CN109407704B - Intelligent unmanned aerial vehicle return control system - Google Patents
Intelligent unmanned aerial vehicle return control system Download PDFInfo
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- CN109407704B CN109407704B CN201811528106.3A CN201811528106A CN109407704B CN 109407704 B CN109407704 B CN 109407704B CN 201811528106 A CN201811528106 A CN 201811528106A CN 109407704 B CN109407704 B CN 109407704B
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- 230000009286 beneficial effect Effects 0.000 abstract description 2
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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Abstract
The utility model provides an unmanned aerial vehicle control system that navigates back of intelligence, includes information acquisition module, instruction receiving module, the planning module that navigates back and control module that navigates back, information acquisition module is used for taking off the back at unmanned aerial vehicle, acquires unmanned aerial vehicle's the coordinate of the point of departure coordinate and the coordinate of passing by the position point in real time, instruction receiving module is used for receiving the return instruction that comes from the ground station, when receiving return instruction, adopts improved ant colony algorithm to plan by the planning module that navigates back to arrive by the current position coordinate the optimal route of navigating back of the point of departure coordinate, the control module that navigates back is used for controlling unmanned aerial vehicle to navigate back along the optimal route of navigating back that plans. The invention has the beneficial effects that: through the coordinate information of the unmanned aerial vehicle route position point of real-time collection, adopt the optimal route of returning voyage of modified ant colony algorithm planning, control unmanned aerial vehicle and return voyage along optimal route of returning voyage, need not artificially to carry out remote control, realized the intellectuality that unmanned aerial vehicle returned voyage, improved unmanned aerial vehicle's work efficiency greatly.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicles, in particular to an intelligent unmanned aerial vehicle return control system.
Background
Along with the development of global navigation positioning technology, the application field of unmanned aerial vehicles is more and more extensive, the aspect of people's life has been related to, unmanned aerial vehicle not only can be used for the military at present, can also be used for in agricultural and other production trades, unmanned aerial vehicle function on the market is more single at present, the mode of flight is fixed, unmanned aerial vehicle adopts the route of returning to the air that just has planned when carrying out the mission and returning to the air when returning to the air according to taking off, perhaps returns to the air through manual operation, has increased the degree of difficulty of returning to the air. If the planning of unmanned aerial vehicle can be intelligent returns the route to if according to the good route of returning a journey of planning, will improve unmanned aerial vehicle's work efficiency greatly, bring the convenience for work and production.
Disclosure of Invention
Aiming at the problems, the invention aims to provide an intelligent unmanned aerial vehicle return control system.
The purpose of the invention is realized by the following technical scheme:
the utility model provides an unmanned aerial vehicle of intelligence control system that returns a journey, includes information acquisition module, instruction receiving module, returns a journey planning module and returns a journey control module, information acquisition module is used for taking off at unmanned aerial vehicle back, acquires unmanned aerial vehicle's the coordinate of the point of departure point and the coordinate of passing through the point of departure point in real time, instruction receiving module is used for receiving the return instruction that comes from the ground station, and when receiving return instruction, by returning a journey planning module and adopting the ant colony algorithm according to the current position coordinate of unmanned aerial vehicle that gathers, the coordinate of passing through the point of departure point and the coordinate of the point of departure point, plan by the current position arrive the optimal route of returning a journey of point, it is used for controlling unmanned aerial vehicle to return a journey along the optimal route of returning a journey that plans.
The beneficial effects created by the invention are as follows: the utility model provides an unmanned aerial vehicle control system that navigates back of intelligence, through the coordinate information of gathering unmanned aerial vehicle route position point in real time, adopts the optimal route of navigating back of improved ant colony algorithm planning, system control unmanned aerial vehicle along the optimal route of navigating back is rewound, need not artificially to carry out remote control, has realized the intellectuality that unmanned aerial vehicle navigated back, has improved unmanned aerial vehicle's work efficiency greatly, brings the convenience for work and production.
Drawings
The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a schematic structural view of the present invention;
reference numerals:
an information acquisition module 1; an instruction receiving module 2; a return planning module 3; a return control module 4; a return voyage determining module 5; and a return flight monitoring module 6.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the intelligent unmanned aerial vehicle return control system of this embodiment includes an information acquisition module 1, an instruction receiving module 2, a return planning module 3, and a return control module 4, where the information acquisition module 1 is configured to acquire coordinates of a take-off point and coordinates of a pass-by position point of an unmanned aerial vehicle in real time after the unmanned aerial vehicle takes off, the instruction receiving module 2 is configured to receive a return instruction from a ground station, and when the return instruction is received, the return planning module 3 plans an optimal return route from a current position coordinate to the take-off point according to the acquired current position coordinate of the unmanned aerial vehicle, the coordinates of the pass-by position point, and the coordinates of the take-off point by using an ant colony algorithm, and the return control module 4 is configured to control the unmanned aerial vehicle to return along the planned optimal return route.
