Autonomous mode switching control method and system for air-ground bimodal robot

Abstract

The invention provides an autonomous mode switching control method and system for an air-ground bimodal robot, and particularly relates to the technical field of robot control, and the scheme comprises the steps: constructing a second-order nonlinear motion response model of a control system of the air-ground bimodal robot; the running state of the air-ground bimodal robot on the target path is obtained in real time, and the current running mode of the air-ground bimodal robot and the output value of the second-order nonlinear motion response model are determined based on the running state; identifying the second-order nonlinear motion response model by adopting an iterative learning algorithm to obtain an estimated value; and on the basis of the output values and the estimated values at the adjacent moments, autonomously switching or keeping the motion modes of the air-ground bimodal robot on the target path. According to the scheme, only one controller is needed to meet control requirements under two different motion models, hardware resources can be greatly saved, the method does not depend on a high-precision model, and operation mode switching can be automatically completed on the basis of maximum energy utilization.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to an autonomous mode switching control method and system for a land-air dual-mode robot. Background Art

[0002] Since the land-air dual-modal robot can adopt different motion modes according to different task requirements, the robot can complete the task more quickly, efficiently and energy-savingly, and the switching between land and air motion modes can improve energy utilization and increase endurance time, it has broad application prospects.

[0003] The control method of the land-air dual-mode robot in the prior art mainly designs two different controllers for the multi-rotor mode and the vehicle mode for independent control. This method not only increases the consumption of hardware resources, but also the response of the motion mode switching is not timely, and cannot meet the requirements of efficient operation. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, an object of the present invention is to provide an autonomous mode switching control method and system for a land-air dual-modal robot, aiming to solve the problem in the prior art that the mode switching response is not timely and cannot meet the requirements of efficient operation.

[0005] In order to achieve the above-mentioned object, the first aspect of the present invention provides an autonomous mode switching control method of a land-air dual-mode robot, comprising:

[0006] Construct a second-order nonlinear motion response model of the control system of the land-air dual-modal robot;

[0007] Acquire the running state of the land-air dual-modal robot on the target path in real time, and determine the current running mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the running state;

[0008] Using an iterative learning algorithm to identify the second-order nonlinear motion response model to obtain an estimated value;

[0009] Based on the output value and the estimated value at adjacent moments, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

[0010] Optionally, the process of constructing a second-order nonlinear motion response model of the control system of the land-air dual-modal robot includes:

[0011] Acquire known nonlinear links, unknown links and operation modes of the control system of the land-air dual-modal robot;

[0012] Based on the parameters corresponding to the operating state, the operating mode, the nonlinear link and the unknown link, a second-order nonlinear motion response model of the control system of the land-air dual-modal robot is constructed.

[0013] Optionally, determining the current operating mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the operating state includes:

[0014] Based on the operating state, determining a current operating mode of the land-air dual-modal robot;

[0015] The current operation mode uses the operation state as an input of the second-order nonlinear motion response model to determine an output value of the second-order nonlinear motion response model.

[0016] Optionally, the adopting an iterative learning algorithm to identify the second-order nonlinear motion response model to obtain an estimated value includes:

[0017] Determine the output error based on the output value at the current moment and the preset expected output value;

[0018] Based on the output error and a preset identification gain, an iterative learning algorithm is used to identify the unknown link to obtain an estimated value.

[0019] Optionally, autonomously switching or maintaining the motion mode of the land-air dual-modal robot on the target path based on the output value and the estimated value at adjacent moments includes:

[0020] Determine the estimation error based on the estimation value at the current moment and the estimation value at the previous moment;

[0021] Based on the estimation error and the output error, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

[0022] Optionally, autonomously switching or maintaining the motion mode of the land-air dual-modal robot on the target path based on the estimation error and the output error includes:

[0023] Calculating an input signal of the second-order nonlinear motion response model based on the estimation error, the output error and a preset control gain;

[0024] Dynamically adjusting the input signal according to the difference in output errors corresponding to two adjacent iterations to obtain an optimized input signal;

[0025] Based on the optimized input signal and the preset mode switching threshold, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

[0026] A second aspect of the present invention provides an autonomous mode switching control system for a land-air dual-mode robot, the system comprising:

[0027] A model building module is used to build a second-order nonlinear motion response model of the control system of the land-air dual-modal robot;

[0028] An operation status information acquisition module is used to acquire the operation status of the land-air dual-modal robot on the target path in real time, and determine the current operation mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the operation status;

[0029] An iterative learning identification module, used for identifying the second-order nonlinear motion response model using an iterative learning algorithm to obtain an estimated value;

[0030] The operating mode autonomous switching module is used to autonomously switch or maintain the motion mode of the land-air dual-modal robot on the target path based on the output value and the estimated value at adjacent moments.

