Tactile teleoperation control method and system for unmanned aerial vehicle hoisting system based on control barrier function

Abstract

The invention discloses an unmanned aerial vehicle (UAV) hoisting system tactile teleoperation control method and system based on a control barrier function, and belongs to the technical field of tactile teleoperation control, the UAV hoisting system comprises a UAV, a payload and a hanging rope, and the control method comprises the following steps: establishing a small-angle second-order integral dynamics model of the UAV, judging whether a nominal control instruction unorm of the input instruction acting on the UAV is safe or not by using the control barrier function, and if so, controlling the UAV to fly by using the nominal control instruction unorm; otherwise, correcting the nominal control instruction unorm, and controlling the UAV to fly by using the corrected optimal control instruction uCBF; in the moving process of the UAV according to the optimal control instruction uCBF, generating tactile force feedback meeting the requirement of output passivity constraint; and tactile force feedback is fed back to an operator through the force feedback hand controller. According to the invention, effective obstacle avoidance of the unmanned aerial vehicle hoisting system can be realized, and meanwhile, the safety and the stability are high.

Description

Technical Field

[0001] The present invention belongs to the technical field of tactile teleoperation control, and in particular relates to a tactile teleoperation control method and system for an unmanned aerial vehicle lifting system based on a control obstacle function. Background Art

[0002] In recent years, low-cost and miniaturized unmanned aerial vehicles (UAVs), represented by rotorcraft, have been widely used in various fields, including military reconnaissance, emergency rescue, light shows, etc., among which the requirements for UAV load transportation capacity are particularly prominent. It should be noted that the UAV lifting system is a typical composite under-actuated system. UAV lifting transportation not only needs to transport the payload to the required location, but also avoid the possibility of collision with obstacles.

[0003] Considering the complexity of the lifting system itself and the unpredictability of the on-site environment, it will be difficult to achieve a fully autonomous control mode for the UAV lifting system for a long time in the future, and the intervention of the operator is indispensable. Teleoperation control currently plays an important role in the field of robotics, allowing humans to work remotely in difficult-to-reach or dangerous environments. Under this control framework, on the one hand, the operator's advanced cognition can be introduced into the control system, and the operator's online decision-making can be fully utilized to improve the transportation operation capability of the UAV lifting system; on the other hand, the interaction between the remote UAV lifting system and the environment is fed back to the local operator, so that the operator has good tactile feedback and reduces the difficulty of the operator's work. How to use the tactile teleoperation control system to reduce the difficulty of the operator's operation and realize the effective obstacle avoidance of the UAV lifting system is a problem that needs to be solved urgently. Summary of the invention

[0004] The object of the present invention is to provide a tactile teleoperation control method and system for an unmanned aerial vehicle (UAV) lifting system based on a control obstacle function, which can reduce the difficulty of operation for operators and achieve effective obstacle avoidance for the UAV lifting system.

[0005] In a first aspect, a tactile teleoperation control method for an unmanned aerial vehicle (UAV) lifting system based on a control obstacle function is provided, wherein the unmanned aerial vehicle (UAV) lifting system comprises an unmanned aerial vehicle (UAV), a payload and a suspension rope, wherein the payload is suspended from the UAV by the suspension rope, and the control method comprises:

[0006] Establish a small-angle second-order integral dynamics model of the UAV to obtain the velocity and position of the UAV;

[0007] Based on the collected input instructions of the operator when operating the force feedback hand controller, the control input instructions for controlling the flight of the UAV are obtained; obtaining the control input instructions includes: using the control obstacle function to determine the nominal control instruction u acting on the UAV by the input instructionnorm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions;

[0008] In the UAV, according to the optimal control instruction u CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm Deviations are generated to generate tactile force feedback that meets the output passivity constraint requirements;

[0009] The tactile force feedback is fed back to the operator through the force feedback hand controller, so that the operator can intuitively feel the obstacle, thereby correcting the operator's next input command.

