CN117369512A - Unmanned aerial vehicle cooperated intelligent control and optimization system - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle cooperative intelligent control and optimization system, which relates to the technical field of unmanned aerial vehicle design, and a main framework of the cooperative control system comprises the following technical modules: a communication and instruction transmission module, an environment sensing and decision module, and a cooperative motion and task division module. The cooperative control system developed by combining multiple technicians rapidly responds to and makes a decision on the rapidly-changing environment based on information exchange and instruction transmission among a plurality of unmanned aerial vehicles, realizes cooperative movement and task division among the plurality of unmanned aerial vehicles, can effectively ensure the maximization of the overall cooperative efficiency, and has important practical value.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle design, in particular to an unmanned aerial vehicle collaborative intelligent control and optimization system.
Background
With the rapid development and application of unmanned aerial vehicle technology, the development of unmanned aerial vehicle technology goes through a plurality of stages, and the ability of single unmanned aerial vehicle can not meet the demand, from initially relying on the autonomous control of single unmanned aerial vehicle to independently accomplish the task, gradually evolve into collaborative work, communication and decision among a plurality of unmanned aerial vehicles. In an early application stage, the development of technologies such as wireless communication, satellite communication and mobile communication provides important support for the development of unmanned aerial vehicle cooperative control, real-time communication between unmanned aerial vehicles becomes possible, and a plurality of unmanned aerial vehicles can realize data transmission and instruction interaction. Meanwhile, the artificial intelligence technology enables the unmanned aerial vehicle to realize intelligent decision making, path planning and task distribution through learning and analysis of a large amount of data, and can make real-time decision making according to environmental changes and task demands, so that the efficiency and adaptability of the whole system are improved. Along with the time, the unmanned aerial vehicles realize information sharing and communication through a communication network, and intelligent decision and task allocation are carried out by integrating sensor data and task demands of a plurality of unmanned aerial vehicles, so that the unmanned aerial vehicles can cooperatively work to complete more complex tasks.
However, despite this wide application of machine learning and artificial intelligence techniques in unmanned aerial vehicle cooperative control, it still suffers from a number of technical drawbacks, including at least the following several prominent aspects: (1) communication between unmanned aerial vehicles based on existing communication and network technology can be influenced by network bandwidth limitation, and real-time coordination between unmanned aerial vehicles can be influenced by delay generated on a large number of data sets transmitted between unmanned aerial vehicles. (2) The sensor may fail or exhibit poor performance for certain weather conditions or disturbances in the environment, and accuracy and stability may also be affected by mechanical vibrations, temperature variations, etc. (3) The limitations of machine learning and artificial intelligence technology require a large amount of training data and computing resources to run the machine learning algorithm in practical application, and greatly reduce the reliability and safety of the unmanned aerial vehicle control system. (4) Dynamic interactions and interactions among multiple unmanned aerial vehicles present new challenges for formation control of unmanned aerial vehicles, and further research on collaborative design is required to improve performance and stability of formation control systems. (5) In a complex environment, the path planning of the unmanned aerial vehicle may be limited by various constraint conditions such as airspace limitation, obstacle avoidance and the like, and an efficient optimization algorithm needs to be designed to solve the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an unmanned aerial vehicle cooperative intelligent control and optimization system
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
The unmanned aerial vehicle collaborative intelligent control and optimization system is based on information communication and instruction transmission among a plurality of unmanned aerial vehicles to rapidly respond to and make a decision on the rapidly-changing environment, so that collaborative movement and task division among the plurality of unmanned aerial vehicles are realized, and the overall collaborative efficiency is ensured to be maximized.
As a preferable technical scheme of the invention, the cooperative control system comprises the following technical modules: a communication and instruction transmission module, an environment sensing and decision module, and a cooperative motion and task division module.
