CN114815830A - Air-ground cluster cooperative networking control system and method based on consistency algorithm - Google Patents
Air-ground cluster cooperative networking control system and method based on consistency algorithm Download PDFInfo
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
- G05D1/0251—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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Abstract
The invention belongs to the technical field of cooperative cluster control of various heterogeneous unmanned machines such as unmanned aerial vehicles/unmanned vehicles and the like, and particularly relates to a system and a method for controlling air-ground cluster cooperative networking based on a consistency algorithm. The invention relates to a set of system and method which are based on a model prediction control algorithm, an artificial potential energy field obstacle avoidance algorithm and a consistency collaborative formation algorithm, can realize the autonomous positioning and obstacle avoidance functions of an intelligent agent and complete a collaborative patrol task. The invention can capture enemy targets through the dynamic capture system, can not only utilize the flexibility of the unmanned vehicle to complete satellite-surrounding type capture tasks with enemy as the center, but also can complete the detection capture tasks that the unmanned vehicle transmits the searched targets to the unmanned vehicle and the unmanned vehicle receives the enemy targets to carry out capture on the basis of the wide field of view of the unmanned vehicle and the high maneuverability of the unmanned vehicle.
Description
Technical Field
The invention belongs to the technical field of cooperative cluster control of various heterogeneous unmanned machines such as unmanned aerial vehicles/unmanned vehicles and the like, and particularly relates to a system and a method for controlling air-ground cluster cooperative networking based on a consistency algorithm.
Background
The unmanned aerial vehicle/unmanned vehicle and other multi-machine air-ground cooperation has very wide application prospect, and can solve the defects of insufficient obstacle crossing capability, low moving speed, small investigation range of the ground robot, poor cruising capability, easy interference, insufficient continuous investigation capability and the like of the small-sized air unmanned aerial vehicle to the greatest extent. However, the current related theoretical research and practical application are still limited, and the degree of organic unification is not sufficient.
In foreign countries, after an unmanned vehicle is launched to the front line, the STORK project in the united states takes charge of information relay between an operator and the unmanned vehicle by taking the unmanned vehicle as a communication relay. The U.S. UTAGS project is to mount a small unmanned aerial vehicle on an unmanned vehicle, and fly the unmanned aerial vehicle after the unmanned vehicle travels to a special environment where the unmanned vehicle is inconvenient to work, and the unmanned aerial vehicle is used as an extension and supplement of the unmanned vehicle. In the English 'black knight' expansion project, the unmanned aerial vehicle plays the role of a scout, and can provide laser aiming signals for the 'black knight' unmanned tank in the shortest time, so that the unmanned vehicle can attack autonomously as soon as possible.
In China, the main research direction focuses on the aspect that unmanned aerial vehicles are used as road information sources for unmanned vehicle path planning through methods such as visual perception and dynamic target detection, and the research direction which is biased to be organically unified with the unmanned aerial vehicles is also biased to be researched by using unmanned aerial vehicles as messengers of distributed unmanned vehicles.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the defects of the prior art are overcome, and a system and a method for controlling the air-ground cluster cooperative networking based on a consistency algorithm are provided. The invention relates to a set of system and method which are based on a model prediction control algorithm, an artificial potential energy field obstacle avoidance algorithm and a consistency collaborative formation algorithm, can realize the autonomous positioning and obstacle avoidance functions of an intelligent agent and complete a collaborative patrol task. The invention can capture enemy targets through the dynamic capture system, can not only utilize the flexibility of the unmanned vehicle to complete satellite-surrounding type capture tasks with enemy as the center, but also can complete the detection capture tasks that the unmanned vehicle transmits the searched targets to the unmanned vehicle and the unmanned vehicle receives the enemy targets to carry out capture on the basis of the wide field of view of the unmanned vehicle and the high maneuverability of the unmanned vehicle.
