CN105138044A

CN105138044A - Fleet formation control device and formation control method based on information physical network

Info

Publication number: CN105138044A
Application number: CN201510401951.4A
Authority: CN
Inventors: 李宏峰; 高振清; 续明进
Original assignee: Beijing Institute of Graphic Communication
Current assignee: Beijing Institute of Graphic Communication
Priority date: 2015-07-10
Filing date: 2015-07-10
Publication date: 2015-12-09
Anticipated expiration: 2035-07-10
Also published as: CN105138044B

Abstract

The invention provides a fleet formation control device and a formation control method based on an information physical network, wherein a fleet comprises navigation vehicles (Ri) and following vehicles (Rj); a fleet intelligent obstacle avoiding device arranged on each vehicle of the fleet comprises a vehicle external sensor module (1), a GPS positioning module (2), a wireless communication module (3) and a central processing unit module (4); the central processing unit module (4) used for realizing communication and application protocols receives speed and position information of the vehicle through the GPS positioning module (2) and processes the speed and position information; and when an infrared sensor (5) senses that an obstacle object exist in the front, the central processing unit module (4) obtains linear speeds and angular speeds of the navigation vehicles (Ri) and the following vehicles (Rj) by means of formula calculation, and the navigation vehicles (Ri) and the following vehicles (Rj) send the obtained linear speeds (vj) and angular speeds (wj) to a vehicle controller (7) through a CAN bus so as to ensure the fleet to advance in a formation manner.

Description

Fleet formation control device and formation control method based on information physical network

Technical Field

The invention belongs to the field of car networking navigation control, and relates to a fleet formation control device with a vehicle-mounted information physical network and a formation control method.

Background

With the rapid development of the automobile industry, the income of people is continuously improved, and more people own the automobile. Meanwhile, the driving conditions of a plurality of vehicles in a team such as self-driving tour and business trip are increasing. We refer to this type of travel collectively as formation travel. The formation driving can effectively integrate resources and utilize the vehicles in the formation to the maximum extent. With the rapid development of computer technology and wireless communication technology, it has become possible for a group system consisting of multiple intelligent vehicles to complete tasks that a single intelligent vehicle cannot or is difficult to complete through coordination and cooperation. The intelligent vehicle formation control mainly refers to a control technology that a plurality of intelligent vehicles can overcome environmental limitations and maintain expected formation in the group movement process, and finally all intelligent vehicles can smoothly reach a designated destination. A linear fleet composed of a plurality of vehicles keeps running in a certain formation at a small distance, so that the existing road capacity can be well expanded, the vehicle congestion condition is reduced, the actual traffic road utilization rate is improved, and the smoothness and the safety of traffic are enhanced.

Chinese patent CN1975802 discloses a control method for a motor vehicle formation driving system, in a fleet composed of more than two members, the vehicle navigation systems of each member communicate with each other through GPRS technology to form a network, and the software control of the network is composed of the following four mechanisms: a formation creation mechanism: taking a certain member as a captain, when another member sends an application for joining the formation to the captain, the captain can make two choices of acceptance and rejection, and if the captain makes an acceptance choice, the formation is created and the management authority of the formation is obtained; formation member location reporting mechanism: the members in the formation report their own position information to the captain periodically, and send to the next member in the formation after organizing by captain; a formation message sending mechanism: the formation member can choose to send an announcement message or whisper with a certain member; the formation termination mechanism: when the formation time set by the captain is over, or the captain actively terminates. The patent realizes communication among the members in the formation, and the members in the formation can select a bulletin mode or a whisper mode, so that a certain message can be conveniently sent to all members in the formation or point-to-point communication with a certain member in the formation. The patent does not intelligently control the progress of fleet formation.

Chinese patent CN101408433 discloses a fleet navigation system, a piloting navigation device, a slave navigation device and a navigation method thereof. The team navigation system comprises a navigation device, a team navigation device and a team navigation device, wherein the navigation device is used for receiving the information of the starting place and the destination, and generating and sending out a navigation path; receiving satellite navigation signals, calculating and sending out current position coordinates; the slave navigation device is used for receiving and displaying a navigation path sent by the pilot navigation device; and receiving the current position coordinate sent by the piloting navigation device, and displaying the current position of the piloting navigation device according to the current position coordinate. The invention also provides a pilot navigation device, a slave navigation device and a navigation method thereof. The navigation device sends the optimal path to the slave navigation device, so that the slave navigation device can realize the navigation function only by configuring the mobile communication function and the display function, and does not need to configure a full-function navigation device for each vehicle in the motorcade, thereby reducing the overall navigation cost of the motorcade. However, this patent requires a driver to manually control vehicle traveling factors such as vehicle speed, and thus cannot intelligently perform intelligent traveling of the entire fleet formation.

Chinese patent CN102256207 discloses a motorcade navigation method. The master vehicle navigation equipment and each slave vehicle navigation equipment are respectively distinguished on a background server by a unique identifier; the main vehicle navigation equipment wirelessly sends information including a current position, a target position and an affiliated mark to a background server; the background server formulates a navigation route according to the received current position and target position information of the main vehicle navigation equipment and sends the navigation route to the main vehicle navigation equipment in a wireless mode; wirelessly transmitting information including a current position and an belonging identifier from a car navigation device to a background server; the background server wirelessly transmits the current position information of each slave vehicle to the master vehicle navigation equipment; the background server wirelessly transmits position information including the position of the master vehicle navigation device and the position information of the slave vehicle navigation device corresponding to the unique identifier to the slave vehicle navigation device. However, this patent requires a driver to manually control vehicle traveling factors such as vehicle speed, and thus cannot intelligently perform intelligent traveling of the entire fleet formation.

