Control method, control device and computer-readable storage medium for movable carrier

Abstract

A control method, a control device, and a computer-readable storage medium for a movable carrier are provided. The control method for a movable carrier includes: obtaining a control rod amount for controlling the movable carrier (S201); determining a predicted trajectory of the movable carrier according to the control rod amount (S202); generating a control instruction sequence according to the predicted trajectory to control the movable carrier to move according to the predicted trajectory (S203).

Description

Technical Field

[0001] The present disclosure relates to the field of control, and in particular to a control method, a control device, and a computer-readable storage medium for a movable carrier. Background Art

[0002] For a movable carrier such as a drone, a user can control the drone through a remote control device. In many scenarios, when a user wants the drone to fly along a desired trajectory, the user can push and pull the joystick of the remote control device to make the drone fly along a trajectory corresponding to the control stick amount.

[0003] In the prior art, the remote control device sends the control stick amount to the drone, and achieves closed-loop speed control by mapping the stick amount into speed instructions. However, since closed-loop position control is not currently implemented, the actual flight trajectory of the drone often does not match the user's expected trajectory. For example, when the user controls the drone to fly in a straight line by pushing and pulling the joystick of the remote control device, the drone does not fly the straight line that the user expects. The above situation will affect the drone's execution of its flight mission. For example, when the drone performs a shooting mission, it will reduce the quality of the video and affect the user experience. Summary of the invention

[0004] The present disclosure provides a control method for a movable carrier, comprising:

[0005] obtaining a control lever amount for controlling the movable carrier;

[0006] determining a predicted trajectory of the movable carrier based on the control lever amount;

[0007] A control instruction sequence is generated according to the predicted trajectory to control the movable carrier to move according to the predicted trajectory.

[0008] The present disclosure also provides a control device for a movable carrier, comprising:

[0009] A memory for storing executable instructions;

[0010] A processor is configured to execute the executable instructions stored in the memory to perform the following operations:

[0011] obtaining a control lever amount for controlling the movable carrier;

[0012] determining a predicted trajectory of the movable carrier based on the control lever amount;

[0013] A control instruction sequence is generated according to the predicted trajectory to control the movable carrier to move according to the predicted trajectory.

[0014] The present disclosure also provides a computer-readable storage medium storing executable instructions, which, when executed by one or more processors, can enable the one or more processors to execute the above control method.

[0015] The present disclosure also provides a movable carrier, comprising:

[0016] the control device mentioned above; and

[0017] The controller is used to receive the control instruction sequence generated by the control device and use the control instruction sequence to control the movable carrier.

[0018] The present disclosure also provides a remote control device of a movable carrier, comprising:

[0019] the control device mentioned above; and

[0020] The communication unit is used to send the control instruction sequence generated by the control device to the movable carrier, so that the controller of the movable carrier controls the movable carrier using the control instruction sequence.

[0021] The present invention predicts the trajectory according to the control stick amount, and then uses the predicted trajectory to control the drone. Through the above measures, when the user controls the drone by using the stick, the actual flight trajectory of the drone is highly consistent with the trajectory expected by the user, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1 This is a schematic diagram of the structure of the drone.

[0024] Figure 2 The present invention is a flow chart of a control method for a movable carrier according to an embodiment of the present invention.

[0025] Figure 3 FIG. 1 is a schematic diagram of a control method according to an embodiment of the present disclosure executed on a drone.

[0026] Figure 4 The diagram is a schematic diagram of a control method according to an embodiment of the present disclosure executed on a remote control device.

[0027] Figure 5 FIG. 1 is a schematic diagram of a control method according to another embodiment of the present disclosure executed on a drone.

[0028] Figure 6 This is a schematic diagram of a control method according to another embodiment of the present disclosure executed on a remote control device.

[0029] Figure 7 It is a schematic diagram of the structure of the control device of the movable carrier according to the embodiment of the present disclosure.

[0030] Figure 8 It is a schematic diagram of the structure of the movable carrier according to the embodiment of the present disclosure.

[0031] Fig. 9 Schematic diagram of the structure of the remote control device according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The technical solution of the present disclosure will be described clearly and completely below in conjunction with the embodiments and the drawings in the embodiments. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0033] An embodiment of the present disclosure provides a control method for a movable carrier. The control method of the embodiment of the present disclosure is suitable for controlling various movable carriers. For the convenience of description, a drone is used as an example in the embodiment of the present disclosure, but the movable carrier is not limited to a drone, but is applicable to various other movable carriers such as unmanned vehicles, unmanned ships, and robots.

[0034] First, the drone to which the control method of this embodiment is applicable is described. Figure 1 As shown, an example of a drone is given. The drone is provided with a flight controller. The flight controller may include one or more processors, and the processors may generate control instructions individually or collectively to control the flight of the drone.

[0035] The drone is also provided with a driver, such as a motor. The motor may be coupled to one or more power units of the drone. The power unit may include a rotor. The flight controller may control the action of the driver so that the driver drives the rotor to rotate, thereby generating power for the drone.

[0036] The drone is also provided with sensors. The sensors may include but are not limited to various types of sensors such as GPS receivers and inertial measurement units (IMUs). The drone can obtain the position and attitude parameters of the drone through sensors. The position parameters may include: the position, linear velocity, and linear acceleration of the drone. The attitude parameters may include: the attitude angle, attitude angular velocity, and attitude angular acceleration of the drone. The attitude angle may include the heading angle, pitch angle, and roll angle of the drone.

[0037] The drone is also equipped with a shooting device, which can be mounted on the drone body through a stabilizing gimbal to capture images or record videos during the movement of the drone.