[0010] Optionally, the step of establishing a small-angle second-order integral dynamics model of the UAV to obtain the speed and position of the UAV specifically includes:

[0011] Establish world coordinate system O w -X w Y w Z w and the UAV's body coordinate system O B -X B Y B Z B ;

[0012] The UAV dynamics model is established based on the UAV's mass, inertia matrix, angular velocity and torque in the body coordinate system;

[0013] Using the small angle assumption, the UAV dynamics model is transformed into a small angle second-order integral dynamics model of UAV.

[0014] Optionally, in the body coordinate system, O B X B Pointing to the front of the UAV, O B Y B Point to the right side of the UAV, O B Z B Vertically downward;

[0015] The dynamic model of the UAV is:

[0016]

[0017] in, is the derivative of q1, q1 is the position vector of the UAV in the world coordinate system {W}, v1 is the velocity vector of the UAV in the world coordinate system {W}, is the derivative of v1, m is the mass of the UAV, R is the rotation matrix of the UAV from the body coordinate system {B} to the world coordinate system {W}, and F i is the force generated by the i-th rotor, g is the gravitational constant, is the derivative of R, Ω is the angular velocity of the UAV in the body coordinate system, is the derivative of Ω, J is the inertia matrix of the UAV, and M is the torque of the UAV in the body coordinate system {B};

[0018] Using the small angle assumption that the Euler angle is less than 10°, the small angle second-order integral dynamics model of the UAV is:

[0019]

[0020] Where q1 is the position vector, is the velocity vector, is the acceleration vector, I is the unit matrix, and are the expected accelerations of the UAV's position vector in the x, y, and z directions, respectively.

[0021] Optionally, the control input instruction for controlling the flight of the UAV is obtained based on the collected input instruction of the operator when operating the force feedback hand controller; obtaining the control input instruction includes: using the control obstacle function to determine the nominal control instruction u acting on the UAV by the input instruction norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions, specifically including:

[0022] Based on the collected input commands of the operator when operating the force feedback hand controller, and according to the established teleoperation forward channel between the operator and the UAV, a position-velocity workspace mapping is defined to obtain the UAV reference velocity given by the operator;

[0023] Based on the current speed and reference speed of the UAV, obtain the nominal control command u given by the operator to the UAV norm ;

[0024] Based on the nominal control command u of the UAV norm The obstacle information around the detected UAV is used to determine whether the UAV lifting system can avoid obstacles by controlling the obstacle function;

[0025] If the obstacle can be avoided, the nominal control command u normSafety, use nominal control instructions u norm Control the UAV to fly, otherwise, convert the obstacle avoidance control problem into the first QP optimization problem, and obtain the optimal control instruction u by solving the first QP optimization problem CBF , and use the optimal control instruction u CBF Control the drone flight.

[0026] Optionally, the position-velocity workspace mapping is expressed as:

[0027]

[0028] Among them, v des is the UAV reference speed given by the operator, k n is a positive real number, q m is the position vector of the end effector of the force feedback hand controller, It is the safety area of ​​the end effector of the force feedback hand controller;

[0029] The nominal control instruction u norm for:

[0030]

[0031] Where v1 is the current speed of the UAV, and Δt is the time constant;

[0032] The control obstacle function H(q λ,o )for:

[0033]

[0034] Among them, q λ,o =q λ -q o ,q o is the position coordinate of the obstacle in the world coordinate system, q λ is the coordinate of any point on the rope in the world coordinate system, ||q λ,o || 2 Q λ,o The second norm, R o is the sphere radius of the obstacle.

[0035] Optionally, the first QP optimization problem is:

[0036]

[0037] Among them, u is the control instruction, q λ,o =q λ -q o ,q o is the position coordinate of the obstacle in the world coordinate system, q λis the coordinate of any point on the rope in the world coordinate system, K is the positive gain matrix, and They are the control barrier function H(q λ,o )'s first and second order differentials.