Scholars at home and abroad make a great deal of leading-edge research on formation control, and obtain certain research results, which mainly comprise: based on behavioral methods, artificial potential field methods, virtual structure methods, navigation following methods, etc.

The control method based on the behaviors is mainly used for enabling a vehicle group to generate the required overall behaviors through designing basic behaviors of the vehicles and local control rules, wherein the behaviors comprise collision avoidance, obstacle avoidance, driving to a target, formation of a formation, maintenance of the formation, transformation of the formation and the like. The method is easy to select a proper controller according to the current specific situation, but the local control rule for achieving the overall behavior cannot be clearly indicated, and the stability of formation control is difficult to guarantee.

The artificial potential field method is mainly used for representing the constraint relation between the environment and each vehicle in the formation by designing an artificial potential field and a potential field function, and analyzing and controlling the constraint relation on the basis of the constraint relation. The basic idea is that the intelligent vehicle moves in a virtual force field, the obstacle is surrounded by a repulsive force field, and the repulsive force generated by the obstacle rapidly increases along with the decrease of the distance between the intelligent vehicle and the obstacle; the target point is surrounded by the attraction potential field, and the attraction generated by the attraction potential field is reduced along with the approach of the intelligent vehicle to the target point; the intelligent vehicle moves along the direction of the minimum potential energy under the action of the resultant force. The disadvantage of this queuing method is that the design of the potential field function is difficult and there is a problem of local extreme points.

The virtual structure method is that the formation of the intelligent vehicle is imagined to be a rigid virtual structure, each point fixed on the structure corresponds to the position of each intelligent vehicle, and the intelligent vehicle can adjust the motion thereof according to the movement of the corresponding fixed point on the rigid body to form the appointed formation. The method requires the formation to move according to the virtual structure of the rigid body, and the centralized control mode causes the movement of the multi-intelligent vehicle system to lack flexibility and adaptability.

The piloting following method is characterized in that some intelligent vehicles in a formation are used as pilots, other intelligent vehicles are used as followers, and the formation control problem is converted into the problem that the followers track the positions and the directions of the pilots. The piloting following method has the advantages that the formation motion is completely determined by the track of a pilot, the control is simple and convenient to realize, the whole formation is not influenced even if the pilot is interfered, the formation control problem can be simplified into an independent tracking problem, and each intelligent vehicle only needs to obtain the state information of the piloting robot, so that the cooperation problem among the formations is greatly simplified; the method has the disadvantages that the control of a pilot and a follower is relatively independent, the pilot is difficult to obtain the tracking error feedback of the follower, and the global optimality is lost.

Besides the four formation control methods, the vehicle formation also comprises MPC model prediction control, formation control based on graph theory and some neural network formation control methods, and the three methods are relatively complex to control and difficult to realize.

At present, the domestic research on vehicle formation is mostly in the theoretical research of control models, control strategies and the like, and a feasible method for controlling the driving of the vehicle formation applied to the practice is not provided.

Therefore, the technical problem which is urgently solved in the field when the intelligent vehicles are autonomously formed to run in real time and can avoid the obstacles is formed.

Disclosure of Invention

The invention discloses a fleet formation control device and a control method based on an information physical network, which can realize real-time autonomous formation driving of intelligent vehicles avoiding obstacles.

In a first aspect, the invention discloses a fleet formation control device based on an cyber-physical network, wherein the fleet comprises piloting vehicles (R)_i) And a following vehicle (R)_j) Each vehicle (R) being arranged in a vehicle fleet_i、R_j) The intelligent obstacle avoidance device for the motorcade comprises a vehicle exterior sensor module, a GPS positioning module, a wireless communication module and a central processing unit module.

The vehicle exterior sensor module includes an infrared sensor for detecting an obstacle in front and a speed sensor for detecting a vehicle traveling speed.

The GPS positioning module is arranged on each vehicle (R)_i、R_j) The middle position of the front end is used for acquiring the position information of the vehicle in real time and synchronizing time.

The wireless communication module comprises a wireless network card and a wireless router and is used for establishing network connection with other vehicles in the motorcade.

The central processing unit module for realizing communication and application protocol receives and processes speed and position information of the vehicle through the wireless communication module, wherein when the infrared sensor detects that an obstacle exists in front, the central processing unit module obtains a following vehicle (R) through calculation of a formula (F-1)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) The formula (F-1) is as follows:

wherein,

γ_ij＝θ_i+ψ_ij-θ_j、γ_kj＝θ_k-θ_j、α₁、α₂is constant,. l_ijFor piloting vehicles (R)_i) And a following vehicle (R)_j) Relative distance between, d denotes the following vehicle (R)_j) Center of mass to axle center distance, wherein a virtual vehicle R is defined_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kOf (2) a connection line_jk，_jkIndicating a following vehicle (R)_j) Closest distance to the obstacle, θ_kAs a virtual vehicle R_kDirection tangent to the obstacle, theta_i、θ_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Angle of (v) v_i、v_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Linear velocity of phi_ijAnd l_ijRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) Relative angle and distance between; following vehicle (R)_j) Linear velocity (v) obtained by central processing unit module through CAN bus_j) And angular velocity (ω)_j) Sending to the vehicle controller for adjusting the following vehicle (R)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) Therefore, the obstacle avoidance of the fleet is ensured under the formation condition.