[0031] Optionally, the system also includes an output error calculation module and an estimated error calculation module, wherein the output error calculation module is used to determine the output error based on the output value at the current moment and the preset expected output value; the estimated error calculation module is used to identify the unknown links of the control system of the land-air dual-modal robot based on the output error and the preset identification gain using an iterative learning algorithm to obtain an estimated value; and determine the estimated error based on the estimated value at the current moment and the estimated value at the previous moment.

[0032] A third aspect of the present invention provides an intelligent terminal, which includes a memory, a processor, and a land-to-air dual-modal robot autonomous mode switching control program stored in the memory and executable on the processor. When the land-to-air dual-modal robot autonomous mode switching control program is executed by the processor, any step of the above-mentioned land-to-air dual-modal robot autonomous mode switching control method is implemented.

[0033] A fourth aspect of the present invention provides a computer-readable storage medium, on which is stored a land-to-air dual-modal robot autonomous mode switching control program, which, when executed by a processor, implements any step of the above-mentioned land-to-air dual-modal robot autonomous mode switching control method.

[0034] Compared with the prior art, the beneficial effects of this solution are as follows:

[0035] The present invention designs a second-order nonlinear motion response model for the control system of the land-air dual-modal robot, which can use one controller to simultaneously meet the control requirements under two different motion models, which can not only greatly save hardware resources, but also realize fast and efficient switching control of the land-air dual-modal robot; introduces an iterative learning identification method to identify uncertainty factors and compensate for them in the control input, which can improve the control accuracy and anti-interference performance of the system and enhance the robustness of the system; proposes an autonomous mode switching control strategy for the land-air dual-modal robot, so that the robot can autonomously complete the operation mode switching and greatly improve the flexibility of system control on the basis of maximizing energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0037] Figure 1 It is a flow chart of the autonomous mode switching control method of the land-air dual-mode robot of the present invention;

[0038] Figure 2 This is a flow chart of the autonomous mode switching control strategy of the land-air dual-mode robot of the present invention;

[0039] Figure 3 It is a schematic diagram of the autonomous mode switching control system module of the land-air dual-mode robot of the present invention;

[0040] Figure 4 It is a schematic diagram of the structure of the intelligent terminal of the present invention. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0042] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0043] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0046] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] In view of the problem in the prior art that the mode switching response is not timely and the requirement of efficient operation cannot be met, the present invention proposes an autonomous mode switching control method for a land-air dual-mode robot. The method mainly designs the aerial multi-rotor mode and the ground vehicle mode of the land-air dual-mode robot into a second-order nonlinear motion response model, and establishes an iterative learning control method under a unified framework, that is, the same controller can be used to realize motion control in two different modes, saving hardware resources; then, an iterative learning identification method is introduced to identify uncertainty factors and compensate for them in the control input, so as to further improve the control accuracy and anti-interference performance of the system; finally, the switching control strategy based on iterative learning control realizes the autonomous mode switching from the ground vehicle mode to the aerial multi-rotor mode, so as to ensure the efficient utilization of the energy of the land-air dual-mode robot and the accessibility to any position in three-dimensional space.

[0049] The embodiment of the present invention provides a method for controlling the autonomous mode switching of a land-air dual-mode robot, which is deployed on electronic devices such as computers and servers and is applied to scenarios with diverse road conditions. It is aimed at situations where the land-air dual-mode robot needs to flexibly and autonomously switch between the aerial multi-rotor mode and the ground vehicle mode under different road conditions. Specifically, Figure 1 As shown, the steps of the method in this embodiment include:

[0050] Step S100: constructing a second-order nonlinear motion response model of the control system of the land-air dual-modal robot.

