An unmanned vehicle chassis and reconnaissance load game coordination data acquisition method

Abstract

The application discloses a game coordination data acquisition method for an unmanned vehicle chassis and a reconnaissance load, belongs to the technical field of data acquisition control, and solves the problems of low data acquisition quality and efficiency in the prior art. The method comprises the following steps: constructing a cooperative control discrete state space equation based on a kinematic model of the unmanned vehicle chassis and the reconnaissance load; constructing a game optimization problem based on the discrete state space equation; acquiring a reference trajectory point of path planning and a corresponding attitude angle of the unmanned vehicle chassis in each control period; calculating a reference azimuth angle and a reference pitch angle of the reconnaissance load corresponding to the reference trajectory point; solving a target function of the game optimization problem based on the reference trajectory point, the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, the reference azimuth angle and the reference pitch angle of the reconnaissance load to obtain an optimal control sequence in the current control period; and controlling the attitude of the unmanned chassis and the reconnaissance load based on the optimal control sequence to perform data acquisition. Efficient and high-quality data acquisition is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted reconnaissance payload data collection technology, and in particular to a method for collaborative data collection between an unmanned vehicle chassis and a reconnaissance payload. Background Art

[0002] Reconnaissance payloads utilize the differences in reflected or radiated light waves between the target and the background to achieve real-time monitoring, tracking and positioning, target identification, and confrontation assessment of targets such as confrontation fortifications and ground equipment. They have technical advantages such as high resolution, wide spectrum, and diverse carrying platforms, and have become an indispensable and important equipment in modern confrontation.

[0003] Research on data collection technology for vehicle-mounted reconnaissance payloads is fundamental to building training databases for autonomous target recognition algorithms and has become a crucial topic. Existing solutions mostly focus on data collection methods for the reconnaissance payload itself, neglecting the dynamic coordination between the payload and the unmanned platform during operation. In particular, they lack the integration and utilization of the unmanned platform's environmental perception and path planning results. As a result, the data collection process exhibits significant hysteresis and lacks global optimality, reducing both data collection quality and overall efficiency. Summary of the Invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a method for collaborative data collection between an unmanned vehicle chassis and a reconnaissance payload, so as to solve the problems of low quality and efficiency of existing data collection.

[0005] On the one hand, an embodiment of the present invention provides a method for collaborative data collection between an unmanned vehicle chassis and a reconnaissance payload, comprising the following steps:

[0006] Based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the reconnaissance payload, a discrete state-space equation for the coordinated control of the unmanned vehicle chassis and the reconnaissance payload is constructed; and a game optimization problem is constructed based on the discrete state-space equation;

[0007] In each control cycle, the reference trajectory point of the unmanned vehicle chassis path planning and the corresponding attitude angle of the unmanned vehicle chassis are obtained; the reference azimuth angle and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point are calculated based on the target position to be collected and the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point;

[0008] Solving the objective function of the game optimization problem based on the reference trajectory point, the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, and the reference azimuth angle and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point to obtain the optimal control sequence within the current control cycle;

[0009] The postures of the unmanned chassis and the reconnaissance payload are controlled based on the optimal control sequence to perform data collection.

[0010] Based on the further improvement of the above method,

[0011] Based on the kinematic model of the unmanned vehicle chassis and the azimuth axis kinematic model of the reconnaissance payload, the discrete state space equations for the lateral coordinated control of the unmanned vehicle chassis and the reconnaissance payload are constructed.

[0012] Based on the kinematic model of the unmanned vehicle chassis and the pitch axis kinematic model of the reconnaissance payload, the discrete state space equations for the vertical collaborative control of the unmanned vehicle chassis and the reconnaissance payload are constructed.

[0013] Based on the further improvement of the above method, the discrete state space equation of the lateral cooperative control is expressed as:

[0014]

[0015] Among them, the state quantity at the kth moment is Control variable u1(k)=[v(k)ω1(k)] T , control quantity

[0016]

[0017] (x(k), y(k)) represents the coordinates of the center of mass of the unmanned vehicle chassis at the kth moment, (x r (k),y r (k)) represents the reference coordinate of the center of mass of the unmanned vehicle chassis at the kth moment, represents the heading angle of the unmanned vehicle chassis at the kth moment, represents the azimuth angle of the reconnaissance payload at the kth moment, ω1(k) represents the yaw angular velocity of the unmanned vehicle chassis at the kth moment, represents the azimuth velocity of the reconnaissance payload at the kth moment, v(k) represents the center of mass velocity of the unmanned vehicle chassis at the kth moment, represents the center-of-mass velocity of the reconnaissance payload at the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, T represents the control cycle duration, and the superscript T represents transposition.