The technical solution of the invention is as follows:
an air-ground cluster cooperative networking control system based on a consistency algorithm comprises a cloud control subsystem and a multi-agent subsystem;
the cloud control subsystem comprises a positioning information acquisition module, a cloud end controller module, a serial port communication module and a WIFI communication module;
the positioning information acquisition module is used for acquiring pose information of each unmanned aerial vehicle and each target object in the multi-agent subsystem, and is also used for acquiring positioning data of each object in the multi-agent subsystem, and the positioning data is acquired by dynamic capturing software in an upper computer;
the serial port communication module is used for realizing a communication function between the positioning information acquisition module and the cloud-end controller module, reading data from a reserved interface of the slave capture software, and packaging the data according to a self-defined communication protocol to be sent to the cloud-end controller module;
the cloud-end controller module is used for processing a mode set by a user, a target position and acquired positioning information to obtain control quantity which each unmanned aerial vehicle should have, so that pose information of an intelligent body (an omnidirectional mobile robot or an unmanned aerial vehicle) is not directly broadcast into a local area network of the WIFI module any more, but is communicated with the cloud-end controller module through a serial port communication module to upload the pose information to the cloud-end controller module, the cloud-end controller module resolves the control quantity on line according to the provided pose information and sends the resolved control quantity back to the local, an upper computer broadcasts the control quantity into the local area network, a single chip microcomputer on each intelligent body is not used as a controller embedded with a complex algorithm, but is only responsible for receiving the control quantity in the local area network, splitting a data packet to extract the control information and executing direct control on the intelligent body, in a cloud control system, simple controllers carried by the intelligent bodies are called as 'edge control nodes', the real-time performance of the whole system can be guaranteed;
the WIFI communication module is used for establishing a communication network to support mutual communication among the members of the multi-agent subsystem, and can send information such as control quantity obtained by operation of the cloud-end controller to the multi-agent subsystem. Particularly, the serial communication module and the WIFI communication module adopt the same communication protocol;
the multi-agent subsystem comprises a quad-rotor unmanned aerial vehicle module and an omnidirectional mobile unmanned vehicle module, the quad-rotor unmanned aerial vehicle module and the omnidirectional mobile unmanned aerial vehicle module are used as main actuators to execute commands sent by an upper control layer, pose control and formation control are completed, and the multi-agent subsystem has the functions of: the device comprises a positioning function, an obstacle avoidance function, a detection function and an enclosure function.
The positioning function is as follows: for the unmanned aerial vehicle, a combined navigation mode is adopted to position the unmanned aerial vehicle, and the reliability and stability of flight are ensured; for the unmanned vehicle, the positioning information of the network communication subsystem is received, wheel control is solved, and the robustness and effectiveness of the multi-intelligent system are improved due to the stable operation of the hardware system of the unmanned vehicle.
Obstacle avoidance function: surrounding obstacles are timely and accurately identified through a sensor, and an unmanned vehicle group capable of automatically avoiding obstacles are designed through intelligent obstacle avoiding algorithms such as an artificial potential energy field.
And (3) detection function: unmanned aerial vehicle has wide field of vision, and unmanned vehicle has better flexibility, searches for and explores through unmanned aerial vehicle's air, realizes the detection function to the enemy.
A trapping function: through the communication between the unmanned vehicle cluster and the unmanned vehicle cluster, the unmanned vehicle realizes the task of carrying out enclosure on the enemy target after receiving the information transmitted by the unmanned vehicle through air search based on a consistency algorithm.
A method for controlling air-ground cluster cooperative networking based on a consistency algorithm comprises the following steps:
the method comprises the following steps: and (3) setting up a positioning information acquisition environment of the unmanned aerial vehicle, so that information such as the position, the speed, the posture and the like of each unmanned aerial vehicle and the unmanned vehicle can be acquired.
Step two: building a cloud end controller, transmitting the information obtained in the step one to the cloud end controller through a serial port communication module, calculating by the cloud end controller according to a set cooperative mode and target information to obtain the control quantity of each unmanned aerial vehicle, and returning the control quantity to an upper computer;
step three: a WIFI communication network is built, and control information in an upper computer can be sent to an information receiving layer through the WIFI communication network, namely a multi-agent subsystem consisting of a piloting unmanned aerial vehicle and a follower unmanned vehicle;
step four: and determining formation control information at a formation control layer based on consistency, and processing the information by an obstacle avoidance algorithm based on an artificial potential energy field and a dynamic role assignment based on a Hungarian algorithm on the unmanned aerial vehicle and the unmanned vehicle to form a final control quantity U.
Step five: and tracking the control quantity U in the fourth step by the mechanical execution layers of the unmanned aerial vehicle and the unmanned vehicle respectively provided with the double-ring PID and the fuzzy PID, acquiring an execution result by the positioning information acquisition module, and repeating the process again.
