derivative with respect to time is acceleration. For control force in an inertial frame, It is a unit vector along the z-axis of the inertial frame; Force interference in the input channel.

[0061] Design the following position loop interference observer:

[0062] ;

[0063] in, To assist in designing variables, for The first derivative, This is an estimate of the force interference in the input channel. Here is the gain matrix. The speed of a fully driven variable configuration UAV.

[0064] Step 3.3, design the attitude loop interference observer;

[0065] For the attitude loop, establish the attitude control equations considering disturbances:

[0066] ;

[0067] in, Angular velocity of fully driven variable configuration UAV The derivative with respect to time, i.e., angular acceleration; To match the inertia and angular velocity of fully driven variable configuration UAVs Related variables, , The inertia matrix for a fully driven variable configuration UAV. for The reverse, For the control torque of the fully driven variable configuration UAV, For lumped torque disturbance; where, .

[0068] Design of a fixed-time convergence attitude loop disturbance observer based on the sliding membrane method:

[0069] ;

[0070] in, , and As an auxiliary variable, it has no actual physical meaning. for The first derivative; To estimate the residuals; This is an estimate of the torque interference in the input channel. These are the parameters for the attitude loop disturbance observer to be designed; they have no actual physical meaning. It is a 2-norm function.

[0071] For disturbances with an upper bound on the rate of change, the above attitude loop disturbance observer can converge in a fixed time (regardless of the initial state of the system).

[0072] Step 3.4: Integrate the position loop interference observer and attitude loop interference observer into the PID controller;

[0073] Integrating the aforementioned position loop disturbance observer and attitude loop disturbance observer into the PID controller, the design equations for the position loop controller and attitude loop controller are as follows:

[0074] ;

[0075] in, For the control force of a fully driven variable configuration UAV in a geographic coordinate system, For the control torque of a fully driven variable configuration UAV; The designed diagonal gain matrix; intermediate quantity , , These are the tracking errors for position, velocity, and angular velocity, respectively. The desired location for a fully driven variable configuration UAV. The current location of the fully driven variable configuration UAV; The desired speed for a fully driven variable-configuration UAV; For the desired angular velocity of the fully driven variable configuration UAV, where, The desired attitude angle is given for the preset trajectory that is directly input. For the desired roll angle, For the desired pitch angle, The desired yaw angle; The current attitude angle, The parameters are those of the PID controller to be designed; This accelerates the anticipated development of fully driven variable-configuration unmanned aerial vehicles (UAVs).

[0076] Step 4: Based on the obstacle control function, establish a quadratic programming problem, optimize the obstacle avoidance calculation strategy, and avoid collision behavior, including:

[0077] Step 4.1: Establish a quadratic programming problem based on the control barrier function method;

[0078] The drone is modeled as a cuboid to closely match the actual physical model of the drone and more accurately reflect the size and shape of the actual aircraft.

[0079] Assume the center of mass of the fully driven variable configuration UAV is located at the center of the cuboid model (center coordinates are denoted as ). (i.e., the centroid position of the cuboid model), the length, width, and height of the cuboid are respectively... (The cross-section of the cuboid is a square, with the same length and width), then the eight vertices of the cuboid are determined by the current position and orientation, specifically as follows: , recorded as ;in, The location of the center of mass. Let the vertex coordinates be... These are the vertices of the cuboid model. This is the rotation matrix used to transform the coordinates from the body coordinate system to the geographic coordinate system. for A simplified version.

[0080] The planes that make up the narrow passage are defined by the hyperplane equation. Defined as:

[0081] ;

[0082] in, The normalization coefficient is a The vector satisfies And the rule for choosing positive and negative values ​​is This represents the side that is passable without collision; Let be the coordinate vector of any point in three-dimensional space. Let be the constant term in the plane equation. Based on the above description, define... Directed distance between the plane for:

[0083] ;

[0084] The drone is modeled as a cuboid, and a control obstacle function is selected based on this model. Constructing security constraints specifically includes: security sets Safety filter.

[0085] Security constraints are represented by a set called a security set. For any satisfy The set ( For any The number represents the planar obstacle. These are the vertices of the cuboid model. The number of planar obstacles; due to the relative degrees of the control input of the fully driven variable configuration UAV. Therefore, a safety filter is designed to perform safety filtering on the output of the PID controller, so that the control law after safety filtering is... Satisfying the inequality:

[0086] ;

[0087] in, To find the maximum value function, For control laws, , For security filter parameters, It is a 6-dimensional real number field.

[0088] If the inequality has a feasible solution, then This is the Effective Exponential Control Barrier Function (ECBF). When this inequality safety constraint is satisfied, the system state is confined within the defined safety zone, thereby preventing collisions.