As a preferable technical scheme of the invention, the communication and instruction transfer module adopts binary coding to code the unmanned aerial vehicle control instruction, and meanwhile, a decoding algorithm is used at a receiving end to convert the coded instruction back to an original instruction; using a wireless local area network as a communication protocol, and using a standard frequency band to establish a hot spot network between unmanned aerial vehicles; and automatically adjusting the data transmission rate according to the channel quality, and adjusting the time and the frequency of data transmission between the unmanned aerial vehicles by adopting an adaptive modulation method and a carrier sensing multiple access protocol, so as to reduce the collision and the interference between the unmanned aerial vehicles to the greatest extent.
As a preferable technical scheme of the invention, the environment sensing and decision module is provided with a plurality of sensors including a high-definition camera, a laser radar and an ultrasonic sensor, and the sensing accuracy and the robustness of the unmanned aerial vehicle to the surrounding environment are improved by fusing the data of the plurality of sensors and adopting a sensor fusion algorithm; and meanwhile, performing target detection and tracking on the sensor data subjected to preprocessing, feature extraction and data analysis through a computer vision algorithm, modeling and predicting the environment through a machine learning algorithm, and generating an adaptive path of each group of unmanned aerial vehicle through a planning algorithm and a task allocation algorithm.
As a preferable technical scheme of the invention, the cooperative motion and task division module generates a smooth flight path for each unmanned aerial vehicle through a path planning algorithm, and collision and obstacle avoidance are avoided by using a flight path prediction and dynamic obstacle avoidance algorithm type obstacle detection and obstacle avoidance algorithm. Meanwhile, the cooperative motion and task division among a plurality of unmanned aerial vehicles are realized through the cooperative control strategy design based on a distributed control algorithm and a collective control algorithm.
As a preferable technical scheme of the invention, the motion and task division module for the unmanned aerial vehicle intelligent system is constructed with a data model and a data process, and the specific data process is as follows: the construction process is as follows:
(1) Building a state and action model: describing the state and the action of the unmanned aerial vehicle by using vectors in a limited-dimensional vector space, wherein n-dimensional vectors x_i= (x_1, x_2,., x_n) represent the state of the unmanned aerial vehicle i, and cover information including position, speed, pitch angle, roll angle and yaw angle of the unmanned aerial vehicle i in a three-dimensional space; let m-dimensional vector u_i= (u_1, u_2,) u_m represent the actions of unmanned aerial vehicle i, information including speed, angular velocity, acceleration;
(2) And (3) building a motion model: a mapping relation of R n X R m- > R n is set, the state of the unmanned aerial vehicle is changed to X_i (t+1) =F (X_i (t), U_i (t)) (1), and at the time t+1, the state of the unmanned aerial vehicle i is determined by the state X_i (t) at the time t and the action U_i (t);
(3) And (3) constructing an objective function: by constructing an objective function to find an optimal path from the start point to the end point and taking into account complex problems including risk and consumption, and in the categories of path length minimization and time of flight minimization, the objective function is set to minimize the total movement distance, expressed as: min Σd (x_i (t+1), x_i (t)). (2), wherein D: R n X R n R is a function of calculating the distance of movement between the two states, Σ represents the sum of all positions; the state X_i of the unmanned aerial vehicle i needs to meet a certain range, X_i (t) epsilon S, wherein S is a limited dimensional space, and meanwhile, the distance between the two unmanned aerial vehicles needs to be maintained within a range;
(4) And (3) constructing a path planning algorithm: the position of the drone is projected onto a coordinate system, and then the optimal solution is found independently in each dimension, specifically, for the kth iteration during each iteration: first find the point x_1 that minimizes the objective function at the 1 st dimension coordinate, thenUpdate x_1 (k+1) =x_1, repeating the operation on each dimension (e.g., x_2, x_3,., x_n) until the convergence condition is satisfied; defining a continuous and microobjective function to measure the degree of cooperation between unmanned aerial vehicles, calculating the gradient of the objective function with respect to the position parameters of the unmanned aerial vehicles, and then updating the parameters according to the negative gradient direction: for the kth iteration: first, calculating the gradient of an objective function at the current unmanned plane position x (k)Secondly updating the position of the unmanned aerial vehicleWhere α is the learning rate.