The positioning information acquisition environment in the first step is realized by measuring, tracking and recording the motion trail of an object in a three-dimensional space through a VICON motion capture system and transmitting processed image information to a ground station.
The serial port communication module in the second step writes a data reading program by C + + on a PC (personal digital computer), encapsulates the pose information into a data packet by a custom communication protocol, and mutually sends the pose data packet and a control quantity data packet between a local client and a cloud end, wherein the period is 0.02 second; the cloud-end controller in the step mainly comprises three parts, namely an unpacking function of a data packet, a control algorithm and an encapsulating function for encapsulating control quantity into the data packet;
the controlled object and the upper computer mentioned in the third step are in bidirectional data transmission through data transmission, the communication between the controlled object and the upper computer adopts UDP + WiFi protocol communication, and the application layer protocol is customized according to the requirement of the communication data format. In the control program of the agent, the format of the position information data packet received from the PC is defined as "start value identification + data length + agent number ID + destination ID + function word + position posture information + check bit". Wherein the start value of the useful data is identified as two "0 xaas", if these two identical bytes appear consecutively, it marks that the following data is the useful data we need. The number of the agent (source ID number) and the destination ID are each represented by one byte. The position information is divided into X, Y, Z three directions, each direction is represented by four bytes, and the angle information is obtained by attitude calculation of the positioning light-reflecting ball on the additional plate and is represented by four bytes. The settings of the function words are mainly divided into three categories, where 0x07 represents the subsequent data as control information, 0x05 represents the subsequently received data as position information, and 0x06 represents speed information. The check bit is the accumulation of the number of bytes from the number, and is used for judging whether the received data packet is complete.
The final control quantity U mentioned in the fourth step and the fifth step is the control quantity executed by the intelligent agent, and the effect can be achieved because a single chip microcomputer of the intelligent agent is provided with a unpacking function of a corresponding control quantity data packet, required control information is extracted from the control quantity data packet, and a self mechanism (motor) is directly controlled to operate;
in addition, it should be particularly noted that the invention integrates a scheduling strategy facing to the control task requirement, and can ensure the control real-time requirement within 12ms of the unmanned aerial vehicle and 20ms of the unmanned aerial vehicle.
Advantageous effects
(1) The invention can endow the space-ground cooperative system with flexibility and universality by using a modular characteristic. Each module plays its own role, and can be replaced more conveniently as required when being popularized and applied.
(2) The self-defined communication protocol used in the communication process of the serial communication module and the cloud controller module can ensure that the communication stage has higher safety and confidentiality, because the self-defined communication protocol not only belongs to mask communication, but also can ensure that personnel who do not know the protocol cannot accurately know the meaning of the communication content, and even can add an encryption algorithm into the self-defined communication protocol to ensure that the safety of the communication is higher. This benefits from the alternative benefit of benefit 1.
(3) The cloud-end controller can concentrate primary information such as pose information of the intelligent agent and the target object in the cloud-end controller instead of flooding in the whole information network. The primary information is converted into high-efficiency information such as the control quantity of each intelligent agent after the resolving processing of the cloud-end controller, and then the high-efficiency information is broadcasted in the wifi local area network. The processing mode can reduce the burden of a communication network and prevent the intelligent agent from blocking the self acceptance window due to too much invalid information, so that the robustness of the system is lowered due to the fact that real control quantity information is delayed or missed.
(4) The multi-agent subsystem and the cloud-end controller are decoupled, and only a certain amount of information is exchanged through a WiFi local area network. This feature makes possible a versatile expansion of the multi-agent subsystem. This subsystem not only can include unmanned aerial vehicle and unmanned vehicle described in the claim, can add the intelligent object such as robot, arm even, only need add its corresponding characteristic in control mode can.
(5) The enclosure function of the invention can lead the subsystems of the multiple intelligent agents to be organically unified under the framework of a consistency algorithm, and the situation that the formation seems to be still in actual individual battles can not occur. And the obstacle avoidance function is not integrated in the cloud-end controller, but realized by an airborne sensor and a single chip microcomputer of the airborne sensor. The characteristics make the whole system more close to actual combat, and ensure the real-time performance of the system.
(6) The double-loop PID and the fuzzy PID can be flexibly replaced by algorithms such as active disturbance rejection, model predictive control and the like. This also represents another advantage of the system, namely, the characteristic of fast verification of algorithm innovation is achieved by taking a complete scheme as a framework to carry out partial replacement on the algorithm of which a student focuses on a part.