[0038] The flight of the drone can be controlled by a remote control device, which communicates wirelessly with the flight controller of the drone. The remote control device is provided with an operating element for the user to operate, such as a joystick. The user generates a control stick amount by operating the joystick, and the control stick amount is sent to the flight controller via wireless communication. The flight controller generates a control instruction based on the control stick amount to control the action of the driver, thereby controlling the flight of the drone.

[0039] The control method of the movable carrier of this embodiment is as follows: Figure 2 As shown, including:

[0040] Step S201: Acquire a control lever amount for controlling the movable carrier;

[0041] Step S202: determining a predicted trajectory of the movable carrier according to the control rod amount;

[0042] Step S203: generating a control instruction sequence according to the predicted trajectory to control the movable carrier to move according to the predicted trajectory.

[0043] The control method of this embodiment can be executed in the flight controller of the UAV, or in the remote control device. Figure 3 , taking the execution on the flight controller as an example to illustrate.

[0044] In step S201, a control stick amount for controlling the drone is generated at the remote control device. The user can manually control the flight of the drone by pushing and pulling the joystick of the remote control device to generate the control stick amount, and the control stick amount corresponds to the user's desired trajectory. For example, when the user wants to control the drone to fly in a straight line, the user can push and pull the joystick of the remote control device to generate the control stick amount corresponding to the desired straight line trajectory.

[0045] In step S202, after the flight controller receives the control stick amount sent by the remote control device, it will generate a control instruction based on the control stick amount, but will not directly control the drone based on the generated control instruction, but will predict the trajectory based on the control stick amount. The generated control instruction is used as the initial control instruction, and the trajectory of the drone is predicted based on the initial control instruction, and then the predicted trajectory is tracked to obtain an optimized control instruction, and the drone is controlled based on the optimized control instruction. Specifically:

[0046] First, an initial command sequence is generated according to the control lever amount.

[0047] Assuming that the control stick quantity generated by the remote control device is received at the current moment, this embodiment can obtain an instruction sequence according to the control stick quantity and use the instruction sequence as the initial instruction sequence.

[0048] The initial command sequence of this embodiment may include: an initial linear velocity command sequence and an initial linear acceleration command sequence. In one example, the control stick amount may be mapped to a linear velocity command. The linear velocity command at the current moment is filtered according to the linear velocity command filtered at the previous moment, and the initial linear velocity command sequence v_cmd is obtained according to the uniform acceleration model. k , k = 1 ... N, and according to the air resistance model, find the linear acceleration corresponding to the linear velocity in the initial linear velocity instruction sequence, so as to obtain the initial linear acceleration instruction sequence a_cmd k , k=1...N, where k represents each instruction moment after the current moment. This embodiment does not limit the number of instructions in the initial linear velocity instruction sequence and the initial linear acceleration instruction sequence (i.e., the value of N), and the time interval between adjacent instructions, which can be set according to actual conditions and control effects.

[0049] In other examples, the control stick amount can also be mapped to a linear acceleration instruction, and the linear velocity corresponding to the linear acceleration in the initial linear acceleration instruction sequence can be found to obtain the initial linear velocity instruction sequence; or the control stick amount can also be mapped to both the linear velocity instruction sequence and the linear acceleration instruction sequence. In the above manner, a smooth and continuous initial instruction sequence can be generated, so that the predicted trajectory determined according to the initial instruction sequence is smooth and continuous, which can ensure the control effect of the flight trajectory of the drone.

[0050] After the initial instruction sequence is generated, the predicted trajectory of the UAV is determined according to the initial instruction sequence. In the process of determining the predicted trajectory, the kinematic model of the UAV is first obtained, and then the trajectory of the initial instruction sequence is predicted using the kinematic model to obtain the predicted trajectory. The predicted trajectory points in the predicted trajectory are characterized by predicted position parameters. The predicted position parameters include: predicted position; or, predicted position and predicted linear velocity.

[0051] The following description is made by taking the uniform acceleration model as an example, but the present embodiment is not limited thereto, and any other type of kinematic model may also be used, such as but not limited to: a uniform velocity model, a nonlinear model, etc.

[0052] The uniform acceleration model is as follows:

[0053] p k+1 =p k +v k ·Δt+0.5·a k ·Δt 2 (1)

[0054] v k+1 =v k +a k ·Δt (2)

[0055] Among them, Δt represents the time interval between adjacent moments; p k represents the position at time k, v k represents the linear velocity at time k, a k represents the linear acceleration at time k, v k+1 represents the linear velocity at time k+1, a k+1 represents the linear acceleration at time k+1.

[0056] In this embodiment, the position parameters of the drone at the current moment are first obtained, and then the position parameters at the current moment, the initial linear velocity instruction sequence, and the initial linear acceleration instruction sequence are input into formulas (1) and (2) to obtain the predicted position parameters of the predicted trajectory points. Specifically, the position p of the drone at the current moment can be obtained through the drone's sensor. 0 , Linear speed v 0 and linear acceleration a 0 . Then position p 0 , Linear speed v 0 and linear acceleration a 0 As the initial value of formula (1) and (2), the initial value and the initial linear velocity instruction sequence v_cmd k Each linear velocity instruction and initial linear acceleration instruction sequence a_cmd in k Substituting each linear acceleration command in formula (1) and (2) for iterative calculation, we can get each predicted trajectory point P of the predicted trajectory. 1 , P 2 , ..., P N ,The predicted position parameters of the predicted trajectory points include two parameters : the predicted position and the predicted linear speed.

[0057] In the trajectory prediction process, the influence of the following factors also needs to be considered to correct the initial linear acceleration command sequence.

