Unmanned aerial vehicle landing method, system and device based on multi-stage control

Abstract

The invention discloses an unmanned aerial vehicle landing method, system and device based on multi-stage control, and the method comprises the steps: determining a dynamic model based on the state data of an unmanned aerial vehicle; determining a first control parameter of the first stage based on the kinetic model, and controlling the movement of the unmanned aerial vehicle in the first stage based on the first control parameter; determining a second control parameter of a second stage based on first end point state data of the unmanned aerial vehicle at the end point of the first stage, and controlling the movement of the unmanned aerial vehicle in the second stage based on the second control parameter; and determining a third control parameter of a third stage based on second end point state data of the unmanned aerial vehicle at the end point of the second stage, and controlling the unmanned aerial vehicle to land based on the third control parameter. Through a three-stage control strategy, in combination with a wind field model, a ground contact force compensation model, ground contact impact force analysis and the like, accurate and stable landing of the unmanned aerial vehicle in a complex environment is achieved, and landing safety and stability are improved.

Description

Technical Field

[0001] This specification relates to the field of unmanned aerial vehicle (UAV) control, and particularly to a UAV landing method, system, and device based on multi-stage control. Background Art

[0002] With the rapid development of UAV technology, UAVs are increasingly widely used in fields such as monitoring and transportation. As the last stage of a flight mission, the safety and reliability of UAV landing are directly related to the success of the entire flight mission. Currently, certain progress has been made in UAV landing control technology, but in complex environments, many challenges still remain.

[0003] Currently, the following main deficiencies exist in the prior art:

[0004] 1. Most landing control methods do not fully consider the influence of the wind field on the UAV landing process. Especially in complex wind field environments, UAVs are prone to problems such as unstable attitudes and position offsets, which affect the safety and accuracy of landing.

[0005] 2. Existing methods usually control the landing process as a whole, lacking refined control strategies for different landing stages and making it difficult to optimize according to the characteristics of each stage.

[0006] 3. When approaching the ground, the ground effect will significantly affect the dynamic characteristics of the UAV, and the prior art lacks an effective ground contact force prediction and compensation mechanism.

[0007] 4. The impact force control at the moment of touchdown is insufficient, easily causing damage or bouncing of the UAV structure and affecting landing stability.

[0008] Therefore, there is an urgent need for a UAV landing control method that can adapt to complex wind field environments, perform fine control in stages, and have the ability to predict and compensate for ground contact forces, so as to improve the safety, stability, and accuracy of the UAV landing process. Summary of the Invention

[0009] To solve the technical problems of poor landing stability and low accuracy of UAVs in complex environments in the prior art and achieve the technical effect of improving the landing accuracy and stability of UAVs, this specification provides a UAV landing control method with a three-level hierarchical control architecture.

[0010] The technical solution adopted by this specification to solve its technical problems is as follows: Provide a drone landing control method based on multi-stage control, including: determining a dynamic model based on the state data of the drone; determining a first control parameter for a first stage based on the dynamic model, and controlling the movement of the drone in the first stage based on the first control parameter; determining a second control parameter for a second stage based on the first end state data of the drone at the end of the first stage, and controlling the movement of the drone in the second stage based on the second control parameter; determining a third control parameter for a third stage based on the second end state data of the drone at the end of the second stage, and controlling the landing of the drone based on the third control parameter.

[0011] Further, the determining the first control parameter for the first stage based on the dynamic model includes: obtaining wind field data of the environment where the drone is located; constructing a wind field model based on the wind field data; determining the first control parameter for the first stage based on the dynamic model and the wind field model.

[0012] Further, the wind field model includes a horizontal wind direction angle component and a vertical pitch angle component; the first control parameter includes a state weight matrix and a control weight matrix; the determining the first control parameter for the first stage based on the dynamic model and the wind field model includes: dynamically adjusting the state weight matrix and the control weight matrix based on the horizontal wind direction angle component and the vertical pitch angle component.

[0013] Further, the determining the second control parameter for the second stage based on the first end state data of the drone at the end of the first stage includes: determining an initial motion trajectory based on the first end state data; determining an estimated ground contact force based on the initial motion trajectory; constructing a compensation model based on the estimated ground contact force; adjusting the initial motion trajectory based on the compensation model, and determining the second control parameter.