When the infrared sensor detects that no obstacle exists in front, the central processing unit module calculates to obtain a piloting vehicle (R) through a formula (F-2)_i) Linear velocity (v) to be controlled according to reference trajectory_i) And angular velocity (ω)_i) The formula (F-2) is as follows:

<math> <mrow> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>v</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>ω</mi> <mi>i</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>y</mi> <mi>ie</mi> </msub> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msubsup> <mi>y</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>v</mi> </mrow> <mi>r</mi> </msub> </mfrac> <msub> <mover> <mi>v</mi> <mo>.</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mi>ie</mi> </msub> </mfrac> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>sin</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mi>ie</mi> </msub> </mfrac> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </math>

wherein v is_r、ω_rLinear and angular velocities, k, respectively, of the reference track₂、k₃Are all constants, a is more than 0 and less than 1, and pose error e_i＝[x_ie，y_ie，θ_ie]^TThe convergence to zero is made to be zero,

<math> <mrow> <mi>s</mi> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>s</mi> <mn>2</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </math>

the central processing unit module obtains a following vehicle (R) through calculation of a formula (F-3)_j) Linear velocity ((v) desired to be controlled according to reference trajectory_j) And angular velocity (ω)_j) The formula (F-3) is as follows:

γ_vj＝-ω_il_ijdsin(ψ_ijd+e_j3)、

wherein k is₄、k₅、k₆、k_vAre all positive and real, psi_ijdAnd l_ijdRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) The desired angle and the distance between the vehicles,

pose error

Preferably, the off-board sensor module is in a vehicle R_i、R_jInfrared sensors are respectively arranged on the left front side and the right front side for detecting front obstacles, and a speed sensor is arranged on the wheel shaft for detecting the vehicle R_i、R_jThe speed of travel.

Preferably, the GPS positioning module comprises a GPS antenna and a patch cord, and the power supply voltage of the GPS positioning module is 5V.

Preferably, the wireless communication module adopts FPV5.8G200MW transmitting and receiving set, and the power supply voltage is 12V.

Preferably, the central processing unit module is an embedded DSP processing unit.

In a second aspect, the invention discloses a fleet formation control method based on an cyber-physical network.

In a first step, the fleet of vehicles is formed, wherein the fleet of vehicles comprises a pilot vehicle R_iAnd following vehicle R_jEach vehicle R arranged in a fleet_i、R_jThe intelligent obstacle avoidance device for the motorcade comprises a vehicle exterior sensor module, a GPS positioning module, a wireless communication module and a central processing unit module.

The GPS positioning module is arranged on each vehicle R_i、R_jThe middle position of the front end is used for acquiring the position information of the vehicle in real time and synchronizing time.

The central processing unit module for realizing communication and application protocol receives and processes the speed and position information of the vehicle through the wireless communication module.

In the second step, an infrared sensor (5) detects whether an obstacle exists in front, if so, the third step is executed, and if not, the fourth step is executed;

in the third step, the central processing unit module (4) establishes a coordinate system and obtains a following vehicle (R) through calculation of a formula (F-1)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) The formula (F-1) is as follows:

wherein, γ_ij＝θ_i+ψ_ij-θ_j、γ_kj＝θ_k-θ_j、α₁、α₂Is constant,. l_ijFor piloting vehicles (R)_i) And a following vehicle (R)_j) Relative distance between, d denotes the following vehicle (R)_j) Center of mass to axle center distance, wherein a virtual vehicle R is defined_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kOf (2) a connection line_jk，_jkIndicating a following vehicle (R)_j) Closest distance to the obstacle, θ_kAs a virtual vehicle R_kDirection tangent to the obstacle, theta_i、θ_jRespectively a pilot vehicle (R)_i) And follow-up vehicleVehicle (R)_j) Angle of (v) v_i、v_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Linear velocity of phi_ijAnd l_ijRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) Relative angle and distance between, following vehicles (R)_j) Linear velocity (v) obtained by a central processing unit module (4) via a CAN bus_j) And angular velocity (ω)_j) Sent to a vehicle controller (7) for adjusting the following vehicle (R)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) Therefore, the obstacle avoidance of the fleet is ensured under the formation condition;

in the fourth step, the central processing unit module (4) obtains a piloted vehicle (R) through calculation of a formula (F-2)_i) Linear velocity (v) to be controlled according to reference trajectory_i) And angular velocity (ω)_i) The formula (F-2) is as follows:

<math> <mrow> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>v</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>ω</mi> <mi>i</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>y</mi> <mi>ie</mi> </msub> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msubsup> <mi>y</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <msub> <mover> <mi>v</mi> <mo>·</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>sin</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mi>F</mi> <mo>-</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

wherein v is_r、ω_rLinear and angular velocities, k, respectively, of the reference track₂、k₃All are normal numbers, a is more than 0 and less than 1, and pose error e_i＝[x_ie，y_ie，θ_ie]^TThe convergence to zero is made to be zero,

the central processing unit module (4) obtains a following vehicle (R) through calculation of a formula (F-3)_j) Linear velocity ((v) desired to be controlled according to reference trajectory_j) And angular velocity (ω)_j) The formula (F-3) is as follows:

