CN114489136A - Unmanned intelligent agent cluster formation control method - Google Patents
Unmanned intelligent agent cluster formation control method Download PDFInfo
- Publication number
- CN114489136A CN114489136A CN202210104502.3A CN202210104502A CN114489136A CN 114489136 A CN114489136 A CN 114489136A CN 202210104502 A CN202210104502 A CN 202210104502A CN 114489136 A CN114489136 A CN 114489136A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- obstacle
- position information
- time interval
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 20
- 230000004888 barrier function Effects 0.000 claims description 21
- 230000001133 acceleration Effects 0.000 claims description 12
- 239000003795 chemical substances by application Substances 0.000 claims description 10
- 230000002349 favourable effect Effects 0.000 abstract description 2
- 238000011217 control strategy Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a cluster formation control method for unmanned intelligent agents; calculating the expected speed between adjacent numbered unmanned aerial vehicles through a distributed control algorithm; acquiring obstacle position data of an obstacle through acquisition equipment, and determining a collision time interval of the unmanned aerial vehicle; whether the unmanned aerial vehicle enters the collision area or not is determined according to the collision time interval, when the unmanned aerial vehicle enters the collision area, corresponding obstacle avoidance measures are started, a conflict-free and feasible multi-agent task planning solution is achieved, a plurality of agent cluster members sense the directions of each other according to shared information in the movement process, automatic coordination is achieved, the safe distance among the agent cluster members in the cooperative movement process is guaranteed, meanwhile, the pace is achieved according to cluster tasks, a favorable array type is formed, and the advantages of the unmanned aerial vehicle cluster are exerted to a greater extent.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a cluster formation control method for unmanned aerial vehicles.
Background
In a plurality of applications such as target search and rescue, agricultural value guarantor, security protection monitoring, single unmanned aerial vehicle is difficult to satisfy the demand of operation on a large scale in efficiency and duration, and it is crucial to carry out formation operation through a plurality of unmanned aerial vehicles. Through the effective formation control strategy of the unmanned aerial vehicle, a plurality of problems in the application scene of large-area operation of the unmanned aerial vehicle can be effectively solved.
At present, most unmanned aerial vehicle formation control methods are used for formation control in a mode of obtaining global coordinates of unmanned aerial vehicles. Considering that in an area where the GPS cannot be used, the position information of each drone in the global coordinate system cannot be obtained, such a control method fails.
Disclosure of Invention
The invention aims to provide a cluster formation control method for unmanned intelligent agents, and aims to solve the technical problem that in the prior art, in areas where a GPS cannot be used, the position information of each unmanned aerial vehicle in a global coordinate system cannot be obtained, and the control method is invalid.
In order to achieve the purpose, the invention provides an unmanned intelligent agent cluster formation control method, which comprises the following steps:
a plurality of unmanned aerial vehicles are deployed in the area, and each unmanned aerial vehicle actively detects the position information of adjacent unmanned aerial vehicles;
numbering the unmanned aerial vehicles from front to back, and calculating the expected speed between adjacent numbered unmanned aerial vehicles through a distributed control algorithm so as to control the distance;
acquiring obstacle position data of an obstacle through acquisition equipment, and determining a collision time interval between the unmanned aerial vehicle and the obstacle through the position information;
and determining whether the unmanned aerial vehicle enters a collision area or not according to the collision time interval, and starting corresponding obstacle avoidance measures according to the relative position between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle enters the collision area.
The method comprises the following steps of deploying a plurality of unmanned aerial vehicles in an area, and actively detecting the position information of adjacent unmanned aerial vehicles by each unmanned aerial vehicle:
the inside laser scanner that is provided with of unmanned aerial vehicle, through laser scanner acquires adjacent unmanned aerial vehicle's positional information, positional information includes relative angle and relative distance.
