CN112396298A - Unmanned helicopter multi-machine cooperative task planning method - Google Patents
Unmanned helicopter multi-machine cooperative task planning method Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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Abstract
The invention discloses a multi-machine collaborative task planning method for an unmanned helicopter, which is used for developing multi-machine task planning by a ground measurement and control station or a command center, considering the requirements of task scenes and the scheduling of limited resources, combining the performance and the load performance of an unmanned helicopter platform, realizing the collaborative task planning of a plurality of unmanned helicopters, increasing the planning of multi-machine carried task loads and the autonomous avoidance function of multi-machine flight routes, improving the multi-machine task planning efficiency and meeting the requirements of multi-machine collaborative planning, trial flight and simulation verification of the unmanned helicopter.
Description
Technical Field
The invention relates to the technical field of command and control, relates to multi-machine cooperative task planning application similar to an unmanned helicopter, in particular to a multi-machine cooperative task planning method for an unmanned helicopter, and can be widely applied to the design process of other aircrafts.
Background
The multi-machine collaborative task planning technology is an important component of the modern national defense command and control technology and is the extension and promotion of the single-machine task planning technology. The method is combined with a data link communication technology and an artificial intelligence technology, and has wide application in many fields such as cluster collaborative planning, cluster collaborative simulation, cluster detection and intelligent obstacle avoidance. With the continuous improvement of the demand of people on the performance of collaborative task planning, how to rapidly and safely realize the multi-machine collaborative task planning based on the task scene and provide an effective planning route for the collaborative execution of tasks by a plurality of unmanned helicopters has become one of the research focuses of the unmanned helicopter command control technology.
The unmanned helicopter is an unmanned aircraft operated by radio remote control equipment, the ground monitoring station is a command control center of an unmanned helicopter system and is responsible for implementing multi-machine cooperative resource allocation and cooperative task planning of the unmanned helicopter according to the existing combat resources in a given task scene and in combination with the task requirements of the task scene, the task planning result is injected into the unmanned helicopter system, the implementation of the given task planning is completed through the flight of the unmanned helicopter, and the state monitoring and evaluation of the task are executed.
At present, a common task planning method for an unmanned helicopter is mainly used for executing flight route planning of a single machine or multiple machines by combining with a specific task flight area, and the defects of the task planning method mainly comprise the following steps: the requirements of the existing resources for the task scene are not considered; and the task load control of multiple cooperation machines is designed without combining the performance and the operation requirements of the task load equipment. Therefore, this method cannot meet the requirement of maximizing the synergy of users for task scenarios.
Disclosure of Invention
The invention aims to provide a multi-machine cooperative task planning method for an unmanned helicopter, which is used for solving the problem that the conventional multi-machine cooperative task planning for the unmanned helicopter cannot meet the cooperative efficiency maximization requirement of a user for a task scene.
In order to realize the task, the invention adopts the following technical scheme:
a multi-helicopter collaborative task planning method for an unmanned helicopter comprises the following steps:
step 1, setting information of a task scene, including a flying point, a task area, a no-flying area and a threat area;
step 2, according to the setting of a task scene, combining the unmanned helicopter platform, the load and the fuel condition, performing task resource evaluation, and calculating task resources required by the execution of the task scene;
step 3, according to the task resource calculation result, implementing task resource scheduling management;
step 4, judging whether the actual condition of the current task resource meets the requirement of the calculation result, if so, taking the calculation result of the task resource as the task planning input;
step 5, considering the flight performance and the load detection capability of the unmanned helicopter platform according to the terrain complexity of a mission area, a no-fly area and a threat area, setting the flight heights and obstacle avoidance spaces of all stages of the unmanned helicopter, and calculating the collaborative flight routes of the plurality of unmanned helicopters;
step 6, designing flight parameters of a flight route according to the flight performance of the unmanned helicopter and by combining task scene requirements and referring to the design logic of the flight control system of the unmanned helicopter;
step 7, aiming at flight safety detection of flight route facilities, carrying out optimization adjustment of a flight route according to a detection result, wherein the optimization adjustment comprises adjustment of longitude and latitude, flight height and flight speed of a waypoint;
step 8, judging whether the planned route passes the safety detection, if so, executing step 9, otherwise, executing step 7 again;
and 9, implementing the load task operation setting of the flight path, setting a control instruction of the airborne task equipment of the unmanned helicopter on the basis of the completed flight path, and completing the planning and design of the task load in the flight process.