[0014] Further, the determining the third control parameter for the third stage based on the second end state data of the drone at the end of the second stage includes: predicting the time distribution of the touchdown impact force based on the second end state data; determining the damping-stiffness parameter based on the time distribution of the touchdown impact force.

[0015] Further, the determining the third control parameter for the third stage based on the second end state data of the drone at the end of the second stage includes: predicting the phase distribution of the touchdown impact force based on the second end state data; determining the frequency-phase parameter based on the phase distribution of the impact force.

[0016] Furthermore, the demarcation points of the first stage, the second stage, and the third stage are adaptively adjusted based on at least one of the state of the drone, the environmental state, and the landing scenario.

[0017] The beneficial effects of this specification are as follows: Through a three-level hierarchical control architecture, compared with the prior art, it has the technical effect of significantly improving the landing accuracy and stability of the drone in a complex wind field environment. Specifically, first, through dynamic disturbance suppression and adaptive weight optimization, it effectively resists wind field interference and maintains the stability of the drone's trajectory; second, based on the compensation mechanism for ground contact force prediction, it effectively reduces the touchdown impact; finally, the time-phase two-dimensional shock absorption design, through the collaborative optimization of the dynamic damping coefficient and the dynamic phase, further reduces the landing impact, thus comprehensively solving the technical problems of poor landing stability and low accuracy of the drone in a complex wind field environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this specification and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0019] Figure 1 is a flowchart of a method for landing a drone based on multi-stage control according to an embodiment of this specification.

[0020] Figure 2 is a flowchart of determining the first control parameter of the first stage according to an embodiment of this specification.

[0021] Figure 3 is a flowchart of determining the second control parameter of the second stage according to an embodiment of this specification.

[0022] Figure 4 is a flowchart of determining the third control parameter of the third stage according to an embodiment of this specification.

[0023] Figure 5 is a module diagram of a system for landing a drone based on multi-stage control according to an embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The present specification will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that in the accompanying drawings or the description of the specification, similar or identical parts are all denoted by the same reference numerals, and the implementation manners not illustrated or described in the accompanying drawings are in the forms known to those of ordinary skill in the art. In the description of the present specification, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0025] In addition, the described embodiments are only some embodiments of the present specification, rather than all embodiments. The components of the embodiments of the present specification usually described and illustrated in the accompanying drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present specification provided in the accompanying drawings is not intended to limit the scope of the present specification to be protected, but merely represents the selected embodiments of the present specification. All other embodiments obtained by those skilled in the art based on the embodiments of the present specification without creative efforts fall within the scope of protection of the present specification.

[0026] It should be noted that the term "including" will be used in the embodiments of the present specification to indicate the existence of the features stated thereafter, but does not exclude the addition of other features.

[0027] The embodiments of the present specification provide a method for an unmanned aerial vehicle (UAV) to land based on multi-stage control. This method divides the UAV landing process into three stages and adopts different control strategies respectively to achieve the safe and stable landing of the UAV.

[0028] Figure 1 is a flowchart of the method for an unmanned aerial vehicle to land based on multi-stage control shown in the embodiments of the present specification. In some embodiments, this method can be executed by a UAV control platform, for example, a server, a terminal device, a processing device, an electronic device, etc.

[0029] Step 110, determine a dynamic model based on the state data of the UAV.

[0030] In this embodiment, the state data of the UAV may include data such as the position, speed, acceleration, attitude, angular velocity, and angular acceleration of the UAV. These state data can be obtained by sensors carried on the UAV, such as an inertial measurement unit (IMU), a global positioning system (GPS), a vision sensor, etc.

[0031] After obtaining the state data of the UAV, a dynamic model of the UAV can be determined based on the state data.

[0032] In some embodiments, a six-degree-of-freedom rigid body dynamic model can be adopted, and this model can be expressed as:

[0033] m·a = F + mg

[0034] J·ω · +ω×(J·ω) = M

[0035] where m represents the mass of the UAV, a represents the acceleration of the UAV, F represents the resultant force acting on the UAV, g represents the acceleration due to gravity, J represents the inertia matrix of the UAV, ω represents the angular velocity of the UAV, and ω · represents the angular acceleration, and M represents the torque acting on the UAV.