The method comprises the following steps of deploying a plurality of unmanned aerial vehicles in an area, and actively detecting the position information of adjacent unmanned aerial vehicles by each unmanned aerial vehicle:
the scanning period of the laser scanner is 0.05S, and the position information is converted into vector coordinates.
Wherein numbering the unmanned aerial vehicles from front to back, calculating the expected speed between adjacent numbered unmanned aerial vehicles through a distributed control algorithm, and thereby controlling the distance in the steps of:
no. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle are the position information of the other side of initiative scanning respectively, calculate No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle's expectation speed through distributed control algorithm, thereby carry out speed control and make No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle keep the distance, rethread No. 3 unmanned aerial vehicle scans No. 2 unmanned aerial vehicle's position information, accomplish whole unmanned aerial vehicle's scanning and control in proper order, form chain form formation.
Wherein, obtaining the obstacle position data of the obstacle through the acquisition equipment, and through the step that the position information confirms the collision time interval of unmanned aerial vehicle and obstacle:
the acquisition equipment comprises a laser radar and a binocular camera, and the laser radar and the binocular camera jointly acquire the position information of the dynamic barrier.
Wherein, obtaining the obstacle position data of the obstacle through the acquisition equipment, and through the step that the position information confirms the collision time interval of unmanned aerial vehicle and obstacle:
determining a state equation met by a flight track of the dynamic obstacle through a quasi-linear variable parameter model, calculating the acceleration and the speed of the dynamic obstacle by using the position information, wherein the position information at least comprises a plurality of continuously acquired position point data;
calculating system parameters and thrust acceleration of the dynamic barrier according to the state equation, the acceleration of the dynamic barrier and the speed of the dynamic barrier, and performing inversion prediction on the flight track of the dynamic barrier according to the system parameters and the thrust acceleration of the dynamic barrier;
calculating the dynamic distance between the unmanned aerial vehicle and the dynamic barrier in any global dimension through the flight path, and determining a time interval with the dynamic distance smaller than the distance of the collision area as a local time interval in a calibration dimension;
and performing union operation on all the local time intervals to form a collision time interval.
Wherein, whether the unmanned aerial vehicle enters a collision area is determined according to the collision time interval, and when the unmanned aerial vehicle enters the collision area, corresponding obstacle avoidance measures are started according to the relative position between the unmanned aerial vehicle and the dynamic obstacle:
judging according to whether an intersection exists between the local time intervals under different dimensions; judging that the unmanned aerial vehicle enters a collision area when intersection occurs; and judging that the unmanned aerial vehicle does not enter a collision area when no intersection exists.
According to the unmanned intelligent agent cluster formation control method, unmanned aerial vehicles are numbered from front to back, and the expected speed between adjacent numbered unmanned aerial vehicles is calculated through a distributed control algorithm, so that the distance is controlled; acquiring obstacle position data of an obstacle through acquisition equipment, and determining a collision time interval between the unmanned aerial vehicle and the obstacle through the position information; whether the unmanned aerial vehicle enters a collision area is determined according to the collision time interval, when the unmanned aerial vehicle enters the collision area, corresponding obstacle avoidance measures are started according to the relative position between the unmanned aerial vehicle and the dynamic barrier, a conflict-free and feasible multi-agent task planning solution is realized, a plurality of agent cluster members sense the directions of each other according to shared information in the motion process, automatic coordination is realized, the safe distance between each other in the coordinated motion process is guaranteed, meanwhile, the pace consistency is realized according to cluster tasks, a favorable array type is formed, and the advantages of the unmanned aerial vehicle are exerted to a greater extent.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a method for controlling formation of a cluster of unmanned intelligent agents according to the present invention.