[0038] Optionally, the UAV is configured to control the CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm The deviation is used to generate tactile force feedback that meets the output passivity constraint requirements, including:

[0039] According to the optimal control instruction u CBF and the nominal control command u norm , get the nominal tactile force feedback F norm ;

[0040] According to the designed energy storage function, determine the output passivity constraint requirements that the tactile force feedback needs to meet;

[0041] The tactile force feedback problem is transformed into a second QP optimization problem, and the tactile force feedback is obtained by solving the second QP optimization problem.

[0042] Optionally, the nominal tactile force feedback F norm for:

[0043] F norm =k f (u CBF -u norm )

[0044] Among them, k f is the positive gain that adjusts the amplitude of tactile force feedback;

[0045] The energy storage function V(q λ )for:

[0046]

[0047] Among them, q λ are the coordinates of any point on the suspension rope, The velocity at any point on the rope, k e is a constant parameter used to adjust the stored energy;

[0048] The output passivity constraint requirements that the tactile force feedback needs to meet are:

[0049]

[0050] Where F is the tactile force feedback, y = F, ρ(y) = F, u = u norm , V(q λ ), where k is a constant greater than 0.

[0051] Optionally, the second QP optimization problem is:

[0052]

[0053] Among them, u is the control instruction, u norm is the nominal control instruction, and They are the control barrier function H(q λ,o ), K is the positive gain matrix, F is the tactile force feedback, k is a constant greater than 0, is the energy storage function V(q λ ) is the first-order differential of .

[0054] In a second aspect, a tactile teleoperation control system for a UAV lifting system based on a control obstacle function is provided, wherein the UAV lifting system comprises a UAV, a payload and a suspension rope, wherein the payload is suspended from the UAV by the suspension rope, and the control system comprises:

[0055] UAV position acquisition module, used to establish a small-angle second-order integral dynamics model of the UAV and obtain the speed and position of the UAV;

[0056] The control input command acquisition module is used to acquire the control input command for controlling the flight of the UAV based on the input command of the operator when operating the force feedback hand controller; acquiring the control input command includes: using the control obstacle function to determine the nominal control command u acting on the UAV by the input command norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions;

[0057] The tactile force feedback generation module is used to generate the optimal control command u in the UAV. CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm Deviations are generated to generate tactile force feedback that meets the output passivity constraint requirements;

[0058] The tactile control module is used to feed back the tactile force feedback to the operator through the force feedback hand controller, so that the operator can intuitively feel the obstacle and thus correct the operator's next input instruction.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention simplifies the establishment of the UAV second-order integral dynamics model, and according to the interaction between the UAV, the payload, the suspension rope and the obstacle environment, uses the control obstacle function to transform the obstacle avoidance problem into a quadratic programming (QP) optimization problem, thereby realizing the effective obstacle avoidance of the UAV lifting system in a non-structural environment; in addition, the present invention also proposes a tactile teleoperation force feedback model to meet the output passivity constraint requirements. The operator can intuitively perceive the existence of obstacles and guide the UAV lifting system to other appropriate directions, so that the UAV lifting teleoperation control system can meet the safety and stability requirements at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flowchart of the steps of the tactile teleoperation control method of the unmanned aerial vehicle lifting system based on the control obstacle function of the present invention;

[0062] Figure 2 It is a structural block diagram of the UAV lifting system of the present invention;

[0063] Figure 3 It is a simplified schematic diagram of the obstacle avoidance of the UAV lifting system of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0065] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0066] Example 1

[0067] like Figure 1 As shown, a tactile teleoperation control method for a UAV lifting system based on a control obstacle function comprises the following steps (S1-S4).

[0068] like Figure 2As shown, the UAV lifting system includes a UAV, a payload and a suspension rope, and the payload is suspended on the UAV through the suspension rope; the operator controls the action of the force feedback hand controller, and the control system generates control instructions through the action of the force feedback hand controller and obstacle information, and uses the wireless communication module to issue control instructions to control the movement of the UAV. During the movement of the UAV, the control system generates tactile force feedback and sends tactile instructions containing tactile force feedback to the feedback hand controller to correct the operator's next operation.