(7) The VICON motion capture system has the advantages of high precision, good real-time performance and the like, can even reach millimeter-level pose judgment precision, and can bring accurate feedback for algorithm verification. If a wide experiment scene is sought, the positioning environment can be completely replaced by satellite positioning such as Beidou.
(8) The serial port communication module can realize data communication with a period of 0.02 second, namely, the control rate of 50 times per second, and compared with other space-ground cooperative systems, the real-time performance of the serial port communication module is greatly improved. This means that when an emergency occurs in the experimental environment, the system can also perform corresponding response processing in a very short time, and the provided robustness is improved.
(9) The definition of data length bit in the definition of data packet format in the communication protocol of the invention also provides an interface for the expansion and change of communication content, and the meaning contained in the function word can be correspondingly adjusted according to the difference of data length. The universality of the system is enhanced.
(10) The single chip microcomputer in the multi-agent only needs to disassemble the data packet and control the self structure to operate according to the control information in the data packet, thereby greatly saving the calculation power of the airborne single chip microcomputer and leading the single chip microcomputer to be more concentrated on the functions related to self safety.
Drawings
FIG. 1 is an air-to-ground cluster cooperative networking control system;
FIG. 2 is a diagram of a communication protocol format;
FIG. 3 is a schematic illustration of a communication flow;
FIG. 4 is a diagram of the variation of the control quantity in the air-ground cooperative control process;
FIG. 5 is a diagram of the effect of the dynamic role assignment algorithm;
FIG. 6 is a diagram of the effect of formation of cooperative patrols;
FIG. 7 is a diagram of the surrounding capturing effect of the unmanned vehicle;
FIG. 8 is a diagram of the effect of detection enclosure;
fig. 9 is a diagram of actual positions and expected positions of the unmanned aerial vehicle and the unmanned aerial vehicle;
fig. 10 is a diagram of the position error of the drone;
fig. 11 is an unmanned vehicle position error diagram.
Detailed Description
In order to make the objects, technical solutions and features of the present invention clearer, the technical solutions of the present invention are clearly and completely described below with reference to the accompanying drawings.
An air-ground cluster cooperative networking control system based on a consistency algorithm comprises a cloud control subsystem and a multi-agent subsystem, as shown in fig. 1;
the cloud control subsystem comprises a positioning information acquisition module, a cloud end controller module, a serial port communication module and a WIFI communication module;
the positioning information acquisition module is used for acquiring the pose information of each unmanned aerial vehicle and target object in the multi-agent subsystem, and the positioning data of the multi-agent subsystem and other objects are acquired by the motion capture system in the positioning information acquisition module and are acquired by motion capture software in an upper computer;
the serial port communication module is responsible for realizing the communication function between the positioning information acquisition module and the cloud end controller module; the module is provided with corresponding software, reads data from a reserved interface of the slave software, packages the data according to a self-defined communication protocol and sends the data to the cloud-end controller module.
The cloud end controller module is responsible for processing a mode set by a user, a target position and the obtained positioning information to obtain the control quantity of each unmanned aerial vehicle; the existence of the module can ensure that the pose information of an intelligent agent (an omnidirectional mobile robot or an unmanned aerial vehicle) is not directly broadcasted into a local area network of the WIFI module any more, but the pose information is uploaded to the cloud-end controller through the communication between the serial port communication module and the cloud-end controller. The cloud end controller resolves the control quantity on line according to the provided pose information and sends the control quantity back to the local, and the control quantity is broadcasted to the local area network by the upper computer. The single chip microcomputer on each intelligent agent does not serve as a controller embedded with a complex algorithm any longer, but is only responsible for receiving control quantity in a local area network, splitting a data packet, extracting control information and executing direct control on the intelligent agent. In a cloud control system, the simple controllers carried by the agents are called as 'edge control nodes', and the real-time performance of the whole system can be guaranteed.
The WIFI communication module is responsible for establishing a communication network to support mutual communication among members of the multi-agent subsystem, and can send information such as control quantity obtained by operation of the cloud-end controller to the multi-agent subsystem. Particularly, the serial communication module and the WIFI communication module adopt the same communication protocol.