[0018] Based on the further improvement of the above method, the vertical cooperative control discrete state space equation is expressed as:

[0019]

[0020] Among them, the state quantity at the kth moment is Control quantity [v(k)ω2(k)] T , control quantity

[0021]

[0022] (x(k), y(k)) represents the coordinates of the center of mass of the unmanned vehicle chassis at the kth moment, (x r (k),y r (k)) represents the reference coordinate of the center of mass of the unmanned vehicle chassis at the kth moment, represents the pitch angle of the unmanned vehicle chassis at the kth moment, represents the pitch angle of the reconnaissance payload at the kth moment, ω2(k) represents the pitch angular velocity of the unmanned vehicle chassis at the kth moment, represents the pitch angular velocity of the reconnaissance payload at the kth moment, v(k) represents the center of mass velocity of the unmanned vehicle chassis at the kth moment, represents the center-of-mass velocity of the reconnaissance payload at the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, T represents the control cycle duration, and the superscript T represents transposition.

[0023] Based on a further improvement of the above method, constructing a game optimization problem based on the discrete state space equation includes:

[0024] A lateral control prediction model is constructed with the state quantity of the lateral cooperative control discrete state space equation as the output quantity and the control quantity of the lateral cooperative control discrete state space equation as the input control quantity. The objective function of the lateral control game optimization is constructed with the goal of optimizing the lateral tracking of the unmanned chassis path and the azimuth axis aiming of the reconnaissance payload.

[0025] A vertical control prediction model is constructed with the state quantity of the vertical cooperative control discrete state space equation as the output quantity and the control quantity of the vertical cooperative control discrete state space equation as the input control quantity; the objective function of the vertical control game optimization is constructed with the vertical response of the unmanned chassis path and the optimal pitch axis aiming of the reconnaissance payload as the goal.

[0026] Based on the further improvement of the above method, the lateral control prediction model is expressed as:

[0027]

[0028] in,

[0029]

[0030]

[0031] γ1(·) represents the output of the lateral control prediction model, u1(·) represents the lateral control amount of the unmanned vehicle chassis, Represents the lateral control amount of the reconnaissance load, N c Represents the control time domain, N p represents the state time domain, k represents the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, and T represents the length of the control cycle.

[0032] Based on the further improvement of the above method, the objective function and constraints of the horizontal control game optimization are:

[0033]

[0034] Among them, J1(·) and J2(·) represent the objective functions of the lateral control game optimization, γ1(k+i) represents the output of the lateral control prediction model at time k+i, and γ 1r (k+i) represents the reference output at the k+i moment, u1(k+i) represents the lateral control value of the unmanned vehicle chassis at the k+i moment, represents the lateral control amount of the reconnaissance load at the k+i moment, N c represents the control time domain,

[0035] q x ,q y and Respectively represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and heading angle, Represents the weight coefficient of the reconnaissance payload state quantity, r v Represents the weight coefficient of the center of mass speed control of the unmanned vehicle chassis, Represents the weight coefficient of the yaw rate control of the unmanned vehicle chassis, Represents the weight coefficient of the reconnaissance payload center of mass velocity control quantity, Represents the weight coefficient of the reconnaissance payload azimuth velocity control quantity.

[0036] Based on the further improvement of the above method, the vertical control prediction model is expressed as:

[0037]

[0038] in,

[0039]

[0040]

[0041] γ2(·) represents the output of the vertical control prediction model, u2(·) represents the vertical control quantity of the unmanned vehicle chassis, Represents the vertical control quantity of the reconnaissance load, N c Represents the control time domain, N p represents the state time domain, k represents the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, and T represents the length of the control cycle.

[0042] Based on the further improvement of the above method, the objective function and constraints of the vertical control game optimization are:

[0043]

[0044] Among them, J 21 (·) and J 22 (·) represents the objective function of the vertical control game optimization, γ2(k+i) represents the output of the vertical control prediction model at time k+i, and γ 2r (k+i) represents the reference output at the k+i moment, u2(k+i) represents the vertical control value of the unmanned vehicle chassis at the k+i moment, represents the vertical control amount of the reconnaissance load at the k+i moment, N c represents the control time domain,

[0045] q x ,q y and Respectively represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and pitch angle, Represents the weight coefficient of the reconnaissance payload state quantity, r v Represents the weight coefficient of the unmanned vehicle chassis center of mass speed control amount, Represents the weight coefficient of the yaw rate control of the unmanned vehicle chassis, Represents the weight coefficient of the reconnaissance payload center of mass velocity control quantity, Represents the weight coefficient of the reconnaissance payload pitch velocity control amount.

[0046] Based on the further improvement of the above method, the reference azimuth and reference pitch angles of the reconnaissance payload corresponding to the reference trajectory point are calculated using the following formula according to the target position to be collected and the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point:

[0047]

[0048] in, To detect the reference azimuth of the payload in the inertial coordinate system, is the reference pitch angle of the reconnaissance payload in the inertial coordinate system, is the coordinate of the target to be collected in the vehicle coordinate system, z o is the vertical coordinate of the reconnaissance payload in the vehicle coordinate system, is the reference heading angle of the unmanned vehicle chassis in the inertial coordinate system, Reference pitch angle of the reconnaissance payload in the inertial coordinate system.