Further, the implementing task resource scheduling management includes:
and distributing the number of unmanned helicopters, photoelectric loads, weapon ammunition and fuel charge required for executing the task.
Further, the step 4 further includes:
and if the actual condition of the current task resource does not meet the requirement of the calculation result, taking the actual condition of the current task resource as task planning input.
Further, the algorithm used for calculating the coordinated flight routes of the multiple unmanned helicopters is an A-x search algorithm.
Further, the flight parameters include flight height and flight speed.
Further, the flight safety detection of the flight route facility relates to flight height, flight speed, flight segment length, oil consumption evaluation and link visibility.
Further, the method is loaded in the form of a computer program in a memory of a computer, the computer comprising a processor and the memory, the computer program realizing the steps of the method when being executed by the processor.
Further, the method is loaded in a computer readable storage medium in the form of a computer program which, when executed by a processor, performs the steps of the method.
Compared with the prior art, the invention has the following technical characteristics:
the invention aims at the set task scene according to the existing combat resources and combines the task requirements of the task scene, realizes the multi-machine cooperative resource allocation and cooperative task planning of the unmanned helicopter, improves the resource efficiency required by the multi-machine cooperative task planning, increases the functions of flight route safety detection and task load planning superposition, and meets the user use requirements of the multi-machine cooperative task planning of the unmanned helicopter.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The invention provides a multi-machine cooperative task planning method for an unmanned helicopter, which is used for developing multi-machine task planning by a ground measurement and control station or a command center, considers the requirement of a task scene and the scheduling of limited resources, combines the performance and the load performance of an unmanned helicopter platform, realizes the cooperative task planning of a plurality of unmanned helicopters, increases the planning of multi-machine carrying task loads and the autonomous avoidance function of multi-machine flight routes, improves the multi-machine task planning efficiency, and meets the requirements of multi-machine cooperative planning, trial flight and simulation verification of the unmanned helicopter. Referring to fig. 1, the multi-helicopter collaborative mission planning method of the invention comprises the following steps:
step 1, setting information of a task scene, including a flying point, a task area, a no-flying area, a threat area and the like; in this embodiment, the flight control system includes 1 flight point, 1 mission area, a plurality of no-fly zones, and a plurality of threat zones.
Step 2, according to the setting of a task scene, combining the unmanned helicopter platform, the load and the fuel condition, performing task resource evaluation, and calculating task resources required by the execution of the task scene; for example, according to the distribution conditions and the position information of a task area, a no-fly area and a threat area in a task scene, the distance required by the unmanned helicopter to fly in the process of completing the task can be calculated, and the required fuel charge is obtained by combining the performance of the unmanned helicopter; and selecting weapon ammunition and the number according to the number and the attribute of the target to be destroyed, and simultaneously preparing corresponding photoelectric loads.
Step 3, according to the task resource calculation result, task resource scheduling management is implemented, and the number of unmanned helicopters, photoelectric loads, weapons and ammunition and the fuel charge required by task execution are distributed; according to the result obtained by calculation, resource allocation is firstly carried out, and N unmanned helicopters carrying corresponding loads and fuel oil are included.
And 4, judging whether the actual condition of the current task resource meets the requirement of the calculation result, if so, taking the calculation result of the task resource as task planning input, and if not, taking the actual condition of the current task resource as task planning input, and acquiring a task planning object and inputting the task planning object into the N unmanned helicopters. The actual condition of the current task resource refers to the actual task resource; when the task resource calculation is carried out in the steps 2 and 3, the task is completed by priority of the calculated result from the perspective of the task scene; however, the mission resources of the base, including the number of unmanned helicopters, the number of weapons and ammunition, etc., may not meet the demand of the calculation result, i.e., the actual situation may be less than the calculation result, in which case the actual situation is used as the mission planning input.