The invention also comprises a device for matching and executing the unmanned plane cooperative intelligent control and optimization system, which is used for realizing the data system, and at least comprises the following hardware technical modules: unmanned aerial vehicle fuselage, flight control device, battery management device, load equipment device.
As a preferable technical scheme of the invention, the unmanned aerial vehicle body is provided with a receiver and a control chip to realize the triggering of corresponding programs for the transmission of hot spot network information among unmanned aerial vehicles.
As a preferable technical scheme of the invention, the flight control device measures the gesture and acceleration of the unmanned aerial vehicle by using the gyroscope and the accelerometer through the inertial measurement unit, controls the gesture and the motion of the unmanned aerial vehicle according to the set control algorithm through the flight controller, and adopts the gesture control algorithm to adjust the flight action in real time according to the sensor data so as to ensure the stability and the accuracy of the unmanned aerial vehicle.
As a preferable technical scheme of the invention, the load equipment device reserves a slot position and an interface of the load equipment comprising a camera, a sensor and a load, and provides a stable platform and vibration isolation measures for the load equipment through a pan-tilt vibration prevention system so as to ensure that the load equipment can work without being interfered.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: the unmanned aerial vehicle collaborative intelligent control and optimization system constructed by the invention can rapidly respond to and make a decision on the rapidly-changing environment based on information exchange and instruction transmission among a plurality of unmanned aerial vehicles, realize collaborative motion and task division among unmanned aerial vehicles, keep collaborative work and stable motion in a dynamic environment, and improve collaborative efficiency. The invention is also beneficial to maximizing the overall cooperative efficiency, reasonably distributing the tasks through an optimization algorithm and a task distribution strategy, thereby expanding the task execution capacity, enabling each unmanned aerial vehicle to contribute to the overall cooperative target to the greatest extent while completing the tasks of the unmanned aerial vehicle, improving the task execution efficiency, the task coverage range and the response speed, and reducing the resource waste. According to the invention, through information exchange and instruction transmission among a plurality of unmanned aerial vehicles, abnormal conditions are processed in time and countermeasures are made, and real-time collaborative decision-making and dynamic optimization are realized, so that the autonomous capacity and adaptability of the unmanned aerial vehicles are improved, and the safety and reliability of task execution are enhanced. In general, the unmanned aerial vehicle cooperative intelligent control and optimization system can improve cooperative performance, quickly adapt to environmental changes, maximize overall cooperative efficiency and improve task execution capacity through cooperative control and optimization among a plurality of unmanned aerial vehicles.
The following examples describe in detail the technical advantages of the various technical details of the present invention and their advantages.
Detailed Description
The following examples illustrate the invention in detail. The raw materials and the equipment used by the invention are conventional commercial products, and can be directly obtained through market purchase.
In the following description of embodiments, for purposes of explanation and not limitation, specific details are set forth, such as particular system architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Example 1
The main body of the invention develops the following technical modules: the system comprises a communication and instruction transmission module, an environment sensing and decision module and a cooperative motion and task division module, wherein a data model is constructed based on the modules, so that cooperative and stable work among a plurality of groups of unmanned aerial vehicles is realized, resource waste is reduced to the greatest extent through an optimized path planning algorithm, and cooperative motion among the plurality of unmanned aerial vehicles and the most task division are realized.
The communication and instruction transfer module utilizes an edge computing technology to place data processing and computing tasks on edge computing nodes which are closer to the unmanned aerial vehicle to form a distributed computing system, realize distributed storage and cooperative processing of data, establish a communication link with the unmanned aerial vehicle, process and analyze collected sensor data, generate control instructions and timely send the control instructions to the unmanned aerial vehicle, thereby avoiding performance bottlenecks of a single node, fully utilizing computing capabilities of a plurality of computing nodes, reducing dependence of the single unmanned aerial vehicle on communication network bandwidth, and improving real-time cooperative effect among the unmanned aerial vehicles.