[0069] As an option, the force feedback hand controller includes a force feedback human-computer interaction device with 3 position degrees of freedom, 3 joint degrees of freedom and 3 position degrees of freedom force feedback outputs, and the wireless communication module is WIFI wireless network communication.

[0070] The control methods specifically include:

[0071] S1: Establish a small-angle second-order integral dynamics model of the UAV to obtain the velocity and position of the UAV.

[0072] Specifically, step S1 includes the following sub-steps (S11-S13):

[0073] S11: Establish world coordinate system O w -X w Y w Z w and the UAV's body coordinate system O B -X B Y B Z B .

[0074] In the body coordinate system, O B X B Pointing to the front of the UAV, O B Y B Point to the right side of the UAV, O B Z B Vertically downward; the length of the rope is L.

[0075] S12: Establish a UAV dynamic model based on the mass, inertia matrix, angular velocity and torque of the UAV in the body coordinate system.

[0076] In this embodiment, the dynamic model of the UAV is:

[0077]

[0078] in, is the derivative of q1, q1 is the position vector of UAV in the world coordinate system {W}, q1=[q 1,x ,q 1,y ,q 1,z] T , v1 is the velocity vector of the UAV in the world coordinate system {W}, is the derivative of v1, m is the mass of the UAV, R is the rotation matrix of the UAV from the body coordinate system {B} to the world coordinate system {W}, and F i is the force generated by the i-th rotor, g is the gravitational constant, is the derivative of R, Ω is the angular velocity of the UAV in the body coordinate system, is the derivative of Ω, J is the inertia matrix of the UAV, and M is the torque of the UAV in the body coordinate system {B}.

[0079] The rotation matrix R of the UAV from the body coordinate system {B} to the world coordinate system {W} is:

[0080]

[0081] Where φ, θ and ψ represent the roll angle, pitch angle and yaw angle of the UAV, respectively, and s φ and c φ They represent sin(φ) and cos(φ) respectively. Similarly, s θ and c θ Respectively represent sin(θ) and cos(θ), s ψ and c ψ They represent sin(ψ) and cos(ψ) respectively.

[0082] S13: Using the small angle assumption, the UAV dynamics model is transformed into a small angle second-order integral dynamics model of the UAV.

[0083] For simplicity, we use the small angle assumption and assume that the Euler angles (roll, pitch, and yaw) are less than 10 degrees. In this case, sin(φ)≈φ and cos(φ)≈1. Based on the above assumptions, the desired roll angle φ of the UAV is des , pitch angle θ des and lift It can be expressed as:

[0084]

[0085] in, and are the expected accelerations of the UAV's position vector in the x, y, and z directions, respectively.

[0086] In the teleoperation control system, the input given by the operator is the desired speed of the UAV, which includes the Euler angles φ and θ required by the UAV. (The desired yaw angle is assumed to be zero.) Therefore, let Then the small-angle second-order integral dynamics model of UAV is:

[0087]

[0088] Where q1 is the position vector, is the velocity vector, is the acceleration vector, I is the unit matrix, and u1 is the control input instruction

[0089] S2: Based on the collected input instructions of the operator when operating the force feedback hand controller, the control input instructions for controlling the flight of the UAV are obtained; the control input instructions are obtained by using the control obstacle function to determine the nominal control instructions u acting on the UAV by the input instructions. norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input command.

[0090] Specifically, step S2 includes the following sub-steps (S21-S24):

[0091] S21: Based on the collected input instructions of the operator when operating the force feedback hand controller, and according to the established teleoperation forward channel between the operator and the UAV, a position-velocity workspace mapping is defined to obtain a UAV reference velocity given by the operator.

