Intelligent network connection hovercar air-ground convergence decision control method
Through intelligent networking technology and polynomial decoupling planning, real-time trajectory tracking of flying cars in air-ground inlet decision control is achieved, solving the adverse impact of air-ground inlet of flying cars on ground traffic flow, and improving traffic efficiency and safety.
Patent Information
- Application Number
- CN202510226492.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-27
- Publication Date
- 2025-07-01
AI Technical Summary
The prior art lacks in decision-making and control of air-ground recess in flying cars, and it is difficult to effectively deal with abnormal traffic conditions, resulting in adverse effects on ground traffic flow during air-ground conversion, affecting safety and traffic efficiency.
Through intelligent networking technology, the lane information and vehicle information of the landing target area are obtained in real time, and the lane and node gaps are decided to be transferred into the lane and node gaps. Five-order polynomial decoupling is used to plan the entry path and expected speed of the flying car to achieve real-time trajectory tracking control.
It effectively reduces the adverse impact of the influx of air-ground vehicles on ground traffic flow, improves the traffic efficiency and safety of air-ground conversion, and ensures smooth traffic passing on ground traffic.
Smart Images
Figure CN120233672A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent connected transportation, and more particularly, to a method for decision-making control of air-ground merging of an intelligent connected flying vehicle. Background Art
[0002] With the enhancement of information interaction capabilities, the interactive applications of intelligent connection among vehicles have gradually increased. As one of the main carriers of the low-altitude economy, the application and development of intelligent flying vehicle technology have gradually highlighted its importance.
[0003] The energy consumption of a flying vehicle is huge during the takeoff and landing process. To ensure that the flying vehicle does not have a safety accident due to the tightness of the cruising range and the energy power distribution, the flying vehicle should jointly decide the air-ground merging timing according to its mission attributes and ground traffic conditions. Therefore, it is a very important link in the application technology of flying vehicles to switch the air-ground motion mode and make a joint decision at an appropriate time to perform the merging action.
[0004] However, there is not much research on jointly making decisions for air-ground merging according to mission attributes and traffic conditions, and there are also few related technologies. To fill this gap in the field, it is urgent to propose a solution to cope with possible abnormal traffic conditions, ensure the safety of the flying vehicle when completing its passing mission, improve the traffic efficiency of the flying vehicle when completing the air-ground conversion process mission, minimize the adverse impact on the ground traffic flow caused by the flying vehicle performing the air-ground merging mission, and ensure the smooth passing of the ground traffic. Summary of the Invention
[0005] According to the above-mentioned technical problems, a method for decision-making control of air-ground merging of an intelligent connected flying vehicle is provided. The present invention selects the traffic mode according to the urban road section passing efficiency and passing mission attributes. It obtains the lane information and vehicle information of the landing target area in real time, and decides the merging lane and node gap to reduce the impact on the ground traffic flow and improve the passing efficiency of the road section.
[0006] The technical means adopted by the present invention are as follows:
[0007] A method for decision-making control of air-ground merging of an intelligent connected flying vehicle, comprising:
[0008] S1. Interact and share vehicle status information with surrounding vehicles and send an air-ground merging instruction;
[0009] S2. After the flying vehicle makes a decision to merge into the ground traffic, adjust its own flight attitude so that the horizontal flight movement direction of its body is consistent with the ground traffic driving direction;
[0010] S3. At the current moment t = t0, based on the minimum landing time t minDetermine the preset working area S for the flying car to land;
[0011] S4. Establish a cost function for the gaps of six merging nodes on the three lanes in the preset landing working area after Δt time, and solve the cost functions of the six merging node gaps to obtain the target merging gaps;
[0012] S5. Based on the target merging gaps, decouple and plan the merging path and the desired speed using a fifth-degree polynomial to obtain the real-time kinematic parameters of the flying car, and plan the merging trajectory corresponding to the t0 moment for the flying car;
[0013] S6. Use the real-time kinematic parameters of the flying car obtained during path planning and speed planning, as well as the positions and motion parameters of ground vehicles, as control quantities to achieve real-time trajectory tracking control of the flying car.
[0014] Furthermore, step S1 specifically includes:
[0015] S11. When the network connection information of the flying car shares that the ground traffic condition is good and the air traffic task is about to end, send ground-air merging information to the surrounding vehicles and execute the ground-air merging task;
[0016] S12. According to the real-time air motion state, send a request for the ground-air merging task of the vehicle itself to the upper-layer flying cars and the lower-layer flying cars;
[0017] S13. After the request instruction is sent, sense the position information and motion states of the upper-layer flying cars and the lower-layer flying cars, and execute the ground-air merging descent task.
[0018] Furthermore, step S3 specifically includes:
[0019] S31. Utilize the vertical motion of the flying car, that is, the maximum allowable landing acceleration a in the z-axis direction zmax , to calculate the minimum landing time t min ;
[0020] S32. At the current moment t = t0, the flying car is in longitudinal motion. Based on the driving speed v in the x-axis direction x0 and the maximum deceleration b xmax , calculate the minimum landing time t min and the minimum distance D that the flying car can travel in the x-axis direction within this time period;
[0021] S33. At the initial moment, the coordinate value of the flying car in the x-axis direction is x0. Determine that the flying car is at least in the low-altitude flight state within the longitudinal range [x0, x0 + D], and then use the ground traffic area where x ≥ x0 + D as the preset working area for the flying car to land.