[0049] Compared with the prior art, the unmanned vehicle chassis and the reconnaissance payload are cooperated by constructing a game optimization problem based on the discrete state space equation of the unmanned vehicle chassis and the reconnaissance payload, and the game optimization problem is constructed based on the discrete state space equation, so that an optimization target of optimal path planning and target aiming cooperation is constructed; the reference azimuth angle and the reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point of the path planning are calculated in each control period, so that the optimal control sequence in the current control period is obtained by solving the game optimization target based on the reference trajectory point, the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, the reference azimuth angle and the reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point; and the attitude of the unmanned chassis and the reconnaissance payload is controlled based on the optimal control sequence, so that the path planning and aiming of the target tracking are cooperatively optimal, and the quality and efficiency of the data collection of the target to be collected are improved.

[0050] In the present application, the above technical solutions can be combined with each other to realize more preferred combination schemes. Other features and advantages of the present application will be described in the subsequent specification, and some advantages will become apparent from the specification, or will be understood by implementing the present application. The purposes and other advantages of the present application can be realized and obtained from the contents specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0051] The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and serve to explain the principles of the application, and are not intended to limit the scope of the application.

[0052] Figure 1 The flowchart of the game cooperative data collection method of the unmanned vehicle chassis and the reconnaissance payload of the embodiment of the present application;

[0053] Figure 2 The schematic diagram of the lateral motion model of the unmanned vehicle chassis of the embodiment of the present application. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present application will be specifically described below in combination with the drawings, wherein the drawings constitute a part of this application, and are used to illustrate the principles of the embodiments of the present application, and are not intended to limit the scope of the present application.

[0055] As an intelligent sensing and control device, the unmanned vehicle platform of the vehicle-mounted reconnaissance load plans a path according to the identified target and the perceived surrounding environment information, and realizes tracking of the target; the reconnaissance load is carried on the unmanned vehicle chassis, and monitors and collects relevant monitoring data of the target, so as to realize visual detection and visual measurement. During the travel of the unmanned vehicle, the reconnaissance load should always aim at the target, so as to improve the quality and efficiency of target data collection. However, the existing reconnaissance load data collection control mainly focuses on the data collection method of the reconnaissance load itself, ignores the dynamic coordination process between the reconnaissance load and the unmanned platform during operation, especially lacks the fusion and utilization of the environment perception and path planning results of the unmanned platform, so the data collection process is obviously delayed and does not have global optimality, which reduces the quality and overall efficiency of data collection.

[0056] Based on this, one specific embodiment of the present application discloses a game coordination data collection method for an unmanned vehicle chassis and a reconnaissance load, as shown in Figure 1 The method comprises the following steps:

[0057] S1, constructing a cooperative control discrete state space equation of the unmanned vehicle chassis and the reconnaissance load based on a kinematic model of the unmanned vehicle chassis and a kinematic model of the reconnaissance load; and constructing a game optimization problem based on the discrete state space equation;

[0058] S2, in each control period, obtaining a reference trajectory point of path planning of the unmanned vehicle chassis and a corresponding attitude angle of the unmanned vehicle chassis; calculating a reference azimuth angle and a reference pitch angle of the reconnaissance load corresponding to the reference trajectory point according to the position of the target to be collected and the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point;

[0059] S3, based on the reference trajectory point, the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, the reference azimuth angle and the reference pitch angle of the reconnaissance load corresponding to the reference trajectory point, solving a target function of the game optimization problem to obtain an optimal control sequence in the current control period;

[0060] S4, controlling the attitude of the unmanned chassis and the reconnaissance load based on the optimal control sequence to perform data collection.

[0061] It should be noted that the control of the unmanned vehicle chassis mainly includes control of the yaw angle (lateral direction), and the control of the reconnaissance load mainly includes control of the azimuth angle (lateral direction) and the pitch angle (vertical direction). The attitude angle of the unmanned vehicle chassis includes the heading angle and the pitch angle.

[0062] Compared with the prior art, the method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload based on the game theory provided in this embodiment constructs a discrete state-space equation for the collaborative control of the unmanned vehicle chassis and the reconnaissance payload based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the reconnaissance payload; constructs a game optimization problem based on the discrete state-space equation, thereby constructing an optimization goal for the optimal coordination of path planning and target aiming; calculates the reference azimuth and reference pitch angles of the reconnaissance payload corresponding to the reference trajectory points of the path planning in each control cycle, and solves the game optimization goal based on the reference trajectory points, the attitude angles of the unmanned vehicle chassis corresponding to the reference trajectory points, and the reference azimuth and reference pitch angles of the reconnaissance payload corresponding to the reference trajectory points, thereby obtaining the optimal control sequence within the current control cycle; thereby controlling the attitudes of the unmanned vehicle chassis and the reconnaissance payload based on the optimal control sequence, so that the path planning and aiming coordination of target tracking are optimized, thereby improving the quality and efficiency of data collection of the target to be collected.