Preferably, the optimal return route is the shortest return route from the current position to the departure point within the coordinate range of the route position point.
Preferably, the unmanned aerial vehicle landing system further comprises a return flight determining module 5, and after the unmanned aerial vehicle lands on the ground, the return flight determining module 5 sends a landing notice of the unmanned aerial vehicle to the ground station.
Preferably, still include the monitoring module that navigates back 6, the monitoring module that navigates back 6 is used for navigating back at unmanned aerial vehicle in-process, reports to the police promptly when monitoring that unmanned aerial vehicle the condition that deviates from the optimal route of navigating back appears.
This preferred embodiment provides an unmanned aerial vehicle control system that navigates back of intelligence, through the coordinate information of gathering unmanned aerial vehicle route position point in real time, adopts the optimal route of navigating back of improved ant colony algorithm planning, system control unmanned aerial vehicle along optimal route of navigating back need not artificially to carry out remote control, has realized the intellectuality that unmanned aerial vehicle navigated back, has improved unmanned aerial vehicle's work efficiency greatly, brings the convenience for work and production.
Preferably, the return flight planning module 3 plans an optimal return flight route from the current position coordinate to the departure point coordinate by using an ant colony algorithm, the number of the points of the unmanned aerial vehicle route position is set as n, the number of ants is set as m, the current position of the unmanned aerial vehicle is set as s, the departure point is set as q, and after the ant colony completes traversal of all the route position points, only when the searched route length d of the return flight route (s, q) is reachedsqSatisfy the requirement of (wherein, dbFor the optimal return path length in this traversal, dwThe worst return path length in this traversal), the pheromone concentration of the return path is updated, and the formula for updating the pheromone concentration of each segment of sub-path in the return path is as follows:
wherein (s, q) represents a path from the current position s to the departure point q, (i, j) represents a path from the position point i to the position point j,representing the pheromone concentration on the path (i, j) at time t,denotes the concentration of the pheromone, Δ τ, on the path (i, j) at time t +1ijThe sum of the pheromone concentrations left on the path (i, j) for an ant colony after one cycle, the pheromone concentration on the path (i, j) at the initial time being Represents the concentration of pheromone left on the path (i, j) after the kth ant completes one traversal, Q represents the total amount of pheromone released by the ant k after one traversal, DkThe path length searched for the kth ant, rho is pheromone volatilization factor, theta is adjustment parameter, andwherein Z isjNumber of branch paths at position j, ZiThe number of branch paths at position point i.
When the preferred embodiment is used for updating the global pheromone, only partial ants with relatively short paths are allowed to update the global pheromone, so that the searching process is effectively enhancedGuidance enables search of ants to be more concentrated in a neighborhood of a relatively short path, so that the convergence speed of the algorithm is improved; in addition, when pheromone updating is carried out on a relatively short path, overall information of the path and local information of the path are comprehensively considered, DkThe overall information of the path is reflected, the global searching capability of the algorithm is facilitated, the theta reflects the local information of the path, the pheromone amount on each sub-segment path is dynamically adjusted by utilizing the local information, and the pheromone concentration of the path with more branch paths is increased, so that the excessive concentration of the pheromones is avoided, and the algorithm is prevented from being premature.
Preferably, the pheromone concentration on the initial time path (i, j) isThenThe calculation formula of (2) is as follows:
wherein T is a pheromone concentration constant, dijIs the length of the path (i, j), di(max) is the maximum length of the branch path at position i, dj(max) is the maximum value of the length of the branch path at position j, ZjThe number of branch paths at position j, a and B are weight coefficients, and a + B is 1.
The preferred embodiment adopts a new initial pheromone distribution strategy, increases the initial pheromone concentration of the path with shorter length, thereby increasing the exploration capacity of the path with shorter length, improving the convergence rate of the algorithm, and in addition, increases the pheromone concentration of the path with more branch paths, thereby increasing the diversity of subsequent solutions, leading the algorithm not to be easy to fall into the local optimal solution, and improving the global search capacity of the algorithm.
Preferably, the return flight planning module 3 plans an optimal return flight route from the current position to the departure point by using an improved ant colony algorithm, in the routing process of the ant colony, the ant determines a position point to be transferred next according to the pheromone concentration and the local heuristic function between the position points of each path, and the strategy that the ant k transfers from the position point i to another position point j at the time t is set as follows:
in the formula (I), the compound is shown in the specification,probability of selecting position point j from position point i for ant k, allowedkIs the set of branch paths that ant k may select at position point i,indicates the pheromone concentration, η, on the path (i, j) at time tijRepresenting local heuristic functions, alpha and beta representing respectivelyAnd ηijDegree of influence on the selection of position points, dijIs the length of the path (i, j), djgThe path length from the position point j to the departure point g,is the average of the branch path lengths at location point i.