[0022] Furthermore, step S4 specifically includes:
[0023] S41. Determine the coordinate regions of the nearest neighbors x0 + D of the ground traffic operation directions within the preset working area for the flying car to land, and the N gaps distributed in different ground lanes as the preselected gap set G;
[0024] S42. Define the coordinates of the flying car at any position as P i (X i , Y i , Z i ), and define the set M i constituted by P si as the path set;
[0025] S43. Define the acceleration of the flying car with the longitudinal direction as the x-axis direction as Define the speed of the flying car with the longitudinal direction as the x-axis direction as Define the acceleration of the i-th cooperative vehicle on the ground as Define the speed of the i-th cooperative vehicle on the ground as
[0026] S44. Define the acceleration of the flying car with the transverse direction as the y-axis direction as Define the speed of the flying car with the transverse direction as the y-axis direction as Define the acceleration of the i-th cooperative vehicle on the ground as Define the speed of the i-th cooperative vehicle on the ground as
[0027] S45. Define the acceleration of the flying car with the vertical direction as the z-axis direction as Define the speed of the flying car with the vertical direction as the z-axis direction as
[0028] S46. Define the distance between the i-th vehicle on the ground at any j moment when performing the air-ground merging task as The distance is
[0029] S47. Use the intelligent driving model of vehicle longitudinal car-following (IDM) to estimate the maximum deceleration a max of the vehicle behind after the flying car merges into this gap, and the relative speed v rel between the flying car and the vehicle behind after merging into this arbitrary gap, and predict the cost B i paid by the flying car for merging into each gap in each lane;
[0030] S48. In order to minimize the influence of the decision cost of lane gap selection, design the gap selection cost function as follows:
[0031]
[0032]
[0033] Among them, β i-j is the weight coefficient, i ∈ (1, 6), j ∈ (1, 4), v x is the longitudinal vehicle speed of the ground vehicle, and v y is the lateral vehicle speed of the ground vehicle;
[0034] S49. Compare the magnitudes of all cost function values corresponding to the preselected gap set G, and select the gap that minimizes the cost function as the target merging gap of the flying car at the current time t = t0.
[0035] Furthermore, step S5 includes: determining the starting point of the planned path, decoupling the three-dimensional planning problem into path planning and speed planning, and using two solvers to perform planning and solution respectively, specifically including:
[0036] S51. Path planning:
[0037] S511. Represent the path as:
[0038] P(s) = (X(s), Y(s), Z(s))
[0039] where P(s) represents the three-dimensional spatial position of the flying car at any time; X(s) represents the position of the flying car in the x-axis movement direction at any time; Y(s) represents the position of the flying car in the y-axis movement direction at any time; Z(s) represents the position of the flying car in the z-axis movement direction at any time; s represents the path length parameter, s = s(t);
[0040] S512. Set the constraint conditions as starting point constraint, ending point constraint, and collision-free constraint respectively, as follows:
[0041]
[0042] Among them, represents the three-dimensional spatial position of the flying car at time t0; represents the path length parameter at time t0; P start represents the three-dimensional spatial position of the flying car at time t0, that is, the starting position of the air-ground merging; represents the distance traveled by the ground vehicle No. 5 along the traffic flow direction from the start time after a time period of Δt; represents the distance traveled by the ground vehicle No. 2 along the traffic flow direction from the start time after a time period of Δt; P goal represents the three-dimensional spatial position of the flying car at the end of the air-ground merging, that is, the merging target point position; P obs represents the predicted path; d safeIndicates the safety distance;
[0043] S513. Set the optimization goal of path planning as the shortest path, as follows:
[0044]
[0045] Among them, Indicates the three-dimensional direction velocity vector during the process of the flying car merging into the air and ground;
[0046] S514. Update the path, as follows:
[0047]
[0048] Among them, P new Indicates the new path position of the flying car; P near Indicates the known node position of the flying car; η represents the update step size; P rand Indicates the new sample node position of the flying car;
[0049] S515. Use a fifth-degree polynomial to fit the optimized smooth path, as follows:
[0050] P(s) = a0 + a1s + a2s 2 + a3s 3 + a4s 4 + a5s 5
[0051] Among them, a0 represents the coefficient of the 0th term of the fifth-degree polynomial for path optimization; a1 represents the coefficient of the 1st term of the fifth-degree polynomial for path optimization; a2 represents the coefficient of the 2nd term of the fifth-degree polynomial for path optimization; a3 represents the coefficient of the 3rd term of the fifth-degree polynomial for path optimization; a4 represents the coefficient of the 4th term of the fifth-degree polynomial for path optimization; a5 represents the coefficient of the 5th term of the fifth-degree polynomial for path optimization; among which each coefficient is optimized and solved by the cost function and constraint conditions;
[0052] S52. Velocity planning:
[0053] S521. Set the relationship between velocity and path, as follows:
[0054]
[0055] S522. Set the relationship between time and path, as follows:
[0056]
[0057] S523. Set the velocity constraint conditions, as follows:
[0058]
[0059] Among them, v xmin represents the minimum driving speed of the flying car along the traffic flow direction; v xmax represents the maximum driving speed of the flying car along the traffic flow direction;
[0060] S524. Set the acceleration constraint conditions as follows:
[0061]
[0062] Among them, a xmax represents the maximum driving acceleration of the flying car along the traffic flow direction; a z represents the acceleration of the flying car along the Z-axis direction; a y represents the acceleration of the flying car along the Y-axis direction;
[0063] S525. Set the speed optimization goal as the shortest time, as follows:
[0064]
[0065] S526. Divide the path into n segments, each with a length of Δs, and the planned speed v i satisfies:
[0066]
[0067] Among them, represents the square of the optimized speed of the flying car at the (i + 1)-th step; represents the square of the optimized speed of the flying car at the i-th step; a i represents the optimized acceleration of the flying car at the i-th step; Δs represents the path length at the i-th step;
[0068] S527. Transform the speed planning problem into an optimization problem, as follows:
[0069]
[0070] Among them, ξ represents the weight coefficient used to control the shortest time optimization of speed and the minimization of acceleration in the speed optimization problem;
[0071] S528. Use the dynamic programming method to iteratively update the speed distribution to ensure meeting the constraints and optimizing the goal.