[0063] During implementation, the data acquisition control of the present invention coordinates the control of the unmanned vehicle chassis and the reconnaissance payload in both the lateral and vertical directions. Therefore, based on the kinematic models of the unmanned vehicle chassis and the reconnaissance payload, the discrete state space equations for the coordinated control of the unmanned vehicle chassis and the reconnaissance payload are constructed, including:

[0064] Based on the kinematic model of the unmanned vehicle chassis and the azimuth axis kinematic model of the reconnaissance payload, the discrete state space equations for the lateral coordinated control of the unmanned vehicle chassis and the reconnaissance payload are constructed.

[0065] Based on the kinematic model of the unmanned vehicle chassis and the pitch axis kinematic model of the reconnaissance payload, the discrete state space equations for the vertical coordinated control of the unmanned vehicle chassis and the reconnaissance payload are constructed.

[0066] Take horizontal collaborative control as an example. Figure 2 As shown in Figure 2, the lateral kinematic model of the unmanned vehicle chassis is expressed as:

[0067]

[0068] in, are the horizontal and vertical coordinates and heading angle of the center of mass of the unmanned vehicle chassis in the inertial coordinate system (the angle between the axis of the unmanned vehicle chassis and the X-axis), ω1 is the yaw angular velocity of the unmanned vehicle chassis; v represents the center of mass velocity of the unmanned vehicle chassis.

[0069] The azimuth axis kinematic model of the reconnaissance payload is expressed as:

[0070]

[0071] in, represents the azimuth of the reconnaissance payload in the inertial coordinate system, It represents the azimuth velocity of the reconnaissance payload in the vehicle coordinate system. Since the reconnaissance payload is stationary relative to the vehicle coordinate system, the azimuth velocity is the direct lateral control variable of the reconnaissance payload.

[0072] The discrete state space equation for the lateral coordinated control of the unmanned vehicle chassis and the reconnaissance payload, constructed based on the lateral kinematic model of the unmanned vehicle chassis and the azimuth axis kinematic model of the reconnaissance payload, is expressed as:

[0073]

[0074] Among them, the state quantity at the kth moment is Control variable u1(k)=[v(k)ω1(k)] T , control quantity

[0075]

[0076] (x(k), y(k)) represents the coordinates of the center of mass of the unmanned vehicle chassis at the kth moment, (x r (k),y r (k)) represents the reference coordinate of the center of mass of the unmanned vehicle chassis at the kth moment, represents the heading angle of the unmanned vehicle chassis at the kth moment, represents the azimuth angle of the reconnaissance payload at the kth moment, ω1(k) represents the yaw angular velocity of the unmanned vehicle chassis at the kth moment, represents the azimuth velocity of the reconnaissance payload at the kth moment, v(k) represents the center of mass velocity of the unmanned vehicle chassis at the kth moment, represents the center-of-mass velocity of the reconnaissance payload at the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, T represents the control cycle duration, and the superscript T represents transposition.

[0077] During implementation, since the reconnaissance payload is fixed to the unmanned vehicle, the center of mass speed of the reconnaissance payload is consistent with the center of mass speed of the unmanned vehicle chassis, and the control amount It has no actual control significance and is only set up for the completeness of constructing the problem.

[0078] The state quantities of the discrete state space equation of lateral cooperative control include three state quantities of the chassis without vehicle, x(k), y(k), And a state variable of the reconnaissance payload The control quantities of the discrete state space equation of lateral cooperative control include: the lateral control quantity of the unmanned vehicle chassis - the center of mass velocity v(k) and the yaw angular velocity ω1(k), and the lateral control quantity of the reconnaissance payload - the center of mass velocity and azimuthal velocity

[0079] Correspondingly, the discrete state space equation of vertical cooperative control is expressed as:

[0080]

[0081] Among them, the state quantity at the kth moment is u2(k)=[v(k)ω2(k)] T , A2=A1,B2=B1,C2=C1,

[0082] (x(k), y(k)) represents the coordinates of the center of mass of the unmanned vehicle chassis at the kth moment, (x r (k),y r (k)) represents the reference coordinate of the center of mass of the unmanned vehicle chassis at the kth moment, represents the pitch angle of the unmanned vehicle chassis at the kth moment, represents the pitch angle of the reconnaissance payload at the kth moment, ω2(k) represents the pitch angular velocity of the unmanned vehicle chassis at the kth moment, represents the pitch angular velocity of the reconnaissance payload at the kth moment, v(k) represents the center of mass velocity of the unmanned vehicle chassis at the kth moment, represents the center-of-mass velocity of the reconnaissance payload at the kth moment, T represents the duration of the control cycle, and the superscript T represents the transpose.