[0058] In order to achieve the desired trajectory, it is necessary to use a flight controller to achieve this; the UAV will be affected by air resistance during flight, and the initial linear acceleration command sequence also needs to overcome the air resistance; the position and attitude are decoupled during trajectory prediction, and considering the coupling between position and attitude, the influence of the UAV's centripetal acceleration also needs to be considered.

[0059] In this embodiment, the initial linear acceleration instruction sequence is corrected using at least one of the following corrections: the linear acceleration control value acc_ctrl of the UAV's flight controller, the centripetal acceleration acc_cent of the UAV, and the air resistance acc_air to which the UAV is subjected. For example, when the initial linear acceleration instruction sequence is corrected considering the above three factors, the corrected initial linear acceleration instruction sequence a_cmd′ k =a_cmd k +acc_ctrl-acc_cent-acc_air.

[0060] After obtaining the predicted trajectory of the drone, step S203 is used to perform command optimization, that is, to generate a control command sequence according to the predicted trajectory to control the drone to move along the predicted trajectory. A trajectory tracking controller can be used to track the predicted trajectory points and generate a control command sequence. The following describes step S203 using a model predictive controller as an example, but the trajectory tracking controller that can be used in this embodiment is not limited to this, and can also be various other trajectory tracking controllers such as a proportional integral differential controller, a pure tracking controller, and a linear quadratic regulator.

[0061] When the model predictive controller is used to track the predicted trajectory point, the predicted trajectory P obtained in step S202 can be firstly 1 , P 2 , ..., P N Perform interval sampling to obtain predicted trajectory points after interval sampling, and input the predicted trajectory points after interval sampling into the model predictive controller to generate the control instruction sequence. The specific method of interval sampling is that in the predicted trajectory, a predicted trajectory point is sampled every several predicted trajectory points, and the sampled predicted trajectory points form a new predicted trajectory as the predicted trajectory after interval sampling. The predicted trajectory P obtained in step S202 1 , P 2 , ..., P N By performing interval sampling, the entire predicted trajectory can be represented by fewer trajectory points, thereby reducing the amount of calculation and improving calculation efficiency.

[0062] In one example, if the time interval between adjacent predicted trajectory points in step S202 is 0.04 seconds, sampling can be performed from each predicted trajectory point, and every 5 predicted trajectory points are sampled, for a total of 8 predicted trajectory points. 1 At the beginning, sampling is performed every 5 predicted trajectory points; from the predicted trajectory point P 2 Initially, every 5 prediction trajectory points are sampled once; and so on, the number of prediction trajectory points after interval sampling is M=8, the time interval between these prediction trajectory points is 0.2 seconds, and the 8 prediction trajectory points after interval sampling are sequentially input into the model predictive controller.

[0063] The model predictive controller is described as follows:

[0064]

[0065] x 1 (k+1)=x 1 (k)+ΔT·x 2 (k)+0.5·ΔT 2 ·u(k) (4)

[0066] x 2 (k+1)=x 2 (k)+ΔT·u(k) (5)

[0067] -v max ≤x 2 (k)≤v max (6)

[0068] -a max ≤u(k)≤a max (7)

[0069] Wherein, formula (3) represents the cost function, which includes the penalty term J of the predicted position parameter p_err , the penalty term J for linear acceleration and rate of change of linear acceleration acc , J jerk .x i 、x ri They represent the predicted input and reference input of the cost function, respectively, and u represents the linear acceleration command. Formulas (4)-(7) represent the constraints, and formulas (4) and (5) represent the prediction models, which are used to output the predicted position x respectively. 1 and predicted linear velocity x 2 Similar to the kinematic model in step S202, the prediction model here also adopts the uniform acceleration model. Formulas (6) and (7) respectively represent the predicted linear velocity x 2 And the value range of linear acceleration command u.

[0070] The model predictive controller is used to track the predicted position parameters of the predicted trajectory points after interval sampling to generate a position control instruction sequence. When a group of predicted trajectory points after interval sampling is input, the predicted position and predicted linear velocity of the predicted trajectory points after interval sampling are used as the reference input of the cost function, and the predicted position and predicted linear velocity output by the prediction model are used as the predicted input of the cost function to perform model predictive control, and a group of linear velocity control instructions and linear acceleration control instructions are obtained. The first linear velocity control instruction and the first linear acceleration instruction in the group of linear velocity control instructions and linear acceleration control instructions are input as the control instructions corresponding to the group of predicted trajectory points after interval sampling into the flight controller, and the flight controller uses the control instructions to control the UAV.

[0071] During the movement of the UAV, the above steps can be repeated to update the predicted trajectory according to the control stick values ​​at different times. Multiple groups of predicted trajectory points after interval sampling are input into the model predictive controller in sequence for rolling prediction, and the linear velocity control instruction sequence and linear acceleration control instruction sequence corresponding to the predicted trajectory points after each interval sampling can be obtained.

[0072] It can be seen that the present disclosure does not directly use the control instructions mapped by the control stick quantity to control the drone, but performs trajectory prediction according to the control instructions mapped by the control stick quantity, and then uses the trajectory tracking controller to track the predicted trajectory to obtain the optimized control instructions, and controls the drone according to the optimized control instructions. Through the above measures, the process of controlling the flight of the drone through the joystick has the position planning capability, and realizes the position closed-loop control. Under the control of the optimized control instructions, the actual flight trajectory of the drone is highly consistent with the trajectory expected by the user and corresponding to the control stick quantity. For example, when the user wants to control the drone to fly in a straight line, by pushing and pulling the remote control device's remote control stick, the drone can fly out of the straight line expected by the user, thereby solving the long-standing technical problem of the prior art that the drone cannot fly in a straight line under the user's stick control. On this basis, the shooting quality of the drone is also improved, such as enhancing the stability of the synthesized video in the time-lapse photography mode and the beauty of the video.