The environment sensing and decision-making module comprises a plurality of sensors such as a high-definition camera, a laser radar, an ultrasonic sensor and the like, wherein the high-definition camera can be used for collecting video information and providing high-resolution and high-precision image information to identify and track the ground and a target; the laser radar can be used for generating point cloud data, performing three-dimensional modeling and obstacle detection on the surrounding environment, and is very important for improving the obstacle avoidance capability of the unmanned aerial vehicle; the ultrasonic sensor can be used for short-range detection and provides data such as distance, speed and the like of objects in the surrounding environment. By fusing the data of a plurality of sensors, a more complete and accurate environment model is established, and optimization such as data quality control and noise reduction is performed. The unmanned aerial vehicle perception effect can be improved, so that the unmanned aerial vehicle can better know the condition of the surrounding environment, and can execute tasks more accurately. Sensor data subjected to preprocessing, feature extraction and data analysis, such as image data acquired by a high-definition camera, are subjected to target detection and tracking by extracting features such as shape, size, color and texture of a target by adopting a traditional visual algorithm or a deep learning method, so that perception of surrounding environment is finished, pedestrians, vehicles, buildings and other objects in the surrounding environment can be understood more clearly, and the accuracy and reliability of unmanned aerial vehicle control are further improved. And by utilizing various data sources such as historical data, sensor data and model data, an environment model can be established, so that prediction is performed, and the efficiency and flexibility of unmanned aerial vehicle cooperative control are improved.
Under the condition that the target position, flight constraint such as minimum turning radius, maximum speed and environmental information factors are considered, the cooperative motion and task division module generates a smooth flight path for each unmanned aerial vehicle through a path planning algorithm, and an optimal path for enabling the unmanned aerial vehicle to reach a target point is calculated. And predicting the position and the speed of the unmanned aerial vehicle in a future period by using the motion model and the target track information of the unmanned aerial vehicle. In this way, possible conflicts can be found in time in the flight process, and corresponding avoidance strategies can be adopted. The dynamic obstacle avoidance algorithm performs obstacle detection and path adjustment by using machine learning or a traditional obstacle avoidance algorithm according to the real-time perceived obstacle information, so as to avoid collision with the obstacle and maintain higher flight safety. By constructing a data model and a data process, each unmanned aerial vehicle can independently make decisions and adjustments according to environmental information and task demands, and through cooperative control among a plurality of unmanned aerial vehicles, the unmanned aerial vehicles can cooperate and cooperate with each other while completing tasks.
On the basis, the project group, a third party manufacturer and college scientific researchers jointly develop a specific executable data process, which comprises the following steps:
(1) Building a state and action model: describing the state and the action of the unmanned aerial vehicle by using vectors in a limited-dimensional vector space, wherein n-dimensional vectors x_i= (x_1, x_2,., x_n) represent the state of the unmanned aerial vehicle i, and cover information including position, speed, pitch angle, roll angle and yaw angle of the unmanned aerial vehicle i in a three-dimensional space; let m-dimensional vector u_i= (u_1, u_2,) u_m represent the actions of unmanned aerial vehicle i, information including speed, angular velocity, acceleration;
(2) And (3) building a motion model: a mapping relation of R < n > R < m > -R < n > is set, the state of the unmanned aerial vehicle is changed to X_i (t+1) =F (X_i (t), U_i (t)) (1), and at a time t+1, the state of the unmanned aerial vehicle i is determined by the state X_i (t) at the time t and the action U_i (t);
(3) And (3) constructing an objective function: by constructing an objective function to find an optimal path from the start point to the end point and taking into account complex problems including risk and consumption, and in the categories of path length minimization and time of flight minimization, the objective function is set to minimize the total movement distance, expressed as: min Σd (x_i (t+1), x_i (t)). (2), wherein D: R n X R n R is a function of calculating the distance of movement between the two states, Σ represents the sum of all positions; the state X_i of the unmanned aerial vehicle i needs to meet a certain range, X_i (t) epsilon S, wherein S is a limited dimensional space, and meanwhile, the distance between the two unmanned aerial vehicles needs to be maintained within a range;
(4) And (3) constructing a path planning algorithm: the position of the drone is projected onto a coordinate system, and then the optimal solution is found independently in each dimension, specifically, for the kth iteration during each iteration: first find the point x_1 that minimizes the objective function at the 1 st dimension coordinate, then update x_1 (k+1) =x_1, so repeat the operation on each dimension (e.g., x_2, x_3, x_n) until the convergence condition is satisfied; defining a continuous and microobjective function to measure the degree of cooperation between unmanned aerial vehicles, calculating the gradient of the objective function with respect to the position parameters of the unmanned aerial vehicles, and then updating the parameters according to the negative gradient direction: for the kth iteration: first, calculating the gradient of an objective function at the current unmanned plane position x (k)Secondly updating the position of the unmanned aerial vehicleWhere α is the learning rate.