[0083] The state quantities of the discrete state space equation of vertical cooperative control include three state quantities of the chassis, x(k), y(k), And a state variable of the reconnaissance payload The control quantities of the discrete state space equation of vertical cooperative control include: the vertical control quantity of the unmanned vehicle chassis - the center of mass velocity v(k) and the elevation velocity ω2(k), and the vertical control quantity of the reconnaissance payload - the center of mass velocity and pitch angular velocity

[0084] Then, the game optimization problem is constructed based on the discrete state space equation. Specifically, it includes:

[0085] A lateral control prediction model is constructed with the state quantity of the lateral cooperative control discrete state space equation as the output quantity and the control quantity of the lateral cooperative control discrete state space equation as the input control quantity. The objective function of the lateral control game optimization is constructed with the goal of optimizing the lateral tracking of the unmanned chassis path and the azimuth axis aiming of the reconnaissance payload.

[0086] A vertical control prediction model is constructed with the state quantity of the vertical cooperative control discrete state space equation as the output quantity and the control quantity of the vertical cooperative control discrete state space equation as the input control quantity; the objective function of the vertical control game optimization is constructed with the vertical response of the unmanned chassis path and the optimal pitch axis aiming of the reconnaissance payload as the goal.

[0087] The lateral cooperative control discrete state space equation is taken as an example for illustration. In implementation, the state quantity ξ1 of the lateral cooperative control discrete state space equation is taken as the output quantity γ1 of the lateral control prediction model, the control quantity u1 of the lateral cooperative control discrete state space equation is taken as the input control quantity of the lateral control prediction model, and the lateral control prediction model is constructed, that is, the output quantity γ1(k) of the lateral control prediction model is equal to ξ1(k), wherein the coordinates x(k), y(k) and the heading angle of the unmanned vehicle chassis embody the tracking effect of the unmanned platform, and the azimuth angle of the reconnaissance load embodies the azimuth aiming effect of the reconnaissance load.

[0088] Then, the system outputs at the k+1 time and the k+2 time can be predicted through the iterative equation:

[0089]

[0090] According to the formula, the state quantity and the output quantity in the prediction time domain N p can be calculated from the current state quantity ξ1(k) and the control quantity in the control time domain N c , so as to establish the following lateral control prediction model:

[0091]

[0092] wherein,

[0093] represents the reference heading angle of the unmanned vehicle chassis corresponding to the reference track point, T represents the control period length, v represents the center of mass speed of the unmanned vehicle chassis, γ1(·) represents the output quantity of the lateral control prediction model, u1(·) represents the lateral control quantity of the unmanned vehicle chassis, represents the lateral control quantity of the reconnaissance load, N c represents the control time domain, and N p represents the state time domain, and k represents the k time.

[0094] Then, the target function of the lateral game optimization is constructed with the optimal unmanned chassis path tracking and reconnaissance load azimuth aiming as the target, that is, the optimization target is to make the state quantity as close as possible to the reference state quantity, and the control quantity as small as possible, so the target function and the constraint condition of the lateral control game optimization are:

[0095]

[0096]

[0097] wherein, J 11 (·) and J​12 (·) represents the objective function of the lateral control game optimization, γ1(k+i) represents the model output of the lateral control prediction model at time k+i, and γ 1r (k+i) represents the reference output at the k+i moment, represents the reference heading angle of the unmanned vehicle at the k+i moment, represents the reference azimuth of the reconnaissance payload at time k+i. ST represents the constraint condition.

[0098] q x ,q y, Respectively represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and heading angle, Represents the weight coefficient of the reconnaissance payload state quantity, r v Represents the weight coefficient of the center of mass speed control of the unmanned vehicle chassis, Represents the weight coefficient of the yaw rate control of the unmanned vehicle chassis, Represents the weight coefficient of the reconnaissance payload center of mass velocity control quantity, Represents the weight coefficient of the reconnaissance payload azimuth velocity control quantity.

[0099] Optimization target J 11 (k) represents the relevant output of the predicted unmanned vehicle chassis (coordinates (x, y) and heading angle) within the prediction time domain ) and the reference output (reference trajectory point coordinates (x r ,y r ) and the reference heading angle ) as small as possible, and control the time domain N c The amount of chassis control within the unmanned vehicle is as small as possible.

[0100] Optimization target J 12 (k) represents the output of the predicted reconnaissance payload (azimuth) in the prediction time domain. ) and the reference output (reference azimuth ) as small as possible, and control the time domain N c The control amount of the reconnaissance payload within is as small as possible.

