Unmanned vehicle chassis and reconnaissance load game collaborative data acquisition method

Abstract

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

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted reconnaissance load data collection technology, and in particular to a method for collecting data by game coordination between an unmanned vehicle chassis and a reconnaissance load. 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 confrontations.

[0003] The research on vehicle-mounted reconnaissance payload data collection technology is the basis for building an autonomous target recognition algorithm training database, and has also become a very important topic. Most of the existing solutions focus on the data collection method of the reconnaissance payload itself, but ignore the dynamic coordination process between the reconnaissance payload and the unmanned platform during operation, especially the lack of integration and utilization of the unmanned platform's environmental perception and path planning results. Therefore, the data collection process is significantly delayed and does not have global optimality, which reduces the quality and overall efficiency of data collection. 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 collecting data by game coordination 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; based on the discrete state space equation, a game optimization problem is constructed;

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

[0008] 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, solving the objective function of the game optimization problem to obtain the optimal control sequence in the current control cycle;

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

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

[0011] Based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the azimuth axis 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 quantity u1(k)=[v(k)ω1(k)] T , control amount

[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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle at the kth moment, T represents the duration of the control cycle, 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 amount

[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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle at the kth moment, T represents the duration of the control cycle, and the superscript T represents transposition.

[0023] Based on the further improvement of the above method, constructing the 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; an objective function of 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 load;

[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 duration 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+ith moment, u1(k+i) represents the lateral control amount of the unmanned vehicle chassis at the k+ith moment, represents the lateral control amount of the reconnaissance load at the k+ith moment, N c represents the control time domain,

[0035] q x ,q y and They represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and heading angle, respectively. Represents the weight coefficient of the reconnaissance load 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 azimuth velocity control amount of the reconnaissance payload.

[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 amount of the unmanned vehicle chassis, Represents the vertical control amount of the reconnaissance load, N c represents the control time domain, N prepresents 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 duration 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+ith moment, u2(k+i) represents the vertical control value of the chassis of the unmanned vehicle at the k+ith moment, represents the vertical control amount of the reconnaissance load at the k+ith moment, N c represents the control time domain,

[0045] q x ,q y and They represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and pitch angle, respectively. Represents the weight coefficient of the reconnaissance load state quantity, r v Represents the weight coefficient of the unmanned vehicle chassis center of mass speed control quantity, 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 angle 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 load 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.

[0049] Compared with the prior art, the present invention constructs a discrete state space equation for the coordinated control of an unmanned vehicle chassis and a reconnaissance payload based on a kinematic model of the unmanned vehicle chassis and a kinematic model of the reconnaissance payload; constructs a game optimization problem based on the discrete state space equation, thereby constructing an optimization target for the coordinated optimality of path planning and target aiming; calculates a reference azimuth angle and a reference pitch angle of the reconnaissance payload corresponding to a reference trajectory point of path planning in each control cycle, thereby 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, and the reference azimuth angle and the reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point, and obtaining an optimal control sequence in the current control cycle; thereby controlling the attitude of the unmanned chassis and the reconnaissance payload based on the optimal control sequence, so that the path planning and aiming of target tracking are coordinated optimally, thereby improving the quality and efficiency of data collection of the target to be collected.

[0050] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0052] Figure 1 This is a flow chart of a method for collaborative data collection between an unmanned vehicle chassis and a reconnaissance payload according to an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of the lateral motion model of the unmanned vehicle chassis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0055] As an intelligent perception and control device, the unmanned vehicle platform with the onboard reconnaissance payload plans the path according to the identified target and the perceived surrounding environment information to achieve target tracking; the reconnaissance payload is mounted on the chassis of the unmanned vehicle to monitor the target and collect relevant monitoring data for visual inspection and visual measurement. During the movement of the unmanned vehicle, the reconnaissance payload should always be aimed at the target to improve the quality and efficiency of target data collection. However, the existing reconnaissance payload data collection control mainly focuses on the data collection method of the reconnaissance payload itself, ignoring the dynamic coordination process between the reconnaissance payload and the unmanned platform during operation, especially the lack of integration and utilization of the unmanned platform's environmental perception and path planning results. Therefore, 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, a specific embodiment of the present invention discloses a method for collecting data in a collaborative game between an unmanned vehicle chassis and a reconnaissance payload, such as Figure 1 As shown, the following steps are included:

