A dynamic motion planning method and system for a land-air dual-mode UAV, a land-air dual-mode UAV, and a storage medium

Abstract

The present invention discloses a dynamic motion planning method, system, land-air dual-mode UAV, and storage medium. The method comprises: designing a disturbance observer based on the dynamic model of the land-air dual-mode UAV using uncertainty and disturbance estimation methods; using the disturbance observer to estimate disturbance factors in the environment to obtain a disturbance estimate; using a motion planner to calculate and update the dynamic boundaries of the land-air dual-mode UAV in real time based on the disturbance estimate; and performing path search and trajectory optimization based on the updated dynamic boundaries of the land-air dual-mode UAV to obtain a trajectory planning result. The present invention combines the disturbance observer with the land-air dual-mode UAV motion planning technology to achieve environmental disturbance information perception at the planning level. It also proposes a disturbance adaptive safety boundary adjustment mechanism to ensure that the planned trajectory meets real-time dynamic constraints, thereby improving the land-air dual-mode UAV's ability to respond to environmental disturbance factors.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) motion planning, and in particular to a dynamic motion planning method and system for a land-air dual-mode UAV, a land-air dual-mode UAV, and a computer-readable storage medium. Background Art

[0002] UAV motion planning technology aims to enable drones to autonomously plan executable flight paths in complex environments through algorithms and strategies to complete specific missions. Existing motion planning methods primarily target single-mode drones and fail to account for real-time disturbances in the environment. They also lack effective responses to invisible disturbances (such as wind and friction), resulting in a high probability of motion planning failure in highly disturbed environments.

[0003] Although existing technologies have developed disturbance observers (DOBs) for estimating disturbance information and improving the tracking performance of controllers, these are currently mostly applied only in the control field, and their integration with the planning field has not been further explored. That is, disturbance observers have not yet been combined with UAV motion planning technology to consider disturbance information at the planning level, thereby enhancing the system's anti-interference ability.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0005] The main purpose of the present invention is to provide a dynamic motion planning method and system for a land-air dual-mode UAV, a land-air dual-mode UAV and a computer-readable storage medium, aiming to solve the problem that the existing UAV motion planning methods are mainly aimed at single-mode UAVs and do not consider environmental disturbance factors, resulting in a high possibility of failure of UAV motion planning in a highly disturbed environment.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a dynamic motion planning method for a land-air dual-mode UAV, the dynamic motion planning method for the land-air dual-mode UAV comprising:

[0007] Based on the dynamic model of the dual-mode UAV in flight mode and land mode, a disturbance observer is designed using uncertainty and disturbance estimation methods, and the disturbance observer is used to estimate the disturbance factors in the environment to obtain a disturbance estimation value;

[0008] Calculate and update the dynamic boundaries of the dual-mode land-air UAV in real time using a motion planner based on the disturbance estimate and the dynamic equations in the dynamic model;

[0009] Based on the updated dynamic boundaries of the land-air dual-mode UAV, the improved motion dynamics A* algorithm is used for path search, and the improved B-spline algorithm is used for trajectory optimization to obtain the trajectory planning result of the land-air dual-mode UAV.

[0010] Preferably, the dynamic model of the land-air dual-mode UAV in flight mode includes:

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] in, Indicates the quality of the drone; Represents the position vector of the drone in the three-dimensional space in the world coordinate system, Respectively represent the UAV in the three-dimensional space under the world coordinate system Axis position; represents transpose; Represents three-dimensional space; Represents the acceleration vector of the drone in the three-dimensional space in the world coordinate system, Respectively represent the UAV in the three-dimensional space under the world coordinate system acceleration of the axis; Indicates the total thrust produced by the four propellers; represents the gravitational acceleration vector, represents the acceleration due to gravity; represents the disturbance vector under the flight mode, Respectively represent the flight mode in the body coordinate system Axis disturbances; represents the attitude angle vector of the drone, Respectively represent the roll angle, pitch angle and yaw angle of the drone; represents the Euler angle vector, Represents the roll angle The cosine and sine of Represents the pitch angle The cosine and sine of Represents the yaw angle The cosine and sine of ; represents the moment of inertia matrix, Indicates is a diagonal matrix with diagonal elements; Respectively represent the UAV in the body coordinate system Moment of inertia about the axis; represents the rotational torque vector, Respectively represent the UAV in the body coordinate system Rotational torque on the shaft; Represents the attitude angular acceleration vector; represents the coupling vector, represents the attitude angular velocity vector, They represent the roll angular velocity, pitch angular velocity and yaw angular velocity of the UAV in the body coordinate system respectively; represents the unknown perturbation vector, Respectively expressed in the body coordinate system unknown disturbances on the axis;

[0025] The dynamic model of the land-air dual-mode UAV in the land mode includes:

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] in, represents the driving force vector, Represent the first wheel speed and the second wheel speed respectively; represents the disturbance vector in the land mode, Respectively represent the land mode in the body coordinate system Axis disturbance, They represent the longitudinal friction and lateral friction acting on the UAV in the body coordinate system respectively; represents the yaw acceleration; Represent the mass matrix, force coordinate transformation matrix and disturbance coordinate transformation matrix respectively: Indicates the width of the drone: represents the reaction torque, Indicates the longitudinal friction force on the first wheel, Indicates the lateral friction force on the first wheel: Indicates the longitudinal friction force on the second wheel, Indicates the lateral friction force on the second wheel: Indicates the longitudinal friction force on the third wheel, Indicates the lateral friction force on the third wheel: Indicates the longitudinal friction force on the fourth wheel, Indicates the lateral friction force on the fourth wheel: Represent the distances from the rear wheel and front wheel to the center of mass of the drone respectively.

[0034] Preferably, the method of designing a disturbance observer based on the dynamic model of the land-air dual-mode UAV in flight mode and land mode by adopting an uncertainty and disturbance estimation method, and estimating the disturbance factors in the environment by using the disturbance observer to obtain a disturbance estimation value specifically includes:

[0035] Define a second-order system:

[0036] ;

[0037] in, denote the control output vector and disturbance vector respectively;

[0038] According to the dynamic equations in the dynamic model of the land-air dual-mode UAV in flight mode, the control output vector and disturbance vector in flight mode are expressed as:

[0039] ;

[0040] in, represent the control output vector and disturbance vector under the flight mode respectively;

[0041] According to the dynamic equations in the dynamic model of the land-air dual-mode UAV in the land mode, the control output vector and disturbance vector in the land mode are expressed as:

[0042] ;

[0043] in, denote the control output vector and disturbance vector under land mode respectively; Represents the mass matrix The inverse matrix of

[0044] The difference between the second-order system state vector and the control output vector is used as the disturbance estimate output by the disturbance observer. , where the second-order system state vector corresponds to the acceleration vector in the dynamic equation :

[0045] ;

[0046] According to the control output vector , reference control output vector and the perturbation estimate relationship , we can get:

[0047] ;

[0048] Using a low-pass filter to filter the high-frequency noise in the second-order system, we can get:

[0049] ;

[0050] in, represents the inverse Laplace transform, is a low-pass filter, Represents the convolution operation;

[0051] Combine and , we can get:

[0052] ;

[0053] make , the disturbance estimate The final expression is:

[0054] ;

[0055] in, represents the time constant, represents the Laplace variable, represents the integration time threshold of the disturbance observer, represents the time variable, Represents the first-order system state vector, corresponding to the velocity vector in the dynamic equation; express The corresponding first-order system state vector is, represents the initial first-order system state vector; express The corresponding baseline control output vector.