The multi-agent subsystem comprises a quad-rotor unmanned aerial vehicle module and an omnidirectional mobile unmanned aerial vehicle module, and the quad-rotor unmanned aerial vehicle module and the omnidirectional mobile unmanned aerial vehicle module are used as main actuators to execute commands sent by an upper control layer so as to complete pose control and formation control. According to different actions, the functions of the multi-agent subsystem can be divided into: the device comprises a positioning function, an obstacle avoidance function, a detection function and an enclosure function.
The positioning function is as follows: for the unmanned aerial vehicle, a combined navigation mode is adopted to position the unmanned aerial vehicle, and the reliability and stability of flight are ensured; for the unmanned vehicle, the positioning information of the network communication subsystem is received, wheel control is solved, and the robustness and effectiveness of the multi-intelligent system are improved due to the stable operation of the hardware system of the unmanned vehicle.
Obstacle avoidance function: surrounding obstacles are timely and accurately identified through a sensor, and an unmanned vehicle group capable of automatically avoiding obstacles are designed through intelligent obstacle avoiding algorithms such as an artificial potential energy field.
And (3) detection function: unmanned aerial vehicle has wide field of vision, and unmanned vehicle has better flexibility, searches for and explores through unmanned aerial vehicle's air, realizes the detection function to the enemy.
A trapping function: through the communication between the unmanned vehicle cluster and the unmanned vehicle cluster, the unmanned vehicle realizes the task of carrying out enclosure on the enemy target after receiving the information transmitted by the unmanned vehicle through air search based on a consistency algorithm.
A method for controlling air-ground cluster cooperative networking based on a consistency algorithm comprises the following steps:
the method comprises the following steps: and (3) setting up a positioning information acquisition environment of the unmanned aerial vehicle, so that information such as the position, the speed, the posture and the like of each unmanned aerial vehicle and the unmanned vehicle can be acquired.
Step two: building a cloud end controller, transmitting the information obtained in the step one to the cloud end controller through a serial port communication module, calculating by the cloud end controller according to a set cooperative mode and target information to obtain the control quantity of each unmanned aerial vehicle, and returning the control quantity to an upper computer;
step three: a WIFI communication network is built, and control information in an upper computer can be sent to an information receiving layer through the WIFI communication network, namely a multi-agent subsystem consisting of a piloting unmanned aerial vehicle and a follower unmanned vehicle;
step four: and determining formation control information on a formation control layer based on consistency, and processing the information by an obstacle avoidance algorithm based on an artificial potential field and dynamic role allocation based on a Hungarian algorithm on unmanned aerial vehicles and unmanned vehicles respectively to form a final control quantity U.
Step five: and tracking the control quantity U in the fourth step by the mechanical execution layers of the unmanned aerial vehicle and the unmanned vehicle respectively provided with the double-ring PID and the fuzzy PID, acquiring an execution result by the positioning information acquisition module, and repeating the process again.
The positioning information acquisition environment in the first step is realized by measuring, tracking and recording the motion trail of an object in a three-dimensional space through a VICON motion capture system and transmitting processed image information to a ground station.
The serial port communication module in the second step writes a data reading program by C + + on a PC (personal digital computer), encapsulates the pose information into a data packet by a custom communication protocol, and mutually sends the pose data packet and a control quantity data packet between a local client and a cloud end, wherein the period is 0.02 second; the cloud-end controller in the step mainly comprises three parts, namely an unpacking function of a data packet, a control algorithm and an encapsulating function for encapsulating control quantity into the data packet;
the controlled object and the upper computer mentioned in the third step are in bidirectional data transmission through data transmission, the communication between the controlled object and the upper computer adopts UDP + WiFi protocol communication, and the application layer protocol is customized according to the requirement of the communication data format. As shown in fig. 2, in the control program of the agent, the format of the position information packet received from the PC is defined as "start value identification + data length + agent number ID + destination ID + function word + position posture information + check bit". Wherein the start value of the useful data is identified as two "0 xaas", if these two identical bytes appear consecutively, it marks that the following data is the useful data we need. The number of the agent (source ID number) and the destination ID are each represented by one byte. The position information is divided into X, Y, Z three directions, each direction is represented by four bytes, and the angle information is obtained by attitude calculation of the positioning light-reflecting ball on the additional plate and is represented by four bytes. The settings of the function words are mainly divided into three categories, where 0x07 represents the subsequent data as control information, 0x05 represents the subsequently received data as position information, and 0x06 represents speed information. The check bit is the accumulation of the number of bytes from the number, and is used for judging whether the received data packet is complete. The above-mentioned communication relationship is shown in fig. 3.