[0072] Furthermore, step S6 includes: dividing the control part into a flight control stage and a ground control stage during the process of the flying car executing the air-ground merging, where:
[0073] S61. Flight stage:
[0074] The predictive control model (MPC) is used to adjust the attitude angle and thrust in real time. During the flight phase, a six-degree-of-freedom dynamics model is used to track the planned trajectory, which is the trajectory path jointly decided and planned in steps S4 and S5.
[0075] S62. Ground control phase: After merging into the ground traffic flow after the air-ground merge, ensure safe driving on the center line of the lane and maintain a safe distance from the vehicles in front and behind in the merging gap for lateral and longitudinal control.
[0076] Furthermore, step S61 specifically includes:
[0077] S611. Construct the dynamics equation for the flight phase as follows:
[0078]
[0079] where F(t) represents the thrust, M(t) represents the moment, L represents the lever arm length, and I represents the moment of inertia of the flying car about the z-axis;
[0080] S612. Set the control objectives for the flight phase as follows:
[0081]
[0082] where P i represents the three-dimensional spatial position of the flying car at the i-th step; P i ref represents the updated three-dimensional spatial position of the flying car at the i-th step; u i represents the control quantity at the i-th step; n represents the number of segments of the path;
[0083] Furthermore, step S62 specifically includes:
[0084] S621. The lateral control uses a pure tracking algorithm to construct the dynamics equation for lateral control through the heading error and lateral error as follows:
[0085]
[0086] where δ represents the steering angle of the flying car; θ error represents the heading error; d error represents the lateral error; L represents the wheelbase of the flying car;
[0087] S622. Control the longitudinal speed through the predictive tracking control model to construct the longitudinal control dynamics model for the air-ground merge of the flying car as follows:
[0088]
[0089] where v(t) represents the longitudinal speed of the vehicle, $a$ represents the longitudinal acceleration, and $u(t)$ represents the control input;
[0090] S623. Set the longitudinal speed objective function as follows:
[0091]
[0092] where $v$ des represents the desired vehicle speed, $t$ k represents the discrete time step, and $N$ represents the prediction window length;
[0093] S624. Input the control variation function as follows:
[0094]
[0095] where $t$ k+1 represents the discrete time step;
[0096] S625. Obtain the total longitudinal speed control objective function as follows:
[0097]
[0098] where $\lambda$ represents the weight coefficient for balancing speed tracking and control input smoothness;
[0099] S626. Control the safety distance during longitudinal tracking by designing traffic flow theory and a safe distance model for the vehicle. The formula for the safe distance model is as follows:
[0100]
[0101] where $D$ min represents the minimum safety distance, $v$ rel represents the relative speed between the flying vehicle and the vehicle behind after merging into the gap, $t$ r represents the driver's reaction time, and $a$ max represents the maximum deceleration of the vehicle behind after merging into the gap;
[0102] S627. Calculate the safety distance corresponding to the time interval $T$ as follows:
[0103] $D$ s $=$ $v$ x $\cdot$ $T$
[0104] where $v$ x represents the real-time speed of the flying vehicle at the moment of longitudinal merging, and $T$ represents the time difference between the flying vehicle and the ground vehicle behind at the last merging tracking attitude during air-ground merging;
[0105] S628. Optimize the longitudinal tracking safety model at the merging moment to satisfy:
[0106]
[0107] Among them, D s represents the safe vehicle distance corresponding to the time interval T; dynamically calculate according to the above formula to optimize the selection of the merging gap, and after the following vehicle of the merging gap makes a real-time safe gap judgment and yields itself, make a real-time dynamic decision on the disturbance of the traffic flow behind itself.
[0108] S629. Convert the lateral and longitudinal control signals into actual thrust, brake, throttle and steering commands to control the attitude of the flying vehicle during the air-to-ground merging process.
[0109] Furthermore, an air-to-ground merging decision control method for an intelligent connected flying vehicle provided by the present invention further includes the following steps:
[0110] S7. Since the flying vehicle merging from the air into the ground traffic is a dynamic update process, the trajectory planned in step S5 is only subjected to trajectory tracking control for one time step Δt by step S6, so that the state of the flying vehicle is updated from S(t0) to S(t0 + Δt) at the moment of t0+Δt. At this time, the ground traffic state is also updated accordingly;
[0111] S8. If the ground vehicles do not meet the safe distance during the merging process, in order to reduce the influence of the ground effect on the flying vehicle and the ground traffic flow, it should take off again in time, or maintain a certain height for tracking and cruising to find the next merging lane and the gap of the merging node;
[0112] S9. If the merging condition is met, then enter the moment of t0+Δt, and repeat steps S3 to S6 until the flying vehicle safely merges into the ground traffic.
[0113] Compared with the prior art, the present invention has the following advantages:
[0114] An air-to-ground merging decision control method for an intelligent connected flying vehicle provided by the present invention can cope with possible abnormal traffic conditions, ensure the safety of the flying vehicle when completing its traffic task, improve the traffic efficiency of the flying vehicle when completing the air-to-ground conversion process task, minimize the adverse impact on the ground traffic flow caused by the flying vehicle performing the air-to-ground merging task, and ensure the smooth passage of the ground traffic.