[0051] Specifically, according to the driving dynamics response principle and motion control requirements of the land-air dual-mode robot (hereinafter referred to as the robot) in the air multi-rotor mode and the ground vehicle mode, the control system of the land-air dual-mode robot is designed as a second-order nonlinear motion response model. The second-order nonlinear motion response model can not only reflect that the robot is subjected to the forces such as ground reaction force, gravity and resistance generated by the drive system in the ground vehicle mode, but also reflect that the robot is subjected to the forces such as lift, thrust, gravity and air resistance in the air multi-rotor mode. The control system characterized by the second-order nonlinear motion response model can perceive the robot's operating state (such as position, speed, acceleration, posture, etc.) in real time, and adjust the control input signal (such as the torque of the driving wheel, the magnitude and direction of the thrust, etc.) according to the preset target and current state, so that the robot can autonomously select the motion mode under the autonomous mode switching control mechanism.

[0052] In a preferred embodiment, the process of constructing a second-order nonlinear motion response model of the control system of the land-air dual-modal robot includes:

[0053] Acquire the known nonlinear links, unknown links and operation modes of the control system of the land-air dual-modal robot; and construct a second-order nonlinear motion response model of the control system of the land-air dual-modal robot based on the parameters corresponding to the operation state, the operation mode, the nonlinear links and the unknown links.

[0054] Specifically, the control system of the air-land dual-modal robot includes a known nonlinear link part and an unknown link part, wherein the known nonlinear link part is a fixed part in the control system and will not change with the change of the operating state or the change of the control input. However, the unknown link part will change with the change of the operating state. Therefore, the focus of model construction is to solve the change of motion response caused by the unknown link part. Obviously, the output of the control system is affected by the operating state and the unknown link part of the robot. This embodiment uses the motion response generated by the operating state and the unknown link part as the output of the entire second-order nonlinear motion response model.

[0055] Based on this, this embodiment constructs the following second-order nonlinear motion response model, which is expressed as follows:

[0056]

[0057] Where t is time; j is the number of iterations; p(t, j), v(t, j) and y(t, j) are position, velocity and output value respectively, y(t, j) is the output of the second-order nonlinear motion response model; u(t, j) represents the input signal; B k and C k is the input matrix and output matrix of the system; f k (·) and d k (·) are the known nonlinear links and unknown links of the control system respectively; k is the mode index, k = 1 represents the ground vehicle mode, k = 2 represents the aerial multi-rotor mode; sat(·) is the saturation function, which is used to limit the amplitude range of the input signal in the system to achieve control of system performance to avoid overload. Among them, the input matrix B k It is used to represent the effect of the input signal u(t, j) on the speed of the robot in the current operating mode, generally representing a constant. In the ground vehicle mode, the input matrix B k It reflects the proportional relationship between the output torque of the wheel drive motor and the motor speed; in the aerial multi-rotor mode, the input matrix B k It reflects the proportional relationship between the motor input torque and the motor speed on the multi-rotor. Output matrix C k It is used to describe the position state of the robot in the current operation mode, which will directly affect the operation state information included in the output of the model. k(·) indicates known nonlinear behaviors in the system. These known nonlinear behaviors usually exist due to the physical characteristics or design of the robot. For the land-air dual-modal robot, the known nonlinear links mainly include friction and air resistance. Unknown links d k (·) represents the uncertainty or unmodeled dynamic characteristics in the system, which are usually introduced by factors such as parameter changes, environmental changes, or inaccurate models. For the land-air dual-modal robot, the unknown links are mainly introduced by random interference from the external environment.

[0058] It can be seen that this embodiment uniformly designs the aerial multi-rotor mode and the ground vehicle mode as a second-order nonlinear model, and establishes an iterative learning control method under a unified framework, that is, the same controller can be used to achieve motion mode switching control in two different modes, which can not only improve the response efficiency of motion mode switching, but also greatly save hardware resources and improve energy utilization.

[0059] Step S200: Acquire the operating state of the land-air dual-modal robot on the target path in real time, and determine the current operating mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the operating state.

[0060] Specifically, based on the above-mentioned second-order nonlinear motion response model, the operating state of the land-air dual-modal robot on the target path is obtained in real time, and the current operating mode of the robot at the current moment is determined according to the operating state at the current moment; if the robot is traveling on a road with a large slope in a ground vehicle mode, then in the current operating mode, the operating state can be used as the input of the second-order nonlinear motion response model to calculate the output value of the second-order nonlinear motion response model, that is, the motion response output.