[0092] Specifically, the position-velocity workspace mapping is expressed as:

[0093]

[0094] Among them, v des is the UAV reference speed given by the operator, k n is a positive real number; q m is the position vector of the end effector of the force feedback hand controller, is the safety area of ​​the end effector of the force feedback hand controller {|q mx |,|q m y|,|q mz |≤15mm}, in the safe area, the UAV does not respond to any control instructions from the operator except in the hovering state to avoid operational errors.

[0095] S22: Based on the current speed and reference speed of the UAV, obtain the nominal control instruction u given by the operator to the UAV norm .

[0096] The nominal control command u given by the operator to the UAV norm for:

[0097]

[0098] Among them, v1 is the current speed of the UAV, Δt is the time constant.

[0099] The current speed of the UAV is the speed obtained by the small-angle second-order integral dynamics model of the UAV.

[0100] S23: Based on the nominal control command u of the UAV norm The obstacle information around the detected UAV is used to determine whether the UAV lifting system can avoid obstacles by controlling the obstacle function.

[0101] S24: If the obstacle can be avoided, the nominal control command u norm Safety, use nominal control instructions u norm Control the UAV to fly, otherwise, convert the obstacle avoidance control problem into the first QP optimization problem, and obtain the optimal control instruction u by solving the first QP optimization problem CBF , and use the optimal control instruction u CBF Control the drone flight.

[0102] Specifically, if the nominal control command is safe, the nominal control command is used as the control input command of the UAV. If the nominal control command is unsafe, the optimal control command is used as the control input command of the UAV. The optimal control command u is obtained through the CBF controller. CBF .

[0103] Specifically, Figure 3 As shown, in the world coordinate system {W}, consider a single static obstacle q o , the radius of the sphere is R o In the UAV lifting system, the coordinate of the UAV is q1, and its corresponding control input is u1; the coordinate of the payload is q2, and its corresponding control input is u2.

[0104] The position q of any point on the rope λ It can be expressed as:

[0105] q λ =λq1+(1-λ)q2,λ∈[0,1] (7)

[0106] Among them, the effective load coordinate q2=q1+Lp, p is the direction of the suspension rope.

[0107] The control input u at any point on the suspension rope λ for:

[0108] u λ =λu1+(1-λ)u2 (8)

[0109] The UAV lifting system includes the UAV, the payload and the suspension rope, and is expected to avoid the radius R o The spherical obstacle is controlled by the obstacle function H(q λ,o ) is designed as follows:

[0110]

[0111] Among them, q λ,o =q λ -q o ,q o is the position coordinate of the obstacle in the world coordinate system, ||q λ,o || 2 for q λ,o The second norm of , positive invariant set This ensures collision-free movement of the drone lifting system.

[0112] As an option, whether a collision with an obstacle will occur can be determined by solving the value of the control obstacle function, that is, if the value of the control obstacle function is greater than 0, no collision will occur, and if it is less than or equal to 0, a collision will occur.

[0113] According to the control obstacle function constraints, the safety constraints for collision-free motion are derived as follows:

[0114]

[0115] Where K is a positive gain matrix. The above obstacle avoidance constraints ensure that the control input drives the state q of the UAV lifting system i ,i=1,2,λ, to ensure that the positive direction of the set C is not deformed, thus ensuring the safety constraint H(q λ,o )≥0.

[0116] The obstacle avoidance control problem of the UAV lifting system is transformed into the first QP optimization problem, namely:

[0117]

[0118] Among them, u is the control instruction, q λ,o =q λ -q o ,q o is the position coordinate of the obstacle in the world coordinate system, q λ is the coordinate of any point on the rope in the world coordinate system, K is the positive gain matrix, and H(q λ,o )'s first and second order differentials.

[0119] S3: The UAV is controlled according to the optimal control instruction u CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm The deviation of is used to generate tactile force feedback that meets the output passivity constraint requirement.