[0036] In some embodiments, the dynamic model can also be a particle model, an elastic body model, a linearized model, etc., and this specification does not limit this.

[0037] Step 120: Based on the dynamic model, determine the first control parameter in the first stage, and control the movement of the UAV in the first stage based on the first control parameter.

[0038] In this embodiment, the first stage refers to the process in which the UAV flies from a predetermined position (for example, the starting position of the landing mission) to a predetermined height above the landing point. In this stage, the UAV needs to maintain a stable flight state and accurately navigate to above the landing point.

[0039] In some embodiments, based on the above dynamic model, the linear quadratic regulator (LQR) control method can be used to determine the first control parameter. For example, the LQR control method can determine the optimal control parameter by minimizing the following performance index:

[0040]

[0041] where x represents the state vector, u represents the control input vector, and Q and R are the state weight matrix and the control weight matrix respectively.

[0042] Furthermore, the optimal feedback gain matrix can be obtained by solving the Riccati equation, and then the control input vector, that is, the first control parameter, can be obtained.

[0043] In some embodiments, based on the above dynamic model, the MPC method can be used to determine the first control parameter, and the control input is determined by solving the finite-time optimal control problem. Specifically, the MPC method determines the optimal control sequence by minimizing the following performance index:

[0044]

[0045] where N represents the prediction horizon; represents the state prediction at time t for time k; Denote the predicted control input at time t for time k; Q and R are the state weight matrix and the control weight matrix respectively; Q f is the weight matrix of the terminal state.

[0046] In some embodiments, in order to effectively handle the wind field interference during the UAV landing process, when determining the first control parameter, a wind field variable is introduced. Specifically, the wind field data of the environment where the UAV is located can be obtained; based on the wind field data, a wind field model is constructed; based on the dynamic model and the wind field model, the first control parameter in the first stage is determined.

[0047] In some embodiments, when the wind speed is relatively high, the position and speed control gains are increased to improve the wind resistance ability of the UAV; when the wind direction changes greatly, the attitude angle and angular velocity control gains are increased to improve the attitude stability of the UAV.

[0048] In some embodiments, since the wind field may be in a dynamically changing state and the wind field disturbances are complex, when determining the first control parameter, the first control parameter is dynamically and adaptively adjusted according to the wind field disturbance situation. Specifically, the wind field model includes a horizontal wind direction angle component and a vertical pitch angle component; based on the horizontal wind direction angle component and the vertical pitch angle component, the state weight matrix and the control weight matrix are dynamically adjusted. A more detailed description can be seen later Figure 2 and its description, which will not be elaborated here.

[0049] In some embodiments, after determining the first control parameter, based on these control parameters, the rotational speeds of each rotor of the UAV can be calculated, thereby controlling the movement of the UAV in the first stage. Specifically, the control input u of the UAV can be expressed as:

[0050] u = [F, τx, τy, τz] T

[0051] where F represents the total thrust, and τx, τy, and τz represent the torques about the x-axis, y-axis, and z-axis respectively. These control inputs can be converted into the rotational speeds of each rotor to control the movement of the UAV.

[0052] Step 130, based on the first end state data of the UAV at the end of the first stage, determine the second control parameter in the second stage, and control the movement of the UAV in the second stage based on the second control parameter.

[0053] When the UAV reaches the end of the first stage, that is, at a predetermined height above the landing point, the system records the state data of the UAV at this time, including data such as the position, speed, acceleration, attitude, angular velocity, and angular acceleration of the UAV. These data are called the first end state data.

[0054] Based on the first end - state data, the system determines the second control parameter for the second stage. The second stage refers to the process of the UAV descending from a predetermined height to near the ground. In this stage, the UAV needs to maintain a stable descent speed and accurately align with the landing point.

[0055] In some embodiments, based on the first end - state data, an initial motion trajectory is first determined. The initial motion trajectory refers to the expected motion path of the UAV from the end of the first stage to the end of the second stage.