[0061] If it is determined based on the output value that the robot may face difficulties in moving forward steadily when driving at the current speed on the slope in the ground vehicle mode, then the operating state and / or operating mode need to be adjusted. However, in order to achieve the purpose of normal driving under the corresponding road conditions with the lowest energy efficiency, it is necessary to use the motion response output of the model to accurately adjust the operating state and operating mode according to the road conditions at the current moment. For specific operating steps, please refer to steps S300 and S400.

[0062] Step S300: using an iterative learning algorithm to identify the second-order nonlinear motion response model to obtain an estimated value.

[0063] In order to accurately calculate the motion response output of the model, this embodiment targets the unknown link d k (·) Set up an uncertainty compensation mechanism, that is, to compensate for the unknown links in the model. kSpecifically, based on the output value at the current moment and the preset expected output value, the output error is determined; based on the output error and the preset identification gain, the iterative learning algorithm is used to identify the unknown link to obtain the estimated value of the motion response output of the model.

[0064] In a preferred embodiment, the expression of the designed iterative learning identification algorithm is:

[0065]

[0066] in, is d k The estimated value of (·), Γ k is the identification gain, y d (t) is the expected output, and its value is affected by the robot's operating mode and operating state, and the purpose is to enable the robot to operate normally at the current moment. This iterative learning identification algorithm adopts a data-driven approach to characterize the process of estimating the unknown links of the system at the current moment using the operating state data and operating mode data collected in the previous moment during the historical iteration process, so as to identify the uncertain factors and compensate them in the control input, which is beneficial to improve the control accuracy and anti-interference performance of the system.

[0067] In specific applications, the robot's current position, speed, acceleration and other motion state information can be obtained through sensors, and an identification gain value is given. The iterative learning identification algorithm is used to calculate the estimated value of the unknown link and store it in the memory.

[0068] It should be noted that this embodiment uses an iterative learning identification algorithm to identify uncertain factors in the system and compensate for them in the control input. As other preferred implementations, neural network, fuzzy control and other algorithms can also be used to estimate unknown links.

[0069] Step S400: Based on the output value and the estimated value at adjacent moments, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

[0070] In actual driving, when the robot's height from the ground or the slope is less than or equal to the preset corresponding threshold, the ground vehicle mode is usually adopted; when the robot's height from the ground or the slope is greater than the preset corresponding threshold and the thrust is sufficient, the aerial multi-rotor mode is usually adopted. During the entire operation process, the control system needs to be able to perceive the robot's state (such as position, speed, acceleration, posture, etc.) in real time, and adjust the control input (such as the torque of the drive wheel, the size and direction of the thrust, etc.) according to the target path and the operating state at adjacent moments, so as to ensure the safe driving of the robot while efficiently utilizing energy, and to achieve the robot's accessibility to any position in three-dimensional space.

[0071] Based on this, this embodiment provides a control strategy for switching the autonomous operation mode of the land-air dual-mode robot. Through this control strategy, it is possible to perform iterative learning and identification according to the above-mentioned second-order nonlinear motion response model and the above-mentioned iterative learning and identification algorithm, and according to the identification estimation values ​​at adjacent moments and the expected output value yd(t), calculate the output error and estimated error information of the control system, and continuously update the control input signal u(t, j+1) to improve the tracking accuracy of the land-air dual-modal robot to complete the target route, so that the land-air dual-modal robot can track the expected motion trajectory. Among them, the controller expression for controlling the input signal u(t, j+1) is:

[0072]

[0073] Among them, u(t, j+1) is the control input, γ k and H k are the control gains for the output error and the estimated error, respectively. e(t, j) is the output error, which represents the difference between the expected output and the current output of the robot, e(t, j+1)=y d (t)-y(t,j+1),y d (t) is the expected output value, y(t, j+1) is the current output of the robot, ω k (t, j+1) is the estimation error, which represents the difference between the estimated value at the current moment and the estimated value at the previous moment.