γ_vj＝-ω_il_ijdsin(ψ_ijd+e_j3)、

pose error

The fleet formation control device and the control method based on the cyber-physical network, disclosed by the invention, take the actual road obstacle situation into consideration, enhance the obstacle avoidance capability of vehicle formation and enable a fleet to keep relatively stable running. The invention can obtain the following beneficial effects:

the formation control problem is simplified into an independent tracking problem, the cooperation problem among the formations is simplified, and the defect of high requirement on communication quality is overcome;

on the premise that the intelligent vehicle formation keeps relatively stable running, the actual road obstacle situation is considered, and the obstacle avoidance capability of the vehicle formation is enhanced;

the communication part between vehicles is a great technical change in the fields of traffic transportation and networks by means of a physical information network, and can improve traffic safety, relieve traffic congestion and improve driving efficiency and comfort. And the car networking technology provides guarantee for the intelligent vehicle formation to travel.

Drawings

Fig. 1 is an overall block diagram of control of a fleet formation control apparatus based on an cyber-physical network according to an embodiment of the present invention;

fig. 2 is a schematic obstacle avoidance position relationship graph of a fleet formation control device based on an cyber-physical network according to an embodiment of the invention;

FIG. 3 is a schematic platoon position relationship graph of a platoon formation control device based on an cyber-physical network according to an embodiment of the invention;

fig. 4 is a flowchart of a fleet formation control method based on an cyber-physical network according to an embodiment of the present invention.

The invention is further explained below with reference to the figures and examples.

Detailed Description

The embodiment of the invention describes a fleet formation control device based on an information physical network, wherein the fleet comprises pilot vehicles R_iAnd following vehicle R_j. According to the control overall block diagram of the formation control device of the fleet based on the cyber-physical network as shown in fig. 1, each vehicle R provided in the fleet_i、R_jThe fleet formation control device comprises an off-board sensor module 1, a GPS positioning module 2, a wireless communication module 3 and a central processing unit module 4.

The vehicle exterior sensor module 1 includes an infrared sensor 5 for detecting an obstacle in front and a speed sensor 6 for detecting a vehicle running speed.

The GPS positioning module 2 is arranged on each vehicle R_i、R_jThe middle position of the front end is used for acquiring the position information of the vehicle in real time and synchronizing time.

The wireless communication module 3 comprises a wireless network card and a wireless router and is used for establishing network connection with other vehicles in the motorcade.

The central processing unit module 4 for realizing communication and application protocol receives and processes the speed and position information of the vehicle through the wireless communication module 3, and when the infrared sensor 5 detects that an obstacle exists in front of the vehicle, the central processing unit module 4 calculates and obtains a following vehicle R through a formula F-1_jLinear velocity v of_jAnd angular velocity ω_j。

Fig. 2 is a schematic diagram of coordinates of obstacle avoidance position relationship of a fleet formation control device based on cyber-physical network according to an embodiment of the present invention, and a plane coordinate system { O, X, Y } is established, wherein an X axis represents an east position, a Y axis represents a north position, (X axis represents a north position, and the like_i，y_i，θ_i，v_i，ω_i) And (x)_j，y_j，θ_j，v_j，ω_j) Respectively representing piloted vehicles R_iAnd following vehicle R_jPosition, angle ofLinear and angular velocities, #_ijAnd l_ijRespectively representing piloted vehicles R_iAnd following vehicle R_jAnd d represents the distance from the center of mass of the vehicle to the axis, and represents the closest distance between the following vehicle and the obstacle. We define a virtual vehicle R_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kThe connecting line of (2).

According to the piloted vehicle R_iFollowing vehicle R_jAnd a virtual vehicle R_kEstablishing a velocity relationship equation

(formula 1)

Wherein gamma is_ij＝θ_i+ψ_ij-θ_j，γ’_kj＝θ_k+ψ_kj-θ_jDue to v_kDirection always perpendicular to_jkDirection, i.e.Therefore (equation 1) can be written as

(formula 2)

Wherein gamma is_kj＝θ_k-θ_j. From (equation 2), the following vehicle R can be obtained_jThe linear velocity and the angular velocity, i.e., the formula F-1, are as follows:

wherein,

γ_ij＝θ_i+ψ_ij-θ_j、ψ_kj＝θ_k-θ_j、

α₁、α₂is constant,. l_ijFor piloting vehicles R_iAnd following vehicle R_jD represents the following vehicle R_jCenter of mass to axle center distance, wherein a virtual vehicle R is defined_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kOf (2) a connection line_jk，_jkIndicating a following vehicle R_jClosest distance to the obstacle, θ_kAs a virtual vehicle R_kDirection tangent to the obstacle, theta_i、θ_jRespectively a piloted vehicle R_iAnd following vehicle R_jAngle of (v) v_i、v_jRespectively a piloted vehicle R_iAnd following vehicle R_jLinear velocity of phi_ijAnd l_ijRespectively representing piloted vehicles R_iAnd following vehicle R_jRelative angle and distance.