[0056] Preferably, the method of calculating and updating the dynamic boundary of the land-air dual-mode UAV in real time using a motion planner based on the disturbance estimation value and the dynamic equations in the dynamic model specifically includes:

[0057] Let the disturbance estimate under flight mode be Based on the dynamic model of the land-air dual-mode UAV in flight mode, the dynamic boundary of the land-air dual-mode UAV in flight mode is derived. Expressed as:

[0058] ;

[0059] in, Respectively represent the flight mode in the world coordinate system Perturbation estimates on the axis; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate;

[0060] Let the perturbation estimate under land mode be Based on the dynamic model of the land-air dual-mode UAV in the land mode, the dynamic boundary of the land-air dual-mode UAV in the land mode is derived. Expressed as:

[0061] ;

[0062] in, Respectively represent the land mode in the world coordinate system The perturbation estimate on the axis, They represent the UAV’s position in land mode. The minimum and maximum accelerations that the axis can achieve, Indicates the UAV is in land mode The maximum tensile force that the shaft can generate; They represent the UAV’s position in land mode. The minimum and maximum accelerations that the axis can achieve, Indicates the UAV is in land mode The maximum tensile force that the shaft can generate;

[0063] according to and The relationship between the dynamic boundary of the land-air dual-mode UAV under the flight mode is Expressed as:

[0064] ;

[0065] according to and The dynamic boundary of the land-air dual-mode UAV in the land mode is Expressed as:

[0066] ;

[0067] According to the expression formula of the dynamic boundary of the land-air dual-mode UAV, the dynamic boundary of the land-air dual-mode UAV is calculated and updated in real time.

[0068] Preferably, based on the updated dynamic boundary of the land-air dual-mode UAV, the improved motion dynamics A* algorithm is used to perform path search, and the improved B-spline algorithm is used to perform trajectory optimization to obtain the trajectory planning result of the land-air dual-mode UAV, specifically including:

[0069] Based on the updated dynamic boundary of the dual-mode land-air UAV, a trajectory cost function for the path search problem is designed to obtain an improved motion dynamics A* algorithm;

[0070] Based on the navigation map, the improved motion dynamics A* algorithm is used to perform path search to obtain an initial path;

[0071] Based on the updated dynamic boundary of the dual-mode land-air UAV, an optimization cost function for the trajectory optimization problem is designed, and an improved B-spline algorithm is obtained;

[0072] Based on the initial path, the improved B-spline algorithm is used to perform trajectory optimization to obtain a trajectory planning result of the land-air dual-mode UAV.

[0073] Preferably, the trajectory cost function of the path search problem is designed based on the updated dynamic boundary of the land-air dual-mode UAV, specifically including:

[0074] Based on the high energy consumption disadvantage of the dual-mode UAV in the flight mode, the extra energy cost is added to the flight trajectory, and the trajectory cost function is designed. Expressed as:

[0075] ;

[0076] in, represents the acceleration input, represents the norm, represents the height of the motion primitive, represents the height threshold of the motion primitive in flight mode, Respectively represent the scaling factors used to adjust the height cost term and the time cost term, Indicates taking the maximum value;

[0077] Based on the updated dynamic boundary of the land-air dual-mode UAV, the discrete acceleration input is obtained by discretizing the acceleration boundary at equal intervals. ;

[0078] Based on the trajectory cost function, the discrete acceleration input All generated The total cost of motion primitives Expressed as:

[0079] ;

[0080] in, Indicates the total number of motion primitives, Indicates the serial number, Respectively represent The acceleration and terminal height of each motion primitive, is the time over which the motion primitive is integrated forward.

[0081] Preferably, the optimized cost function of the trajectory optimization problem is designed based on the updated dynamic boundary of the land-air dual-mode UAV to obtain an improved B-spline algorithm, which specifically includes:

[0082] Parameterize the initial trajectory as control points of Order B-spline curve, where Represents the first control points, Respectively represent Control points in the world coordinate system The position of the axis, Represent the 0th control point, the 1st control point and the control points, the optimized cost function of the design Expressed as:

[0083] ;

[0084] in, represents the smoothness cost, represents the collision cost, represents the dynamic feasibility cost, Respectively The corresponding weight;

[0085] Among them, the smoothness cost of the design Expressed as:

[0086] ;

[0087] in, represents the curvature penalty term, represents the elastic uniform term, For the control points, For the control points, is the normalization parameter, represents the regularization weight;

[0088] The collision cost of design Expressed as:

[0089] ;

[0090] ;

[0091] in, Indicates the control points The distance to the nearest obstacle, represents the collision cost mapping function, Indicates the safety distance threshold;

[0092] Design of dynamic feasibility cost based on the updated dynamic boundary of the land-air dual-mode UAV , the dynamic feasibility cost of the design Expressed as:

[0093] ;

[0094] ;

[0095] in, Indicates the Acceleration control points along The acceleration value of the axis, represents the dynamic feasibility cost mapping function, They represent the flight mode along The lower and upper limits of the axis's acceleration, They represent the land mode along The lower and upper acceleration limits of the axis.

[0096] To achieve the above-mentioned object of the invention, the present invention further provides a dynamic motion planning system for a land-air dual-mode UAV, the dynamic motion planning system for the land-air dual-mode UAV comprising:

[0097] A disturbance estimation module is configured to design a disturbance observer based on the dynamic model of the dual-mode UAV in flight mode and land mode, using uncertainty and disturbance estimation methods, and use the disturbance observer to estimate the disturbance factors in the environment to obtain a disturbance estimate value;

[0098] A dynamic boundary adjustment module is configured to calculate and update the dynamic boundary of the land-air dual-mode UAV in real time using a motion planner based on the disturbance estimation value and the dynamic equations in the dynamic model;

[0099] Path search and trajectory optimization module: used to perform path search using the improved motion dynamics A* algorithm based on the updated dynamic boundaries of the land-air dual-mode UAV, and to perform trajectory optimization using the improved B-spline algorithm to obtain the trajectory planning result of the land-air dual-mode UAV.