[0074] According to the above formula, the output error (also called tracking error) e(t, j) is determined based on the output value at the current moment and the expected output value; the estimated error ω is determined based on the estimated value at the current moment and the estimated value at the previous moment. k (t, j+1); based on the output error e(t, j) and the estimated error ω k (t, j+1), and the control gain γ of the output error k and the control gain H of the estimation error k, calculate the input signal u(t, j+1) of the second-order nonlinear motion response model; as the number of iterations increases, the input signal in the current iteration process will be optimized and adjusted according to the size of the input signal in the previous iteration process, specifically, the input signal is dynamically adjusted according to the difference between the output errors corresponding to the current iteration and the previous iteration to obtain the optimized input signal; then based on the optimized input signal and the preset mode switching threshold, the motion mode of the land-air dual-mode robot on the target path is autonomously switched or maintained, which can reduce the dependence of the control strategy on the model and enhance the robustness of the control strategy. Among them, the preset mode switching threshold includes but is not limited to the driving height threshold, the slope threshold of the target road surface, and the driving thrust and resistance threshold, which can be flexibly adjusted according to the application requirements, but all the switching mechanisms of the operating modes based on the concept of the present invention are within the protection scope of the present invention. It should be stated that this embodiment uses a custom controller to control the input. As other preferred implementations, fuzzy control, predictive control and other control algorithms can also be selected according to different application requirements and the performance of the drone to achieve personalized control.

[0075] In a preferred embodiment, assuming that the land-air dual-mode robot is traveling on a target road with a certain slope, the initial operation mode is the ground vehicle mode, and the autonomous mode switching control strategy for selecting whether to switch to the air multi-rotor mode is as follows: Figure 2 It is known that the control system of the land-air dual-mode robot satisfies the second-order nonlinear motion response model shown in equation (1), and the control gain γ of the output error is k , the control gain H of the estimation error k , and the identification gain Γ k The three are fixed values. Given the initial speed v(0), the road slope θ, the initial iterative control input u0(t) = 0, and the initial estimate value k=1 represents the ground vehicle mode, and k=2 represents the aerial multi-rotor mode. If the road surface slope θ is greater than the preset slope threshold, it is directly switched to the aerial multi-rotor mode. If the road surface slope θ is less than or equal to the preset slope threshold, the iterative learning identification algorithm (such as formula (2)) provided in step S300 is used for iterative learning identification, mainly inputting the working mode k=1 to the control system, and continuously updating the estimated value of the unknown link of the system through iterative learning identification, thereby updating the control input u(t, j+1) according to the expression (3) of the controller; and passing the latest control input u to the actuator, and controlling the output of the land-air dual-mode robot (including but not limited to position information, speed information and other operating status information) through the actuator. The estimated error and output error are further updated according to the updated operating status information. It is worth noting that in actual operation, the initial speed is usually small. In the entire iterative learning control process, the robot mainly starts from the target driving path with a certain slope at a small initial speed v(0), and continuously increases the running speed during the climbing process, so as to identify the uncertainty factors of the system and compensate for them in the control input during the iterative learning process, so as to ensure that the robot can drive efficiently and normally on the slope. When iterative learning is performed during driving on the target road surface, the current output error (i.e., tracking error) is less than the output error of the previous iteration, indicating that the driving speed corresponding to the current operating state is closer to the expected output speed. Therefore, the number of iterations is increased by 1 to further increase the driving speed, thereby updating the output and then updating the output error. According to this logic, the driving speed is gradually increased after multiple iterations until the latest driving speed reaches the maximum speed in the ground vehicle mode, or the thrust generated by the actuator according to the latest driving speed exceeds the thrust threshold in the ground vehicle mode, and then the robot switches to the aerial multi-rotor mode. When the robot is running in the aerial multi-rotor mode, the height above the ground is less than the minimum flight height in the aerial multi-rotor mode, and then it switches back to the ground vehicle mode. By repeating this logic repeatedly, the autonomous mode switching control of the land-air dual-modal robot under resistance terrain is realized. This control strategy can enable the robot to autonomously complete mode switching based on maximizing energy utilization.