The final control quantity U mentioned in the fourth and fifth steps is the control quantity executed by the agent, and the effect thereof is that the corresponding unpacking function of the control quantity data packet is provided on the single chip of the agent, the required control information is extracted from the control quantity data packet, and the self mechanism (motor) is directly controlled to operate, and the whole control flow is shown in fig. 4.
In addition, it should be particularly noted that the invention integrates a scheduling strategy facing to the control task requirement, and can ensure the control real-time requirement within 12ms of the unmanned aerial vehicle and 20ms of the unmanned aerial vehicle.
Examples
The first example is as follows: and after the whole system is built, verifying the role dynamic allocation algorithm of the unmanned vehicle in the multi-agent subsystem. As shown in fig. 5, the role dynamic assignment algorithm used by the multi-agent subsystem achieves a good effect: as can be seen from the figure, firstly, the differences of different robots, namely No. 1, No. 2 and No. 3, can be seen through the identification balls on the acrylic plate on the omnidirectional mobile robot. In the initial formation state, the robot No. 1 is positioned in the front, and the robots No. 2 and No. 3 are respectively positioned in the rear. When the No. 1 robot is placed behind the No. 3 robot, a formation with the No. 3 robot in front and the No. 1 and No. 2 robots in back is formed.
Example two: the invention carries out formation experiment verification of cooperative control of the unmanned aerial vehicle and the unmanned vehicles on the basis of unmanned aerial vehicle position and attitude control and cooperative control of multiple unmanned vehicles. The control model of the unmanned aerial vehicle predicts a control algorithm, the unmanned aerial vehicle adds pilot information on the basis of the algorithm of first-order consistency, one unmanned aerial vehicle serves as a pilot, and the other unmanned aerial vehicle serves as a follower. Five ground mobile robots are used as followers. The follower takes the pilot as the center, encloses a fixed formation, and maintains the formation during the movement of the follower. Meanwhile, a multi-target dynamic role allocation task is realized based on the Hungarian algorithm, so that the followers can select the self formation positions at the minimum cost, and the stability of the formation form is maintained. And the obstacle avoidance is carried out by adopting an artificial potential energy field based method, so that obstacles on the driving route of a pilot are avoided, and the driving safety is guaranteed. In addition, the unmanned vehicle divides the control into two parts, a bottom layer control and an upper layer control. The bottom control board arranged on the bottom layer of the vehicle is responsible for motor drive control to realize control of the rotating speed of each omnidirectional wheel of the unmanned vehicle, and the upper control board adjusts the rotating speed difference of each motor after receiving broadcast information to realize control of front-back and left-right movement of the trolley, thereby realizing control of the formation of the trolley. The upper control panel and the bottom control panel are in communication connection through a CAN.
With the method in example two, three simulation scenarios for cooperative formation of unmanned aerial vehicles and unmanned vehicles are as follows:
a) collaborative patrol
Two formation modes are designed to complete the air-ground cooperative patrol task of the unmanned aerial vehicle and the unmanned vehicle.
Set for fixed formation mode, regard as the pilot with an unmanned aerial vehicle, another unmanned aerial vehicle is as the follower, and 5 unmanned vehicles are as the follower, regard as the center with the pilot, enclose into a circular, keep formation when following the pilot motion.
A satellite surrounding formation mode is designed, an unmanned aerial vehicle is used as a center, and 5 unmanned vehicles perform circular motion around the unmanned aerial vehicle. The cooperative patrol formation is shown in fig. 6.
b) Unmanned vehicle enclosure
By utilizing the flexibility of the unmanned vehicle, when an enemy enters the military range of the enemy, the unmanned vehicle starts from each position by taking the enemy target as the center, and performs central satellite surrounding type capture on the enemy. The unmanned vehicle is enclosed as shown in fig. 7.
c) Detection enclosure
By utilizing the wide visual field of the unmanned aerial vehicle and the high performance of the unmanned aerial vehicle, when finding that the enemy enters the military range of the enemy, the unmanned aerial vehicle locks a target by utilizing the height advantage of the unmanned aerial vehicle and directly reaches the sky of the enemy. And after receiving the signal of the unmanned aerial vehicle, the unmanned aerial vehicle carries out satellite type enclosure catching task by taking the unmanned aerial vehicle as the center. The detection enclosure task is shown in fig. 8.