[0100] To achieve the above-mentioned purpose of the invention, the present invention also provides a land-air dual-mode UAV, which includes: a disturbance observer and a motion planner, and the disturbance observer and motion planner are used to jointly implement the steps of the dynamic motion planning method of the land-air dual-mode UAV as described above.

[0101] In order to achieve the above-mentioned purpose of the invention, the present invention also provides a computer-readable storage medium, which stores a dynamic motion planning program for a land-air dual-mode UAV. When the dynamic motion planning program for the land-air dual-mode UAV is executed by a disturbance observer and a motion planner, the steps of the dynamic motion planning method for the land-air dual-mode UAV as described above are implemented.

[0102] In the present invention, based on the dynamic model of the land-air dual-mode UAV in flight mode and land mode, an uncertainty and disturbance estimation method is adopted to design a disturbance observer, and the disturbance observer is used to estimate the disturbance factors in the environment to obtain a disturbance estimation value; according to the disturbance estimation value and the dynamic equations in the dynamic model, a motion planner is used to calculate and update the dynamic boundaries of the land-air dual-mode UAV in real time; based on the updated dynamic boundaries of the land-air dual-mode UAV, an improved motion dynamics A* algorithm is used to perform path search, and an improved B-spline algorithm is used to perform trajectory optimization, so as to obtain a trajectory planning result of the land-air dual-mode UAV. At the control level, the present invention combines the dynamic model of the land-air dual-mode UAV to design a disturbance observer, and uploads the estimated disturbance information to the motion planner; at the planning level, a disturbance adaptive safety boundary adjustment mechanism is proposed, which uses the disturbance information to adjust the dynamic boundary of the land-air dual-mode UAV in real time, and then performs path search and trajectory optimization based on the adjusted dynamic boundary of the land-air dual-mode UAV, so as to realize the perception of disturbance information in the environment at the planning level and plan a trajectory that meets the real-time dynamic constraints of the UAV, thereby improving the ability of the land-air dual-mode UAV to respond to environmental disturbance factors and significantly enhancing the adaptability of the land-air dual-mode UAV in a large disturbance environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a flow chart of a preferred embodiment of the dynamic motion planning method of the land-air dual-mode UAV of the present invention;

[0104] Figure 2 This is another flow chart of a preferred embodiment of the method for dynamic motion planning of a land-air dual-mode UAV of the present invention;

[0105] Figure 3 This is a structural diagram of a preferred embodiment of the dynamic motion planning system for the land-air dual-mode UAV of the present invention;

[0106] Figure 4 It is a structural diagram of a preferred embodiment of the land-air dual-mode UAV of the present invention. DETAILED DESCRIPTION

[0107] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0108] UAV motion planning technology aims to enable drones to autonomously plan executable flight paths in complex environments to complete specific missions through algorithms and strategies. Existing motion planning methods primarily target single-modal drones and fail to consider real-time disturbances in the environment. This can lead to two problems: First, the planner may generate conservative trajectories. For example, in tailwind conditions, the drone can achieve greater acceleration, but a planner with fixed dynamic bounds cannot fully exploit the wind. Second, the planner may generate unfeasible trajectories. For example, in headwind conditions, the drone's actual maximum acceleration is reduced, and the planner fails to adjust for this, resulting in a trajectory exceeding the drone's dynamic bounds and causing the controller to incur large tracking errors. Overall, existing UAV motion planning technologies only utilize visual cameras or lidar to perceive environmental obstacles at the planning level, failing to consider real-time disturbances in the environment. This lacks effective countermeasures for invisible disturbances (such as wind and friction), leading to a high probability of motion planning failure in highly disturbed environments.

[0109] Although existing technologies have developed disturbance observers (DOBs) for estimating disturbance information and improving the tracking performance of controllers, these are currently mostly applied only in the control field, and their integration with the planning field has not been further explored. That is, disturbance observers have not yet been combined with UAV motion planning technology to consider disturbance information at the planning level, thereby enhancing the system's anti-interference ability.

[0110] In order to solve the above technical problems, the present invention provides a dynamic motion planning method for a land-air dual-mode UAV. Based on the dynamic model of the land-air dual-mode UAV in flight mode and land mode, an uncertainty and disturbance estimation method is used to design a disturbance observer, and the disturbance observer is used to estimate the disturbance factors in the environment to obtain a disturbance estimation value; according to the disturbance estimation value and the dynamic equations in the dynamic model, a motion planner is used to calculate and update the dynamic boundaries of the land-air dual-mode UAV in real time; based on the updated dynamic boundaries of the land-air dual-mode UAV, an improved motion dynamics A* algorithm is used to perform path search, and an improved B-spline algorithm is used to perform trajectory optimization to obtain a trajectory planning result of the land-air dual-mode UAV. At the control level, the present invention combines the dynamic model of the land-air dual-mode UAV to design a disturbance observer, and uploads the estimated disturbance information to the motion planner; at the planning level, a disturbance adaptive safety boundary adjustment mechanism is proposed, which uses the disturbance information to adjust the dynamic boundary of the land-air dual-mode UAV in real time, and then performs path search and trajectory optimization based on the adjusted dynamic boundary of the land-air dual-mode UAV, so as to realize the perception of disturbance information in the environment at the planning level and plan a trajectory that meets the real-time dynamic constraints of the UAV, thereby improving the ability of the land-air dual-mode UAV to respond to environmental disturbance factors and significantly enhancing the adaptability of the land-air dual-mode UAV in a large disturbance environment.

[0111] The application content will be further explained below through description of embodiments in conjunction with the accompanying drawings.

[0112] A preferred embodiment of the dynamic motion planning method of the land-air dual-mode UAV of the present invention is as follows: Figure 1 and Figure 2 As shown, specifically including:

[0113] S1. Based on the dynamic model of the dual-mode UAV in flight mode and land mode, a disturbance observer is designed using uncertainty and disturbance estimation methods. The disturbance observer is used to estimate the disturbance factors in the environment to obtain a disturbance estimation value.