[0120] Step S3 specifically includes:

[0121] S31: According to the optimal control instruction u CBF and the nominal control command u norm , get the nominal tactile force feedback F norm .

[0122] Specifically, the UAV follows the optimal control instruction u CBF When the UAV moves, the control input of the UAV is inconsistent with the nominal control command given by the operator to the UAV. Therefore, in order to inform the operator of the situation of the obstacle at the end, the nominal tactile force feedback F norm Designed for:

[0123] F norm =k f (u CBF -u norm ) (12)

[0124] Among them, k f >0 is the positive gain for adjusting the amplitude of tactile force feedback; the main end operator can intuitively sense the existence of obstacles and guide the drone lifting system to other appropriate directions.

[0125] S32: According to the designed energy storage function, determine the output passivity constraint requirements that the tactile force feedback F needs to satisfy.

[0126] Energy storage function V(q λ )for:

[0127]

[0128] Among them, q λ are the coordinates of any point on the suspension rope, The velocity vector of any point on the rope, k e is a constant parameter used to adjust the amount of stored energy.

[0129] The tactile force feedback from the end to the operator through the force feedback hand controller can be obtained by finding the closest norm At the same time, the tactile force feedback F needs to satisfy the output passivity constraint:

[0130]

[0131] Among them, in the strict unconditioned condition, F is the tactile force feedback, y = F, ρ(y) = F, u = u norm , V(q λ ), where k is a constant greater than 0.

[0132] S33: The tactile force feedback problem is converted into a second QP optimization problem. Combined with the obstacle-free motion requirements of the UAV lifting system, the tactile force feedback is obtained by solving the second QP optimization problem.

[0133] Specifically, the second QP optimization problem is:

[0134]

[0135] Among them, u is the control instruction, u norm is the nominal control command, K is the positive gain matrix, F is the tactile force feedback, is the energy storage function V(q λ ) is the first-order differential of .

[0136] S4: Feedback the tactile force feedback to the operator through the force feedback hand controller, so that the operator can intuitively feel the obstacle, thereby correcting the operator's next input command.

[0137] The operator of the present invention operates the force feedback hand controller, and the force feedback hand controller transmits the position information q m Send to computer; location informationq m The signal is transmitted to the slave end through the wireless communication module and becomes the desired speed control input signal u of the UAV in the UAV lifting system. norm , realize the trajectory generation and tracking of UAV; the UAV lifting system realizes the obstacle-free movement of the payload in the obstacle or dangerous area environment according to the control obstacle function optimization algorithm. At the same time, the distance signal d between the UAV lifting system and the obstacle i,o , including the distance d between the drone and the obstacle i,o , the distance between the payload and the obstacle d 2,o and the distance d between the rope and the obstacle λ,o , acting on obstacles respectively, and the obstacles provide the UAV lifting system with a safety input signal u CBF; Further, in order to inform the operator of the situation of the obstacles on the slave side and meet the passivity constraint of the system output, tactile force feedback F is generated and fed back to the operator on the master side, so that the operator can intuitively perceive the existence of the obstacles and guide the UAV lifting system to other suitable directions; In a non-structural environment, the present invention introduces the operator into the control and decision-making of the UAV lifting system, effectively solving the problems of establishing the remote operation channel of the UAV lifting system, obstacle avoidance of the UAV lifting system and tactile force feedback, thereby reducing the operating difficulty of the operator and realizing the effective obstacle avoidance of the UAV lifting system.

[0138] Example 2

[0139] A tactile teleoperation control system for a UAV lifting system based on a control obstacle function is provided. The UAV lifting system includes a UAV, a payload and a suspension rope. The payload is suspended on the UAV through the suspension rope. The control system includes:

[0140] UAV position acquisition module, used to establish a small-angle second-order integral dynamics model of the UAV and obtain the speed and position of the UAV;

[0141] The control input command acquisition module is used to acquire the control input command for controlling the flight of the UAV based on the input command of the operator when operating the force feedback hand controller; acquiring the control input command includes: using the control obstacle function to determine the nominal control command u acting on the UAV by the input command norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions;

[0142] The tactile force feedback generation module is used to generate the optimal control command u in the UAV. CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm Deviations are generated to generate tactile force feedback that meets the output passivity constraint requirements;

[0143] The tactile control module is used to feed back the tactile force feedback to the operator through the force feedback hand controller, so that the operator can intuitively feel the obstacle and thus correct the operator's next input instruction.