Following vehicle R_jLinear velocity v obtained by the central processing unit module 1 through the CAN bus 8_jAnd angular velocity ω_jSent to the vehicle controller 7 for adjustment of the following vehicle R_jLinear velocity v of_jAnd angular velocity ω_jTherefore, the obstacle avoidance of the fleet is ensured under the formation condition.

Referring to fig. 3, which is a schematic formation position relationship diagram of the formation control apparatus for a fleet of vehicles based on cyber-physical networks according to the embodiment of the present invention, when the infrared sensor 5 detects that there is no obstacle in front, a pilot vehicle R is established_iIs described by the following differential equation

(formula 4)

Piloted vehicle R_iAttitude error in plane of

(formula 5)

q_r＝[x_r，y_r，θ_r]^TThe position coordinate and the course of a certain point of the virtual four-wheel vehicle on the reference track; v. of_r、ω_rRespectively linear and angular velocity of the reference track. The differential equation of the pose error is

(formula 6)

The switching function of the variable structure control is designed as

<math> <mrow> <mi>s</mi> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>s</mi> <mn>2</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> </math>

(formula 7)

Because the sliding mode is gradually stable, the system finally realizes the pose error e under the sliding motion_i＝[x_ie，y_ie，θ_ie]^TConverging to zero. To make the system have a sliding mode, the arrival condition must be satisfiedUsing the power law of approximation

(formula 8)

Wherein > 0, k₁＞0，0＜a＜1，-|s|^asgn(s) are guaranteed to arrive globally for a limited time. Assuming that the initial value s (0) > 0, it can be obtained from the equation (8)

(formula 9)

WhereinThen the achievable time is available from the sliding mode s (0) to s-0

(formula 10)

To mitigate the jitter problem, the sign function can be replaced by a continuous function

(formula 11)

Let α be arctan (v)_ry_ie) Then, then

<math> <mrow> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </math>

(formula 12)

<math> <mrow> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <mo>=</mo> <mfrac> <msub> <mi>v</mi> <mi>r</mi> </msub> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </math>

<math> <mrow> <mover> <mi>s</mi> <mo>·</mo> </mover> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>ω</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>-</mo> <msub> <mi>v</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>ω</mi> <mi>r</mi> </msub> <mo>-</mo> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <msub> <mover> <mi>v</mi> <mo>·</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <mo>-</mo> <msub> <mi>ω</mi> <mi>i</mi> </msub> <msub> <mi>x</mi> <mi>ie</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>sin</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> </math>

(formula 13)

Suppose s₁→0，s₂→ 0, the central processing unit module 4 calculates and obtains the piloted vehicle (R) by the formula (F-2)_i) Linear velocity (v) to be controlled according to reference trajectory_i) And angular velocity (ω)_i) The formula (F-2) is as follows:

<math> <mrow> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>v</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>ω</mi> <mi>i</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>y</mi> <mi>ie</mi> </msub> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msubsup> <mi>y</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mi>ϵ</mi> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <msub> <mover> <mi>v</mi> <mo>·</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>sin</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> </mrow> </math>

(F-2) wherein, in the above,

v_r、ω_rlinear and angular velocities, k, respectively, of the reference track₂、k₃All are normal numbers, a is more than 0 and less than 1,

pose error e_i＝[x_ie，y_ie，θ_ie]^TThe convergence to zero is made to be zero,

<math> <mrow> <mi>s</mi> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>s</mi> <mn>2</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mtable> </mtable> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </math>

consider the following Lyapunov function

V_{s} = \frac{1}{2} s^{T} s

(formula 15)

The Lyapunov function is derived as:

<math> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msub> <mover> <mi>V</mi> <mo>·</mo> </mover> <mi>s</mi> </msub> <mo>=</mo> <msub> <mi>x</mi> <mi>ie</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>ω</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>-</mo> <msub> <mi>v</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mrow> <mo>(</mo> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mover> <mi>θ</mi> <mo>·</mo> </mover> <mi>ie</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <msub> <mover> <mi>v</mi> <mo>·</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <mrow> <mo>&PartialD;</mo> <msub> <mi>y</mi> <mi>ie</mi> </msub> </mrow> </mfrac> <msub> <mover> <mi>y</mi> <mo>·</mo> </mover> <mi>ie</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <mo>-</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msubsup> <mi>x</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>-</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msubsup> <mi>x</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <msubsup> <mi>y</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>-</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <msub> <mi>y</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>≤</mo> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> </math>

(formula 16)

Thus, the switching function can converge to zero within a limited time. When s → 0, θ_ie→α＝：-arctan(v_ry_ie) Then the system pose error converges to zero.

The central processing unit module 4 is used for following vehicles R_jIn the actual position and angle of

x_j＝x_i-dcosθ_i+l_ijcos(ψ_ij+θ_i)

y_j＝y_i-dsinθ_i+l_ijsin(ψ_ij+θ_i)

θ_j＝θ_j(formula 17)

While the desired following vehicle R_jIs positioned as

x_jr＝x_i-dcosθ_i+l_ijdcos(ψ_ijd+θ_i)

y_jr＝y_i-dsinθ_i+l_ijdsin(ψ_ijd+θ_i)

θ_jr＝θ_i(formula 18)

Wherein psi_ijdAnd l_ijdRespectively representing piloted vehicles R_iAnd following vehicle R_jThe expected angle and the distance between the vehicles are obtained by the following equations (formula 17) and (formula 18) to obtain the pose error equation