[0114] In one implementation of this embodiment, the dynamic model of the land-air dual-mode UAV in flight mode includes:

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] in, Indicates the quality of the drone; Represents the position vector of the drone in the three-dimensional space in the world coordinate system, Respectively represent the UAV in the three-dimensional space under the world coordinate system Axis position; represents transpose; Represents three-dimensional space; Represents the acceleration vector of the drone in the three-dimensional space in the world coordinate system, Respectively represent the UAV in the three-dimensional space under the world coordinate system acceleration of the axis; Indicates the total thrust produced by the four propellers; represents the gravitational acceleration vector, represents the acceleration due to gravity; represents the disturbance vector under the flight mode, Respectively represent the body coordinate system in flight mode Axis disturbances; represents the attitude angle vector of the drone, Respectively represent the roll angle, pitch angle and yaw angle of the drone; represents the Euler angle vector, Represents the roll angle The cosine and sine of Represents the pitch angle The cosine and sine of Represents the yaw angle The cosine and sine of ; represents the moment of inertia matrix, Indicates is a diagonal matrix with diagonal elements and the rest of the elements are zero; Respectively represent the UAV in the body coordinate system Moment of inertia about the axis; represents the rotational torque vector, Respectively represent the UAV in the body coordinate system Rotational torque on the shaft; Represents the attitude angular acceleration vector; represents the coupling vector, represents the attitude angular velocity vector, They represent the roll angular velocity, pitch angular velocity and yaw angular velocity of the UAV in the body coordinate system respectively; represents the unknown perturbation vector, Respectively expressed in the body coordinate system unknown disturbances on the axis;

[0129] The dynamic model of the land-air dual-mode UAV in the land mode includes:

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] in, Represents the position vector in the world coordinate system under land mode (the height of land mode is 0), where Respectively represent the UAV in the world coordinate system Axis position; represents the driving force vector, Represents the first wheel speed and the second wheel speed respectively. The wheels on the same side have the same speed. ; represents the disturbance vector in the land mode, Respectively represent the land mode in the body coordinate system Axis disturbance, They represent the longitudinal friction and lateral friction of the drone in the body coordinate system, which can be understood Respectively represent the UAV in the body coordinate system The disturbance force on the axis can also be expressed as That is, the drone is in the body coordinate system It is expressed by the longitudinal friction and lateral friction on the shaft; represents the yaw acceleration; Represent the mass matrix, force coordinate transformation matrix and disturbance coordinate transformation matrix respectively: Indicates the width of the drone: represents the reaction torque, Indicates the longitudinal friction force on the first wheel, Indicates the lateral friction force on the first wheel: Indicates the longitudinal friction force on the second wheel, Indicates the lateral friction force on the second wheel: Indicates the longitudinal friction force on the third wheel, Indicates the lateral friction force on the third wheel: Indicates the longitudinal friction force on the fourth wheel, Indicates the lateral friction force on the fourth wheel: Represent the distances from the rear wheel and front wheel to the center of mass of the drone respectively.

[0138] In one implementation of this embodiment, the design of a disturbance observer based on the dynamic model of the dual-mode land-air UAV in flight mode and land mode using an uncertainty and disturbance estimation method, and the use of the disturbance observer to estimate the disturbance factors in the environment to obtain a disturbance estimate value specifically includes:

[0139] The difference between the actual output and the expected output of the system is mainly caused by disturbances. The core idea of the disturbance observer is to reversely calculate the disturbance estimate by comparing the actual output with the expected output and combining it with the system dynamics model:

[0140] Define a second-order system:

[0141] ;

[0142] in, represents the state vector of the second-order system, which corresponds to the acceleration vector of the dynamic equation of the land-air dual-mode UAV in the land-air dual-mode UAV dynamic model, and therefore can be represented by the same symbol; denote the control output vector and disturbance vector respectively;

[0143] According to the dynamic equations in the dynamic model of the land-air dual-mode UAV in flight mode , the control output vector and disturbance vector in flight mode are expressed as:

[0144] ;

[0145] in, represent the control output vector and disturbance vector under the flight mode respectively;

[0146] According to the dynamic equations in the dynamic model of the land-air dual-mode UAV in the land mode , the control output vector and disturbance vector in land mode are expressed as:

[0147] ;

[0148] in, denote the control output vector and disturbance vector under land mode respectively; Represents the mass matrix The inverse matrix of

[0149] formula Describes a control system in reality. , that is, giving the system a control quantity , which is ultimately reflected in the system status However, in the real environment, there are disturbance factors such as wind and friction. , the final control quantity and disturbance factors are reflected on the system state together. The basic idea of the disturbance estimator is to calculate the difference between the actual system state and the control quantity given to the system. The difference is the estimated disturbance value, that is, ;

[0150] Therefore, the difference between the second-order system state vector (i.e., the actual system state) and the control output vector (i.e., the control quantity given to the system) is used as the disturbance estimate output by the disturbance observer. , where the second-order system state vector corresponds to the acceleration vector in the dynamic equation :

[0151] ;

[0152] According to the control output vector , reference control output vector and the perturbation estimate relationship (Let the reference control the output Subtract the estimated disturbance , realizing the system’s compensation for disturbances), we can get:

[0153] ;

[0154] Using a low-pass filter to filter the high-frequency noise in the second-order system, we can get:

[0155] ;

[0156] in, represents the inverse Laplace transform, is a low-pass filter, Represents the convolution operation; because the disturbance information is usually a low-frequency signal, the above formula adds a low-pass filter to the disturbance of the system to filter out useless high-frequency noise in the system;

[0157] Combine and , we can get:

[0158] ;

[0159] make , the disturbance estimate The final expression is:

[0160] ;

[0161] in, represents the time constant, represents the Laplace variable, represents the integration time threshold of the disturbance observer, represents the time variable, Represents the first-order system state vector, corresponding to the velocity vector in the dynamic equation; express The corresponding first-order system state vector is, represents the initial first-order system state vector; express The corresponding baseline control output vector.

[0162] Based on the dynamics model of a dual-mode UAV operating in both land and air, this paper employs the Uncertainty and Disturbance Estimator (UDE) method to design disturbance observers for both the flight and land modes of the dual-mode UAV. These disturbance observers estimate environmental disturbances. The UDE method first calculates the difference between the actual system state and the applied control variable, then applies a low-pass filter to this difference to obtain a disturbance estimate. In summary, based on the dynamics model of a dual-mode UAV operating in both land and air, this paper designs disturbance observers for both flight and land modes. These disturbance observers calculate the deviation signal between the actual system state and the applied control variable in real time, then low-pass filter the difference, thereby obtaining a dynamic estimate of the external disturbance. In fact, this invention is the first to design a disturbance observer for a land-air dual-modal UAV based on uncertainty and disturbance estimation theory, and for the first time to combine the disturbance information output by the disturbance observer with a motion planner to improve the planning effect of the motion planner, thereby realizing the perception of disturbance information in the environment at the planning level (previously, disturbance information was mostly only used in the control field, and no further exploration of its combination with the planning field was made).