[0056] Specifically, the initial motion trajectory can be represented as positions, velocities, and accelerations at a series of time points:

[0057] {(t_i,p_i,v_i,a_i)|i = 0,1,...,N}

[0058] where t_i represents the time point, p_i represents the position, v_i represents the velocity, a_i represents the acceleration, and N represents the number of trajectory points.

[0059] Based on the initial motion trajectory, an estimated ground contact force is determined. The estimated ground contact force refers to the force that may be generated when the UAV contacts the ground at the end of the second stage.

[0060] Furthermore, based on the estimated ground contact force, a compensation model is constructed. The compensation model is used to adjust the initial motion trajectory to reduce the landing impact. Based on the compensation model, the initial motion trajectory is adjusted, and the second control parameter is determined. Based on these control parameters, the system can calculate the control input sequence of the UAV in the second stage, thereby controlling the motion of the UAV in the second stage. A more detailed description can be found later Figure 3 and its description, which will not be elaborated here.

[0061] Step 140, based on the second end - state data of the UAV at the end of the second stage, determines the third control parameter for the third stage and controls the UAV to land based on the third control parameter.

[0062] When the UAV reaches the end of the second stage, that is, the position near the ground, the system records the state data of the UAV at this time, including data such as the position, velocity, acceleration, attitude, angular velocity, and angular acceleration of the UAV. These data are called the second end - state data.

[0063] Based on the second end - state data, the system determines the third control parameter for the third stage. The third stage refers to the process of the UAV finally landing on the ground from the position near the ground. In this stage, the UAV needs to control the landing speed to reduce the landing impact and maintain a stable attitude.

[0064] In some embodiments, the third control parameter is determined using an impedance control method. The impedance control method enables the UAV to make a smooth transition when it touches the ground by simulating the behavior of a spring-damper system. Specifically, the impedance control process can be expressed as:

[0065]

[0066] where F represents the control force, M represents the mass matrix of the UAV, xd, represent the desired position, velocity, and acceleration respectively, x, represent the actual position and velocity respectively, and Kp and Kd represent the position gain and velocity gain respectively.

[0067] In some embodiments, to achieve a smoother landing of the UAV, during the control process of the third stage, the time distribution of the impact force is predicted before touching the ground, and the damping-stiffness parameters are dynamically adjusted based on the prediction results to achieve advanced shock absorption. In addition, the phase distribution of the impact force when touching the ground is predicted, and based on the impact force phase distribution, the frequency-phase parameters are determined to control the vibration characteristics of the UAV during the landing process. For a more detailed description, see Figure 4 and its description, which will not be elaborated here.

[0068] In some embodiments, the separation points of the first stage, the second stage, and the third stage are adaptively adjusted based on at least one of the state of the UAV, the environmental state, and the landing scenario.

[0069] In some embodiments, the end position of the first stage is adaptively adjusted according to the state of the UAV, the environmental state, and the landing scenario.

[0070] Specifically, the end height h_1 of the first stage can be expressed as:

[0071] h_1 = h_1_base + Δh_1(state, env, scene)

[0072] where h_1_base represents the base end height, and Δh_1 represents the adjustment term based on the state of the UAV, the environmental state, and the landing scenario.

[0073] In this embodiment, when the battery power of the UAV is low, h_1 is decreased to shorten the flight time; when the environmental wind speed is high, h_1 is increased to provide more adjustment space; when the landing scenario is complex (such as having obstacles), h_1 is increased to improve safety.

[0074] In some embodiments, the end position of the second stage is adaptively adjusted according to the state of the UAV, the environmental state, and the landing scenario.

[0075] Specifically, the end height h_2 of the second stage can be expressed as:

[0076] h_2 = h_2_base + Δh_2(state, env, scene)

[0077] Among them, h_2_base represents the base end height, and Δh_2 represents the adjustment term based on the UAV state, environmental state, and landing scene.

[0078] In this embodiment, when the attitude of the UAV is unstable, h_2 is increased to provide more adjustment time; when the environmental wind speed changes suddenly, h_2 is increased to reduce the impact of the wind on landing; when the landing site is uneven, h_2 is increased to improve the landing accuracy.