[0057] S1. Constructing a discrete state space equation for cooperative 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; constructing a game optimization problem based on the discrete state space equation;

[0058] S2. In each control cycle, obtain the reference trajectory point of the path planning of the unmanned vehicle chassis and the corresponding attitude angle of the unmanned vehicle chassis; calculate the reference azimuth angle and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point according to the target position 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, and the reference azimuth angle and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point, solving the objective function of the game optimization problem to obtain the optimal control sequence in the current control cycle;

[0060] S4. Control the postures of the unmanned chassis and the reconnaissance payload based on the optimal control sequence to collect data.

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

[0062] Compared with the prior art, the unmanned vehicle chassis and reconnaissance payload game collaborative data collection method provided in this embodiment constructs a discrete state space equation for 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 of optimal coordination between path planning and target aiming; calculates the reference azimuth and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point of the path planning in each control cycle, thereby solving the game optimization goal based on the reference trajectory point, the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point, and the reference azimuth and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point, and obtaining the optimal control sequence in the current control cycle; thereby controlling the attitude of the unmanned chassis and the reconnaissance payload based on the optimal control sequence, so that the path planning and aiming coordination of target tracking is optimal, 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 performs coordinated control of the unmanned vehicle chassis and the reconnaissance load in the lateral and vertical directions respectively. Therefore, based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the reconnaissance load, a discrete state space equation for coordinated control of the unmanned vehicle chassis and the reconnaissance load is constructed, including:

[0064] Based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the azimuth axis 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 collaborative control of the unmanned vehicle chassis and the reconnaissance payload are constructed.

[0066] Take horizontal collaborative control as an example. Figure 2 As shown, 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 load in the vehicle body coordinate system. Since the reconnaissance load is stationary relative to the vehicle body coordinate system, the azimuth velocity is the direct lateral control quantity of the reconnaissance load.

[0072] The discrete state space equation of the lateral cooperative 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 quantity u1(k)=[v(k)ω1(k)] T , control amount

[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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle at the kth moment, T represents the duration of the control cycle, and the superscript T represents transposition.

[0077] During implementation, since the reconnaissance payload is fixedly connected 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. 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 azimuth 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 chassis of the unmanned vehicle 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 mass center velocity of the reconnaissance payload at the kth moment, T represents the control cycle duration, and the superscript T represents transposition.

[0083] The state quantities of the discrete state space equation of vertical 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 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 construction of discrete state space equations. 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; an objective function of 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 load;

[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 discrete state space equation of lateral cooperative control is used as an example for explanation. During implementation, the state quantity ξ1 of the discrete state space equation of lateral cooperative control is used as the output quantity γ1 of the lateral control prediction model, the control quantity u1 of the discrete state space equation of lateral cooperative control is used as the output quantity γ1 of the lateral control prediction model, The lateral control prediction model is constructed as the input control quantity of the lateral control prediction model, that is, the output of the lateral control prediction model is γ1(k)=ξ1(k), where the coordinates x(k), y(k) of the chassis of the unmanned vehicle and the heading angle Reflect the tracking effect of unmanned platform and detect the azimuth of payload Reflects the azimuth aiming effect of the reconnaissance payload.

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

[0089]

[0090] Continue to iterate according to this formula, and the prediction time domain N p The state and output can be obtained by the current state ξ1(k) and the control time domain N c The control quantity within is calculated, so as to establish the following lateral control prediction model:

[0091]

[0092] in,

[0093] represents the reference heading angle of the unmanned vehicle chassis corresponding to the reference trajectory point, T represents the control cycle duration, v represents the center of mass speed of the unmanned vehicle chassis, 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, and k represents the kth moment.