[0163] S2. Calculate and update the dynamic boundary of the land-air dual-mode UAV in real time using a motion planner based on the disturbance estimation value and the dynamic equations in the dynamic model.

[0164] In one implementation of this embodiment, the calculation and updating of the dynamic boundaries of the land-air dual-mode UAV in real time using a motion planner based on the disturbance estimate and the dynamic equations in the dynamic model specifically includes:

[0165] Let the disturbance estimate under flight mode be Based on the dynamic model of the land-air dual-mode UAV in flight mode, the dynamic boundary of the land-air dual-mode UAV in flight mode is derived. Expressed as:

[0166] ;

[0167] in, Respectively represent the flight mode in the world coordinate system Perturbation estimates on the axis; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate;

[0168] Let the perturbation estimate under land mode be Based on the dynamic model of the land-air dual-mode UAV in the land mode, the dynamic boundary of the land-air dual-mode UAV in the land mode is derived. Expressed as:

[0169] ;

[0170] in, Respectively represent the land mode in the world coordinate system The perturbation estimate on the axis, They represent the UAV’s position in land mode. The minimum and maximum accelerations that the axis can achieve, Indicates the UAV is in land mode The maximum tensile force that the shaft can generate; They represent the UAV’s position in land mode. The minimum and maximum accelerations that the axis can achieve, Indicates the UAV is in land mode The maximum tensile force that the shaft can generate;

[0171] according to and The relationship ( ), the dynamic boundary of the land-air dual-mode UAV under the flight mode Expressed as:

[0172] ;

[0173] according to and The relationship ( ), the dynamic boundary of the land-air dual-mode UAV in the land mode is Expressed as:

[0174] ;

[0175] According to the expression formula of the dynamic boundary of the land-air dual-mode UAV, the dynamic boundary of the land-air dual-mode UAV is calculated and updated in real time.

[0176] The present invention uploads the disturbance information estimated by the disturbance observer to the motion planner and proposes a disturbance adaptive safety boundary adjustment mechanism, which can dynamically adjust the dynamic boundaries of the land-air dual-mode UAV in real time, providing accurate dynamic boundary information for path search and trajectory optimization.

[0177] S3. Based on the updated dynamic boundaries of the land-air dual-mode UAV, the improved motion dynamics A* algorithm is first used to perform path search, and then the improved B-spline algorithm is used to perform trajectory optimization to obtain the trajectory planning result of the land-air dual-mode UAV.

[0178] In one implementation of this embodiment, based on the updated dynamic boundary of the land-air dual-mode UAV, the improved motion dynamics A* algorithm is used to perform path search, and the improved B-spline algorithm is used to perform trajectory optimization to obtain the trajectory planning result of the land-air dual-mode UAV, specifically including:

[0179] Based on the updated dynamic boundary of the dual-mode land-air UAV, a trajectory cost function for the path search problem is designed to obtain an improved motion dynamics A* algorithm;

[0180] Based on the navigation map, the improved motion dynamics A* algorithm is used to perform path search to obtain an initial path;

[0181] Based on the updated dynamic boundary of the dual-mode land-air UAV, an optimization cost function for the trajectory optimization problem is designed, and an improved B-spline algorithm is obtained;

[0182] Based on the initial path, the improved B-spline algorithm is used to perform trajectory optimization to obtain a trajectory planning result of the land-air dual-mode UAV.

[0183] Specifically, the present invention adopts a hierarchical planning method, which uses the Kinodynamic A* algorithm (motion dynamics A* algorithm) for path search at the front end and the B-spline algorithm for trajectory optimization at the back end. The specific process of using the Kinodynamic A* algorithm for path search includes:

[0184] Step 1: State space modeling: Define high-dimensional state nodes containing position, velocity, and acceleration, and constrain their dynamic parameter ranges (such as upper and lower limits on velocity / acceleration);

[0185] Step 2: State expansion and pruning: Generate motion primitives through forward integration of discrete acceleration (this acceleration refers to the real-time acceleration boundary calculated above, also known as the dynamic boundary, and discretization refers to discretizing the acceleration into n acceleration values of different sizes at equal intervals). Invalid motion primitives are eliminated through collision detection and environmental constraints (such as obstacles).

[0186] Step 3: Design a trajectory cost function, calculate and select the lowest-cost motion primitive as the new extended trajectory: After removing the motion primitives that collide with obstacles, calculate the cost of each remaining motion primitive and select the motion primitive with the lowest cost as the new extended trajectory;

[0187] Step 4: Check whether a trajectory to the end point is found: Check whether the new extended trajectory reaches the end point. If so, use the new extended trajectory as the initial trajectory; if not, use the end point of the new extended trajectory as the new extended node and jump to step 2 to continue the search process.

[0188] The Kinodynamic A* algorithm fails to consider obstacle distances during the search process, resulting in the searched trajectory often being close to obstacles. Furthermore, the smoothness and dynamic feasibility of each trajectory segment need to be improved. Therefore, the B-spline algorithm is required to optimize the initial trajectory. The specific process of trajectory optimization using the B-spline algorithm is as follows:

[0189] Step 1: First parameterize the initial trajectory with control points Order B-spline curve;

[0190] Step 2: Design the optimization cost function for the trajectory optimization problem;

[0191] Step 3: Use numerical methods (such as gradient descent) to iteratively solve the optimization problem, adjust the trajectory position, and ultimately solve a collision-free, smooth, and dynamically feasible trajectory.

[0192] This paper designs a Kinodynamic A* path search algorithm, in which aerial trajectories introduce additional energy costs and tend to plan ground trajectories. At the same time, a B-spline trajectory optimization algorithm is designed to optimize the planned trajectory through the dynamic boundaries of the dynamic changes of the dual-mode land and air UAV, ensuring that the trajectory remains feasible in the face of disturbances in the real environment.

[0193] In one implementation of this embodiment, designing a trajectory cost function for the path search problem based on the updated dynamic boundary of the land-air dual-mode UAV specifically includes:

[0194] Considering the high energy consumption disadvantage of the dual-mode UAV flight mode, the dual-mode UAV is inclined to plan a ground trajectory by adding an additional energy cost to the flight trajectory. It switches to the flight mode only when encountering insurmountable obstacles. The designed trajectory cost function Expressed as:

[0195] ;

[0196] in, represents the acceleration input, represents the norm, represents the height of the motion primitive, represents the height threshold of the motion primitive in flight mode, Respectively represent the scaling factors used to adjust the height cost term and the time cost term, Indicates taking the maximum value; the motion primitive is the basic trajectory segment generated by the discrete control quantity (acceleration), which can be combined for the motion planning of the UAV.