[0079] In the embodiments of this specification, the separation points of the first stage, the second stage, and the third stage are adaptively adjusted based on at least one of the UAV state, environmental state, and landing scene. Specifically, the adjustment method of the separation point is:

[0080] h_1 = h_1_base + α_1 * state_factor + β_1 * env_factor + γ_1 * scene_factor

[0081] h_2 = h_2_base + α_2 * state_factor + β_2 * env_factor + γ_2 * scene_factor

[0082] Among them, state_factor represents the UAV state factor, env_factor represents the environmental state factor, scene_factor represents the landing scene factor, and α_1, β_1, γ_1, α_2, β_2, γ_2 represent the corresponding weight coefficients.

[0083] The UAV state factor can be expressed as:

[0084] state_factor = w_1 * battery_level + w_2 * stability_index + w_3 * control_error

[0085] Among them, battery_level represents the battery power level, stability_index represents the attitude stability index, control_error represents the control error, and w_1, w_2, w_3 represent the corresponding weight coefficients.

[0086] The environmental state factor can be expressed as:

[0087] env_factor = w_4 * wind_speed + w_5 * wind_gust + w_6 * visibility

[0088] Among them, wind_speed represents the wind speed, wind_gust represents the gust intensity, visibility represents the visibility, and w_4, w_5, and w_6 represent the corresponding weight coefficients.

[0089] The landing scenario factor can be expressed as:

[0090] scene_factor = w_7 * terrain_roughness + w_8 * obstacle_density + w_9 * landing_area_size

[0091] Among them, terrain_roughness represents the terrain roughness, obstacle_density represents the obstacle density, landing_area_size represents the landing area size, and w_7, w_8, and w_9 represent the corresponding weight coefficients.

[0092] In some embodiments, when the battery power of the UAV is lower than 30%, h_1 and h_2 are reduced to accelerate the landing process; when the attitude stability index is lower than 0.6, h_1 and h_2 are increased to provide more adjustment time; when the control error is greater than 0.5 meters, h_1 and h_2 are increased to improve the control accuracy.

[0093] In some embodiments, when the wind speed is greater than 5 m / s, h_1 and h_2 are increased to reduce the impact of the wind on landing; when the gust intensity is greater than 2 m / s, h_1 and h_2 are increased to cope with sudden wind conditions; when the visibility is lower than 50 meters, h_1 and h_2 are increased to improve safety.

[0094] In some embodiments, when the terrain roughness is greater than 0.3, h_2 is increased to improve the landing accuracy; when the obstacle density is greater than 0.2, h_1 is increased to improve the obstacle avoidance ability; when the landing area size is less than 10 square meters, h_1 and h_2 are increased to improve the landing accuracy.

[0095] It should be noted that the above description of the overall process is only for illustration and example, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the above process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0096] Figure 2 is a flowchart for determining the first control parameter according to the embodiments shown in this specification.

[0097] Step 210: Obtain the wind field data of the environment where the drone is located.

[0098] The wind field data can be obtained by the wind speed sensor carried on the drone, or by a weather station or weather forecast.

[0099] Step 220: Based on the wind field data, construct a wind field model.

[0100] Based on the obtained wind field data, construct a wind field model. Specifically, the wind field model can be expressed as:

[0101]

[0102] where ||w|| represents the wind speed magnitude, θ(t) is the horizontal wind direction angle (the angle with the drone's heading), is the vertical pitch angle (the inclination angle of the wind field relative to the ground).

[0103] In some embodiments, assuming that the wind field is a random perturbation distribution (such as Gaussian distribution, colored noise distribution, etc.), the horizontal wind direction angle and the vertical pitch angle satisfy the following conditions:

[0104]

[0105] where α and β are attenuation coefficients, ξ θ(t) and is white noise, representing the random fluctuation of the wind direction.

[0106] Step 230: Based on the dynamic model and the wind field model, determine the first control parameter in the first stage.

[0107] In some embodiments, the wind field model may include a horizontal wind direction angle component and a vertical pitch angle component. The first control parameter includes a state weight matrix and a control weight matrix. Accordingly, based on the horizontal wind direction angle component and the vertical pitch angle component, dynamically adjust the state weight matrix and the control weight matrix.