(formula 19)

To further analyze the error equation of equation (equation 19), assume ψ_ijd、l_ijdIs constant, the distance between the vehicles is l_ijDecomposing into a horizontal direction and a vertical direction to obtain:

<math> <mrow> <msub> <mi>l</mi> <mi>ijx</mi> </msub> <mo>=</mo> <msub> <mi>x</mi> <msub> <mi>i</mi> <mi>front</mi> </msub> </msub> <mo>-</mo> <msub> <mi>x</mi> <msub> <mi>j</mi> <mi>front</mi> </msub> </msub> <mo>=</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <mo>-</mo> <mi>d</mi> <mi>cos</mi> <msub> <mi>θ</mi> <mi>j</mi> </msub> </mrow> </math>

<math> <mrow> <msub> <mi>l</mi> <mi>ijy</mi> </msub> <mo>=</mo> <msub> <mi>y</mi> <msub> <mi>i</mi> <mi>front</mi> </msub> </msub> <mo>-</mo> <msub> <mi>y</mi> <msub> <mi>j</mi> <mi>front</mi> </msub> </msub> <mo>=</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mi>j</mi> </msub> <mo>-</mo> <mi>d</mi> <mi>cos</mi> <msub> <mi>θ</mi> <mi>j</mi> </msub> </mrow> </math>

(formula 20)

Differentiating the formula (equation 20) to obtain:

(formula 21)

Is provided with

<math> <mrow> <msubsup> <mi>l</mi> <mi>ij</mi> <mn>2</mn> </msubsup> <mo>=</mo> <msubsup> <mi>l</mi> <mi>ijx</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>l</mi> <mi>ijy</mi> <mn>2</mn> </msubsup> <mo>,</mo> <msub> <mi>ψ</mi> <mi>ij</mi> </msub> <mo>=</mo> <mi>arctan</mi> <mrow> <mo>(</mo> <mfrac> <msub> <mi>l</mi> <mi>ijy</mi> </msub> <msub> <mi>l</mi> <mi>ijx</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>θ</mi> <mi>i</mi> </msub> <mo>+</mo> <mi>π</mi> <mo>.</mo> </mrow> </math>

From kinematic analysis:

(formula 22)

Wherein gamma is_j＝ψ_ij+e_j3. The dynamic error differential equation obtained by the joint formula 19 and 22 is

(formula 23)

The CPU module 4 designs a following vehicle R by backstepping_jObtains the following vehicle (R) by the formula (F-3)_j) Linear velocity ((v) desired to be controlled according to reference trajectory_j) And angular velocity (ω)_j) The formula (F-3) is as follows:

γ_vj＝-ω_il_ijdsin(ψ_ijd+e_j3)、

pose error

Parameter k_vIs to ensure that even v_iProgressive stability of the system at 0. Consider the following Lyapunov function

V_{j} = \frac{1}{2} (e_{j 1}^{2} + e_{j 2}^{2}) + \frac{1}{k_{5}} (1 - \cos e_{j 3})

(formula 27)

V_j≧ 0 and if and only if e_jWhen equal to 0, V_j0 is true. Differentiating the formula (formula 27) and combining the formula (formula 23), (formula 24), (formula 25) and (F-3) to obtain

For all v_iNot less than 0The speed control input of the expressions (24), (25) and (F-3) can make the dynamic errors of the systems of the expressions (19) and (23) gradually stable.

In one embodiment, the off-board sensor module 1 is in a vehicle R_i、R_jAn infrared sensor 5 is respectively arranged at the left front side and the right front side for detecting front obstacles, and a speed sensor 6 is arranged on the wheel shaft for detecting the vehicle R_i、R_jThe speed of travel.

In one embodiment, the GPS positioning module 2 contains a GPS antenna and a patch cord with a 5V supply voltage.

In one embodiment, the wireless communication module 3 adopts FPV5.8G200MW transmitting and receiving set, and the power supply voltage is 12V.

In one embodiment, the central processing unit module 4 is an embedded DSP processing unit.

In another embodiment, the off-board sensor module 1 further comprises a visual measuring device for monitoring obstacles.

Fig. 4 is a flowchart of a fleet formation control method based on an cyber-physical network according to an embodiment of the present invention, and the example further illustrates the fleet formation control method based on the cyber-physical network in detail.

In a first step S1, a platoon formation is performed, wherein the platoon comprises a lead vehicle R_iAnd following vehicle R_jEach vehicle R arranged in a fleet_i、R_jThe intelligent obstacle avoidance device for the motorcade comprises a vehicle external sensor module 1, a GPS positioning module 2, a wireless communication module 3 and a central processing unit module 4.

The central processing unit module 4 for implementing communication and application protocol receives and processes the speed and position information of the vehicle through the wireless communication module 3.