[0089] Based on the UAV dynamics model and differentiation rule in step 2.1 above, we obtain The derivatives are:

[0090] ;

[0091] ;in, Let be the symmetric inverse cross product matrix of the airframe angular velocity of a fully driven variable configuration UAV. for , , for The corresponding symmetric inverse cross product matrix.

[0092] Using safety constraints to solve the Quadratic Programming (QP) problem:

[0093] ;

[0094] in, This refers to the safety control quantity after secondary planning. The dependent variable is a quadratic programming problem. The control variable is the output of the original PID controller. To minimize the value of the function, Let be the square of the L2 norm function. (subject to) means to be subject to.

[0095] The optimization objective of the above quadratic programming problem is to minimize deviation from the original control objective while ensuring compliance with safety constraints. The UAV's cuboid modeling features are defined by 8 fixed points, and there are a total of n obstacle planes. Therefore, the above quadratic programming problem is an optimization problem involving 8n inequalities. Under ideal conditions, the optimal solution to the above quadratic programming problem can ensure that the fully driven variable-configuration UAV can achieve collision-free passage in narrow channels. However, experimental verification shows that running this QP problem at 500Hz on an STM32F7 microcontroller (216MHz) cannot meet the real-time requirements. While introducing an onboard computer can solve the insufficient computing power problem, high-frequency communication with the underlying attitude loop will also fail to meet the requirements for precise control and rapid response due to communication latency.

[0096] To address the above issues, this invention employs a constraint merging strategy (step 4.2), which reduces a multi-point / multi-faceted constraint inequality to a single inequality through reasonable scaling, thereby obtaining an analytical solution to the QP problem and avoiding online solution optimization issues. Specifically:

[0097] Step 4.2, optimize obstacle avoidance calculation strategy to reduce online computation:

[0098] The purpose of the following steps is to merge multiple constraints into a single constraint, thereby reducing the computational cost onboard. Assume a total set of safe zones comprised of all obstacle planes. A collection of single security zones ,pass Sets are composed of intersection or union operations, and the level of set operations is denoted by the level of the set operations. .in, For the first The non-obstacle region is divided by an obstacle plane. For plane numbering (sequence number of the set). A set of planes, Represents the total number of A plane, Let be the set consisting of all set operations. This represents the total number of rounds of nested operations on the set.

[0099] Step 4.2.1, for all constraint functions Take the index:

[0100] ;

[0101] in, For design parameters; As an intermediate variable, The superscript indicates the nested rounds, which is 0 here since set operations have not yet been performed. The subscript... For plane numbering.

[0102] Step 4.2.2, in each round of set relation operations, the following operation rules shall be followed:

[0103] ;

[0104] in, superscript , The subscript represents the round of nested operations on the set. For plane numbering; superscript Indicates the round of nested operations on sets, subscript The subscript for summation calculations represents the function number. For the first In nested round operations The set of operational planes of combinations, , These are the sets that are intersected or joined in nested set operations.

[0105] Step 4.2.3, calculate the control barrier function. :

[0106] ;

[0107] in, It is a logarithmic function with the natural exponent as its base; The subscript 1 indicates that only one function remains after the last calculation, and the superscript... This represents the total number of rounds of nested operations on the set. For the design parameters, the following relationship must be satisfied:

[0108] ;

[0109] in, The parameters for determining the scaling boundary are set to:

[0110] ;

[0111] in, It is an intermediate quantity (without actual physical meaning). For the first The set involved in the operation during the set operation. It is a set The number of elements in a set For the first The number of sets involved in a set operation. This is the boundary function of the actual obstacle region.

[0112] when The value satisfies Sometimes, ;when The value satisfies Sometimes, , This represents the set of practically feasible regions. The control barrier function obtained in step 4.2.3 The corresponding set of security zones is represented.

[0113] Step 4.2.4: After the above operations, the safe region composed of complex multi-plane sets can be represented by a single function. The corresponding safety constraints for ensuring collision-free flight of drones are as follows:

[0114] ;

[0115] in, These are parameters for a security filter and have no actual physical meaning. They are respectively The second and first derivatives with respect to time can be simplified as follows: , This is an intermediate quantity with no actual meaning. for Neutral and control laws Irrelevant constant terms, the remaining terms after removing these terms. , For the control law in the remaining terms The transpose of , for The transpose of , and the corresponding quadratic programming problem is as follows:

[0116] ;

[0117] Step 4.2.5, based on the extremum condition, original feasibility, dual feasibility, and complementary relaxation condition, it can be deduced that the solution to the quadratic programming problem in 4.2.4 exists in analytical form:

[0118] ;

[0119] ;

[0120] in, To take 0 and The minimum value among them, It is an intermediate quantity and has no actual physical meaning.