Detailed Description
Referring to fig. 1, the present invention provides a method for controlling a cluster formation of unmanned aerial vehicles, where a plurality of unmanned aerial vehicles are deployed in an area, and each unmanned aerial vehicle actively detects position information of adjacent unmanned aerial vehicles;
numbering the unmanned aerial vehicles from front to back, and calculating the expected speed between adjacent numbered unmanned aerial vehicles through a distributed control algorithm so as to control the distance;
acquiring obstacle position data of an obstacle through acquisition equipment, and determining a collision time interval between the unmanned aerial vehicle and the obstacle through the position information;
and determining whether the unmanned aerial vehicle enters a collision area or not according to the collision time interval, and starting corresponding obstacle avoidance measures according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
In step S1, a laser scanner is disposed inside the unmanned aerial vehicle, and position information of an adjacent unmanned aerial vehicle is obtained through the laser scanner, where the position information includes a relative angle and a relative distance; the scanning period of the laser scanner is 0.05S, and the position information is converted into vector coordinates.
In step S2, No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle are the active scanning position information of the other side respectively, calculate No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle 'S expectation speed through the distributed control algorithm, thereby carry out speed control and make No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle keep the distance, rethread No. 3 unmanned aerial vehicle scans No. 2 unmanned aerial vehicle' S position information, accomplish scanning and control of whole unmanned aerial vehicle in proper order, form chain form formation.
In step S3, the collecting device includes a laser radar and a binocular camera, and the laser radar and the binocular camera jointly acquire position information of the dynamic obstacle; determining a state equation met by a flight track of the dynamic obstacle through a quasi-linear variable parameter model, calculating the acceleration and the speed of the dynamic obstacle by using the position information, wherein the position information at least comprises a plurality of continuously acquired position point data; calculating system parameters and thrust acceleration of the dynamic barrier according to the state equation, the acceleration of the dynamic barrier and the speed of the dynamic barrier, and performing inversion prediction on the flight track of the dynamic barrier according to the system parameters and the thrust acceleration of the dynamic barrier; calculating the dynamic distance between the unmanned aerial vehicle and the dynamic barrier in any global dimension through the flight path, and determining a time interval with the dynamic distance smaller than the distance of the collision area as a local time interval in a calibration dimension; and performing union operation on all the local time intervals to form a collision time interval.
In step S4, the type of the obstacle is determined, when the obstacle is a stationary object, all the drones decelerate and brake to detour, and when the obstacle is a moving object, the determination is made according to whether there is an intersection between the local time intervals in different dimensions; judging that the unmanned aerial vehicle enters a collision area when intersection occurs; judging that the unmanned aerial vehicle does not enter a collision area when no intersection exists; after the unmanned aerial vehicle is avoided, the unmanned aerial vehicle returns to step S1 to be redeployed.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. An unmanned intelligent agent cluster formation control method is characterized by comprising the following steps:
a plurality of unmanned aerial vehicles are deployed in the area, and each unmanned aerial vehicle actively detects the position information of adjacent unmanned aerial vehicles;
numbering the unmanned aerial vehicles from front to back, and calculating the expected speed between adjacent numbered unmanned aerial vehicles through a distributed control algorithm so as to control the distance;
acquiring obstacle position data of an obstacle through acquisition equipment, and determining a collision time interval between the unmanned aerial vehicle and the obstacle through the position information;
and determining whether the unmanned aerial vehicle enters a collision area or not according to the collision time interval, and starting corresponding obstacle avoidance measures according to the relative position between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle enters the collision area.
2. The method of claim 1, wherein a plurality of drones are deployed in the area, and in the step of actively detecting the position information of neighboring drones by each drone:
the inside laser scanner that is provided with of unmanned aerial vehicle, through laser scanner acquires adjacent unmanned aerial vehicle's positional information, positional information includes relative angle and relative distance.
3. The method of claim 2, wherein a plurality of drones are deployed in the area, and in the step of actively detecting the position information of neighboring drones by each drone:
the scanning period of the laser scanner is 0.05S, and the position information is converted into vector coordinates.