[0108] In some embodiments, when the horizontal wind direction angle component is between 0° and 90°, increase the weights of the position and speed in the x-axis direction; when the horizontal wind direction angle component is between 90° and 180°, increase the weights of the position and speed in the y-axis direction; when the vertical pitch angle component is greater than 30°, increase the weights of the position and speed in the z-axis direction.

[0109] In some embodiments, the specific adjustment method is:

[0110]

[0111] where Q0 is the basic state weight matrix and k is the adaptive gain coefficient.

[0112] R(t) = R0 + ΔR(t), where ΔR(t) = λ * diag(||w(t)||, ||w(t)||, ||w(t)||)

[0113] Here, R0 is the basic control weight matrix, λ is the adjustment coefficient, and ||w(t)|| is the norm of the wind speed.

[0114] By adaptively and dynamically adjusting the state weight matrix and the control weight matrix based on the wind field conditions, the wind field interference can be effectively resisted and the UAV trajectory can be kept stable.

[0115] It should be noted that the above description is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the above process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0116] Figure 3 is a flowchart for determining the second control parameter in the second stage according to the embodiments shown in this specification.

[0117] Step 310, determine the initial motion trajectory based on the first end state data.

[0118] Combined with the foregoing, the initial motion trajectory refers to the expected motion path of the UAV from the end point of the first stage to the end point of the second stage.

[0119] Specifically, the initial motion trajectory can be expressed as the position, velocity, and acceleration at a series of time points:

[0120] {(t_i, p_i, v_i, a_i)|i = 0, 1,..., N}

[0121] Here, t_i represents the time point, p_i represents the position, v_i represents the velocity, a_i represents the acceleration, and N represents the number of trajectory points.

[0122] In some embodiments, the initial motion trajectory planning can be achieved by adjusting the convergence rate and the end attitude constraint based on the Tanh-Poly Hybrid Trajectory.

[0123] In some embodiments, the initial motion trajectory planning can also be performed based on the MPC method described above. In some embodiments, the trajectory planning can also be performed based on traditional algorithms such as the A* algorithm and the Dijkstra algorithm, which are not limited in this specification.

[0124] Step 320, determine the estimated ground contact force based on the initial motion trajectory.

[0125] After determining the initial motion trajectory, the ground contact force when the UAV touches the ground can be predicted by combining the speed, direction, acceleration, etc. of the motion trajectory.

[0126] In some embodiments, the estimated ground contact force can be expressed as:

[0127] F_contact = m * (v_N - v_target) / Δt

[0128] where m represents the mass of the UAV, v_N represents the speed at the end of the trajectory, v_target represents the target landing speed, and Δt represents the contact time.

[0129] In some embodiments, the ground contact force can be estimated by a momentum observer:

[0130]

[0131] where q represents the attitude of the UAV, including position, angle, etc., M(q) is the inertia matrix of the UAV, is the Coriolis force matrix, and G(q) represents the gravity term.

[0132] In some embodiments, when estimating the ground contact force, the ground stiffness, hardness, friction coefficient and other factors can also be considered through dynamic simulation, and the estimated ground contact force can be obtained through simulation calculation. This specification does not limit this.

[0133] Step 330, construct a compensation model based on the estimated ground contact force. The compensation model is used to adjust the initial motion trajectory to reduce the landing impact.

[0134] In some embodiments, the compensation model can be expressed as:

[0135] Δp_i = f(F_contact, t_i)

[0136] Δv_i = g(F_contact, t_i)

[0137] Δa_i = h(F_contact, t_i)

[0138] where Δp_i, Δv_i, and Δa_i represent the adjustment amounts of position, speed, and acceleration respectively, and f, g, and h represent the corresponding adjustment functions.

[0139] Step 340, adjust the initial motion trajectory based on the compensation model and determine the second control parameter.