[0073] Combination of the above Figure 2 and Figure 3The control method of this embodiment is described, wherein the predicted position parameters of the predicted trajectory point include the predicted position and the predicted linear velocity, and the position control instruction sequence includes the control instruction sequence of the linear velocity and the linear acceleration, but the above description is only one implementation of this embodiment. In another implementation of the control method of this embodiment, the predicted position parameters of the predicted trajectory point may include the predicted position, and the position control instruction sequence includes the linear velocity control instruction sequence. In this implementation, in step S202, the position parameters and the initial instruction sequence at the current moment are input into the kinematic model, and the predicted position of each predicted trajectory point in the predicted trajectory can be obtained. In step S203, the predicted position of the predicted trajectory point after interval sampling is tracked by using a model predictive controller to generate a linear velocity control instruction sequence, and the flight control of the drone uses the linear velocity control instruction sequence to control the position of the drone.

[0074] The control method of this embodiment is described above by taking the execution of the control method in the flight controller of the drone as an example. However, the above description is only one implementation of this embodiment. In another implementation of the control method of this embodiment, the control method of this embodiment can be executed in the remote control device of the drone. Figure 4 As shown, at the remote control device, the predicted position parameters are obtained through trajectory prediction in step S202, and then the position control instruction sequence is obtained through instruction optimization in step S203, and the position control instruction sequence is sent to the drone instead of the control stick amount. The position controller in the flight controller uses the position control instruction sequence to control the flight of the drone.

[0075] The above implementation also enables the process of controlling the flight of the drone through a joystick to have position planning capabilities and realizes position closed-loop control. The actual flight trajectory of the drone is highly consistent with the trajectory expected by the user and corresponding to the control stick amount. It also solves the long-standing technical problem in the prior art that the drone cannot fly in a straight line under the user's control of the stick.

[0076] In order to achieve the purpose of brief description, the control method of a movable carrier of another embodiment of the present disclosure is summarized here, and there is no need to repeat the same description.

[0077] In some application scenarios, the user wants to control the drone to fly along a curve rather than a straight line. For example, the control stick amount can be generated by pushing and pulling the remote control device's joystick, and the control stick amount corresponds to the desired curved trajectory. The control method of the movable carrier of this embodiment can make the drone fly along the curved trajectory corresponding to the control stick amount, so that the user's desired trajectory is highly consistent with the actual flight trajectory of the drone.

[0078] See also Figure 2 and Figure 5As shown, in step S201, the control stick amount for controlling the drone is generated at the remote control device. Since the flight trajectory expected by the user is a curve, the control stick amount generated by the remote control device includes two parts: the stick amount corresponding to the linear motion and the stick amount corresponding to the rotational motion.

[0079] In step S202, after receiving the control stick amount sent by the remote control device, the flight controller generates an initial control instruction based on the control stick amount, predicts the trajectory of the drone based on the initial control instruction, and then tracks the predicted trajectory to obtain an optimized control instruction, and controls the drone based on the optimized control instruction. Specifically:

[0080] First, an initial command sequence is generated according to the control lever amount.

[0081] The initial command sequence of this embodiment includes: an initial position command sequence and an initial posture command sequence. The initial position command sequence is similar to the previous embodiment, including: an initial linear velocity command sequence v_cmd k , k = 1...N and the initial linear acceleration command sequence a_cmd k , k = 1 ... N. The initial attitude instruction sequence may include: initial heading angular velocity instruction sequence Similar to the previous embodiment, this embodiment can obtain the initial instruction sequence by filtering, thereby obtaining the above-mentioned initial position instruction sequence and initial posture instruction sequence, and the number of instructions of the above-mentioned two instruction sequences (that is, the value of N) and the time interval between adjacent instructions are limited, which can be set according to actual conditions and control effects.

[0082] The above method can generate a smooth and continuous initial position instruction sequence and an initial attitude instruction sequence, so that the predicted trajectory determined according to the initial position instruction sequence and the initial attitude instruction sequence is smooth and continuous, which can ensure the control effect of the UAV flight trajectory.

[0083] After the initial instruction sequence is generated, the predicted trajectory of the drone is determined according to the initial instruction sequence. Similar to the previous embodiment, in the process of determining the predicted trajectory, the kinematic model of the drone is first obtained, and then the kinematic model is used to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory. The predicted trajectory points in the predicted trajectory are characterized by predicted position parameters and predicted attitude parameters. Among them, the predicted attitude parameters include: predicted heading angle and predicted heading angular velocity.

[0084] For the uniform acceleration model, the uniform acceleration model is as follows:

[0085] p k+1 =p k +v k ·Δt+0.5·a k ·Δt2 (8)

[0086] v k+1 =v k +a k ·Δt (9)

[0087]

[0088]

[0089] Wherein, formulas (8) and (9) are the same as formulas (1) and (2) of the previous embodiment, and are used to predict the position parameters of the predicted trajectory. Formulas (10) and (11) are used to predict the posture parameters of the predicted trajectory; represents the angular velocity of the heading at time k, represents the angular velocity of the course at time k+1, ψ k represents the heading angle at time k, ψ k+1 represents the heading angle at time k+1, represents the heading angular acceleration at time k.

[0090] In this embodiment, the position parameters and posture parameters of the predicted trajectory can be decoupled, and the position parameters and posture parameters can be predicted separately.