Step 5, considering the flight performance and the load detection capability of the unmanned helicopter platform according to the terrain complexity of a mission area, a no-fly area and a threat area, setting the flight heights and obstacle avoidance spaces of all stages of the unmanned helicopter, and calculating the collaborative flight routes of the plurality of unmanned helicopters; the calculation method may be various route planning algorithms in the prior art, such as an a-search algorithm or a modified a-search algorithm.
And 6, designing the flight height and the flight speed of a flight route according to the flight performance of the unmanned helicopter, combining the task scene requirements and referring to the design logic of the flight control system of the unmanned helicopter. For example, for an unmanned helicopter of a certain model, if the flight path of the unmanned helicopter passes through a mountain obstacle, the judgment needs to be carried out, and if the performance of the unmanned helicopter is enough to climb above the obstacle, the flight path of the unmanned helicopter can be planned to pass through the obstacle from above; otherwise the route may be designed to bypass the obstacle.
And 7, aiming at the flight safety detection of flight route facilities, relating to the flight height, the flight speed, the flight section length, the oil consumption evaluation, the link visibility and the like, and implementing the optimized adjustment of the flight route according to the detection result, including the adjustment of the longitude and latitude, the flight height and the flight speed of a waypoint. And sequentially carrying out safety detection on the flight route of the ith unmanned helicopter, and dynamically adjusting the longitude and latitude, the flight height and the flight speed of the waypoint. For example, after detecting an unmanned helicopter, if the fuel consumption is found to be greater than a predetermined plan, the current environmental information (wind speed, wind direction, etc.) can be referred to adjust the course and speed; or if detecting that some unmanned helicopter deviates from the flight path, carrying out the re-planning of the flight path.
Step 8, judging whether the planned route passes the safety detection, if so, executing step 9, otherwise, executing step 7 again;
step 9, implementing load task operation setting of the flight path, setting a control instruction of the unmanned helicopter airborne task equipment on the basis of the completed flight path, and completing planning and design of the task load in the flight process; for example, the waypoint task parameters of each unmanned helicopter can be designed in sequence, the corresponding tasks are executed after the corresponding waypoints are reached, and control instructions of relevant task equipment, such as photoelectric loads, weapons and the like, are set so as to facilitate control.
And step 10, completing the multi-machine cooperative task planning of the unmanned helicopter, ending the process and outputting a planning result.
In the scheme, the data required by the task scene is determined according to the user requirements. By analyzing the task scene environment input by the user and combining with the consideration of task requirements, reasonable scheduling and management calculation of the battle resources are implemented, and the use efficiency of the battle resources is optimized; the input is considered comprehensively by combining a task area, the flight performance of the unmanned helicopter and the load performance of the unmanned helicopter, and the multi-machine collaborative flight route planning is implemented; according to each flight stage of the unmanned helicopter for executing the task, the load operation process is combined, the task load operation in the planned flight route waypoint is added, and the task planning and superposition of the collaborative flight route is completed.
Example 1
The embodiment is an example of a multi-machine collaborative task planning system in a certain topic pre-research project, and the system is developed by using a Qt5.5.1 development tool and a C + + language based on a Windows 7 operating system. The system realizes the function of multi-machine cooperative task planning of the unmanned helicopter according to the method of the invention, and is applied to a certain unmanned helicopter laboratory simulation environment. The implementation process of the method of the present invention is described by taking the system as an example, and the implementation steps are as follows:
1) the user needs to set a task area, a plurality of no-fly areas, a threat area, a take-off point and a landing point.
2) And calculating the number of the unmanned helicopters and the number of the carried resources required by the task execution according to the size range of the task area.
3) Taking a resource calculation result (the number of the unmanned helicopters) as input, combining task information (a task area, a threat area, a no-fly area, a take-off point and a landing point), adopting an improved A-star search algorithm to calculate the collaborative flight routes of the multiple unmanned helicopters, and obtaining the flight route information (position information) of the ith unmanned helicopter.
4) And setting flight route information (flight height and flight speed information) of the ith unmanned helicopter according to the flight performance design of the whole flight stage of the unmanned helicopter and the requirement of a task scene.