[0140] Specifically, based on the compensation model, the initial motion trajectory is adjusted, and the second control parameter is determined. Through the compensation model, the position, velocity, acceleration, etc. of the initial motion trajectory are adjusted, and based on the adjusted motion trajectory, the second control parameter (such as thrust) is determined, so that the sum of the thrust and the estimated ground contact force is as close as possible to the gravity of the UAV within a short time before touchdown (for example, 0.5 s), thereby minimizing the touchdown impact as much as possible.

[0141] It should be noted that the above description is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the above process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0142] Figure 4 It is a flowchart for determining the third control parameter in the third stage according to the embodiments shown in this specification.

[0143] Step 410, based on the second end state data, predict the time distribution of the touchdown impact force.

[0144] In this embodiment, the time distribution of the touchdown impact force refers to the variation law of the reaction force of the ground on the UAV with time during the landing process of the UAV.

[0145] Specifically, the time distribution of the touchdown impact force can be expressed as:

[0146]

[0147] where F_max represents the maximum impact force, t_contact represents the contact time point, and σ represents the standard deviation of the distribution.

[0148] Step 420, based on the time distribution of the touchdown impact force, determine the damping-stiffness parameter.

[0149] In this embodiment, the damping-stiffness parameter is used to control the elastic behavior of the UAV during landing.

[0150] Specifically, the damping coefficient can be expressed as:

[0151] Damping = D_base + ΔD(F_max, σ)

[0152] where D_base represents the basic damping coefficient, and ΔD represents the adjustment term based on the impact force characteristics.

[0153] The stiffness coefficient can be expressed as:

[0154] Stiffness = K_base + ΔK(F_max, σ)

[0155] Among them, K_base represents the basic stiffness coefficient, and ΔK represents the adjustment term based on the impact force characteristics.

[0156] In this embodiment, when the maximum impact force F_max is relatively large, the damping coefficient is increased to absorb more impact energy; when the distribution standard deviation σ is relatively small, the stiffness coefficient is increased to improve the system response speed.

[0157] Step 430: Predict the touchdown impact force phase distribution based on the second end state data.

[0158] The touchdown impact force phase distribution refers to the distribution characteristics of the ground's reaction force on the UAV in the frequency domain during the UAV's landing process.

[0159] Specifically, the touchdown impact force phase distribution can be obtained by performing a Fourier transform on the touchdown impact force time distribution:

[0160] F_impact(ω) = FFT(F_impact(t))

[0161] Step 440: Determine the frequency-phase parameters based on the impact force phase distribution.

[0162] The frequency-phase parameters are used to control the vibration characteristics of the UAV during landing.

[0163] Specifically, the frequency parameter can be expressed as:

[0164] Frequency = F_base + ΔF(|F_impact(ω)|)

[0165] Among them, F_base represents the basic frequency parameter, and ΔF represents the adjustment term based on the impact force amplitude spectrum.

[0166] The phase parameter can be expressed as:

[0167] Phase = P_base + ΔP(arg(F_impact(ω)))

[0168] Among them, P_base represents the basic phase parameter, and ΔP represents the adjustment term based on the impact force phase spectrum.

[0169] By introducing the dynamic damping-stiffness parameters based on the impact force time distribution and the dynamic frequency-phase parameters based on the impact force phase distribution, two-dimensional collaborative optimization can effectively reduce the landing impact and improve the stability and accuracy of the UAV landing.

[0170] It should be noted that the above description is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the above process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0171] Based on the same inventive concept, an embodiment of this specification also provides a drone landing system 500 corresponding to the drone landing method. Figure 5 The module diagram of the drone landing system 500 according to an embodiment of this specification is shown. As Figure 5 shown, the drone landing system 500 includes:

[0172] A construction module 505 that determines a dynamic model based on the state data of the drone;

[0173] A first control module 510 that determines first control parameters for the first stage based on the dynamic model, and controls the movement of the drone in the first stage based on the first control parameters;

[0174] A second control module 520 that determines second control parameters for the second stage based on the first end state data of the drone at the end of the first stage, and controls the movement of the drone in the second stage based on the second control parameters;

[0175] A third control module 530 that determines third control parameters for the third stage based on the second end state data of the drone at the end of the second stage, and controls the drone to land based on the third control parameters.

[0176] For specific details, please refer to other parts of this specification and will not be elaborated here.