[0197] Based on the updated dynamic boundary of the land-air dual-mode UAV, the discrete acceleration input is obtained by discretizing the acceleration boundary at equal intervals. ;

[0198] Based on the trajectory cost function, the discrete acceleration input All generated The total cost of motion primitives Expressed as:

[0199] ;

[0200] in, Indicates the total number of motion primitives, Indicates the serial number, Respectively represent The acceleration and terminal height of each motion primitive, is the time over which the motion primitive is integrated forward.

[0201] The trajectory cost function designed by the present invention is shown in the above formula. This formula can calculate the cost of all motion primitives from the initial position to the current position. This formula weighs the trajectory control amount (acceleration, tending to reduce acceleration to reduce energy consumption), altitude (tending to search for ground tracks to reduce system energy consumption), and total trajectory time (tending to shorten system operation time and improve system efficiency). This allows the planner to search for a collision-free, low-energy, and high-efficiency trajectory.

[0202] In one implementation of this embodiment, the optimization cost function of the trajectory optimization problem is designed based on the updated dynamic boundary of the land-air dual-mode UAV to obtain an improved B-spline algorithm, which specifically includes:

[0203] Parameterize the initial trajectory as control points of Order B-spline curve, where Represents the first Control points (position control points), Respectively represent Control points in the world coordinate system The position of the axis, Represent the 0th control point, the 1st control point and the control points, the optimized cost function of the design Expressed as:

[0204] ;

[0205] in, represents the smoothness cost, which aims to distribute the control points on a straight line as evenly as possible; represents the collision cost, which pushes the control point away from the obstacle based on the distance information; It represents the dynamic feasibility cost, constraining the acceleration of the trajectory to ensure that the land-air dual-mode UAV can execute the trajectory; Respectively The corresponding weight;

[0206] Among them, the smoothness cost of the design Expressed as:

[0207] ;

[0208] in, represents the curvature penalty term, Represents the elastic uniformity term. The smoothness cost is divided into two parts: the curvature penalty term and the elastic uniformity term. The curvature penalty term approximates the local curvature of the trajectory through second-order differences and penalizes the curvature of the trajectory. Its purpose is to suppress sharp turns in the trajectory and improve kinematic feasibility (such as avoiding excessive acceleration or centripetal force). The elastic uniformity term encourages uniform distribution of control points to avoid local over-crowding or over-sparseness. For the control points, For the control points; It is a normalization parameter used to eliminate the influence of uneven distribution of control points, and its value is the average distance between adjacent control points; represents the regularization weight, which balances the curvature penalty and distribution uniformity;

[0209] The collision cost of design Expressed as:

[0210] ;

[0211] ;

[0212] in, Indicates the control points The distance to the nearest obstacle, represents the collision cost mapping function, Indicates the safety distance threshold, which must be greater than the radius or physical size of the drone; Through the function Mapped to collision cost, the collision costs of all control points are accumulated to form the total collision cost, which is used to quantify the obstacle avoidance constraints in trajectory optimization; when the distance between the control point and the obstacle is When , an exponential penalty is imposed, and the closer the distance, the stronger the penalty; when When , there is no collision risk and the cost is zero.

[0213] Design of dynamic feasibility cost based on the updated dynamic boundary of the land-air dual-mode UAV , the dynamic feasibility cost of the design Expressed as:

[0214] ;

[0215] ;

[0216] in, Indicates the acceleration control points along The acceleration value of the axis, represents the dynamic feasibility cost mapping function, They represent the flight mode along The lower and upper limits of the axis's acceleration, They represent the land mode along The design of the dynamic feasibility cost is based on the disturbance adaptive safety boundary adjustment mechanism. The above formula can be used to calculate the dynamic boundary of the land-air dual-mode UAV system, that is, the acceleration boundary, that is, the acceleration lower limit and the acceleration upper limit in real time.

[0217] Among them, the acceleration control point Calculation method:

[0218] ;

[0219] in, is the time between two position control points, For the Speed control points, For the speed control points.

[0220] In another implementation of this embodiment, Figure 2 As shown, a dynamic motion planning method for a land-air dual-mode UAV includes: first determining whether the user has issued a new instruction, that is, whether a new target position is provided; if so, performing disturbance estimation; then dynamically adjusting the dynamic boundary based on the disturbance estimation value; then performing path search and trajectory optimization based on the dynamically adjusted dynamic boundary to obtain a trajectory result; a controller controls the UAV to fly according to the trajectory result; then determining whether the target position is reached; if not, returning to continue disturbance estimation; if so, determining whether a new target position is provided; if no new target position is provided, directly terminating the process.

[0221] In summary, the controller (i.e., disturbance observer) of the present invention estimates the disturbance information of the dual-modal UAV in real time and uploads it to the motion planner. The motion planner dynamically adjusts the UAV's dynamic boundaries based on this disturbance information. Ultimately, the motion planner performs path search and trajectory optimization using the dynamically changing dynamic boundaries to ensure that the final trajectory meets the real-time dynamic feasibility requirements.

[0222] In addition, based on the above-mentioned dynamic motion planning method of the land-air dual-mode UAV, the present invention also provides a dynamic motion planning system of the land-air dual-mode UAV, wherein a preferred embodiment of the dynamic motion planning system of the land-air dual-mode UAV is as follows: Figure 3 As shown, specifically including:

[0223] Disturbance Estimation Module 01: Designs a disturbance observer based on the dynamic model of the dual-mode UAV in flight mode and land mode using uncertainty and disturbance estimation methods, and uses the disturbance observer to estimate the disturbance factors in the environment to obtain a disturbance estimate.

[0224] Dynamic boundary adjustment module 02: used for calculating and updating the dynamic boundary of the land-air dual-mode UAV in real time using a motion planner according to the disturbance estimation value and the dynamic equations in the dynamic model;

[0225] Path search and trajectory optimization module 03: Based on the updated dynamic boundary of the land-air dual-mode UAV, the improved motion dynamics A* algorithm is used to perform path search, and the improved B-spline algorithm is used to perform trajectory optimization to obtain the trajectory planning result of the land-air dual-mode UAV.