5) And (3) detecting and dynamically adjusting flight path safety, and implementing the flight path safety detection and dynamic adjustment of the i-th unmanned helicopter:
a) according to the flight performance of the unmanned helicopter, safety detection of flight height and flight speed is implemented, and dynamic adjustment is carried out until the requirements are met;
b) according to the flight control logic of the unmanned helicopter, the length of a flight range is detected, and the flight range is dynamically adjusted until the flight range meets the requirements;
c) according to the transmission characteristics of the data link of the unmanned helicopter, carrying out visual communication detection on a flight route link, and dynamically adjusting to meet the requirements;
d) according to the flight oil consumption empirical data of the unmanned helicopter, carrying out oil consumption evaluation on a flight route, and dynamically adjusting to meet the requirements;
e) carrying out inter-aircraft cooperative obstacle avoidance evaluation with the flight routes which are safely detected and pass, and dynamically adjusting to meet the requirements;
6) implementing the load task operation setting of the flight route, setting a control instruction of the unmanned helicopter airborne task equipment on the basis of the completed flight route, and completing the planning and design of the task load in the flight process; and sequentially setting the waypoint task parameters of the ith unmanned helicopter.
7) And (5) finishing the task planning of all the unmanned helicopters, outputting a multi-machine collaborative planning result and finishing the process.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equally replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application, and are intended to be included within the scope of the present application.
Claims (8)
1. A multi-helicopter collaborative task planning method for an unmanned helicopter is characterized by comprising the following steps:
step 1, setting information of a task scene, including a flying point, a task area, a no-flying area and a threat area;
step 2, according to the setting of a task scene, combining the unmanned helicopter platform, the load and the fuel condition, performing task resource evaluation, and calculating task resources required by the execution of the task scene;
step 3, according to the task resource calculation result, implementing task resource scheduling management;
step 4, judging whether the actual condition of the current task resource meets the requirement of the calculation result, if so, taking the calculation result of the task resource as the task planning input;
step 5, considering the flight performance and the load detection capability of the unmanned helicopter platform according to the terrain complexity of a mission area, a no-fly area and a threat area, setting the flight heights and obstacle avoidance spaces of all stages of the unmanned helicopter, and calculating the collaborative flight routes of the plurality of unmanned helicopters;
step 6, designing flight parameters of a flight route according to the flight performance of the unmanned helicopter and by combining task scene requirements and referring to the design logic of the flight control system of the unmanned helicopter;
step 7, aiming at flight safety detection of flight route facilities, carrying out optimization adjustment of a flight route according to a detection result, wherein the optimization adjustment comprises adjustment of longitude and latitude, flight height and flight speed of a waypoint;
step 8, judging whether the planned route passes the safety detection, if so, executing step 9, otherwise, executing step 7 again;
and 9, implementing the load task operation setting of the flight path, setting a control instruction of the airborne task equipment of the unmanned helicopter on the basis of the completed flight path, and completing the planning and design of the task load in the flight process.
2. The unmanned helicopter multi-machine cooperative task planning method according to claim 1, wherein the implementation of task resource scheduling management includes:
and distributing the number of unmanned helicopters, photoelectric loads, weapon ammunition and fuel charge required for executing the task.
3. The unmanned helicopter multi-machine cooperative mission planning method of claim 1, wherein said step 4 further comprises:
and if the actual condition of the current task resource does not meet the requirement of the calculation result, taking the actual condition of the current task resource as task planning input.
4. The unmanned helicopter multi-machine collaborative mission planning method of claim 1, wherein the algorithm used to calculate the multiple unmanned helicopter collaborative flight paths is the a-x search algorithm.
5. The unmanned helicopter multi-machine cooperative mission planning method of claim 1, wherein the flight parameters include flight altitude, flight speed.
6. The unmanned helicopter multi-machine collaborative mission planning method of claim 1, wherein the flight safety detection for flight route facilities relates to flight altitude, flight speed, flight segment length, oil consumption evaluation and link visibility.
7. The unmanned helicopter multi-aircraft cooperative mission planning method of claim 1, wherein said method is loaded in a memory of a computer in the form of a computer program, said computer comprising a processor and said memory, wherein the computer program when executed by the processor implements the steps of said method.