[0115] For the above reasons, the present invention can be widely promoted in the fields of intelligent connected transportation, etc. Description of the Drawings
[0116] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0117] Figure 1 This is the flowchart of the method of the present invention.
[0118] Figure 2 This is a schematic diagram for establishing the position coordinate axes of the flying car and the ground vehicle provided by the embodiment of the present invention.
[0119] Figure 3 This is a schematic diagram of the flying car's air-ground merging provided by the embodiment of the present invention.
[0120] Figure 4 This is the flowchart of the lane and node gap decision-making process provided by the embodiment of the present invention.
[0121] Figure 5 This is the flowchart of the trajectory planning and control process provided by the embodiment of the present invention.
[0122] Figure 6 This is the flowchart of the air-ground decision-making merging provided by the embodiment of the present invention.
[0123] Figure 7 This is the dynamic update flowchart of the flying car merging from the air to the ground provided by the embodiment of the present invention. Detailed implementation manners
[0124] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0125] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.
[0126] As Figure 1 shown, the present invention provides an intelligent connected flying vehicle ground-air merging decision control method, including:
[0127] S1. Interact and share vehicle state information with surrounding vehicles and send a ground-air merging instruction;
[0128] S2. After the flying vehicle makes a decision to merge into ground traffic, adjust its own flight attitude so that the horizontal flight movement direction of its body is consistent with the ground traffic driving direction;
[0129] S3. At the current moment t = t0, based on the minimum landing time t min determine the preset landing working area S of the flying vehicle;
[0130] S4. Establish a cost function for the six merging node gaps of the three lanes in the preset landing working area after Δt moments, and solve the cost function of the six merging node gaps to obtain the target merging gap;
[0131] S5. Based on the target merging gap, decouple and plan the merging path and desired speed using a fifth-order polynomial to obtain the real-time kinematic parameters of the flying vehicle, and plan the merging trajectory corresponding to the moment t0 for the flying vehicle;
[0132] S6. Use the real-time kinematic parameters of the flying vehicle obtained during path planning and speed planning and the position and motion parameters of ground vehicles as control quantities to achieve real-time trajectory tracking control of the flying vehicle. As Figure 2 shown, the coordinate axis orientation established for the positions of the flying vehicle and ground vehicles.
[0133] Specifically, as a preferred implementation manner of the present invention, step S1 specifically includes:
[0134] S11. When the flying vehicle shares network information that the ground traffic state is good and the air traffic task is about to end, send ground-air merging information to surrounding vehicles and execute the ground-air merging task;
[0135] S12. Send a request for the vehicle to perform an air-ground merging task to the upper and lower flying vehicles according to the real-time in-air motion state;
[0136] S13. After the request instruction is sent, sense the position information and motion state of the upper and lower flying vehicles, and execute the air-ground merging descent task. As Figure 3 shown, it is a schematic diagram of the air-ground merging of a flying vehicle.
[0137] In specific implementation, as a preferred implementation manner of the present invention, step S3 specifically includes:
[0138] S31. Utilize the vertical motion of the flying vehicle, that is, the maximum allowable landing acceleration a in the z-axis direction zmax , to calculate the minimum landing time t min ;
[0139] S32. At the current moment t = t0, the flying vehicle is in longitudinal motion. Based on the driving speed v in the x-axis direction x0 and the maximum deceleration b xmax , calculate the minimum landing time t min and the minimum distance D that the flying vehicle can travel in the x-axis direction during this period;
[0140] S33. At the initial moment, the coordinate value of the flying vehicle in the x-axis direction is x0. Determine that the flying vehicle is in a low-altitude flight state at least within the longitudinal range [x0, x0 + D], and then use the ground traffic area where x ≥ x0 + D as the preset working area for the flying vehicle to land.
[0141] In specific implementation, as a preferred implementation manner of the present invention, as Figure 4 shown, it is a flowchart of the lane and node gap decision-making process, that is, step S4 specifically includes:
[0142] S41. Determine N gaps in the coordinate area closest to x0 + D in the ground traffic operation direction within the preset working area for the flying vehicle to land and distributed in different ground lanes as the preselected gap set G;
[0143] S42. Define the coordinate of the flying vehicle at any position as P i (X i , Y i , Z i ), and define the set M i constituted by P si as the path set;
[0144] S43. Define the acceleration of the flying vehicle in the longitudinal direction as the x-axis direction as Define the speed of the flying vehicle in the longitudinal direction as the x-axis direction as Define the acceleration of the i-th cooperative vehicle on the ground as Define the speed of the i-th cooperative vehicle on the ground as
[0145] S44. Define the acceleration of the flying car in the lateral direction (y-axis direction) as Define the speed of the flying car in the lateral direction (y-axis direction) as Define the acceleration of the i-th cooperative vehicle on the ground as Define the speed of the i-th cooperative vehicle on the ground as
[0146] S45. Define the acceleration of the flying car in the vertical direction (z-axis direction) as Define the speed of the flying car in the vertical direction (z-axis direction) as
[0147] S46. Define the distance between the i-th vehicle on the ground and the flying car at any time j when performing the air-ground merging task as The distance is
[0148] S47. The design of the cost function takes into account the impact of the acceleration fluctuation on the following vehicle after the flying car merges into a certain gap in the lane. Therefore, in this invention, the vehicle longitudinal following intelligent driving model IDM (Intelligent Driving Model) is used to estimate the maximum deceleration a of the following vehicle after the flying car merges into the gap max , and the relative speed v between the flying car and the following vehicle after the flying car merges into any gap rel , and predict the cost B paid by the flying car for merging into each gap in each lane i ; In this embodiment, the input of the vehicle longitudinal following intelligent driving model IDM is the longitudinal vehicle speed when the flying car for air-ground merging merges and the distance between the vehicle behind the merging gap and the flying car and the vehicle speed of the following vehicle on the ground The output of the vehicle longitudinal following intelligent driving model (IDM) is the safe acceleration of the following vehicle on the ground To evaluate the impact of the flying car's merging on the vehicle behind the flying car and the following traffic flow, and then feedback it to the decision-making process for further adjustment
[0149] S48. To minimize the impact of the decision cost of lane gap selection, design the gap selection cost function as follows
[0150]
[0151] where, β i-j is the weight coefficient, i ∈ (1, 6), j ∈ (1, 4), vx is the longitudinal vehicle speed of the ground vehicle, v y is the lateral vehicle speed of the ground vehicle;
[0152] S49. Compare the values of all cost functions corresponding to the preselected clearance set G, and select the clearance that minimizes the cost function as the target merging clearance of the flying car at the current moment t = t0.