While three military scenes are realized, parameters are continuously adjusted, the formation precision of the unmanned aerial vehicle and the unmanned vehicle is controlled within 5cm (namely, the error between the expected position and the actual position is within 5 cm), and the expected position and the actual position of the unmanned aerial vehicle and the unmanned vehicle are shown in fig. 9.
The blue track is the reference position of the unmanned aerial vehicle and the unmanned vehicle, and the tracks except the blue track are the actual track positions of the unmanned aerial vehicle and the unmanned vehicle respectively.
The position errors of the unmanned aerial vehicle and the unmanned vehicle are respectively shown in fig. 10 and fig. 11.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A control system for air-ground cluster cooperative networking based on a consistency algorithm is characterized in that:
the control system comprises a cloud control subsystem and a multi-agent subsystem;
the cloud control subsystem comprises a positioning information acquisition module, a serial port communication module, a cloud end controller module and a WIFI communication module;
the positioning information acquisition module is used for acquiring pose information of each unmanned aerial vehicle and each target object in the multi-agent subsystem, and is also used for acquiring positioning data of each object in the multi-agent subsystem, and the positioning data is acquired by dynamic capturing software in an upper computer;
the serial port communication module is used for realizing the communication function between the positioning information acquisition module and the cloud end controller module;
the cloud-end controller module is used for processing a mode set by a user, a target position and the acquired positioning information to obtain the control quantity of each unmanned aerial vehicle;
the WIFI communication module is used for establishing a communication network to support mutual communication among the members of the multi-agent subsystem, and can send control quantity information obtained by operation of the cloud-end controller to the multi-agent subsystem.
2. The air-ground cluster cooperative networking control system based on the consistency algorithm according to claim 1, wherein:
the serial port communication module reads data from a reserved interface of the slave capture software and packages the data according to a user-defined communication protocol and sends the data to the cloud-end controller module.
3. The air-ground cluster cooperative networking control system based on the consistency algorithm according to claim 1 or 2, wherein:
the cloud-end controller module is used for enabling the pose information of the intelligent bodies not to be directly broadcasted into a local area network of the WIFI module any more, but communicating with the cloud-end controller module through the serial port communication module to upload the pose information to the cloud-end controller module, the cloud-end controller module calculates control quantity on line according to the provided pose information and sends the control quantity back to the local area network, the control quantity is broadcasted into the local area network by the upper computer, the single chip microcomputer on each intelligent body is used for receiving the control quantity in the local area network, splitting a data packet to extract the control information and executing direct control on the intelligent bodies, and the serial port communication module and the WIFI communication module adopt the same communication protocol.
4. The air-ground cluster cooperative networking control system based on the consistency algorithm according to claim 1 or 2, wherein:
the multi-agent subsystem comprises a quad-rotor unmanned aerial vehicle module and an omnidirectional mobile unmanned vehicle module, is used for executing commands sent by an upper control layer, completes pose control and formation control, and has the following functions according to different actions: the device comprises a positioning function, an obstacle avoidance function, a detection function and an enclosure function.
5. The air-ground cluster cooperative networking control system based on the consistency algorithm according to claim 4, wherein:
the positioning function is as follows: for the unmanned aerial vehicle, positioning the unmanned aerial vehicle in a combined navigation mode, and for the unmanned aerial vehicle, receiving positioning information of a network communication subsystem and calculating wheel control;
the obstacle avoidance function is as follows: surrounding obstacles are timely and accurately identified through a sensor, and an unmanned vehicle group which can autonomously avoid obstacles are designed through designing an artificial potential field intelligent obstacle avoiding algorithm;
the detection function is as follows: the function of detecting the enemy is realized through the aerial search and exploration of the unmanned aerial vehicle;
the enclosure function is as follows: through the communication between the unmanned vehicle cluster and the unmanned vehicle cluster, after the unmanned vehicle receives the information transmitted by the unmanned vehicle through aerial search, the enemy target is developed and trapped.