8. The unmanned helicopter multi-aircraft cooperative mission planning method of claim 1, wherein said method is loaded in a computer readable storage medium in the form of a computer program that when executed by a processor implements the steps of said method.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114153156A (en) * | 2021-12-09 | 2022-03-08 | 北京航空航天大学 | Forest and grassland fire prevention and extinguishing oriented helicopter field refueling scheduling simulation system |
| CN115456486A (en) * | 2022-11-10 | 2022-12-09 | 深圳市道通智能航空技术股份有限公司 | Task planning method and device of cluster system and electronic equipment thereof |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004003680A2 (en) * | 2002-04-22 | 2004-01-08 | Neal Solomon | System, method and apparatus for automated collective mobile robotic vehicles used in remote sensing surveillance |
| CA2666889A1 (en) * | 2008-05-27 | 2009-11-27 | Wilfred P. So | System and method for multiple aircraft lifting a common payload |
| CN102566580A (en) * | 2011-12-27 | 2012-07-11 | 中国直升机设计研究所 | Unmanned helicopter flight track planning method |
| CN102929285A (en) * | 2012-11-16 | 2013-02-13 | 中国民用航空飞行学院 | Multi-target distribution and flight path planning method for multiple rescue helicopters |
| CN106873628A (en) * | 2017-04-12 | 2017-06-20 | 北京理工大学 | A kind of multiple no-manned plane tracks the collaboration paths planning method of many maneuvering targets |
| CN108762295A (en) * | 2018-02-09 | 2018-11-06 | 华南理工大学 | Integrated unmanned aerial vehicle control system based on software bus |
| CN108958285A (en) * | 2018-07-17 | 2018-12-07 | 北京理工大学 | It is a kind of that path planning method is cooperateed with based on the efficient multiple no-manned plane for decomposing thought |
| CN109558116A (en) * | 2018-10-29 | 2019-04-02 | 中国航空无线电电子研究所 | A kind of unrelated modeling method of open unmanned aerial vehicle platform |
-
2020
- 2020-11-03 CN CN202011213182.2A patent/CN112396298B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004003680A2 (en) * | 2002-04-22 | 2004-01-08 | Neal Solomon | System, method and apparatus for automated collective mobile robotic vehicles used in remote sensing surveillance |
| CA2666889A1 (en) * | 2008-05-27 | 2009-11-27 | Wilfred P. So | System and method for multiple aircraft lifting a common payload |
| CN102566580A (en) * | 2011-12-27 | 2012-07-11 | 中国直升机设计研究所 | Unmanned helicopter flight track planning method |
| CN102929285A (en) * | 2012-11-16 | 2013-02-13 | 中国民用航空飞行学院 | Multi-target distribution and flight path planning method for multiple rescue helicopters |
| CN106873628A (en) * | 2017-04-12 | 2017-06-20 | 北京理工大学 | A kind of multiple no-manned plane tracks the collaboration paths planning method of many maneuvering targets |
| CN108762295A (en) * | 2018-02-09 | 2018-11-06 | 华南理工大学 | Integrated unmanned aerial vehicle control system based on software bus |
| CN108958285A (en) * | 2018-07-17 | 2018-12-07 | 北京理工大学 | It is a kind of that path planning method is cooperateed with based on the efficient multiple no-manned plane for decomposing thought |
| CN109558116A (en) * | 2018-10-29 | 2019-04-02 | 中国航空无线电电子研究所 | A kind of unrelated modeling method of open unmanned aerial vehicle platform |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114153156A (en) * | 2021-12-09 | 2022-03-08 | 北京航空航天大学 | Forest and grassland fire prevention and extinguishing oriented helicopter field refueling scheduling simulation system |
| CN114153156B (en) * | 2021-12-09 | 2023-11-28 | 北京航空航天大学 | Helicopter field oiling dispatching simulation system for forest and grassland fire prevention and extinguishment |
| CN115456486A (en) * | 2022-11-10 | 2022-12-09 | 深圳市道通智能航空技术股份有限公司 | Task planning method and device of cluster system and electronic equipment thereof |
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