[0144] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0145] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0146] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0147] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A tactile teleoperation control method for an unmanned aerial vehicle lifting system based on a control obstacle function, wherein the unmanned aerial vehicle lifting system comprises an unmanned aerial vehicle (UAV), a payload and a suspension rope, wherein the payload is suspended from the UAV by the suspension rope, and wherein: The control method comprises: Establish a small-angle second-order integral dynamics model of the UAV to obtain the velocity and position of the UAV; Based on the collected input instructions of the operator when operating the force feedback hand controller, the control input instructions for controlling the flight of the UAV are obtained; obtaining the control input instructions includes: using the control obstacle function to determine the nominal control instruction u acting on the UAV by the input instruction norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions; In the UAV, according to the optimal control instruction u CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm Deviations are generated to generate tactile force feedback that meets the output passivity constraint requirements; The tactile force feedback is fed back to the operator through the force feedback hand controller, so that the operator can intuitively feel the obstacle, thereby correcting the operator's next input command.

2. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 1 is characterized in that: The step of establishing a small-angle second-order integral dynamics model of the UAV to obtain the speed and position of the UAV specifically includes: Establish world coordinate system O w -X w Y w Z w and the UAV's body coordinate system O B -X B Y B Z B ; The UAV dynamics model is established based on the UAV's mass, inertia matrix, angular velocity and torque in the body coordinate system; Using the small angle assumption, the UAV dynamics model is transformed into a small angle second-order integral dynamics model of UAV.

3. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 2 is characterized in that: In the body coordinate system, O B X B Pointing to the front of the UAV, O B Y B Point to the right side of the UAV, O B Z B Vertically downward; The dynamic model of the UAV is: in, is the derivative of q1, q1 is the position vector of the UAV in the world coordinate system {W}, v1 is the velocity vector of the UAV in the world coordinate system {W}, is the derivative of v1, m is the mass of the UAV, R is the rotation matrix of the UAV from the body coordinate system {B} to the world coordinate system {W}, and F i is the force generated by the i-th rotor, g is the gravitational constant, is the derivative of R, Ω is the angular velocity of the UAV in the body coordinate system, is the derivative of Ω, J is the inertia matrix of the UAV, and M is the torque of the UAV in the body coordinate system {B}; Using the small angle assumption that the Euler angle is less than 10°, the small angle second-order integral dynamics model of the UAV is: Where q1 is the position vector, is the velocity vector, is the acceleration vector, I is the unit matrix, and are the expected accelerations of the UAV's position vector in the x, y, and z directions, respectively.

4. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 1 is characterized in that: The control input instruction for controlling the flight of the UAV is obtained based on the input instruction of the operator when operating the force feedback hand controller; obtaining the control input instruction includes: using the control obstacle function to determine the nominal control instruction u acting on the UAV by the input instruction norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions, specifically including: Based on the collected input commands of the operator when operating the force feedback hand controller, and according to the established teleoperation forward channel between the operator and the UAV, a position-velocity workspace mapping is defined to obtain the UAV reference velocity given by the operator; Based on the current speed and reference speed of the UAV, obtain the nominal control command u given by the operator to the UAV norm ; Based on the nominal control command u of the UAV norm The obstacle information around the detected UAV is used to determine whether the UAV lifting system can avoid obstacles by controlling the obstacle function; If the obstacle can be avoided, the nominal control command u norm Safety, use nominal control instructions u norm Control the UAV to fly, otherwise, convert the obstacle avoidance control problem into the first QP optimization problem, and obtain the optimal control instruction u by solving the first QP optimization problem CBF , and use the optimal control instruction u CBF Control the drone flight.

5. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 4 is characterized in that: The position-velocity workspace mapping is expressed as: Among them, v des is the UAV reference speed given by the operator, k n is a positive real number, q m is the position vector of the end effector of the force feedback hand controller, It is the safety area of ​​the end effector of the force feedback hand controller; The nominal control instruction u norm for: Where v1 is the current speed of the UAV, and Δt is the time constant; The control obstacle function H(q λ,o )for: Among them, q λ,o =q λ -q o ,q o is the position coordinate of the obstacle in the world coordinate system, q λ is the coordinate of any point on the rope in the world coordinate system, ||q λ,o || 2 Q λ,o The second norm, R o is the radius of the obstacle sphere.

6. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 4 is characterized in that: The first QP optimization problem is: Among them, u is the control instruction, q λ,o =q λ -q o ,q o is the position coordinate of the obstacle in the world coordinate system, q λ is the coordinate of any point on the rope in the world coordinate system, K is the positive gain matrix, and They are the control barrier function H(q λ,o )'s first and second order differentials.

7. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 1 is characterized in that: The UAV is controlled according to the optimal control instruction u CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm The deviation is used to generate tactile force feedback that meets the output passivity constraint requirements, including: According to the optimal control instruction u CBF and the nominal control command u norm , get the nominal tactile force feedback F norm ; According to the designed energy storage function, determine the output passivity constraint requirements that the tactile force feedback needs to meet; The tactile force feedback problem is transformed into a second QP optimization problem, and the tactile force feedback is obtained by solving the second QP optimization problem.

8. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 7 is characterized in that: The nominal tactile force feedback F norm for: F norm =k f (in CBF -in norm ) Among them, k f is the positive gain that adjusts the amplitude of tactile force feedback; The energy storage function V(q λ )for: Among them, q λ are the coordinates of any point on the suspension rope, The velocity at any point on the rope, k e is a constant parameter used to adjust the stored energy; The output passivity constraint requirements that the tactile force feedback needs to meet are: Where F is the tactile force feedback, y = F, ρ(y) = F, u = u norm , V(q λ ), where k is a constant greater than 0.

9. The tactile teleoperation control method for a UAV lifting system based on a control obstacle function according to claim 7, characterized in that: The second QP optimization problem is: Among them, u is the control instruction, u norm is the nominal control instruction, and They are the control barrier function H(q λ,o ), K is the positive gain matrix, F is the tactile force feedback, k is a constant greater than 0, is the energy storage function V(q λ ) is the first-order differential of .

10. A tactile teleoperation control system for a UAV lifting system based on a control obstacle function, wherein the UAV lifting system comprises a UAV, a payload and a suspension rope, wherein the payload is suspended from the UAV by the suspension rope, and wherein: The control system comprises: UAV position acquisition module, used to establish a small-angle second-order integral dynamics model of the UAV and obtain the speed and position of the UAV; The control input command acquisition module is used to acquire the control input command for controlling the flight of the UAV based on the input command of the operator when operating the force feedback hand controller; acquiring the control input command includes: using the control obstacle function to determine the nominal control command u acting on the UAV by the input command norm Is it safe? If it is safe, the nominal control instruction u norm As the control input command; otherwise, modify the nominal control command u norm , and use the modified optimal control instruction u CBF As control input instructions; The tactile force feedback generation module is used to generate the haptic feedback in the UAV according to the optimal control instruction u CBF During the movement process, based on the optimal control instruction u CBF and the nominal control command u norm Deviations are generated to generate tactile force feedback that meets the output passivity constraint requirements; The tactile control module is used to feed back the tactile force feedback to the operator through the force feedback hand controller, so that the operator can intuitively feel the obstacle and thus correct the operator's next input instruction.

Images