[0091] The prediction process of the position parameters is similar to that of the previous embodiment. For details, please refer to the description of the previous embodiment. In short, the current position parameters, the initial linear velocity instruction sequence, and the initial linear acceleration instruction sequence are substituted into formulas (8) and (9) for iterative calculation to obtain the predicted position and predicted linear velocity of the predicted trajectory point, and the initial linear acceleration instruction sequence is corrected based on at least one of the following factors: the linear acceleration control amount of the UAV flight controller, the centripetal acceleration of the UAV, and the air resistance of the UAV.

[0092] The attitude parameter prediction process is as follows: first, the attitude parameters of the UAV at the current moment are obtained; the attitude parameters at the current moment, the initial heading angular velocity instruction sequence, and the initial heading angular acceleration instruction sequence are input into the formulas (10) and (11) of the uniform acceleration model to obtain the predicted attitude parameters of each predicted trajectory point in the predicted trajectory. The predicted attitude parameters include: the predicted heading angle and the predicted heading angular velocity of the predicted trajectory point.

[0093] In the above process, the initial heading angular acceleration instruction can be determined according to the initial heading angular velocity instruction sequence and the predicted heading angular velocity of the predicted trajectory point. Specifically,

[0094]

[0095] in, represents the heading angular velocity instruction at time k in the initial heading angular velocity instruction sequence, represents the predicted heading angular velocity of the predicted trajectory point at time k-1, and ρ represents the control coefficient.

[0096] Specifically, the UAV’s heading angle ψ at the current moment can be obtained through the UAV’s IMU 0 , Angular velocity and the angular acceleration of the heading The heading angle ψ 0 , Angular velocity and the angular acceleration of the heading As the initial value of formula (10) and (11), the initial value and the initial heading angular velocity instruction sequence are Substituting the initial heading angular acceleration instruction sequence into formulas (10) and (11) for iterative calculation, the predicted heading angle and predicted heading angular velocity of each predicted trajectory point can be obtained.

[0097] Through the prediction of the above position parameters and posture parameters, the predicted trajectory P is obtained according to the initial position instruction sequence and the initial posture instruction sequence. 1 , P 2 , ..., P N , predict the four parameters of the trajectory point: predicted position, predicted linear velocity, predicted heading angle and predicted heading angular velocity.

[0098] After obtaining the predicted trajectory of the UAV, step S203 is used to perform instruction optimization, that is, to generate a control instruction sequence according to the predicted trajectory to control the UAV to move according to the predicted trajectory. The control instruction sequence includes: a position control instruction sequence and an attitude control instruction sequence.

[0099] In this embodiment, the position parameters and attitude parameters of the predicted trajectory can be decoupled to generate a position control instruction sequence and an attitude control instruction sequence respectively.

[0100] The process of generating the position control instruction sequence is similar to that of the previous embodiment. As an example, a model predictive controller can be used to generate the position control instruction sequence respectively. Similar to the previous embodiment, the position control instruction sequence can include a linear speed control instruction sequence and / or a linear acceleration control instruction sequence.

[0101] A variety of trajectory tracking controllers can track the attitude parameters of the predicted trajectory to generate an attitude control instruction sequence. In one example, a proportional integral derivative controller (PID) can be used. The generated attitude control instruction sequence can be a heading angular velocity control instruction sequence, or a heading angular acceleration control instruction sequence. The attitude control instruction sequence can also include the above two instruction sequences.

[0102] After obtaining the position control instruction sequence and attitude control instruction sequence, these two instruction sequences can be sent to the UAV's flight control, which uses the position control instruction sequence and attitude control instruction sequence to control the position and attitude of the UAV. The position control instruction sequence is input into the position controller, and the attitude control instruction sequence is input into the attitude controller. The position controller and the attitude controller respectively output control quantities to control each driver, so that the UAV flies along the curve corresponding to the control stick quantity.

[0103] It can be seen that the present disclosure does not directly use the control instructions mapped by the control stick quantity to control the drone, but performs trajectory prediction according to the control instructions mapped by the control stick quantity, and then uses the trajectory tracking controller to track the predicted trajectory to obtain the optimized control instructions, and controls the drone according to the optimized control instructions. Through the above measures, the process of controlling the flight of the drone through the joystick has the position planning capability, and realizes the position closed-loop control. Under the control of the optimized control instructions, the actual flight trajectory of the drone is highly consistent with the trajectory expected by the user and corresponding to the control stick quantity. For example, when the user wants to control the drone to fly a curve or a detour, by pushing and pulling the remote control device's remote control stick, the drone can fly out of the user's expected trajectory, thereby solving the technical problem that the actual flight trajectory of the drone is inconsistent with the user's expected trajectory under the user's stick control.

[0104] The control method of this embodiment is described above by taking the execution of the control method on a drone as an example. However, the above description is only one implementation of this embodiment. In another implementation of the control method of this embodiment, the control method of this embodiment can be executed on a remote control device of a drone. Figure 6 As shown, at the remote control device, the predicted position parameters and predicted attitude parameters are obtained through trajectory prediction, and then the position control instruction sequence and attitude control instruction sequence are obtained through instruction optimization, and the position control instruction sequence and attitude control instruction sequence are sent to the UAV instead of the control stick amount. The position controller and attitude controller in the flight controller use the position control instruction sequence and attitude control instruction sequence to control the flight of the UAV respectively.

[0105] Another embodiment of the present disclosure provides a control device for a movable carrier, such as Figure 7 As shown, the control device may include: a processor and a memory. The processor and the memory may be one or more.

[0106] In the control device for the removable carrier of this embodiment, the memory stores executable instructions; the processor can execute the executable instructions stored in the memory to perform the following operations:

[0107] obtaining a control lever amount for controlling the movable carrier;

[0108] determining a predicted trajectory of the movable carrier based on the control lever amount;

[0109] A control instruction sequence is generated according to the predicted trajectory to control the movable carrier to move according to the predicted trajectory.