Example 2
In order to realize cooperative intelligent control and optimization of the unmanned aerial vehicle, the invention develops a device for matching the system, and the device at least comprises the following hardware technical modules: unmanned aerial vehicle fuselage, flight control device, battery management device, load equipment device.
Wherein the receiver equipped with the unmanned aerial vehicle body is used for receiving the wireless signal sent by the hot spot network. The receiver generally refers to a wireless communication module, such as a Wi-Fi module or a bluetooth module. The modules have the function of receiving wireless signals and can receive wireless data sent by other unmanned aerial vehicles within a specified range. And the control chip is responsible for controlling navigation, actions and tasks of the unmanned aerial vehicle. When the receiver receives the hot spot network information among the unmanned aerial vehicles, the control chip analyzes the information and triggers corresponding programs for processing according to preset rules and logic judgment. These programs may be pre-written or may be generated in real-time, depending on the specific application scenario and task requirements.
Wherein the flight control device comprises an inertial measurement unit, a flight controller, a motor controller and the like. The inertial measurement unit is a device integrated with sensors such as a gyroscope and an accelerometer and is used for measuring the angular velocity, acceleration and other motion state information of the unmanned aerial vehicle; measuring the angular speed of the unmanned aerial vehicle, namely the rotation speed of the unmanned aerial vehicle through a gyroscope, wherein an accelerometer is used for measuring the acceleration and the gesture of the unmanned aerial vehicle; by processing and calculating the sensor measurement data, the attitude information of the unmanned aerial vehicle can be obtained, including pitching, rolling, yaw angle and other state parameters such as speed and position of the unmanned aerial vehicle. The flight controller calculates the control quantity required by the unmanned aerial vehicle by receiving the data of the sensor and the flight instruction, sends a control signal to the flight control device, controls the movement and the gesture of the unmanned aerial vehicle, and adopts an embedded computer such as a microcontroller or an FPGA and the like to combine with the sensors such as a gyroscope, an accelerometer, a magnetometer and the like to perform data acquisition, signal processing and control calculation. The flight control device refers to hardware equipment in the unmanned aerial vehicle flight control system, and the control device receives control signals sent by the flight controller and controls the flight attitude and movement of the unmanned aerial vehicle by adjusting the rotating speed and the steering of the motor. Flight control devices typically require a quick response to the flight controller instructions to ensure stability and accuracy of the drone.