4. The unmanned-agent-cluster-formation-control method as claimed in claim 3, wherein in the step of numbering drones from front to back, calculating desired velocities between adjacent numbered drones by means of a distributed-control algorithm, thereby controlling the distance, the step of:
no. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle are the position information of the other side of initiative scanning respectively, calculate No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle's expectation speed through distributed control algorithm, thereby carry out speed control and make No. 1 unmanned aerial vehicle and No. 2 unmanned aerial vehicle keep the distance, rethread No. 3 unmanned aerial vehicle scans No. 2 unmanned aerial vehicle's position information, accomplish whole unmanned aerial vehicle's scanning and control in proper order, form chain form formation.
5. The unmanned intelligent agent cluster formation control method of claim 4, wherein in the step of obtaining obstacle position data of an obstacle through a collection device and determining the collision time interval between the unmanned aerial vehicle and the obstacle through the position information:
the acquisition equipment comprises a laser radar and a binocular camera, and the laser radar and the binocular camera jointly acquire the position information of the dynamic barrier.
6. The unmanned intelligent agent cluster formation control method of claim 5, wherein in the step of obtaining obstacle position data of an obstacle through a collecting device and determining the collision time interval between the unmanned aerial vehicle and the obstacle through the position information:
determining a state equation met by a flight track of the dynamic obstacle through a quasi-linear variable parameter model, calculating the acceleration and the speed of the dynamic obstacle by using the position information, wherein the position information at least comprises a plurality of continuously acquired position point data;
calculating system parameters and thrust acceleration of the dynamic barrier according to the state equation, the acceleration of the dynamic barrier and the speed of the dynamic barrier, and performing inversion prediction on the flight track of the dynamic barrier according to the system parameters and the thrust acceleration of the dynamic barrier;
calculating the dynamic distance between the unmanned aerial vehicle and the dynamic barrier in any global dimension through the flight path, and determining a time interval with the dynamic distance smaller than the distance of the collision area as a local time interval in a calibration dimension;
and performing union operation on all the local time intervals to form a collision time interval.
7. The unmanned aerial vehicle cluster formation control method of claim 6, wherein in the step of determining whether the unmanned aerial vehicle enters the collision area according to the collision time interval, when the unmanned aerial vehicle enters the collision area, the corresponding obstacle avoidance measures are started according to the relative position between the unmanned aerial vehicle and the dynamic obstacle:
judging according to whether an intersection exists between the local time intervals under different dimensions; judging that the unmanned aerial vehicle enters a collision area when an intersection appears; and judging that the unmanned aerial vehicle does not enter a collision area when no intersection exists.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210104502.3A CN114489136A (en) | 2022-01-28 | 2022-01-28 | Unmanned intelligent agent cluster formation control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210104502.3A CN114489136A (en) | 2022-01-28 | 2022-01-28 | Unmanned intelligent agent cluster formation control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114489136A true CN114489136A (en) | 2022-05-13 |
Family
ID=81476712
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210104502.3A Pending CN114489136A (en) | 2022-01-28 | 2022-01-28 | Unmanned intelligent agent cluster formation control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114489136A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115599089A (en) * | 2022-08-02 | 2023-01-13 | 北京理工大学(Cn) | Multi-agent formation control method based on artificial potential field method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105589470A (en) * | 2016-01-20 | 2016-05-18 | 浙江大学 | Multi-UAVs distributed formation control method |