[0226] In addition, based on the above-mentioned dynamic motion planning method and system of the land-air dual-mode UAV, the present invention also provides a land-air dual-mode UAV, wherein the preferred embodiment of the land-air dual-mode UAV is as follows: Figure 4 As shown, it specifically includes a disturbance observer 10 and a motion planner 20. Figure 4 Only some components of the land-air dual-mode UAV are shown, but it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead. In one embodiment, the disturbance observer 10 and the motion planner 20 jointly implement the steps of the dynamic motion planning method for the land-air dual-mode UAV as described above. Specifically, the disturbance observer 10 is used to estimate disturbance factors in the environment, obtain disturbance estimates, and upload the disturbance estimates to the motion planner 20. The motion planner 20 is used to calculate and update the dynamic boundaries of the land-air dual-mode UAV in real time based on the disturbance estimates. Based on the updated dynamic boundaries of the land-air dual-mode UAV, path search and trajectory optimization are performed to obtain a trajectory planning result for the land-air dual-mode UAV.

[0227] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a dynamic motion planning program for a land-air dual-mode UAV. When the dynamic motion planning program for the land-air dual-mode UAV is executed by a disturbance observer and a motion planner, the steps of the dynamic motion planning method for the land-air dual-mode UAV as described above are implemented.

[0228] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or terminal comprising the element.

[0229] Of course, those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium that can be read by a computer. When executed, the program can include the processes in the above-described method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disk, etc.

[0230] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A dynamic motion planning method for a land-air dual-mode UAV, characterized in that: The dynamic motion planning method of the land-air dual-mode UAV includes: Based on the dynamic model of the dual-mode UAV in flight mode and land mode, a disturbance observer is designed using uncertainty and disturbance estimation methods, and the disturbance observer is used to estimate the disturbance factors in the environment to obtain a disturbance estimation value; Calculate and update the dynamic boundaries of the dual-mode land-air UAV in real time using a motion planner based on the disturbance estimate and the dynamic equations in the dynamic model; Based on the updated dynamic boundaries of the land-air dual-mode UAV, an improved kinematic dynamics A* algorithm is used to perform path search, and an improved B-spline algorithm is used to perform trajectory optimization to obtain a trajectory planning result for the land-air dual-mode UAV; The method of designing a disturbance observer based on the dynamic model of the dual-mode UAV in flight mode and land mode by using uncertainty and disturbance estimation method, and using the disturbance observer to estimate the disturbance factors in the environment to obtain the disturbance estimation value specifically includes: Define a second-order system: ； in, Represents the acceleration vector of the drone in the three-dimensional space in the world coordinate system, denote the control output vector and disturbance vector respectively; According to the dynamic equations in the dynamic model of the land-air dual-mode UAV in flight mode, the control output vector and disturbance vector in flight mode are expressed as: ； in, represents the total thrust generated by the four propellers, represents the Euler angle vector, represents the attitude angle vector of the drone, Indicates the quality of the drone, represents the gravitational acceleration vector, represents the disturbance vector under the flight mode, represent the control output vector and disturbance vector under the flight mode respectively; According to the dynamic equations in the dynamic model of the land-air dual-mode UAV in the land mode, the control output vector and disturbance vector in the land mode are expressed as: ； in, denote the control output vector and disturbance vector under land mode respectively; represents the force coordinate transformation matrix, represents the yaw angle, represents the driving force vector, Represents the mass matrix The inverse matrix of represents the perturbation coordinate transformation matrix, represents the disturbance vector under the land mode; The difference between the second-order system state vector and the control output vector is used as the disturbance estimate output by the disturbance observer. , where the second-order system state vector corresponds to the acceleration vector in the dynamic equation : ； According to the control output vector , reference control output vector and the perturbation estimate relationship , we can get: ； Using a low-pass filter to filter the high-frequency noise in the second-order system, we can get: ； in, represents the inverse Laplace transform, is a low-pass filter, Represents the convolution operation; Combine and , we can get: ； make , the disturbance estimate The final expression is: ； in, represents the time constant, represents the Laplace variable, represents the integration time threshold of the disturbance observer, represents the time variable, Represents the first-order system state vector, corresponding to the velocity vector in the dynamic equation; express The corresponding first-order system state vector is, represents the initial first-order system state vector; express The corresponding baseline control output vector.

2. The dynamic motion planning method for a land-air dual-mode UAV according to claim 1 is characterized in that: The dynamic model of the land-air dual-mode UAV in flight mode includes: ； ； ； ； ； ； ； ； ； ； ； ； ； in, Indicates the quality of the drone; Represents the position vector of the drone in the three-dimensional space in the world coordinate system, Respectively represent the UAV in the three-dimensional space under the world coordinate system Axis position; represents transpose; Represents three-dimensional space; Represents the acceleration vector of the drone in the three-dimensional space in the world coordinate system, Respectively represent the UAV in the three-dimensional space under the world coordinate system acceleration of the axis; Indicates the total thrust produced by the four propellers; represents the gravitational acceleration vector, represents the acceleration due to gravity; represents the disturbance vector under the flight mode, Respectively represent the body coordinate system in flight mode Axis disturbances; represents the attitude angle vector of the drone, Respectively represent the roll angle, pitch angle and yaw angle of the drone; represents the Euler angle vector, Represents the roll angle The cosine and sine of Represents the pitch angle The cosine and sine of Represents the yaw angle The cosine and sine of ; represents the moment of inertia matrix, Indicates is a diagonal matrix with diagonal elements; Respectively represent the UAV in the body coordinate system Moment of inertia about the axis; represents the rotational torque vector, Respectively represent the UAV in the body coordinate system Rotational torque on the shaft; Represents the attitude angular acceleration vector; represents the coupling vector, represents the attitude angular velocity vector, They represent the roll angular velocity, pitch angular velocity and yaw angular velocity of the UAV in the body coordinate system respectively; represents the unknown perturbation vector, Respectively expressed in the body coordinate system unknown disturbances on the axis; The dynamic model of the land-air dual-mode UAV in the land mode includes: ； ； ； ； ； ； ； in, represents the driving force vector, Represent the first wheel speed and the second wheel speed respectively; represents the disturbance vector in the land mode, Respectively represent the land mode in the body coordinate system Axis disturbance, They represent the longitudinal friction and lateral friction acting on the UAV in the body coordinate system respectively; represents the yaw acceleration; Represent the mass matrix, force coordinate transformation matrix and disturbance coordinate transformation matrix respectively: Indicates the width of the drone: represents the reaction torque, Indicates the longitudinal friction force on the first wheel, Indicates the lateral friction force on the first wheel: Indicates the longitudinal friction force on the second wheel, Indicates the lateral friction force on the second wheel: Indicates the longitudinal friction force on the third wheel, Indicates the lateral friction force on the third wheel: Indicates the longitudinal friction force on the fourth wheel, Indicates the lateral friction force on the fourth wheel: Represent the distances from the rear wheel and front wheel to the center of mass of the drone respectively.