The load equipment device can provide stable power supply for the load equipment such as cameras, sensors, loads and the like. This may be achieved by an integrated battery pack or external power interface, ensuring that the load device is provided with continuous power support during flight. And simultaneously, a corresponding data transmission and control interface is provided, so that the unmanned aerial vehicle can receive data acquired by the load equipment in real time and process and control the data. This may be achieved by serial, ethernet or wireless communication, etc. to meet the data interaction requirements between the load device and the flight control means. Further, the load device may generate a certain amount of heat during operation, and thermal management is required to ensure that the load device can operate in a suitable temperature range. The load equipment device can adopt components such as a radiator, a fan or a temperature sensor to emit and monitor heat, so that the influence of overheating on the performance and the service life of the equipment is prevented. Considering the quick replacement and maintenance requirements of the load equipment. The design of the load device facilitates the removal and installation of the load device, easy access and maintenance of the device interface and cable. Meanwhile, the load equipment device is also provided with a pluggable modularized structure, so that the load equipment device can be quickly adapted when different types of load equipment are replaced. The load equipment device can integrate fault detection and automatic alarm mechanisms, and monitor and detect the working state of the load equipment in real time. When equipment faults or abnormal conditions are found, the device can automatically alarm and provide corresponding fault diagnosis information so that flight operators can take corresponding measures in time.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
In various embodiments, the hardware implementation of the technology may directly employ existing smart devices, including, but not limited to, industrial personal computers, PCs, smartphones, handheld standalone machines, floor stand-alone machines, and the like. The input device is preferably a screen keyboard, the data storage and calculation module adopts an existing memory, a calculator and a controller, the internal communication module adopts an existing communication port and protocol, and the remote communication module adopts an existing gprs network, a universal Internet and the like.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The functional units in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RandomAcces Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. Unmanned aerial vehicle cooperatees intelligent control and optimizing system, its characterized in that: the cooperative control system rapidly responds to and makes a decision on the rapidly-changing environment based on information exchange and instruction transmission among the plurality of unmanned aerial vehicles, realizes cooperative movement and task division among the plurality of unmanned aerial vehicles, and ensures the maximization of the overall cooperative efficiency.
2. The unmanned aerial vehicle cooperative intelligent control and optimization system according to claim 1, wherein: the cooperative control system comprises the following technical modules: a communication and instruction transmission module, an environment sensing and decision module, and a cooperative motion and task division module.
3. The unmanned aerial vehicle cooperative intelligent control and optimization system according to claim 2, wherein: the communication and instruction transfer module adopts binary coding to code unmanned plane control instructions, and meanwhile, a decoding algorithm is used at a receiving end to convert the coded instructions back to original instructions; using a wireless local area network as a communication protocol, and using a standard frequency band to establish a hot spot network between unmanned aerial vehicles; and automatically adjusting the data transmission rate according to the channel quality, and adjusting the time and the frequency of data transmission between the unmanned aerial vehicles by adopting an adaptive modulation method and a carrier sensing multiple access protocol, so as to reduce the collision and the interference between the unmanned aerial vehicles to the greatest extent.
4. The unmanned aerial vehicle cooperative intelligent control and optimization system according to claim 2, wherein: the environment sensing and decision module is provided with various sensors including a high-definition camera, a laser radar and an ultrasonic sensor, and the sensing accuracy and the robustness of the unmanned aerial vehicle to the surrounding environment are improved by fusing the data of the sensors and adopting a sensor fusion algorithm; and meanwhile, performing target detection and tracking on the sensor data subjected to preprocessing, feature extraction and data analysis through a computer vision algorithm, modeling and predicting the environment through a machine learning algorithm, and generating an adaptive path of each group of unmanned aerial vehicle through a planning algorithm and a task allocation algorithm.
5. The unmanned aerial vehicle cooperative intelligent control and optimization system according to claim 2, wherein: the collaborative motion and task division module generates a smooth flight path for each unmanned aerial vehicle through a path planning algorithm, and collision and obstacle avoidance are avoided by using a track prediction and dynamic obstacle avoidance algorithm and an obstacle detection and obstacle avoidance algorithm. Meanwhile, the cooperative motion and task division among a plurality of unmanned aerial vehicles are realized through the cooperative control strategy design based on a distributed control algorithm and a collective control algorithm.