6. A method for controlling air-ground cluster cooperative networking based on a consistency algorithm is characterized by comprising the following steps:
the method comprises the following steps: setting up a positioning information acquisition environment of the unmanned aerial vehicle, so that the position, speed and attitude information of each unmanned aerial vehicle and the unmanned vehicle are acquired;
step two: building a cloud end controller, transmitting the information obtained in the step one to the cloud end controller through a serial port communication module, and calculating by the cloud end controller according to a set cooperative mode and target information to obtain the control quantity of each unmanned machine and returning the control quantity to an upper computer;
step three: a WIFI communication network is built, and control information in an upper computer can be sent to an information receiving layer through the WIFI communication network, namely a multi-agent subsystem consisting of a piloting unmanned aerial vehicle and a follower unmanned vehicle;
step four: determining formation control information on a formation control layer based on consistency, and processing the information by an obstacle avoidance algorithm based on an artificial potential energy field and dynamic role distribution based on a Hungarian algorithm on an unmanned aerial vehicle and an unmanned vehicle to form a final control quantity U;
step five: and tracking the control quantity U in the fourth step by the mechanical execution layers of the unmanned aerial vehicle and the unmanned vehicle respectively provided with the double-ring PID and the fuzzy PID, and acquiring the execution result by the positioning information acquisition module.
7. The air-ground cluster cooperative networking control method based on the consistency algorithm according to claim 6, wherein:
the positioning information acquisition environment in the first step is realized by measuring, tracking and recording the motion trail of an object in a three-dimensional space through a VICON motion capture system and transmitting processed image information to a ground station.
8. The air-ground cluster cooperative networking control method based on the consistency algorithm according to claim 6 or 7, characterized in that:
the serial port communication module in the second step writes a data reading program by using C + + on the PC, encapsulates the pose information into a data packet by using a custom communication protocol, and mutually sends the pose data packet and a control quantity data packet between the local client and the cloud, wherein the period is 0.02 second; the cloud end controller mainly comprises three parts, namely an unpacking function of a data packet, a control algorithm and an encapsulating function for encapsulating control quantity into the data packet.
9. The air-ground cluster cooperative networking control method based on the consistency algorithm according to claim 6 or 7, characterized in that:
the controlled object and the upper computer mentioned in the third step are transmitted by data in two-way, the communication between them adopts UDP + WiFi protocol communication, the application layer protocol is customized according to the communication data format requirement, in the control program of the intelligent agent, the position information data packet format received from the PC end is defined as 'initial value identification + data length + intelligent agent number ID + target ID + function word + position posture information + check bit', wherein, the initial value for identifying the useful data is two '0 xAA', if the two same bytes appear continuously, the following data is marked as the useful data required by us, the number and the target ID of the intelligent agent are respectively represented by one byte, the position information is divided into X, Y, Z three directions, each direction is represented by four bytes, the angle information is obtained by the posture calculation of the positioning light reflecting ball on the additional plate, the functional words are mainly divided into three types, namely 0x07 for representing the subsequent data as control information, 0x05 for representing the subsequently received data as position information, 0x06 for representing speed information, and check bits for accumulating the number of bytes from the number of bytes for judging whether the received data packet is complete.
10. The air-ground cluster cooperative networking control method based on the consistency algorithm according to claim 6 or 7, characterized in that:
the final control quantity U mentioned in the fourth step and the fifth step is the control quantity executed by the intelligent agent, a single chip microcomputer of the intelligent agent is provided with a unpacking function of a corresponding control quantity data packet, and required control information is extracted from the control quantity data packet to directly control the operation of a mechanism per se.
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| CN115639830A (en) * | 2022-12-15 | 2023-01-24 | 北京航空航天大学 | Air-ground intelligent agent cooperative formation control system and formation control method thereof |
| CN116880434A (en) * | 2023-06-20 | 2023-10-13 | 辽宁工业大学 | Unmanned aerial vehicle-unmanned aerial vehicle cluster cooperative control method based on cloud and fog calculation under network attack |
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| CN115639830A (en) * | 2022-12-15 | 2023-01-24 | 北京航空航天大学 | Air-ground intelligent agent cooperative formation control system and formation control method thereof |
| CN116880434A (en) * | 2023-06-20 | 2023-10-13 | 辽宁工业大学 | Unmanned aerial vehicle-unmanned aerial vehicle cluster cooperative control method based on cloud and fog calculation under network attack |
| CN116880434B (en) * | 2023-06-20 | 2024-01-23 | 辽宁工业大学 | Unmanned aerial vehicle-unmanned aerial vehicle cluster cooperative control method based on cloud and fog calculation under network attack |
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