3. The dynamic motion planning method for a land-air dual-mode UAV according to claim 2, characterized in that: The method of calculating and updating the dynamic boundary of the land-air dual-mode UAV in real time using a motion planner based on the disturbance estimation value and the dynamic equations in the dynamic model specifically includes: Let the disturbance estimate under flight mode be Based on the dynamic model of the land-air dual-mode UAV in flight mode, the dynamic boundary of the land-air dual-mode UAV in flight mode is derived. Expressed as: ； in, Respectively represent the flight mode in the world coordinate system Perturbation estimates on the axis; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; They represent the UAV’s flight mode in The minimum and maximum accelerations that the axis can achieve, Indicates the drone is in flight mode The maximum tensile force that the shaft can generate; Let the perturbation estimate under land mode be Based on the dynamic model of the land-air dual-mode UAV in the land mode, the dynamic boundary of the land-air dual-mode UAV in the land mode is derived. Expressed as: ； in, Respectively represent the land mode in the world coordinate system The perturbation estimate on the axis, They represent the UAV’s position in land mode. The minimum and maximum accelerations that the axis can achieve, Indicates the UAV is in land mode The maximum tensile force that the shaft can generate; They represent the UAV’s position in land mode. The minimum and maximum accelerations that the axis can achieve, Indicates the UAV is in land mode The maximum tensile force that the shaft can generate; according to and The relationship between the dynamic boundary of the land-air dual-mode UAV under the flight mode is Expressed as: ； according to and The dynamic boundary of the land-air dual-mode UAV in the land mode is Expressed as: ； According to the expression formula of the dynamic boundary of the land-air dual-mode UAV, the dynamic boundary of the land-air dual-mode UAV is calculated and updated in real time.

4. The dynamic motion planning method for a land-air dual-mode UAV according to claim 3 is characterized in that: Based on the updated dynamic boundary of the land-air dual-mode UAV, the improved motion dynamics A* algorithm is used to perform path search, and the improved B-spline algorithm is used to perform trajectory optimization to obtain the trajectory planning result of the land-air dual-mode UAV, which specifically includes: Based on the updated dynamic boundary of the dual-mode land-air UAV, a trajectory cost function for the path search problem is designed to obtain an improved motion dynamics A* algorithm; Based on the navigation map, the improved motion dynamics A* algorithm is used to perform path search to obtain an initial path; Based on the updated dynamic boundary of the dual-mode land-air UAV, an optimization cost function for the trajectory optimization problem is designed, and an improved B-spline algorithm is obtained; Based on the initial path, the improved B-spline algorithm is used to perform trajectory optimization to obtain a trajectory planning result of the land-air dual-mode UAV.

5. The dynamic motion planning method for a land-air dual-mode UAV according to claim 4, characterized in that: The trajectory cost function of the path search problem is designed based on the updated dynamic boundary of the land-air dual-mode UAV, specifically including: Based on the high energy consumption disadvantage of the dual-mode UAV in the flight mode, the extra energy cost is added to the flight trajectory, and the trajectory cost function is designed. Expressed as: ； in, represents the acceleration input, represents the norm, represents the height of the motion primitive, represents the height threshold of the motion primitive in flight mode, Respectively represent the scaling factors used to adjust the height cost term and the time cost term, Indicates taking the maximum value; Based on the updated dynamic boundary of the land-air dual-mode UAV, the discrete acceleration input is obtained by discretizing the acceleration boundary at equal intervals. ; Based on the trajectory cost function, the discrete acceleration input All generated The total cost of motion primitives Expressed as: ； in, Indicates the total number of motion primitives, Indicates the serial number, Respectively represent The acceleration and terminal height of each motion primitive, is the time over which the motion primitive is integrated forward.

6. The dynamic motion planning method for a land-air dual-mode UAV according to claim 5, characterized in that: The optimized cost function of the trajectory optimization problem is designed based on the updated dynamic boundary of the land-air dual-mode UAV to obtain an improved B-spline algorithm, which specifically includes: Parameterize the initial trajectory as control points of Order B-spline curve, where Represents the first control points, Respectively represent Control points in the world coordinate system The position of the axis, Represent the 0th control point, the 1st control point and the control points, the optimized cost function of the design Expressed as: ； in, represents the smoothness cost, represents the collision cost, represents the dynamic feasibility cost, Respectively The corresponding weight; Among them, the smoothness cost of the design Expressed as: ； in, represents the curvature penalty term, represents the elastic uniform term, For the control points, For the control points, is the normalization parameter, represents the regularization weight; The collision cost of design Expressed as: ； ； in, Indicates the control points The distance to the nearest obstacle, represents the collision cost mapping function, Indicates the safety distance threshold; Design of dynamic feasibility cost based on the updated dynamic boundary of the land-air dual-mode UAV , the dynamic feasibility cost of the design Expressed as: ； ； in, Indicates the Acceleration control points along The acceleration value of the axis, represents the dynamic feasibility cost mapping function, They represent the flight mode along The lower and upper limits of the axis's acceleration, They represent the land mode along The lower and upper acceleration limits of the axis.

7. A dynamic motion planning system for a land-air dual-mode UAV, characterized in that: The dynamic motion planning system of the land-air dual-mode UAV is applied to the dynamic motion planning method of the land-air dual-mode UAV according to any one of claims 1 to 6, and the dynamic motion planning system of the land-air dual-mode UAV includes: A disturbance estimation module is configured to design a disturbance observer based on the dynamic model of the dual-mode UAV in flight mode and land mode, using uncertainty and disturbance estimation methods, and use the disturbance observer to estimate the disturbance factors in the environment to obtain a disturbance estimate value; A dynamic boundary adjustment module is configured to calculate and update the dynamic boundary of the land-air dual-mode UAV in real time using a motion planner based on the disturbance estimation value and the dynamic equations in the dynamic model; Path search and trajectory optimization module: used to perform path search using the improved motion dynamics A* algorithm based on the updated dynamic boundaries of the land-air dual-mode UAV, and to perform trajectory optimization using the improved B-spline algorithm to obtain the trajectory planning result of the land-air dual-mode UAV.

8. A dual-mode land and air drone, characterized in that: The land-air dual-mode UAV includes: a disturbance observer and a motion planner, which are used to jointly implement the steps of the dynamic motion planning method of the land-air dual-mode UAV as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a dynamic motion planning program for a land-air dual-mode UAV. When the dynamic motion planning program for the land-air dual-mode UAV is executed by a disturbance observer and a motion planner, the steps of the dynamic motion planning method for the land-air dual-mode UAV as described in any one of claims 1 to 6 are implemented.

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