[0110] The operation of determining the predicted trajectory of the movable carrier according to the control rod amount includes:

[0111] generating an initial instruction sequence according to the control lever quantity;

[0112] A predicted trajectory of the movable carrier is determined based on the initial instruction sequence.

[0113] The operation of determining the predicted trajectory of the movable carrier according to the initial instruction sequence includes:

[0114] Acquiring a kinematic model of the movable carrier;

[0115] The kinematic model is used to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory.

[0116] The predicted trajectory points in the predicted trajectory are characterized by the following parameters: predicted position parameters. The predicted position parameters include:

[0117] Predicted location; or,

[0118] Predicted position and predicted linear velocity.

[0119] The initial instruction sequence includes: an initial linear velocity instruction sequence and an initial linear acceleration instruction sequence.

[0120] The operation of using the kinematic model to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory includes:

[0121] Obtaining the position parameters of the movable carrier at the current moment;

[0122] The position parameters of the movable carrier at the current moment, the initial linear velocity instruction sequence and the initial linear acceleration instruction sequence are input into the kinematic model to obtain the predicted position parameters of the predicted trajectory points of the predicted trajectory. The position parameters of the movable carrier at the current moment include: position, linear velocity and linear acceleration.

[0123] The initial linear acceleration instruction sequence is modified according to at least one of the following factors:

[0124] The linear acceleration control amount of the controller of the movable carrier, the centripetal acceleration of the movable carrier, and the air resistance experienced by the movable carrier.

[0125] The predicted trajectory points in the predicted trajectory are further characterized by the following parameters: predicted attitude parameters. The predicted attitude parameters include: predicted heading angle and predicted heading angular velocity.

[0126] The initial instruction sequence also includes: an initial heading angular velocity instruction sequence.

[0127] The operation of using the kinematic model to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory also includes:

[0128] Obtaining the posture parameters of the movable carrier at the current moment;

[0129] Inputting the attitude parameters of the movable carrier at the current moment, the initial heading angular velocity instruction sequence, and the initial heading angular acceleration instruction sequence into the kinematic model to obtain the predicted attitude parameters of the predicted trajectory points of the predicted trajectory sequence;

[0130] The initial heading angular acceleration instruction is determined according to the initial heading angular velocity instruction sequence and the predicted heading angular velocity of the predicted trajectory point of the predicted trajectory. The attitude parameters of the movable carrier at the current moment include: heading angle, heading angular velocity and heading angular acceleration.

[0131] The kinematic model includes at least one of the following: a uniform acceleration model, a uniform velocity model, and a nonlinear model.

[0132] The operation of generating a control instruction sequence according to the predicted trajectory includes:

[0133] The predicted trajectory is sampled at intervals to obtain predicted trajectory points after the interval sampling; and the predicted trajectory points after the interval sampling are tracked by a trajectory tracking controller to generate the control instruction sequence.

[0134] The trajectory tracking controller includes at least one of the following: a model predictive controller, a proportional integral derivative controller, a pure tracking controller, and a linear quadratic regulator.

[0135] The control instruction sequence includes a position control instruction sequence, and the operation of using a trajectory tracking controller to track the predicted trajectory points after interval sampling to generate the control instruction sequence includes:

[0136] The model predictive controller is used to track the predicted position parameters of the predicted trajectory points after interval sampling to generate the position control instruction sequence.

[0137] The position control instruction sequence includes a linear acceleration control instruction sequence and / or a linear speed control instruction sequence.

[0138] The first instruction in the linear acceleration control instruction sequence and / or the linear velocity control instruction sequence is used to control the movable carrier.

[0139] The control instruction sequence includes a posture control instruction sequence, and the operation of using a trajectory tracking controller to track the predicted trajectory points after interval sampling to generate the control instruction sequence includes:

[0140] The predicted attitude parameters of the predicted trajectory points in the predicted trajectory sequence are tracked by the proportional integral differential controller to generate the attitude control instruction sequence. The attitude control instruction sequence includes a heading angular velocity control instruction sequence and / or a heading angular acceleration control instruction sequence.

[0141] The posture control instruction sequence is used to control the movable carrier.

[0142] The processor of this embodiment may include, for example, a general-purpose microprocessor, an instruction set processor and / or related chipsets and / or a special-purpose microprocessor (eg, an application-specific integrated circuit (ASIC)), etc. The processor may also include an onboard memory for cache purposes.

[0143] The memory of this embodiment can be, for example, a non-volatile computer-readable storage medium, and specific examples include but are not limited to: magnetic storage devices, such as magnetic tapes or hard disks (HDDs); optical storage devices, such as compact disks (CD-ROMs); memories, such as random access memory (RAM) or flash memory; and the like.

[0144] Yet another embodiment of the present disclosure provides a computer-readable storage medium storing executable instructions, which, when executed by one or more processors, can enable the one or more processors to execute the control method described in any of the above embodiments.

[0145] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0146] Another embodiment of the present disclosure provides a movable carrier, such as Figure 8As shown, it includes: the control device of the above embodiment; and a controller, which is used to receive the control instruction sequence generated by the control device and use the control instruction sequence to control the movable carrier. In one example, the movable carrier is a drone, and the controller is a flight controller of the drone.

[0147] Another embodiment of the present disclosure provides a remote control device of a movable carrier, such as Fig. 9 As shown, it includes: the control device of the above embodiment; and a communication unit, which is used to send the control instruction sequence generated by the control device to the removable carrier, so that the controller of the removable carrier controls the removable carrier using the control instruction sequence.