[0121] Step 4.2 constructs a safety filter with low computational complexity, high real-time performance, and high security, which is used to control the output of the PID controller. The control quantity that satisfies the control barrier function constraint is obtained by solving the QP problem. .

[0122] The above description is merely an embodiment of the present invention and does not limit the scope of the invention. Any equivalent structural or procedural transformations made based on the description and drawings of this invention, or direct or indirect applications in other related system fields, are similarly included within the protection scope of this invention. Contents not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for obstacle avoidance flight control of a fully driven variable configuration unmanned aerial vehicle, characterized in that, include: Step 1: Design the configuration of the fully driven variable configuration UAV and analyze its motion capabilities; Each rotor and its corresponding motor are mounted at the end of the arm via a tiltable servo, giving the rotor a controllable degree of tilt freedom relative to the frame. Step 2: Perform dynamic modeling and control allocation model optimization for the fully driven variable configuration UAV; Step 3: Design a PID controller that decouples position and attitude and integrate a position loop disturbance observer and an attitude loop disturbance observer; Step 4: Based on the obstacle control function, establish a quadratic programming problem, optimize the obstacle avoidance calculation strategy, and avoid collision behavior.

2. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 1, characterized in that, The rotor tilt direction of the fully driven variable configuration UAV adopts an X-shaped diagonal symmetrical distribution. After each rotor tilts around the arm axis, its horizontal thrust component exhibits an X-shaped diagonal symmetrical distribution in the body coordinate system.

3. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 1, characterized in that, Step 2 includes: Step 2.1, Perform dynamic modeling for the fully driven variable configuration UAV: ； in, For the mass of fully driven variable configuration UAVs; Acceleration in geographic coordinate system; This is the rotation matrix for converting coordinates from the body coordinate system to geographic coordinate system. The coordinates are in the body coordinate system; Geographic coordinates These are the unit vectors along the x, y, and z axes of the geographic coordinate system, respectively. For the control force of fully driven variable configuration UAVs, , , These are the components of the control force along the three axes of the body coordinate system; The gravitational force experienced by a fully driven variable-configuration UAV. It is the acceleration due to gravity; The moment of inertia of the fully driven variable configuration UAV; The rotational angular velocity of the fully driven variable configuration UAV is given by, where, , , These represent the components of the angular velocity of the fully driven variable configuration UAV along the three axes of the body coordinate system. for The first derivative; For the control torque of the fully driven variable configuration UAV, , , These are the components of the control torque along the three axes of the body coordinate system. It is the cross product antisymmetric matrix of the body's angular velocity.

4. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 3, characterized in that, for: 。 5. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 4, characterized in that, Step 2 also includes: Step 2.2, optimize the control allocation model for the fully driven variable configuration UAV: The component of the quadcopter thrust is selected as the virtual control variable: ,in, The thrust of each of the four rotors. For the first Each servo motor tilt angle Number the rotors and establish the following control efficiency equations: ； in, For the total thrust expected of a fully driven variable configuration UAV, The expected total torque for a fully driven variable configuration UAV. For the allocation matrix; The control efficiency equation expands as follows: ； in, The torque coefficient, The length of the arm, for abbreviation of for abbreviation; and for: ； Among them, the function It is the arctangent function in the fourth quadrant, with a range of . .

6. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 5, characterized in that, Step 3 includes: Step 3.1, design a PID controller with decoupled position and attitude; the PID controller of the fully driven variable configuration UAV is set as a parallel structure PID controller; the position loop and attitude loop of the PID controller are set to be decoupled and generate control quantities independently respectively. The desired three-axis force signal is obtained through the position loop, and the attitude loop receives the independently input desired attitude angle signal to control the attitude. Step 3.2, Design the position loop interference observer: ； in, To assist in designing variables, for The first derivative, This is an estimate of the force interference in the input channel. Here is the gain matrix. For the speed of fully driven variable configuration drones, For control force in an inertial frame; Step 3.3, design the attitude loop interference observer; For the attitude loop, a fixed-time convergence attitude loop disturbance observer is designed based on the sliding membrane method: ； in, , and As an auxiliary variable, for The first derivative; It is related to inertia and angular velocity of fully driven variable configuration UAVs. Related variables, ; The inertia matrix for a fully driven variable configuration UAV. for The reverse; For the control torque of a fully driven variable configuration UAV; To estimate the residuals; This is an estimate of the torque interference in the input channel; Parameters for the attitude loop interference observer to be designed; It is a 2-norm function; Step 3.4: Integrate the position loop interference observer and attitude loop interference observer into the PID controller.

7. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 6, characterized in that, Step 3.4 includes: integrating the position loop disturbance observer and attitude loop disturbance observer into the PID controller. The design equations for the position loop controller and attitude loop controller are as follows: ； in, For the control force of a fully driven variable configuration UAV in a geographic coordinate system, For the control torque of a fully driven variable configuration UAV; The designed diagonal gain matrix; intermediate quantity , , These are the tracking errors for position, velocity, and angular velocity, respectively. The desired location for a fully driven variable configuration UAV. The current location of the fully driven variable configuration UAV; The desired speed for a fully driven variable-configuration UAV; For the desired angular velocity of the fully driven variable configuration UAV, where, The desired attitude angle is given for the preset trajectory that is directly input. For the desired roll angle, For the desired pitch angle, The desired yaw angle; The current attitude angle, The parameters are those of the PID controller to be designed; This accelerates the anticipated development of fully driven variable-configuration unmanned aerial vehicles (UAVs).

8. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 7, characterized in that, Step 4 includes: Step 4.1: Establish a quadratic programming problem based on the control barrier function; The drone is modeled as a cuboid, and a control obstacle function is selected based on this model. Constructing security constraints: security sets Security filters; security constraints are represented by sets called security sets. For any satisfy The set, The number represents the planar obstacle. These are the vertices of the cuboid model. Given the number of planar obstacles; design a safety filter to perform safety filtering on the output of the PID controller, so that the control law after safety filtering is... Satisfying the inequality: ； If the above equation has a feasible solution, then For effective exponential control barrier function; in, To find the maximum value function, For control laws, , For security filter parameters, For a 6-dimensional real number field, for The first derivative, for The second derivative; Assuming the center of mass of the fully driven variable configuration UAV is located at the center of the cuboid model, and the length, width, and height of the cuboid model are respectively... The eight vertices of the cuboid are determined by its current position and orientation. ;in, Let the vertex coordinates be... For the first One vertex, This represents the centroid location of the cuboid model. This is the rotation matrix used to transform the coordinates from the body coordinate system to the geographic coordinate system. for abbreviated form; The planes that make up the narrow passage are defined by the hyperplane equation. Defined as: ； in, Let be the normalization coefficient, satisfying The rule for choosing positive and negative values ​​is: This represents the side that is passable without collision; Let be the coordinate vector of any point in three-dimensional space. is the constant term in the plane equation; for The directed distance between the plane and the target plane; Step 4.2: Optimize obstacle avoidance calculation strategy to reduce online computation.

9. The obstacle avoidance flight control method for a fully driven variable configuration UAV according to claim 8, characterized in that, Step 4.1 also includes: establishing a quadratic programming problem using safety constraints: ； in, This is a safety control quantity for secondary planning. The dependent variable is a quadratic programming problem. The control variable is the output of the original PID controller. To minimize the value of the function, Let be the square of the L2 norm function. To be subject to.

10. The obstacle avoidance flight control method for a fully driven variable-configuration UAV according to claim 9, characterized in that, Step 4.2 includes: Step 4.2.1, for all constraint functions Take the index: ； in, For design parameters; As an intermediate variable, The superscript indicates the nested round, with a superscript of 0 indicating the 0th set operation, and the subscript... The sequence number of the set; Step 4.2.2, perform set relation operations for each round based on the following rules: ； in, superscript , Indicates the round of nested operations on sets, subscript For plane numbering; superscript Indicates the round of nested operations on sets, subscript The subscript for summation calculations represents the function number. For the first In nested round operations The set of operational planes of combinations, , These are the sets that undergo intersection and union operations in nested set operations, respectively. Step 4.2.3, calculate the control barrier function. : ； in, It is a logarithmic function with the natural exponent as its base; The subscript 1 indicates that only one function remains after the last calculation, and the superscript... This represents the total number of rounds of nested operations on the set. For the design parameters, the following relationship must be satisfied: ； in, The parameters for determining the scaling boundary are set to: ； For the first The set involved in the operation during the set operation. It is a set The number of elements in a set It is the first The number of sets involved in a set operation. The boundary function of the actual obstacle region; Step 4.2.4 yields the following safety obstacle constraints to ensure collision-free flight of the UAV: ； in, For security filter parameters, They are respectively The second and first derivatives with respect to time are simplified as follows: , As an intermediate quantity, for Neutral and control laws Irrelevant constant terms, the remaining terms after removing these terms. , For the control law in the remaining terms The transpose of , for The transpose of the problem leads to the following quadratic programming problem: ； Step 4.2.5, based on the extremum condition, original feasibility, dual feasibility, and complementary relaxation condition, derive the analytical form of the solution to the quadratic programming problem in 4.2.4: ； ； in, To take 0 and The minimum value among them, This is an intermediate quantity.

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