6. The unmanned aerial vehicle cooperative intelligent control and optimization system according to claim 5, wherein: the method comprises the steps of constructing a data model and a data process for a motion and task division module of an unmanned aerial vehicle intelligent system, wherein the specific data process is as follows:
(1) Building a state and action model: describing the state and the action of the unmanned aerial vehicle by using vectors in a limited-dimensional vector space, wherein n-dimensional vectors x_i= (x_1, x_2,., x_n) represent the state of the unmanned aerial vehicle i, and cover information including position, speed, pitch angle, roll angle and yaw angle of the unmanned aerial vehicle i in a three-dimensional space; let m-dimensional vector u_i= (u_1, u_2,) u_m represent the actions of unmanned aerial vehicle i, information including speed, angular velocity, acceleration;
(2) And (3) building a motion model: a mapping relation of R n X R m- > R n is set, the state of the unmanned aerial vehicle is changed to X_i (t+1) =F (X_i (t), U_i (t)) (1), and at the time t+1, the state of the unmanned aerial vehicle i is determined by the state X_i (t) at the time t and the action U_i (t);
(3) And (3) constructing an objective function: by constructing an objective function to find an optimal path from the start point to the end point and taking into account complex problems including risk and consumption, and in the categories of path length minimization and time of flight minimization, the objective function is set to minimize the total movement distance, expressed as: min Σd (x_i (t+1), x_i (t)). (2), wherein D: R n X R n R is a function of calculating the distance of movement between the two states, Σ represents the sum of all positions; the state X_i of the unmanned aerial vehicle i needs to meet a certain range, X_i (t) epsilon S, wherein S is a limited dimensional space, and meanwhile, the distance between the two unmanned aerial vehicles needs to be maintained within a range;
(4) And (3) constructing a path planning algorithm: the position of the drone is projected onto a coordinate system, and then the optimal solution is found independently in each dimension, specifically, for the kth iteration during each iteration: first find the point x_1 that minimizes the objective function at the 1 st dimension coordinate, then update x_1 (k+1) =x_1, so repeat the operation on each dimension (e.g., x_2, x_3, x_n) until the convergence condition is satisfied; defining a continuous and microobjective function to measure the degree of cooperation between unmanned aerial vehicles, calculating the gradient of the objective function with respect to the position parameters of the unmanned aerial vehicles, and then updating the parameters according to the negative gradient direction: for the kth iteration: first, calculating the gradient of an objective function at the current unmanned plane position x (k)Secondly updating the position of the unmanned aerial vehicleWhere α is the learning rate.
7. A device for performing a coordinated intelligent control and optimization system for an unmanned aerial vehicle in a matched manner, for implementing the system of claim 1, characterized in that: the device at least comprises the following hardware technical modules: unmanned aerial vehicle fuselage, flight control device, battery management device, load equipment device.
8. The apparatus for the coordinated intelligent control and optimization system of an unmanned aerial vehicle of claim 7, wherein: the unmanned aerial vehicle body is provided with a receiver and a control chip to realize the triggering of corresponding programs for hot spot network information transmission among unmanned aerial vehicles.
9. The apparatus for the coordinated intelligent control and optimization system of an unmanned aerial vehicle of claim 7, wherein: the flying control device measures the gesture and the acceleration of the unmanned aerial vehicle by using the gyroscope and the accelerometer through the inertial measurement unit, controls the gesture and the motion of the unmanned aerial vehicle according to the set control algorithm through the flying controller, and adopts the gesture control algorithm to adjust the flying action in real time according to the sensor data so as to ensure the stability and the accuracy of the unmanned aerial vehicle.
10. The apparatus for the coordinated intelligent control and optimization system of an unmanned aerial vehicle of claim 8, wherein: the load equipment device reserves a slot position and an interface of the load equipment comprising a camera, a sensor and a load, and provides a stable platform and vibration isolation measures for the load equipment through a cradle head vibration prevention system, so that the load equipment can work under the condition of no interference.
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