[0101] In order to facilitate the solution, γ 1r (k+i)]} is expressed as Expressed as γ 1r (k+i)]} is expressed as Expressed as

[0102] Then the optimization objective function of the horizontal control game is expressed as:

[0103]

[0104] in,

[0105] Similarly, the state quantity ξ2 of the vertical cooperative control discrete state space equation is used as the output quantity γ2 of the vertical control prediction model, the control quantity u2 of the vertical cooperative control discrete state space equation, The vertical control prediction model is constructed for the input control variable of the vertical control prediction model, which is expressed as:

[0106]

[0107] in,

[0108] represents the reference pitch angle of the unmanned vehicle chassis corresponding to the reference trajectory point, T represents the control cycle duration, v represents the center of mass velocity of the unmanned vehicle chassis, γ2(·) represents the output of the vertical control prediction model, u2(·) represents the vertical control amount of the unmanned vehicle chassis, Represents the vertical control quantity of the reconnaissance load, N c Represents the control time domain, N p Represents the state time domain, and k represents the kth moment.

[0109] The vertical control game optimization objective function and constraints are:

[0110]

[0111]

[0112] Among them, J 21 (·) and J 22 (·) represents the objective function of the vertical control game optimization, γ2(k+i) represents the output of the vertical control prediction model at time k+i, and γ 2r (k+i) represents the reference output at the k+i moment, u2(k+i) represents the vertical control value of the unmanned vehicle chassis, Represents the vertical control quantity of the reconnaissance load, N c represents the control time domain,

[0113] q x ,q y and Respectively represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and pitch angle, Represents the weight coefficient of the reconnaissance payload state quantity, r v Represents the weight coefficient of the unmanned vehicle chassis center of mass speed control amount, Represents the weight coefficient of the yaw rate control of the unmanned vehicle chassis, Represents the weight coefficient of the reconnaissance payload center of mass velocity control quantity, Represents the weight coefficient of the reconnaissance payload pitch velocity control amount.

[0114] Optimization target J 21 (k) represents the relevant output of the predicted unmanned vehicle chassis (coordinates (x, y) and pitch angle) within the prediction time domain ) and the reference output (reference trajectory point coordinates (x r ,y r ) and reference pitch angle ) as small as possible, and control the time domain N c The amount of chassis control within the unmanned vehicle is as small as possible.

[0115] Optimization target J 22 (k) represents the output of the predicted reconnaissance payload (pitch angle ) and the reference output (reference pitch angle ) as small as possible, and control the time domain N c The control amount of the reconnaissance payload within is as small as possible.

[0116] Similarly, Expressed as Expressed as Expressed as Expressed as The vertical control game optimization objective function can be written as follows:

[0117]

[0118] The control system of the vehicle-mounted reconnaissance payload periodically controls the unmanned vehicle chassis and the reconnaissance payload. In each control cycle, the reference trajectory points of the unmanned vehicle chassis's path planning and the corresponding attitude angles of the unmanned vehicle chassis are obtained; the reference azimuth and reference pitch angles of the reconnaissance payload corresponding to the reference trajectory points are calculated based on the target position to be collected and the attitude angles of the unmanned vehicle chassis corresponding to the reference trajectory points.

[0119] During implementation, first convert the target coordinates to be collected from the inertial coordinate system to the vehicle coordinate system:

[0120] Sv=TR wv ·S w

[0121] Among them, Sv is the coordinate of the target to be collected in the vehicle coordinate system; S w is the coordinate of the target to be collected in the inertial coordinate system; TR wv is the transformation matrix from the inertial coordinate system to the vehicle coordinate system.

[0122] The reference azimuth and reference pitch angles of the reconnaissance payload are calculated using the following formula:

[0123]

[0124] in, To detect the reference azimuth of the payload in the inertial coordinate system, is the reference pitch angle of the reconnaissance payload in the inertial coordinate system, is the coordinate of the target to be collected in the vehicle coordinate system, z o is the vertical coordinate of the reconnaissance payload in the vehicle coordinate system, is the reference heading angle of the unmanned vehicle chassis in the inertial coordinate system, is the reference pitch angle of the unmanned vehicle chassis in the inertial coordinate system.

[0125] During implementation, based on the attitude angle of the unmanned vehicle chassis (including heading angle and pitch angle), the geometric relationship between the reconnaissance payload control point and the center of the vehicle coordinate system, the reference azimuth angle and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point can be calculated.

[0126] Based on the reference trajectory points, the attitude angles of the unmanned vehicle chassis corresponding to the reference trajectory points, and the reference azimuth and reference pitch angles of the reconnaissance payload corresponding to the reference trajectory points, the objective function of the game optimization constructed in step S1 is solved to obtain the optimal control sequence within the current control cycle.

[0127] During implementation, a convex iteration approach is used to solve the objective function of the game optimization based on the reference trajectory points, the attitude angles of the unmanned vehicle chassis corresponding to the reference trajectory points, and the reference azimuth and reference pitch angles of the reconnaissance payload corresponding to the reference trajectory points to obtain the optimal control sequence within the current control cycle.