[0153] In specific implementation, as a preferred implementation manner of the present invention, as Figure 5 shown, it is a flow chart of the trajectory planning and control process, that is, step S5 includes: determining the starting point of the planned path, decoupling the three-dimensional planning problem into path planning and speed planning, and using two solvers to perform planning and solution respectively, specifically including:
[0154] S51. Path planning:
[0155] S511. Represent the path as:
[0156] P(s) = (X(s), Y(s), Z(s))
[0157] where P(s) represents the three-dimensional spatial position of the flying car at any moment; X(s) represents the position of the flying car in the x-axis movement direction at any moment; Y(s) represents the position of the flying car in the y-axis movement direction at any moment; Z(s) represents the position of the flying car in the z-axis movement direction at any moment; s represents the path length parameter, s = s(t);
[0158] S512. Set the constraint conditions as the starting point constraint, the ending point constraint, and the collision-free constraint respectively, as follows:
[0159]
[0160] where represents the three-dimensional spatial position of the flying car at the moment t0; represents the path length parameter at the moment t0; P start represents the three-dimensional spatial position of the flying car at the moment t0, that is, the position at the start time of the ground-air merging; represents the distance traveled by the ground vehicle No. 5 along the traffic flow direction from the start time after a time period of Δt; represents the distance traveled by the ground vehicle No. 2 along the traffic flow direction from the start time after a time period of Δt; P goal represents the three-dimensional spatial position of the flying car at the end time of the ground-air merging, that is, the merging target point position; P obs represents the predicted path; d safe represents the safety distance;
[0161] S513. Set the optimization goal of path planning as the shortest path, as follows:
[0162]
[0163] Among them, represents the three-dimensional velocity vector during the process of the flying car merging into the air and ground;
[0164] S514. Update the path as follows:
[0165]
[0166] Among them, P new represents the new path position of the flying car; P near represents the known node position of the flying car; η represents the update step size; P rand represents the new sample node position of the flying car;
[0167] S515. Optimize the smoothed path using a fifth-degree polynomial fitting as follows:
[0168] P(s) = a0 + a1s + a2s 2 + a3s 3 + a4s 4 + a5s 5
[0169] Among them, a0 represents the coefficient of the 0th term of the fifth-degree polynomial for path optimization; a1 represents the coefficient of the 1st term of the fifth-degree polynomial for path optimization; a2 represents the coefficient of the 2nd term of the fifth-degree polynomial for path optimization; a3 represents the coefficient of the 3rd term of the fifth-degree polynomial for path optimization; a4 represents the coefficient of the 4th term of the fifth-degree polynomial for path optimization; a5 represents the coefficient of the 5th term of the fifth-degree polynomial for path optimization; and each coefficient is optimized and solved by the cost function and constraint conditions;
[0170] S52. Speed planning (assigning speeds to the flying car and optimizing the motion time on the above-known path):
[0171] S521. Set the relationship between speed and path as follows:
[0172]
[0173] S522. Set the relationship between time and path as follows:
[0174]
[0175] S523. Set the speed constraint conditions as follows:
[0176]
[0177] Among them, v xminRepresents the minimum driving speed of the flying car along the traffic flow direction; v xmax Represents the maximum driving speed of the flying car along the traffic flow direction;
[0178] S524. Set the acceleration constraint conditions as follows:
[0179]
[0180] where a xmax Represents the maximum driving acceleration of the flying car along the traffic flow direction; a z Represents the acceleration of the flying car along the Z-axis direction; a y Represents the acceleration of the flying car along the Y-axis direction;
[0181] S525. Set the speed optimization goal as the shortest time, as follows:
[0182]
[0183] S526. Divide the path into n segments, each with a length of Δs, and the planned speed v i Satisfies:
[0184]
[0185] where Represents the square of the optimized speed of the flying car at the (i + 1)-th step; Represents the square of the optimized speed of the flying car at the i-th step; a i Represents the optimized acceleration of the flying car at the i-th step; Δs represents the path length at the i-th step;
[0186] S527. Convert the speed planning problem into an optimization problem, as follows:
[0187]
[0188] where ξ represents the weight coefficient used to control the shortest time optimization of speed and the minimization of acceleration in the speed optimization problem;
[0189] S528. Use the dynamic programming method to iteratively update the speed distribution to ensure that the constraints are satisfied and the goal is optimized. In this embodiment, the end state constraint is reduced to the gravitational acceleration g, is reduced to 0, is reduced to 0, is reduced to 0, and are determined by the of the front and rear vehicles within the merging node in the case of intelligent networking. Solve the merging path curve through the above constraint conditions to obtain the planned trajectory.