[0094] Then, the objective function of lateral game optimization is constructed with the goal of unmanned chassis path tracking and optimal reconnaissance load azimuth aiming. That is, the optimization goal is to make the state quantity as close as possible to the reference state quantity, while the control quantity is as small as possible. Therefore, the objective function and constraints of lateral control game optimization are:

[0095]

[0096]

[0097] Among them, J11 (·) 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+ith moment, represents the reference heading angle of the unmanned vehicle at the k+ith moment, represents the reference azimuth of the reconnaissance payload at the k+ith moment. ST represents the constraint condition.

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

[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 relevant output of the predicted reconnaissance payload in the prediction time domain (azimuth ) 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 quantity 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 amount 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+ith moment, u2(k+i) represents the vertical control value of the chassis of the unmanned vehicle, Represents the vertical control amount of the reconnaissance load, N c represents the control time domain,

[0113] q x ,q y and They represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and pitch angle, respectively. Represents the weight coefficient of the reconnaissance load state quantity, r v Represents the weight coefficient of the unmanned vehicle chassis center of mass speed control quantity, 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 the 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 path planning of the unmanned vehicle chassis and the corresponding attitude angles of the unmanned vehicle chassis are obtained; the reference azimuth angle and reference pitch angle 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 elevation angle 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 load 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 load control point and the center of the vehicle coordinate system, the reference azimuth angle and reference pitch angle of the reconnaissance load 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 in the current control cycle.

[0127] During implementation, 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, a convex iteration approach is used to solve the objective function of the game optimization 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, that is, the left division calculation of the matrix. Coming 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 game Nash equilibrium property 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, Coming 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, so as to achieve 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 processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0145] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope 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 path planning of the unmanned vehicle chassis and the corresponding attitude angles of the unmanned vehicle chassis are obtained; Calculate the reference azimuth and reference pitch angle of the reconnaissance payload corresponding to the reference trajectory point according to the target position to be collected and the attitude angle of the unmanned vehicle chassis corresponding to the reference trajectory point; 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, solving the objective function of the game optimization problem to obtain the optimal control sequence in the current control cycle; The postures of the unmanned chassis and the reconnaissance payload are controlled based on the optimal control sequence to collect data.

2. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance load game according to claim 1 is characterized in that: Based on the kinematic model of the unmanned vehicle chassis and the kinematic model of the azimuth axis 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 load 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 quantity u1(k)=[v(k)ω1(k)] T , control amount (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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle at the kth moment, T represents the duration of the control cycle, and the superscript T represents transposition.

4. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance load 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 amount (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 chassis of the unmanned vehicle 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 chassis of the unmanned vehicle at the kth moment, T represents the duration of the control cycle, and the superscript T represents transposition.

5. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance load 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; an objective function of 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 load; 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 load 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 duration of the control cycle.

7. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance load 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+ith moment, u1(k+i) represents the lateral control amount of the unmanned vehicle chassis at the k+ith moment, represents the lateral control amount of the reconnaissance load at the k+ith moment, N c represents the control time domain, q x ,q y and They represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and heading angle, respectively. Represents the weight coefficient of the reconnaissance load state quantity, r v Represents the weight coefficient of the center of mass speed control amount 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 azimuth velocity control amount of the reconnaissance payload.

8. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance load 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 amount of the unmanned vehicle chassis, Represents the vertical 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 duration of the control cycle.

9. The method for collaborative data collection between the unmanned vehicle chassis and the reconnaissance load 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+ith moment, u2(k+i) represents the vertical control value of the chassis of the unmanned vehicle at the k+ith moment, represents the vertical control amount of the reconnaissance load at the k+ith moment, N c represents the control time domain, q x ,q y and They represent the weight coefficients of the three state quantities of the unmanned vehicle chassis x-axis coordinate, y-axis coordinate and pitch angle, respectively. Represents the weight coefficient of the reconnaissance load state quantity, r v Represents the weight coefficient of the unmanned vehicle chassis center of mass speed control quantity, 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 load game according to claim 1 is characterized in that: 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, 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 load 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.

Images