[0148] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. In the absence of conflicts, the features in the embodiments of the present disclosure can be combined arbitrarily. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A control method for a movable carrier, characterized in that: include: acquiring a control lever amount for controlling the movable carrier, wherein the control lever amount is generated based on a user's operation, and the control lever amount is used to generate an initial instruction sequence; determining a predicted trajectory of the movable carrier according to the initial instruction sequence and a current position parameter of the movable carrier, wherein the predicted trajectory is an expected trajectory corresponding to the user's operation, and the current position parameter of the movable carrier includes at least one of a position, a linear velocity, and a linear acceleration; Optimizing the initial instruction sequence according to the predicted trajectory to generate a control instruction sequence; According to the control instruction sequence, the movable carrier is controlled to move according to the predicted trajectory.

2. The control method according to claim 1, characterized in that: Determining the predicted trajectory of the movable carrier according to the initial instruction sequence and the current position parameter of the movable carrier includes: Acquiring a kinematic model of the movable carrier; The kinematic model is used to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory.

3. The control method according to claim 2, characterized in that: The predicted trajectory points in the predicted trajectory are characterized by the following parameters: predicted position parameters.

4. The control method according to claim 3, characterized in that: The predicted position parameters include: Predicted location; or, Predicted position and predicted linear velocity.

5. The control method according to claim 3, characterized in that: The initial instruction sequence includes: an initial linear velocity instruction sequence and an initial linear acceleration instruction sequence.

6. The control method according to claim 5, characterized in that: The using the kinematic model to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory includes: Obtaining the position parameters of the movable carrier at the current moment; The position parameters of the movable carrier at the current moment, the initial linear velocity instruction sequence and the initial linear acceleration instruction sequence are input into the kinematic model to obtain the predicted position parameters of the predicted trajectory points of the predicted trajectory.

7. The control method according to claim 6, characterized in that: The position parameters of the movable carrier at the current moment include: position, linear velocity and linear acceleration.

8. The control method according to claim 5, characterized in that: The initial linear acceleration instruction sequence is modified according to at least one of the following factors: The linear acceleration control amount of the controller of the movable carrier, the centripetal acceleration of the movable carrier, and the air resistance experienced by the movable carrier.

9. The control method according to claim 5, characterized in that: The predicted trajectory points in the predicted trajectory are further characterized by the following parameters: predicted posture parameters.

10. The control method according to claim 9, characterized in that: The predicted attitude parameters include: predicted heading angle and predicted heading angular velocity.

11. The control method according to claim 10, characterized in that: The initial instruction sequence also includes: an initial heading angular velocity instruction sequence.

12. The control method according to claim 11, characterized in that: The using the kinematic model to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory also includes: Obtaining the posture parameters of the movable carrier at the current moment; Inputting the attitude parameters of the movable carrier at the current moment, the initial heading angular velocity instruction sequence, and the initial heading angular acceleration instruction sequence into the kinematic model to obtain the predicted attitude parameters of the predicted trajectory points of the predicted trajectory sequence; Wherein, the initial heading angular acceleration instruction is determined according to the initial heading angular velocity instruction sequence and the predicted heading angular velocity of the predicted trajectory point of the predicted trajectory.

13. The control method according to claim 12, characterized in that: The attitude parameters of the movable carrier at the current moment include: heading angle, heading angular velocity and heading angular acceleration.

14. The control method according to claim 2, characterized in that: The kinematic model includes at least one of the following: a uniform acceleration model, a uniform velocity model, and a nonlinear model.

15. The control method according to claim 1, characterized in that: Generating a control instruction sequence according to the predicted trajectory includes: Performing interval sampling on the predicted trajectory to obtain predicted trajectory points after interval sampling; The predicted trajectory points after interval sampling are tracked by using a trajectory tracking controller to generate the control instruction sequence.

16. The control method according to claim 15, characterized in that: The trajectory tracking controller includes at least one of the following: a model predictive controller, a proportional integral derivative controller, a pure tracking controller, and a linear quadratic regulator.

17. The control method according to claim 16, characterized in that: The control instruction sequence includes a position control instruction sequence, and the use of a trajectory tracking controller to track the predicted trajectory points after interval sampling to generate the control instruction sequence includes: The model predictive controller is used to track the predicted position parameters of the predicted trajectory points after interval sampling to generate the position control instruction sequence.

18. The control method according to claim 17, characterized in that: The position control instruction sequence includes a linear acceleration control instruction sequence and / or a linear speed control instruction sequence.

19. The control method according to claim 18, characterized in that: The first instruction in the linear acceleration control instruction sequence and / or the linear velocity control instruction sequence is used to control the movable carrier.

20. The control method according to claim 16, characterized in that: The control instruction sequence includes a posture control instruction sequence, and the trajectory tracking controller is used to track the predicted trajectory points after interval sampling to generate the control instruction sequence, including: The predicted posture parameters of the predicted trajectory points in the predicted trajectory sequence are tracked by using the proportional-integral-differential controller to generate the posture control instruction sequence.

21. The control method according to claim 20, characterized in that: The attitude control instruction sequence includes a heading angular velocity control instruction sequence and / or a heading angular acceleration control instruction sequence.

22. The control method according to claim 20, characterized in that: The posture control instruction sequence is used to control the movable carrier.

23. A control device for a movable carrier, characterized in that: include: A memory for storing executable instructions; A processor is configured to execute the executable instructions stored in the memory to perform the following operations: acquiring a control lever amount for controlling the movable carrier, wherein the control lever amount is generated based on a user's operation, and the control lever amount is used to generate an initial instruction sequence; determining a predicted trajectory of the movable carrier according to the initial instruction sequence and a current position parameter of the movable carrier, wherein the predicted trajectory is an expected trajectory corresponding to the user's operation, and the current position parameter of the movable carrier includes at least one of a position, a linear velocity, and a linear acceleration; Optimizing the initial instruction sequence according to the predicted trajectory to generate a control instruction sequence; According to the control instruction sequence, the movable carrier is controlled to move according to the predicted trajectory.