In the second step S2, the infrared sensor 5 detects whether there is an obstacle in front, and if there is an obstacle, the third step is executed, and if there is no obstacle, the fourth step is executed;

in the third step S3, the CPU module 4 establishes a coordinate system and calculates the following vehicle (R) by the formula (F-1)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) The formula (F-1) is as follows:

(F-1) wherein, in the above,

γ_ij＝θ_i+ψ_ij-θ_j、γ_kj＝θ_k-θ_j、α₁、α₂is constant,. l_ijFor piloting vehicles (R)_i) And a following vehicle (R)_j) Relative distance between, d denotes the following vehicle (R)_j) Center of mass to axle center distance, wherein a virtual vehicle R is defined_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kOf (2) a connection line_jk，_jkIndicating a following vehicle (R)_j) Closest distance to the obstacle, θ_kAs a virtual vehicle R_kDirection tangent to the obstacle, theta_i、θ_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Angle of (v) v_i、v_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Linear velocity of phi_ijAnd l_ijRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) Relative angle and distance between, following vehicles (R)_j) Linear velocity (v) obtained by the cpu module 4 through the CAN bus_j) And angular velocity (ω)_j) Sent to the vehicle controller 7 for adjustment of the following vehicle (R)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) Therefore, the obstacle avoidance of the fleet is ensured under the formation condition;

in the fourth step S4, the CPU module 4 calculates the piloted vehicle (R) by the formula (F-2)_i) Linear velocity (v) to be controlled according to reference trajectory_i) And angular velocity (ω)_i) The formula (F-2) is as follows:

<math> <mrow> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>v</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>ω</mi> <mi>i</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>y</mi> <mi>ie</mi> </msub> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msubsup> <mi>y</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>v</mi> </mrow> <mi>r</mi> </msub> </mfrac> <msub> <mover> <mi>v</mi> <mo>·</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mi>ie</mi> </msub> </mfrac> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>sin</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mi>ie</mi> </msub> </mfrac> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mi>F</mi> <mo>-</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

wherein,

the central processing unit module 4 obtains a following vehicle (R) through calculation of a formula (F-3)_j) Linear velocity ((v) desired to be controlled according to reference trajectory_j) And angular velocity (ω)_j) The formula (F-3) is as follows:

γ_vj＝-ω_il_ijdsin(ψ_ijd+e_j3)、

pose error

Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments and application fields, and the above-described embodiments are illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto without departing from the scope of the invention as defined by the appended claims.

Claims

1. A fleet formation control device based on an cyber-physical network, wherein the fleet comprises piloted vehicles (R)_i) And a following vehicle (R)_j) Each vehicle (R) being arranged in a vehicle fleet_i、R_j) The intelligent obstacle avoidance device for the motorcade comprises a vehicle exterior sensor module (1), a GPS positioning module (2), a wireless communication module (3) and a central processing unit module (4), wherein,

the vehicle exterior sensor module (1) comprises an infrared sensor (5) for detecting a front obstacle and a speed sensor (6) for detecting the running speed of a vehicle;

the GPS positioning module (2) is arranged on each vehicle (R)_i、R_j) The middle position of the front end is used for acquiring the position information of the vehicle in real time and synchronizing time;

the wireless communication module (3) comprises a wireless network card and a wireless router and is used for establishing network connection with other vehicles in the motorcade;

the central processing unit module (4) for realizing communication and application protocol receives and processes the speed and position information of the vehicle through the wireless communication module (2), wherein when the infrared sensor (5) detects that an obstacle exists in front of the vehicle, the central processing unit module (4) obtains a following vehicle (R) through calculation of a formula (F-1)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) The formula (F-1) is as follows:

wherein,

γ_ij＝θ_i+ψ_ij-θ_j、γ_kj＝θ_k-θ_j、α₁、α₂is constant,. l_ijFor piloting vehicles (R)_i) And a following vehicle (R)_j) Relative distance between, d denotes the following vehicle (R)_j) Center of mass to axle center distance, wherein a virtual vehicle R is defined_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kOf (2) a connection line_jk，_jkIndicating a following vehicle (R)_j) Closest distance to the obstacle, θ_kAs a virtual vehicle R_kDirection tangent to the obstacle, theta_i、θ_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Angle of (v) v_i、v_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Linear velocity of phi_ijAnd l_ijRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) Relative angle and distance between; following vehicle (R)_j) Center through CAN busLinear velocity (v) obtained by the physical unit module (4)_j) And angular velocity (ω)_j) Sent to a vehicle controller (7) for adjusting the following vehicle (R)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) Therefore, the obstacle avoidance of the fleet is ensured under the formation condition;

when the infrared sensor (5) detects that no obstacle exists in front, the central processing unit module (4) calculates and obtains a piloting vehicle (R) through a formula (F-2)_i) Linear velocity (v) to be controlled according to reference trajectory_i) And angular velocity (ω)_i) The formula (F-2) is as follows:

<math> <mrow> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>v</mi> <mi>i</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>ω</mi> <mi>i</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mi>y</mi> <mi>ie</mi> </msub> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>cos</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <msub> <mrow> <mo>|</mo> <mi>s</mi> </mrow> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msubsup> <mi>y</mi> <mi>ie</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>1</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> <msub> <mi>s</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mi>αv</mi> <mi>r</mi> </msub> </mfrac> <msub> <mover> <mi>v</mi> <mo>.</mo> </mover> <mi>r</mi> </msub> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mi>ie</mi> </msub> </mfrac> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mi>sin</mi> <msub> <mi>θ</mi> <mi>ie</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>ϵ</mi> <msup> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> </mrow> <mi>a</mi> </msup> <mfrac> <msub> <mi>s</mi> <mn>2</mn> </msub> <mrow> <mo>|</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>|</mo> <mo>+</mo> <mi>σ</mi> </mrow> </mfrac> <mo>+</mo> <msub> <mi>k</mi> <mn>3</mn> </msub> <msub> <mi>s</mi> <mn>2</mn> </msub> </mrow> <mrow> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <mi>α</mi> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mi>ie</mi> </msub> </mfrac> <msub> <mi>x</mi> <mi>ie</mi> </msub> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mi>F</mi> <mo>-</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