The preferred embodiment improves the heuristic function in the ant transfer strategy, and adopts a sectional local heuristic function to increase the probability of selecting local poor paths, thereby increasing the diversity of algorithm solutions and avoiding the defect that the algorithm is easy to fall into the local optimal solution.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. An intelligent unmanned aerial vehicle return control system is characterized by comprising an information acquisition module, an instruction receiving module, a return planning module and a return control module, wherein the information acquisition module is used for acquiring the coordinates of a flying point and the coordinates of a passing position point of an unmanned aerial vehicle in real time after the unmanned aerial vehicle takes off, the instruction receiving module is used for receiving a return instruction from a ground station, when the return instruction is received, an optimal return route from a current position to the flying point is planned by the return planning module according to the acquired current position coordinates, the coordinates of the passing position point and the coordinates of the flying point of the unmanned aerial vehicle by adopting an ant colony algorithm, the return control module is used for controlling the unmanned aerial vehicle to return along the planned optimal return route, the return planning module plans the optimal return route from the current position coordinates to the flying point coordinates by adopting an ant colony algorithm, setting the number of the unmanned aerial vehicle path position points as n, the number of the ants as m, the current position of the unmanned aerial vehicle as s, and the departure point as q, and after the ant colony completes traversal of all the path position points, only when the path length d of the searched return path (s, q) is dsqSatisfy the requirement ofThen, the pheromone concentration is updated on the return route, wherein dbFor the optimal return path length in this traversal, dwFor the worst return path length in this traversal, the formula for updating the pheromone concentration on each segment of sub-path in the return path is as follows:
wherein (s, q) represents a path from the current position s to the departure point q, (i, j) represents a path from the position point i to the position point j,representing the pheromone concentration on the path (i, j) at time t,denotes the concentration of the pheromone, Δ τ, on the path (i, j) at time t +1ijThe sum of the pheromone concentrations left on the path (i, j) for an ant colony after one cycle, the pheromone concentration on the path (i, j) at the initial time beingRepresents the concentration of pheromone left on the path (i, j) after the kth ant completes one traversal, Q represents the total amount of pheromone released by the ant k after one traversal, DkThe path length searched for the kth ant, rho is pheromone volatilization factor, theta is adjustment parameter, andwherein Z isjNumber of branch paths at position j, ZiThe number of branch paths at position point i; the pheromone concentration on the initial time path (i, j) isThenThe calculation formula of (2) is as follows:
wherein T is a pheromone concentration constant, dijIs the length of the path (i, j), di(max) is the maximum value of the length of the branch path at position point i, dj(max) is the maximum value of the length of the branch path at position j, ZjThe number of branch paths at position j, a and B are weight coefficients, and a + B is 1.
2. An intelligent unmanned aerial vehicle return control system as claimed in claim 1, wherein the optimal return route is the shortest return route from the current position to the departure point within the coordinate range of the approach position point.
3. The system of claim 2, further comprising a return determination module, wherein when the drone lands on the ground, the return determination module sends a notification of landing of the drone to the ground station.
4. The system according to claim 3, further comprising a return monitoring module, wherein the return monitoring module is configured to alarm when the unmanned aerial vehicle deviates from the optimal return path during the return of the unmanned aerial vehicle.
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| CN114115237B (en) * | 2021-11-03 | 2023-08-01 | 中国人民解放军陆军防化学院 | Multi-target radiation reconnaissance method based on path optimization |
| CN114326787B (en) * | 2021-12-03 | 2024-06-21 | 彩虹无人机科技有限公司 | Unmanned aerial vehicle autonomous return route planning method, electronic equipment and medium |
| CN114610045B (en) * | 2022-05-12 | 2023-01-06 | 南京铉盈网络科技有限公司 | Robot path planning method and system based on improved ant colony algorithm |
| CN118151670A (en) * | 2024-05-11 | 2024-06-07 | 长龙(杭州)信息技术有限公司 | Control method and system for emergency return of flight equipment |
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| CN107314772B (en) * | 2017-07-25 | 2020-05-15 | 哈尔滨工业大学（威海） | Unmanned aerial vehicle self-learning waypoint track flight method and system thereof |
| CN107806877A (en) * | 2017-10-11 | 2018-03-16 | 湖北工业大学 | A kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm |
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Effective date of registration: 20191129 Address after: 210000 Lishui Economic Development Zone, Nanjing City, Jiangsu Province Applicant after: Nanjing Lihang bionic Industry Research Institute Co., Ltd. Address before: 528000 K004, Sixth Floor, No. 1 Building, No. 117 Zhangcha Road, Chancheng District, Foshan City, Guangdong Province Applicant before: Foshan Shanchang Technology Co., Ltd. |