[0076] In summary, the present invention designs a second-order nonlinear motion response model for the control system of the land-air dual-mode robot, which can use one controller to simultaneously meet the control requirements under two different motion models. Compared with the prior art that uses two controllers to control the operation and operation mode switching control of the multi-rotor mode and the ground vehicle mode respectively, it can not only greatly save hardware resources, but also realize the fast and efficient switching control of the land-air dual-mode robot. At the same time, for the second-order nonlinear motion response model, an iterative learning identification algorithm is proposed. The algorithm only needs to calculate the output error and the estimation error to calculate the control input signal, and continuously "learns" through multiple experiments to continuously improve the control accuracy of the input signal and enhance the robustness and anti-interference performance of the system. Moreover, based on the second-order nonlinear motion response model and the iterative learning identification algorithm, an autonomous mode switching control strategy for the land-air dual-mode robot is proposed, so that the robot can autonomously complete the operation mode switching on the basis of maximizing energy utilization, which is conducive to greatly improving the flexibility of control.

[0077] like Figure 3 As shown, corresponding to the above-mentioned land-air dual-mode robot autonomous mode switching control method, the embodiment of the present invention further provides a land-air dual-mode robot autonomous mode switching control system, and the above-mentioned land-air dual-mode robot autonomous mode switching control system includes:

[0078] A model building module 310 is used to build a second-order nonlinear motion response model of the control system of the land-air dual-modal robot;

[0079] The operation status information acquisition module 320 is used to acquire the operation status of the land-air dual-modal robot on the target path in real time, and determine the current operation mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the operation status;

[0080] An iterative learning identification module 330, used to identify the second-order nonlinear motion response model using an iterative learning algorithm to obtain an estimated value;

[0081] The operation mode autonomous switching module 340 is used to autonomously switch or maintain the motion mode of the land-air dual-modal robot on the target path based on the output value and the estimated value at adjacent moments.

[0082] Specifically, in this embodiment, the specific functions of the above-mentioned land-to-air dual-modal robot autonomous mode switching control system can also refer to the corresponding description in the above-mentioned land-to-air dual-modal robot autonomous mode switching control method, which will not be repeated here.

[0083] In one embodiment, the system also includes an output error calculation module and an estimated error calculation module, wherein the output error calculation module is used to determine the output error based on the output value at the current moment and the preset expected output value; the estimated error calculation module is used to identify the unknown links of the control system of the land-air dual-modal robot based on the output error and the preset identification gain using an iterative learning algorithm to obtain an estimated value; and determine the estimated error based on the estimated value at the current moment and the estimated value at the previous moment.

[0084] Specifically, in this embodiment, the specific functions of the above-mentioned land-to-air dual-modal robot autonomous mode switching control system can also refer to the corresponding description in the above-mentioned land-to-air dual-modal robot autonomous mode switching control method, which will not be repeated here.

[0085] Based on the above embodiments, the present invention further provides an intelligent terminal, whose principle block diagram can be shown as follows: Figure 4 As shown. The above-mentioned intelligent terminal includes a processor, a memory, a network interface and a display screen connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and an autonomous mode switching control program for a land-air dual-modal robot. The internal memory provides an environment for the operation of the operating system in the non-volatile storage medium and the autonomous mode switching control program for the land-air dual-modal robot. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the autonomous mode switching control program for the land-air dual-modal robot is executed by the processor, the steps of any one of the above-mentioned methods for autonomous mode switching control of the land-air dual-modal robot are implemented. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.

[0086] Those skilled in the art will understand that Figure 4 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the smart terminal to which the scheme of the present invention is applied. The specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0087] In one embodiment, a smart terminal is provided, which includes a memory, a processor, and a land-to-air dual-modal robot autonomous mode switching control program stored in the memory and executable on the processor. When the land-to-air dual-modal robot autonomous mode switching control program is executed by the processor, the steps of any one of the land-to-air dual-modal robot autonomous mode switching control methods provided in the embodiments of the present invention are implemented.

[0088] An embodiment of the present invention also provides a computer-readable storage medium, on which is stored a land-to-air dual-modal robot autonomous mode switching control program. When the land-to-air dual-modal robot autonomous mode switching control program is executed by a processor, the steps of any one of the land-to-air dual-modal robot autonomous mode switching control methods provided in the embodiments of the present invention are implemented.