[0190] In specific implementation, as a preferred implementation manner of the present invention, as Figure 6 shown, it is a flowchart for the decision-making of air-ground merging, that is, step S6, including: during the process of the flying car performing air-ground merging, the control part is divided into a flight control stage and a ground control stage, where:
[0191] S61. Flight stage:
[0192] The predictive tracking control model (MPC) is used to adjust the attitude angle and thrust in real time. The six-degree-of-freedom dynamics model is used to track the planned trajectory during the flight stage, and its trajectory is the trajectory path jointly decided and planned in step S4 and step S5;
[0193] In specific implementation, as a preferred implementation manner of the present invention, step S61 specifically includes:
[0194] S611. Construct the dynamics equation of the flight stage as follows:
[0195]
[0196] Among them, F(t) represents the thrust, M(t) represents the torque, L represents the arm length, and I represents the moment of inertia of the flying car about the z-axis;
[0197] S612. Set the control target of the flight stage as follows:
[0198]
[0199] Among them, P i represents the three-dimensional spatial position of the flying car at the i-th step; P i ref represents the updated three-dimensional spatial position of the flying car at the i-th step; u i represents the control quantity at the i-th step; n represents the number of segments of the path;
[0200] S62. Ground control stage: After merging into the ground traffic flow after air-ground merging, perform lateral and longitudinal control to ensure safe driving on the center line of the lane and maintain a safe distance from the vehicles in front and behind the merging gap.
[0201] In specific implementation, as a preferred implementation manner of the present invention, step S62 specifically includes:
[0202] For lateral control, a pure tracking algorithm is adopted, and the dynamics equation of lateral control is constructed through the heading error and the lateral error as follows:
[0203]
[0204] Among them, δ represents the steering angle of the flying car; θ error represents the heading error; d errorrepresents the lateral error; L represents the wheelbase of the flying car;
[0205] S622. Control the longitudinal speed through the model predictive control (MPC) to construct the longitudinal control dynamics model for the flying car to merge into the air and ground, as follows:
[0206]
[0207] where, v(t) represents the longitudinal speed of the vehicle, represents the longitudinal acceleration, and u(t) represents the control input;
[0208] S623. Set the longitudinal speed objective function, as follows:
[0209]
[0210] where, v des represents the desired vehicle speed, t k represents the discrete time step, and N represents the prediction window length;
[0211] S624. Input the control variation function, as follows:
[0212]
[0213] where, t k+1 represents the discrete time step;
[0214] S625. Obtain the total longitudinal speed control objective function, as follows:
[0215]
[0216] where, λ represents the weight coefficient that balances the speed tracking and the smoothness of the control input; in this embodiment, the total longitudinal speed tracking control objective function of the flying car integrates the longitudinal speed tracking objective and the control input variation control penalty function. This avoids sudden changes in the throttle or brakes caused by excessive changes in the control input, thereby preventing accidental events and driving discomfort, and is beneficial for smooth control input.
[0217] S626. Control the safety distance during longitudinal tracking by designing traffic flow theory and a safety distance model. The formula of the safety distance model is as follows:
[0218]
[0219] where, D min represents the minimum safety distance, v rel represents the relative speed between the flying car and the vehicle behind in the merging gap, t r represents the driver's reaction time, and a max represents the maximum deceleration of the vehicle behind in the merging gap;
[0220] S627. Calculate the safe vehicle distance corresponding to the time interval T as follows:
[0221] D s = v x ·T
[0222] where v x represents the real-time speed of the flying car at the moment of longitudinal merging into the ground, and T represents the time difference between the flying car and the following ground vehicle at the last merging tracking attitude during the air-ground merging;
[0223] S628. The longitudinal tracking safety model for optimizing the merging moment satisfies:
[0224]
[0225] where D s represents the safe vehicle distance corresponding to the time interval T; dynamically calculate according to the above formula to optimize the merging gap selection, and after the following vehicle of the merging gap makes real-time safety gap judgment and its own yielding, make real-time dynamic decision on the disturbance of the traffic flow behind itself.
[0226] S629. Convert the lateral and longitudinal control signals into actual thrust, brake, throttle and steering commands to control the attitude of the flying car during the air-ground merging process.
[0227] Specifically, as a preferred implementation manner of the present invention, an intelligent connected flying car air-ground merging decision control method provided by the present invention, as Figure 7 shown, further includes the following steps:
[0228] S7. Since the merging of the flying car from the air into the ground traffic is a dynamic update process, the trajectory planned in step S5 only performs trajectory tracking control for one time step Δt by step S6, so that the state of the flying car is updated from S(t0) to S(t0 + Δt) at the moment of t0 + Δt, and at this time the ground traffic state is also updated accordingly;
[0229] S8. If the ground vehicle does not meet the safety distance during the merging process, in order to reduce the influence of the ground effect on the flying car and the ground traffic flow, it should take off again in time, or maintain a certain height for tracking and cruising to find the next merging lane and the gap of the merging node;
[0230] S9. If the merging condition is met, then enter the moment of t0 + Δt, and repeat steps S3 to S6 until the flying car safely merges into the ground traffic.
[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A decision control method for air-ground merging of an intelligent networked flying car, characterized in that: include: S1. Share vehicle status information with surrounding vehicles and send vacant space merge instructions; S2. After making the decision to merge into ground traffic, the flying car adjusts its flight attitude so that its horizontal flight direction is consistent with the direction of ground traffic; S3, at the current time t=t0, based on the minimum landing time t min Determine the preset working area S for the flying car to land; S4, establishing a cost function of six merging node gaps of three lanes of the preset landing work area at time Δt, and solving the cost function of the six merging node gaps to obtain a target merging gap; S5. Based on the target merging gap, a fifth-order polynomial is used to decouple the merging path and the expected speed, obtain the real-time kinematic parameters of the flying car, and plan the merging trajectory corresponding to time t0 for the flying car; S6. Using the real-time kinematic parameters of the flying car obtained during path planning and speed planning and the position and motion parameters of the ground vehicle as control quantities, the real-time trajectory tracking control of the flying car is realized.
2. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 1, characterized in that: Step S1 specifically includes: S11. When the flying car network connection information is shared to the ground traffic status is good and the air traffic mission is about to end, the air-ground integration information is sent to the surrounding car to perform the air-ground integration mission; S12, according to the real-time aerial motion status, sending a request for the upper-layer flying car and the lower-layer flying car to perform an air-to-ground merging mission; S13: After the request command is issued, the position information and motion status of the upper-layer flying car and the lower-layer flying car are sensed, and the air-to-ground merging and descending task is performed.
3. The method for controlling the air-ground merging decision of an intelligent networked flying car according to claim 1, characterized in that: Step S3 specifically includes: S31. Using the vertical motion of the flying car, i.e. the maximum allowable landing acceleration a in the z-axis direction zmax , calculate the minimum landing time t min ; S32, at the current time t=t0, the flying car is moving in the longitudinal direction, according to the x-axis speed v x0 and the maximum deceleration b xmax , calculate the minimum landing time t min The minimum distance D that the flying car can travel in the x-axis direction during the time period; S33. At the initial moment, the coordinate value of the flying car in the x-axis direction is x0. It is determined that the flying car is in a low-altitude flight state at least in the longitudinal range of [x0, x0+D]. The ground traffic area with x≥x0+D is the preset working area for the flying car to land.
4. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 1, characterized in that: Step S4 specifically includes: S41, determine the coordinate area of the nearest neighbor x0+D in the ground traffic running direction within the preset working area for the landing of the flying car and N gaps distributed in different ground lanes is the pre-selected gap set G; S42. Define the coordinates of the flying car at any position as P i (X i ,Y i ,Z i ), and define P i The set M si is a set of paths; S43, define the longitudinal direction as the acceleration of the flying car in the x-axis direction as Define the speed of the flying car in the longitudinal direction as the x-axis direction The acceleration of the i-th cooperative vehicle on the ground is defined as Define the speed of the i-th cooperative vehicle on the ground as S44. Define the acceleration of the flying car in the lateral direction as the y-axis direction: Define the flying car speed in the lateral direction as the y-axis direction The acceleration of the i-th cooperative vehicle on the ground is defined as Define the speed of the i-th cooperative vehicle on the ground as S45. Define the vertical direction as the z-axis direction of the flying car acceleration as Define the vertical direction as the z-axis direction of the flying car speed is S46, define the distance between the i-th vehicle on the ground at any time j when performing the open-ground merging task Distance is S47, using the vehicle longitudinal following intelligent driving model to estimate the maximum deceleration a of the following vehicle after the flying vehicle merges into the gap max , and the relative speed v between the flying car and the car behind it after it enters the arbitrary gap rel , and predict the cost B for each gap in each lane that the flying car merges into i ; S48. In order to minimize the impact of the decision cost of lane gap selection, a gap selection cost function is designed as follows: Among them, β i-j is the weight coefficient, i∈(1,6),j∈(1,4),v x is the longitudinal speed of the ground vehicle, v y is the lateral speed of the ground vehicle; S49, comparing all cost function values corresponding to the pre-selected gap set G, and selecting the gap that minimizes the cost function as the target merging gap for the flying car at the current time t=t0.
5. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 1, characterized in that: Step S5 includes: determining the starting point of the planned path, decoupling the three-dimensional planning problem into two parts: path planning and speed planning, and using two solvers to plan and solve them respectively, specifically including: S51, Path Planning: S511, the path is expressed as: P(s)=(X(s),Y(s),Z(s)) Among them, P(s) represents the three-dimensional spatial position of the flying car at any time; X(s) represents the x-axis motion direction position of the flying car at any time; Y(s) represents the y-axis motion direction position of the flying car at any time; Z(s) represents the z-axis motion direction position of the flying car at any time; s represents the path length parameter, s=s(t); S512, set the constraint conditions to be a starting point constraint, an end point constraint and a non-collision constraint, respectively, as follows: in, Indicates the three-dimensional spatial position of the flying car at time t0; represents the path length parameter at time t0; P start It indicates the three-dimensional space position of the flying car at time t0, i.e. the position at the start of the merging into the open space; It indicates the distance traveled by vehicle No. 5 along the direction of traffic flow from the start time in the time period Δt; P represents the distance traveled by vehicle No. 2 along the direction of traffic flow from the start time in the Δt period; goal P represents the three-dimensional spatial position of the flying car at the end of the empty space merging, i.e., the merging target point position; obs represents the predicted path; d safe Indicates safe distance; S513, setting the optimization target of path planning to the shortest path, as follows: in, Represents the three-dimensional velocity vector of the flying car during the merging process into the ground; S514, update the path as follows: Among them, P new represents the new path position of the flying car; P near represents the known node position of the flying car; η represents the update step size; P rand Indicates the new sample node position of the flying car; S515, using the quintic polynomial to fit the optimized smooth path, as follows: P(s)=a0+a1s+a2s 2 +a3s 3 +a4s 4 +a5s 5 Wherein, a0 represents the coefficient of the 0th degree term of the fifth-order polynomial used for path optimization; a1 represents the coefficient of the 1st degree term of the fifth-order polynomial used for path optimization; a2 represents the coefficient of the 2nd degree term of the fifth-order polynomial used for path optimization; a3 represents the coefficient of the 3rd degree term of the fifth-order polynomial used for path optimization; a4 represents the coefficient of the 4th degree term of the fifth-order polynomial used for path optimization; a5 represents the coefficient of the 5th degree term of the fifth-order polynomial used for path optimization; wherein each coefficient is optimized and solved by the cost function and the constraint conditions; S52, speed planning: S521, set the relationship between speed and path as follows: S522, set the relationship between time and path as follows: S523, set speed constraint conditions as follows: Among them, v xmin Indicates the minimum speed of the flying car along the direction of traffic flow; v xmax Indicates the maximum speed of the flying car along the direction of traffic flow; S524, setting acceleration constraint conditions as follows: Among them, a xmax Indicates the maximum acceleration of the flying car along the direction of traffic; a z Indicates the acceleration of the flying car along the Z axis; a y Indicates the acceleration of the flying car along the Y axis; S525, setting the speed optimization target to the shortest time, as follows: S526, divide the path into n segments, each segment length is Δs, the planned speed is v i satisfy: in, represents the square of the optimized speed of the flying car at step i+1; represents the square of the optimized speed of the flying car in step i; a i represents the optimized acceleration of the flying car in the i-th step; Δs represents the path length of the i-th step; S527, convert the speed planning problem into an optimization problem, as follows: Among them, ξ represents the weight coefficient used to control the shortest time optimization of speed and the minimization of acceleration in the speed optimization problem; S528. Iteratively update the velocity distribution using a dynamic programming method to ensure that the constraints are met and the objectives are optimized.
6. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 1, characterized in that: Step S6, comprising: dividing the control part into a flight control phase and a ground control phase during the air-ground merging process of the flying car, wherein: S61. Flight phase: The predictive tracking control model is used to adjust the attitude angle and thrust in real time, and the six-degree-of-freedom dynamics model is used to track the planned trajectory during the flight phase, and the trajectory is the trajectory path planned by the joint decision of step S4 and step S5; S62, Ground Control Stage: After merging into the ground traffic flow after merging into an open space, ensure safe driving on the center line of the lane and maintain a safe distance from the vehicles in front and behind in the merging gap through lateral and longitudinal control.
7. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 6, characterized in that: Step S61 specifically includes: S611. Construct the flight phase dynamic equation as follows: Among them, F(t) represents thrust, M(t) represents torque, L represents the length of the lever arm, and I represents the moment of inertia of the flying car on the z-axis; S612. Set the flight phase control objectives as follows: Among them, P i represents the three-dimensional spatial position of the flying car in step i; P i ref represents the updated three-dimensional spatial position of the flying car in step i; u i represents the control amount of the i-th step; n represents the number of segments in the path.
8. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 6, characterized in that: Step S62 specifically includes: S621, lateral control adopts pure tracking algorithm, and constructs the dynamic equation of lateral control through heading error and lateral error, as follows: Among them, δ represents the steering angle of the flying car; θ error Indicates heading error; d error represents the lateral error; L represents the wheelbase of the flying car; S622, control the longitudinal speed through the predictive tracking control model, and construct a longitudinal control dynamics model of the flying car entering the air and ground, as follows: Where v(t) represents the longitudinal velocity of the vehicle, represents the longitudinal acceleration, u(t) represents the control input; S623, set the longitudinal speed target function as follows: Among them, v des represents the expected vehicle speed, t k represents the discrete time step, and N represents the prediction window length; S624, input control change function, as follows: Among them, t k+1 represents a discrete time step; S625, obtain the overall longitudinal speed control objective function as follows: Where λ represents the weight coefficient for balancing speed tracking and control input smoothness; S626. The safety distance during longitudinal tracking is controlled by designing traffic flow theory and a safety distance model. The formula of the safety distance model is as follows: Among them, D min Indicates the minimum safety distance, v rel represents the relative speed between the flying car and the car merging into the gap, t r represents the driver's reaction time, a max Indicates the maximum deceleration of the vehicle after merging into the gap; S627, calculate the safe vehicle distance corresponding to the time interval T, as follows: D s =v x ·T Among them, v x It indicates the real-time speed of the flying car at the time of longitudinal merging, and T indicates the time difference between the flying car and the ground vehicle behind when they merge into the tracking posture at the time of air-ground merging; S628. Optimize the longitudinal tracking safety model at the time of entry to meet the following requirements: Among them, D s It represents the safe vehicle distance corresponding to the time interval T. It is dynamically calculated according to the above formula to optimize the merging gap selection. After the vehicle merges into the gap, it makes a real-time safe gap judgment and makes a real-time dynamic decision on the disturbance of the traffic flow behind it after giving way. S629, converting the lateral and longitudinal control signals into actual thrust, brake, throttle and steering commands to control the posture of the flying car during the air-ground merging process.
9. The method for controlling the air-ground merging decision of an intelligent networked flying vehicle according to claim 6, characterized in that: The following steps are also included: S7. Since the merging of the flying car from the air into the ground traffic is a dynamic update process, the trajectory planned in step S5 is only tracked and controlled by step S6 for a time step Δt, so that the state of the flying car is updated from S(t0) to S(t0+Δt) at time t0+Δt, and the ground traffic state is also updated accordingly. S8. If the ground vehicles do not meet the safety distance during the merging process, in order to reduce the impact of the ground effect on the flying car and the ground traffic, the flying car should take off in time, or maintain a certain altitude for tracking and cruising to find the next merging lane and merging node gap; S9. If the merging conditions are met, the process proceeds to time t0+Δt and steps S3 to S6 are repeated until the flying car safely merges with the ground traffic.