[0177] Based on the same inventive concept, an embodiment of this specification also provides a drone landing device corresponding to the drone landing method. The drone landing device includes: at least one storage medium storing computer instructions; at least one processor that executes the computer instructions to implement the above drone landing method.

[0178] Based on the same inventive concept, an embodiment of this specification also provides a computer-readable storage medium corresponding to the drone landing method. A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes the steps of the drone landing method described in this specification.

[0179] It should be noted that specific terms are used in this specification to describe the embodiments of this specification. For example, "an embodiment", "one embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0180] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numbers and letters, or the use of other names in this specification are not used to limit the order of the processes and methods in this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0181] Similarly, it should be noted that, in order to simplify the expression of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0182] In some embodiments, numbers describing the components and the quantity of attributes are used. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximate", or "substantially" in some examples. Unless otherwise stated, "about", "approximate", or "substantially" indicate that the said numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values can change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0183] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated by reference into this specification. This excludes the application history files that are inconsistent with or conflict with the content of this specification, as well as the files that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0184] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered to be consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A drone landing method based on multi-stage control, characterized in that, The method includes: Determining a dynamic model based on the state data of the drone; Determining first control parameters for a first stage based on the dynamic model, and controlling the movement of the drone in the first stage based on the first control parameters; Determining second control parameters for a second stage based on the first end state data of the drone at the end of the first stage, and controlling the movement of the drone in the second stage based on the second control parameters; Determining third control parameters for a third stage based on the second end state data of the drone at the end of the second stage, and controlling the landing of the drone based on the third control parameters.

2. The method according to claim 1, wherein The determining of the first control parameters for the first stage based on the dynamic model includes: Obtaining wind field data of the environment where the drone is located; Constructing a wind field model based on the wind field data; Determining the first control parameters for the first stage based on the dynamic model and the wind field model.

3. The method according to claim 2, wherein: The wind field model includes a horizontal wind direction angle component and a vertical pitch angle component; The first control parameters include a state weight matrix and a control weight matrix; The determining of the first control parameters for the first stage based on the dynamic model and the wind field model includes: Dynamically adjusting the state weight matrix and the control weight matrix based on the horizontal wind direction angle component and the vertical pitch angle component.

4. The method according to claim 1, wherein The determining of the second control parameters for the second stage based on the first end state data of the drone at the end of the first stage includes: Determining an initial motion trajectory based on the first end state data; Determining a predicted ground contact force based on the initial motion trajectory; Constructing a compensation model based on the predicted ground contact force; Adjusting the initial motion trajectory based on the compensation model, and determining the second control parameters.

5. The method according to claim 1, characterized in that, The determining of the third control parameters for the third stage based on the second end state data of the drone at the end of the second stage includes: Predicting the time distribution of the touchdown impact force based on the second end state data; Determining damping-stiffness parameters based on the time distribution of the touchdown impact force.

6. The method according to claim 1, characterized in that The determining of the third control parameters for the third stage based on the second end state data of the drone at the end of the second stage includes: Predicting the phase distribution of the touchdown impact force based on the second end state data; Determining frequency-phase parameters based on the impact force phase distribution.

7. The method according to claim 1, wherein The separation points of the first stage, the second stage, and the third stage are adaptively adjusted based on at least one of the state of the drone, the environmental state, and the landing scenario.

8. An unmanned aerial vehicle landing system based on multi-stage control, characterized in that, The system includes: A construction module that determines a dynamic model based on the state data of the drone; A first control module that determines first control parameters for a first stage based on the dynamic model, and controls the movement of the drone in the first stage based on the first control parameters; The second control module determines second control parameters for the second stage based on the first end state data of the UAV at the end of the first stage, and controls the movement of the UAV in the second stage based on the second control parameters; The third control module determines third control parameters for the third stage based on the second end state data of the UAV at the end of the second stage, and controls the UAV to land based on the third control parameters.

9. An unmanned aerial vehicle landing device based on multi-stage control, characterized in that, The device includes: At least one storage medium storing computer instructions; At least one processor that executes the computer instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions, the computer executes the method according to any one of claims 1 to 7.

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