[0128] That is, convex iteration is used to solve the optimization objective function of the lateral control game and the optimization objective function of the vertical control game respectively, and the optimal lateral control sequence and vertical control sequence in the current control cycle are obtained.

[0129] The optimal lateral control sequence obtained is expressed as:

[0130]

[0131] Where X1(k)={ξ1(k)Y 1r (k)} T , Γ 11 =[-L 11full Ψ1L 11full ], Γ 12 =[-L 12full Ψ1L 12full ],Λ 11 =L 11full N1,Λ 12 =L 12full M1, [·]\[·] represents the backslash operator, which is the left division calculation of the matrix. Upcoming Matrix Split into The transpose and Multiplying together, we get Will Split into The transpose and Multiplying to get

[0132] The horizontal optimal solution with the Nash equilibrium property of the game at time k can be expressed as:

[0133] ω 1Nash (k)=[1 0 ··· 0]U1(k) *

[0134]

[0135] Similarly, the optimal vertical control sequence obtained is expressed as:

[0136]

[0137] Where X2(k)={ξ2(k)Y 2r (k)} T , Γ 21 =[-L 21full Ψ2L 21full ], Γ 22 =[-L 22full Ψ1L 22full ],Λ 21 =L 21full N2,Λ 22 =L 22full M2, Upcoming Matrix Split into The transpose and Multiplying together, we get Will Split into The transpose and Multiplying to get

[0138] The vertical optimal solution with the Nash equilibrium property of the game at time k can be expressed as:

[0139] ω 2Nash (k)=[1 0 ··· 0]U2(k) *

[0140]

[0141] During implementation, among the lateral optimal solution and the vertical optimal solution, the center-of-mass speed of the unmanned vehicle in the lateral optimal solution is used as the current center-of-mass speed control value of the unmanned vehicle.

[0142] At time k, the first value in the optimal control sequence is used as the current control variable to control the center of mass velocity, yaw angular velocity of the unmanned vehicle chassis, lateral angular velocity of the reconnaissance payload, pitch velocity of the unmanned vehicle chassis, and pitch angular velocity of the reconnaissance payload.

[0143] According to the solved current control quantity, the unmanned vehicle chassis and reconnaissance payload are made to collect data in this posture, thereby achieving lateral comprehensive optimal control with optimal lateral path tracking and optimal azimuth axis aiming, and vertical comprehensive optimal control with optimal vertical path response and optimal pitch axis aiming, thereby realizing a high-precision and high-stability data collection process of coordinated game between the unmanned chassis and the reconnaissance payload.

[0144] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0145] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for collaborative data collection between an unmanned vehicle chassis and a reconnaissance payload, characterized in that: The following steps are involved: Based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the reconnaissance payload, the discrete state space equations for the coordinated control of the unmanned vehicle chassis and the reconnaissance payload are constructed; Constructing a game optimization problem based on the discrete state space equation; In each control cycle, the reference trajectory points of the unmanned vehicle chassis path planning and the corresponding unmanned vehicle chassis attitude angles are obtained; Calculate the reference azimuth and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point based on the target position to be collected and the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point; Solving the objective function of the game optimization problem based on the reference trajectory point, the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, and the reference azimuth angle and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point to obtain the optimal control sequence within the current control cycle; The postures of the unmanned chassis and the reconnaissance payload are controlled based on the optimal control sequence to perform data collection.

2. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 1 is characterized in that: Based on the kinematic model of the unmanned vehicle chassis and the azimuth axis kinematic model of the reconnaissance payload, the discrete state space equations for the lateral coordinated control of the unmanned vehicle chassis and the reconnaissance payload are constructed. Based on the kinematic model of the unmanned vehicle chassis and the pitch axis kinematic model of the reconnaissance payload, the discrete state space equations for the vertical collaborative control of the unmanned vehicle chassis and the reconnaissance payload are constructed.

3. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 2 is characterized in that: The discrete state space equation of the lateral cooperative control is expressed as: Among them, the state quantity at the kth moment is Control variable u1(k)=[v(k)ω1(k)] T , control quantity (x(k), y(k)) represents the coordinates of the center of mass of the unmanned vehicle chassis at the kth moment, (x r (k),y r (k)) represents the reference coordinate of the center of mass of the unmanned vehicle chassis at the kth moment, represents the heading angle of the unmanned vehicle chassis at the kth moment, represents the azimuth angle of the reconnaissance payload at the kth moment, ω1(k) represents the yaw angular velocity of the unmanned vehicle chassis at the kth moment, represents the azimuth velocity of the reconnaissance payload at the kth moment, v(k) represents the center of mass velocity of the unmanned vehicle chassis at the kth moment, represents the center-of-mass velocity of the reconnaissance payload at the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, T represents the control cycle duration, and the superscript T represents transposition.

4. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 2 is characterized in that: The vertical cooperative control discrete state space equation is expressed as: Among them, the state quantity at the kth moment is Control quantity [v(k)ω2(k)] T , control quantity (x(k), y(k)) represents the coordinates of the center of mass of the unmanned vehicle chassis at the kth moment, (x r (k),y r (k)) represents the reference coordinate of the center of mass of the unmanned vehicle chassis at the kth moment, represents the pitch angle of the unmanned vehicle chassis at the kth moment, represents the pitch angle of the reconnaissance payload at the kth moment, ω2(k) represents the pitch angular velocity of the unmanned vehicle chassis at the kth moment, represents the pitch angular velocity of the reconnaissance payload at the kth moment, v(k) represents the center of mass velocity of the unmanned vehicle chassis at the kth moment, represents the center-of-mass velocity of the reconnaissance payload at the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, T represents the control cycle duration, and the superscript T represents transposition.

5. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 2 is characterized in that: Constructing a game optimization problem based on the discrete state space equation includes: A lateral control prediction model is constructed with the state quantity of the lateral cooperative control discrete state space equation as the output quantity and the control quantity of the lateral cooperative control discrete state space equation as the input control quantity. The objective function of the lateral control game optimization is constructed with the goal of optimizing the lateral tracking of the unmanned chassis path and the azimuth axis aiming of the reconnaissance payload. A vertical control prediction model is constructed with the state quantity of the vertical cooperative control discrete state space equation as the output quantity and the control quantity of the vertical cooperative control discrete state space equation as the input control quantity; the objective function of the vertical control game optimization is constructed with the vertical response of the unmanned chassis path and the optimal pitch axis aiming of the reconnaissance payload as the goal.

6. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 5 is characterized in that: The lateral control prediction model is expressed as: in, γ1(·) represents the output of the lateral control prediction model, u1(·) represents the lateral control amount of the unmanned vehicle chassis, Represents the lateral control amount of the reconnaissance load, N c Represents the control time domain, N p represents the state time domain, k represents the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, and T represents the length of the control cycle.

7. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 6 is characterized in that: The objective function and constraints of the horizontal control game optimization are: Among them, J1(·) and J2(·) represent the objective functions of the lateral control game optimization, γ1(k+i) represents the output of the lateral control prediction model at time k+i, and γ 1r (k+i) represents the reference output at the k+i moment, u1(k+i) represents the lateral control value of the unmanned vehicle chassis at the k+i moment, represents the lateral control amount of the reconnaissance load at the k+i moment, N c represents the control time domain, q x ,q y and Respectively represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and heading angle, Represents the weight coefficient of the reconnaissance payload state quantity, r v Represents the weight coefficient of the center of mass speed control of the unmanned vehicle chassis, Represents the weight coefficient of the yaw rate control of the unmanned vehicle chassis, Represents the weight coefficient of the reconnaissance payload center of mass velocity control quantity, Represents the weight coefficient of the reconnaissance payload azimuth velocity control quantity.

8. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 5 is characterized in that: The vertical control prediction model is expressed as: in, γ2(·) represents the output of the vertical control prediction model, u2(·) represents the vertical control quantity of the unmanned vehicle chassis, Represents the vertical control quantity of the reconnaissance load, N c Represents the control time domain, N p represents the state time domain, k represents the kth moment, represents the reference heading angle of the unmanned vehicle chassis at the kth moment, and T represents the length of the control cycle.

9. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 8 is characterized in that: The objective function and constraints of the vertical control game optimization are: Among them, J 21 (·) and J 22 (·) represents the objective function of the vertical control game optimization, γ2(k+i) represents the output of the vertical control prediction model at time k+i, and γ 2r (k+i) represents the reference output at the k+i moment, u2(k+i) represents the vertical control value of the unmanned vehicle chassis at the k+i moment, represents the vertical control amount of the reconnaissance load at the k+i moment, N c represents the control time domain, q x ,q y and Respectively represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and pitch angle, Represents the weight coefficient of the reconnaissance payload state quantity, r v Represents the weight coefficient of the unmanned vehicle chassis center of mass speed control amount, Represents the weight coefficient of the yaw rate control of the unmanned vehicle chassis, Represents the weight coefficient of the reconnaissance payload center of mass velocity control quantity, Represents the weight coefficient of the reconnaissance payload pitch velocity control amount.

10. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance payload game according to claim 1 is characterized in that: According to the target position to be collected and the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, the reference azimuth and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point are calculated using the following formula: in, To detect the reference azimuth of the payload in the inertial coordinate system, is the reference pitch angle of the reconnaissance payload in the inertial coordinate system, is the coordinate of the target to be collected in the vehicle coordinate system, z o is the vertical coordinate of the reconnaissance payload in the vehicle coordinate system, is the reference heading angle of the unmanned vehicle chassis in the inertial coordinate system, is the reference pitch angle of the unmanned vehicle chassis in the inertial coordinate system.

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