γ_vj＝-ω_il_ijdsin(ψ_ijd+e_j3)、

pose error

2. The cyber-physical-network-based fleet formation control device according to claim 1, wherein the off-board sensor module (1) is located at a vehicle (R)_j、R_j) An infrared sensor (5) is respectively arranged at the left front side and the right front side for detecting front obstacles, and a speed sensor (6) is arranged on the wheel shaft for detecting the vehicle (R)_i、R_j) The speed of travel.

3. The formation control device for the fleet based on cyber-physical network as claimed in claim 1, wherein the GPS positioning module (2) comprises a GPS antenna and a patch cord, and the power supply voltage is 5V.

4. The formation control device for the fleet based on cyber-physical network as claimed in claim 1, wherein the wireless communication module (3) adopts FPV5.8G200MW transmitting and receiving set, and the power supply voltage is 12V.

5. The formation control device for the fleet based on cyber-physical network as claimed in claim 1, wherein the central processing unit module (4) is an embedded DSP processing unit.

6. A fleet formation control method based on an cyber-physical network, wherein,

in a first step (S1), the fleet is formed, wherein the fleet comprises pilot vehicles (R)_i) And a following vehicle (R)_j) Each vehicle (R) being arranged in a vehicle fleet_i、R_j) The intelligent obstacle avoidance device for the motorcade comprises a vehicle exterior sensor module (1), a GPS positioning module (2), a wireless communication module (3) and a central processing unit module (4), wherein,

the central processing unit module (4) for realizing communication and application protocol receives and processes the speed and position information of the vehicle through the wireless communication module (2);

in a second step (S2), the infrared sensor (5) detects whether there is an obstacle in front, and if there is an obstacle, the third step is performed, and if there is no obstacle, the fourth step is performed;

in a third step (S3), the central processing unit module (4) establishes a coordinate system and obtains a following vehicle (R) through calculation by a formula (F-1)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) The formula (F-1) is as follows:

wherein,

γ_ij＝θ_i+ψ_ij-θ_j、γ_kj＝θ_k-θ_j、α₁、α₂is constant,. l_ijFor piloting vehicles (R)_i) And a following vehicle (R)_j) Relative distance between, d denotes the following vehicle (R)_j) Center of mass to axle center distance, wherein a virtual vehicle R is defined_kAt a constant linear velocity v_kDirection theta tangent to the obstacle_kMoving along obstacles, i.e. theta_kIn the direction of movement v_kIs always perpendicular to R_jAnd a virtual vehicle R_kOf (2) a connection line_jk，_jkIndicating a following vehicle (R)_j) Closest distance to the obstacle, θ_kAs a virtual vehicle theta_kDirection tangent to the obstacle, theta_i、θ_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Angle of (v) v_i、v_jRespectively a pilot vehicle (R)_i) And a following vehicle (R)_j) Linear velocity of phi_ijAnd l_ijRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) Relative angle and distance between, following vehicles (R)_j) Linear velocity (v) obtained by a central processing unit module (4) via a CAN bus_j) And angular velocity (ω)_j) Sent to a vehicle controller (7) for adjusting the following vehicle (R)_j) Linear velocity (v) of_j) And angular velocity (ω)_j) Therefore, the obstacle avoidance of the fleet is ensured under the formation condition;

in a fourth step (S4), the central processing unit module (4) calculates and obtains a piloted vehicle (R) through a formula (F-2)_i) Linear velocity (v) to be controlled according to reference trajectory_i) And angular velocity (ω)_i) The formula (F-2) is as follows:

wherein,

v_r、ω_rlinear and angular velocities, k, respectively, of the reference track₂、k₃Are all normal numbers, 0 < a < 1

Pose error ei ═ x_ie，y_ie，θ_ie]^TThe convergence to zero is made to be zero,

γ_vj＝-ω_il_ijdsin(ψ_ijd+e_j3)、

wherein k is₄、k₅、k₆、k_vAre all positive and real, psi_igdAnd l_ijdRespectively representing piloted vehicles (R)_i) And a following vehicle (R)_j) The desired angle and the distance between the vehicles,

pose error

7. The cyber-physical-network-based fleet formation control method according to claim 6, wherein the off-board sensor module (1) is located at a vehicle (R)_i、R_j) An infrared sensor (5) is respectively arranged at the left front side and the right front side for detecting front obstacles, and a speed sensor (6) is arranged on the wheel shaft for detecting the vehicle (R)_i、R_j) The speed of travel.

8. The formation control method for the fleet based on cyber-physical network as claimed in claim 6, wherein the GPS positioning module (2) comprises a GPS antenna and a patch cord, and the power supply voltage is 5V.

9. The cyber-physical network-based fleet formation control method according to claim 6, wherein the wireless communication module (3) transmits and receives a package with FPV5.8G200MW power supply voltage of 12V.

10. The cyber-physical network-based fleet formation control method according to claim 6, wherein said CPU module (4) is an embedded DSP processing unit.