24. The control device according to claim 23, characterized in that The operation of determining the predicted trajectory of the movable carrier according to the initial instruction sequence and the current position parameter of the movable carrier includes: Acquiring a kinematic model of the movable carrier; The kinematic model is used to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory.

25. The control device according to claim 24, characterized in that The predicted trajectory points in the predicted trajectory are characterized by the following parameters: predicted position parameters.

26. The control device according to claim 25, characterized in that The predicted position parameters include: Predicted location; or, Predicted position and predicted linear velocity.

27. The control device according to claim 25, characterized in that The initial instruction sequence includes: an initial linear velocity instruction sequence and an initial linear acceleration instruction sequence.

28. The control device according to claim 27, characterized in that The operation of using the kinematic model to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory includes: Obtaining the position parameters of the movable carrier at the current moment; The position parameters of the movable carrier at the current moment, the initial linear velocity instruction sequence and the initial linear acceleration instruction sequence are input into the kinematic model to obtain the predicted position parameters of the predicted trajectory points of the predicted trajectory.

29. The control device according to claim 28, characterized in that The position parameters of the movable carrier at the current moment include: position, linear velocity and linear acceleration.

30. The control device according to claim 27, characterized in that The initial linear acceleration instruction sequence is modified according to at least one of the following factors: The linear acceleration control amount of the controller of the movable carrier, the centripetal acceleration of the movable carrier, and the air resistance experienced by the movable carrier.

31. The control device according to claim 27, characterized in that The predicted trajectory points in the predicted trajectory are further characterized by the following parameters: predicted posture parameters.

32. The control device according to claim 31, characterized in that The predicted attitude parameters include: predicted heading angle and predicted heading angular velocity.

33. The control device according to claim 32, characterized in that: The initial instruction sequence also includes: an initial heading angular velocity instruction sequence.

34. The control device according to claim 33, characterized in that The operation of using the kinematic model to predict the trajectory of the initial instruction sequence to obtain the predicted trajectory also includes: Obtaining the posture parameters of the movable carrier at the current moment; Inputting the attitude parameters of the movable carrier at the current moment, the initial heading angular velocity instruction sequence, and the initial heading angular acceleration instruction sequence into the kinematic model to obtain the predicted attitude parameters of the predicted trajectory points of the predicted trajectory sequence; Wherein, the initial heading angular acceleration instruction is determined according to the initial heading angular velocity instruction sequence and the predicted heading angular velocity of the predicted trajectory point of the predicted trajectory.

35. The control device according to claim 34, characterized in that The attitude parameters of the movable carrier at the current moment include: heading angle, heading angular velocity and heading angular acceleration.

36. The control device according to claim 24, characterized in that The kinematic model includes at least one of the following: a uniform acceleration model, a uniform velocity model, and a nonlinear model.

37. The control device according to claim 23, characterized in that The operation of generating a control instruction sequence according to the predicted trajectory includes: Performing interval sampling on the predicted trajectory to obtain predicted trajectory points after interval sampling; The predicted trajectory points after interval sampling are tracked by using a trajectory tracking controller to generate the control instruction sequence.

38. The control device according to claim 37, characterized in that The trajectory tracking controller includes at least one of the following: a model predictive controller, a proportional integral derivative controller, a pure tracking controller, and a linear quadratic regulator.

39. The control device according to claim 38, characterized in that The control instruction sequence includes a position control instruction sequence, and the operation of using a trajectory tracking controller to track the predicted trajectory points after interval sampling to generate the control instruction sequence includes: The model predictive controller is used to track the predicted position parameters of the predicted trajectory points after interval sampling to generate the position control instruction sequence.

40. The control device according to claim 39, characterized in that The position control instruction sequence includes a linear acceleration control instruction sequence and / or a linear speed control instruction sequence.

41. The control device according to claim 40, characterized in that The first instruction in the linear acceleration control instruction sequence and / or the linear velocity control instruction sequence is used to control the movable carrier.

42. The control device according to claim 38, characterized in that The control instruction sequence includes a posture control instruction sequence, and the operation of using a trajectory tracking controller to track the predicted trajectory points after interval sampling to generate the control instruction sequence includes: The predicted posture parameters of the predicted trajectory points in the predicted trajectory sequence are tracked by using the proportional-integral-differential controller to generate the posture control instruction sequence.

43. The control device according to claim 42, characterized in that The attitude control instruction sequence includes a heading angular velocity control instruction sequence and / or a heading angular acceleration control instruction sequence.

44. The control device according to claim 42, characterized in that The posture control instruction sequence is used to control the movable carrier.

45. A computer-readable storage medium, characterized in that It stores executable instructions, which, when executed by one or more processors, can enable the one or more processors to execute the control method as claimed in any one of claims 1 to 22.

46. ​​A removable carrier, characterized in that: include: A control device as claimed in any one of claims 23 to 44; as well as The controller is used to receive the control instruction sequence generated by the control device and use the control instruction sequence to control the movable carrier.

47. The removable carrier according to claim 46, characterized in that The movable carrier is a drone.

48. A remote control device for a movable carrier, characterized in that: include: A control device as claimed in any one of claims 23 to 44; as well as The communication unit is used to send the control instruction sequence generated by the control device to the movable carrier, so that the controller of the movable carrier controls the movable carrier using the control instruction sequence.

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