[0089] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0090] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0093] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / terminal equipment and method can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are only illustrative, for example, the division of the above modules or units is only a logical function division, and in actual implementation, other division methods can be used, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0094] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features therein may be replaced by equivalents. However, these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for controlling autonomous mode switching of a land-air dual-mode robot, characterized in that: The following steps are involved: Construct a second-order nonlinear motion response model of the control system of the land-air dual-modal robot; Acquire the running state of the land-air dual-modal robot on the target path in real time, and determine the current running mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the running state; Using an iterative learning algorithm to identify the second-order nonlinear motion response model to obtain an estimated value; Based on the output value and the estimated value at adjacent moments, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

2. The autonomous mode switching control method of the land-air dual-mode robot according to claim 1 is characterized in that: The process of constructing the second-order nonlinear motion response model of the control system of the land-air dual-modal robot includes: Acquire known nonlinear links, unknown links and operation modes of the control system of the land-air dual-modal robot; Based on the parameters corresponding to the operating state, the operating mode, the nonlinear link and the unknown link, a second-order nonlinear motion response model of the control system of the land-air dual-modal robot is constructed.

3. The autonomous mode switching control method of the land-air dual-mode robot according to claim 1 is characterized in that: The determining, based on the operating state, the current operating mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model comprises: Based on the operating state, determining a current operating mode of the land-air dual-modal robot; The current operation mode uses the operation state as an input of the second-order nonlinear motion response model to determine an output value of the second-order nonlinear motion response model.

4. The autonomous mode switching control method of the land-air dual-mode robot according to claim 2 is characterized in that: The adopting of an iterative learning algorithm to identify the second-order nonlinear motion response model to obtain an estimated value includes: Determine the output error based on the output value at the current moment and the preset expected output value; Based on the output error and a preset identification gain, an iterative learning algorithm is used to identify the unknown link to obtain an estimated value.

5. The autonomous mode switching control method of the land-air dual-mode robot according to claim 4 is characterized in that: The autonomously switching or maintaining the motion mode of the land-air dual-modal robot on the target path based on the output value and the estimated value at adjacent moments includes: Determine the estimation error based on the estimation value at the current moment and the estimation value at the previous moment; Based on the estimation error and the output error, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

6. The autonomous mode switching control method of the land-air dual-mode robot according to claim 5 is characterized in that: The autonomously switching or maintaining the motion mode of the land-air dual-modal robot on the target path based on the estimation error and the output error includes: Calculating an input signal of the second-order nonlinear motion response model based on the estimation error, the output error and a preset control gain; Dynamically adjusting the input signal according to the difference in output errors corresponding to two adjacent iterations to obtain an optimized input signal; Based on the optimized input signal and the preset mode switching threshold, the motion mode of the land-air dual-modal robot on the target path is autonomously switched or maintained.

7. An autonomous mode switching control system for a land-air dual-mode robot, characterized in that: The system comprises: A model building module is used to build a second-order nonlinear motion response model of the control system of the land-air dual-modal robot; An operation status information acquisition module is used to acquire the operation status of the land-air dual-modal robot on the target path in real time, and determine the current operation mode of the land-air dual-modal robot and the output value of the second-order nonlinear motion response model based on the operation status; An iterative learning identification module, used for identifying the second-order nonlinear motion response model using an iterative learning algorithm to obtain an estimated value; The operating mode autonomous switching module is used to autonomously switch or maintain the motion mode of the land-air dual-modal robot on the target path based on the output value and the estimated value at adjacent moments.

8. The autonomous mode switching control system of the land-air dual-mode robot according to claim 7 is characterized in that: The system also includes an output error calculation module and an estimated error calculation module, wherein: An output error calculation module is used to determine the output error based on the output value at the current moment and the preset expected output value; The estimated error calculation module is used to identify the unknown links of the control system of the land-air dual-modal robot based on the output error and the preset identification gain, and obtain an estimated value by using an iterative learning algorithm; and determine the estimated error based on the estimated value at the current moment and the estimated value at the previous moment.

9. An intelligent terminal, characterized in that: The intelligent terminal includes a memory, a processor, and a land-to-air dual-modal robot autonomous mode switching control program stored in the memory and executable on the processor. When the land-to-air dual-modal robot autonomous mode switching control program is executed by the processor, the steps of the land-to-air dual-modal robot autonomous mode switching control method as described in any one of claims 1-6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a land-to-air dual-modal robot autonomous mode switching control program, and when the land-to-air dual-modal robot autonomous mode switching control program is executed by the processor, the steps of the land-to-air dual-modal robot autonomous mode switching control method as described in any one of claims 1-6 are implemented.

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