| CN109508035A (en) * | 2018-12-24 | 2019-03-22 | 南京邮电大学 | Multizone stagewise unmanned plane formation paths planning method based on distributed AC servo system |
| CN110502032A (en) * | 2019-08-31 | 2019-11-26 | 华南理工大学 | A kind of unmanned plane cluster formation flight method of Behavior-based control control |
| KR102300324B1 (en) * | 2021-04-30 | 2021-09-09 | 세종대학교산학협력단 | System and method for controlling formation flight based on anti-collision algorithm |
| CN113625762A (en) * | 2021-08-30 | 2021-11-09 | 吉林大学 | Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system |
-
2022
- 2022-01-28 CN CN202210104502.3A patent/CN114489136A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105589470A (en) * | 2016-01-20 | 2016-05-18 | 浙江大学 | Multi-UAVs distributed formation control method |
| CN109508035A (en) * | 2018-12-24 | 2019-03-22 | 南京邮电大学 | Multizone stagewise unmanned plane formation paths planning method based on distributed AC servo system |
| CN110502032A (en) * | 2019-08-31 | 2019-11-26 | 华南理工大学 | A kind of unmanned plane cluster formation flight method of Behavior-based control control |
| KR102300324B1 (en) * | 2021-04-30 | 2021-09-09 | 세종대학교산학협력단 | System and method for controlling formation flight based on anti-collision algorithm |
| CN113625762A (en) * | 2021-08-30 | 2021-11-09 | 吉林大学 | Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115599089A (en) * | 2022-08-02 | 2023-01-13 | 北京理工大学(Cn) | Multi-agent formation control method based on artificial potential field method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sato et al. | Multilayer lidar-based pedestrian tracking in urban environments | |
| CN113424079A (en) | Obstacle detection method, obstacle detection device, computer device, and storage medium | |
| CN107798699A (en) | Depth map estimation is carried out with stereo-picture | |
| CN112859873B (en) | Semantic laser-based mobile robot multi-stage obstacle avoidance system and method | |
| CN105892489A (en) | Multi-sensor fusion-based autonomous obstacle avoidance unmanned aerial vehicle system and control method | |
| US11380103B2 (en) | Coverage device, moving body, control device, and moving body distributed control program | |
| CN110825108B (en) | Cooperative anti-collision method for multiple tracking unmanned aerial vehicles in same airspace | |
| EP4246437A1 (en) | Method and apparatus for detecting motion information of target, and device and medium | |
| US20190086513A1 (en) | Method and apparatus for frame rate boosting in lidar array | |
| KR20220020804A (en) | Information processing devices and information processing methods, and programs | |
| DE112020002287T5 (en) | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD AND PROGRAM | |
| KR102601134B1 (en) | Method for estimating location of vehicle | |
| CN116540784B (en) | Unmanned system air-ground collaborative navigation and obstacle avoidance method based on vision | |
| EP3842885A1 (en) | Autonomous movement device, control method and storage medium | |
| US20210263533A1 (en) | Mobile object and method for controlling mobile object | |
| CN114489136A (en) | Unmanned intelligent agent cluster formation control method | |
| WO2022231879A1 (en) | Data driven resolution function derivation | |
| Fragoso et al. | Dynamically feasible motion planning for micro air vehicles using an egocylinder | |
| Lim et al. | Three-dimensional (3D) dynamic obstacle perception in a detect-and-avoid framework for unmanned aerial vehicles | |
| CN116627140A (en) | Multi-robot cooperative obstacle avoidance method and system | |
| JP6839642B2 (en) | Vehicle control devices, vehicle control methods, and programs | |
| JP7291015B2 (en) | Surrounding object recognition method and surrounding object recognition device | |
| WO2019239471A1 (en) | Driving assistance device, driving assistance system, and driving assistance method | |
| RU2783780C1 (en) | Method for adaptive control of movement characteristics of highly automated vehicle depending on level of uncertainty in assessment of surrounding situation | |
| US20240221186A1 (en) | Processing for machine learning based object detection using sensor data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231016 Address after: 314500 Room 405, Floor 4, Building 3, No. 550, Tongde Road, wutong Street, Tongxiang City, Jiaxing City, Zhejiang Province Applicant after: Zhejiang tongben Technology Co.,Ltd. Address before: 215000 room 206, 3 / F, building 5, phase II, Taicang University Science Park, No. 20, Jianxiong Road, science and education new town, Taicang City, Suzhou City, Jiangsu Province Applicant before: Suzhou Jiyuan System Technology Co.,Ltd. |
|
| TA01 | Transfer of patent application right |