CN112306069A - Plant protection unmanned aerial vehicle elevation air line control optimization method - Google Patents
Plant protection unmanned aerial vehicle elevation air line control optimization method Download PDFInfo
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- 238000013507 mapping Methods 0.000 claims description 5
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- 239000003814 drug Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
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- 239000007788 liquid Substances 0.000 description 2
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/04—Control of altitude or depth
- G05D1/06—Rate of change of altitude or depth
- G05D1/0607—Rate of change of altitude or depth specially adapted for aircraft
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- G—PHYSICS
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- G05B11/00—Automatic controllers
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- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
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Abstract
The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a method for optimizing control of an elevation air route of a plant protection unmanned aerial vehicle, which comprises the following steps: acquiring current flight position data and next flight position data of the plant protection unmanned aerial vehicle; detecting whether the height change value of the next flight segment of the plant protection unmanned aerial vehicle exceeds a set height change value or not; if yes, entering a flight segment height segmentation processing program, setting a middle flight point as a next flight point, and executing the previous step; if not, executing the next step; calculating a range slope value according to the current waypoint position and the next waypoint position; loading the gradient value into a fuzzy PID controller, and acquiring a PID control value under the current gradient to enter the flight operation; and (4) replanning an altitude control waypoint when the operation altitude of the plant protection unmanned aerial vehicle changes by combining an altitude control algorithm and a fuzzy PID control algorithm, and loading the gradient change value into a fuzzy PID controller to generate an optimal flight PID control parameter group so as to ensure the altitude control precision of the plant protection unmanned aerial vehicle.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a method for optimizing control of an elevation air route of a plant protection unmanned aerial vehicle.
Background
Plant protection unmanned aerial vehicle carries out the during operation, need keep with the stable high distance of crops for the liquid medicine is guaranteed the liquid medicine to the prevention and cure effect of plant diseases and insect pests on evenly spraying crops. For this reason, the planned working line needs to be closely attached to the canopy envelope of the crop. However, in actual planting, the distribution topography of crops is not constant, the canopy grows unevenly, the set height needs to be changed frequently when the air route planning is caused by the condition, the plant protection unmanned aerial vehicle needs to frequently carry out the take-off and landing operations with different gradients for realizing the air route tracking, and the error of the height control precision of the plant protection unmanned aerial vehicle is directly caused to be larger.
To solve this problem, optimization is required from both the route planning and the route control. In the course planning, most of plant protection unmanned aerial vehicles on the market at present adopt a high-precision RTK mapping technology and a course planning technology, and utilize a reconstructed three-dimensional topographic map to plan the course and passively optimize the course. This method can provide good coverage of the crop envelope, but has no significant effect in the face of a highly sloping flight path.
In plant protection unmanned aerial vehicle control systems, a PID control algorithm is the most common control algorithm at present. The PID control algorithm uses the difference between the target value and the actual value as a control variable, and uses proportional, integral, and differential parameters to control the target value. PID is in plant protection unmanned aerial vehicle's actual control, and the promotion space to the response speed of unmanned aerial vehicle under the different slopes is great. Therefore, the improved PID control method combined with the high-precision RTK surveying and mapping route is designed, and has important significance for controlling and optimizing the altitude route of the unmanned aerial vehicle for plant protection.
Disclosure of Invention
Based on the above-mentioned shortcomings and drawbacks of the prior art, an object of the present invention is to solve at least one or more of the above-mentioned problems of the prior art, in other words, to provide a method for optimizing the elevation path control of a plant protection drone, which satisfies one or more of the above-mentioned needs.
In order to achieve the purpose, the invention adopts the following technical scheme:
a plant protection unmanned aerial vehicle elevation route control optimization method comprises the following steps:
s1, acquiring current navigation point position data and next navigation point position data of the plant protection unmanned aerial vehicle;
s2, detecting whether the altitude change value of the next flight segment of the plant protection unmanned aerial vehicle exceeds a set altitude change value; if yes, entering a flight segment height segmentation program, setting a middle flight point as a next flight point, and executing the previous step; if not, executing the next step;
s3, calculating a segment slope value according to the current waypoint position and the next waypoint position;
and S4, loading the slope value of the flight into a fuzzy PID controller, and acquiring a PID control value under the current slope so as to enter flight operation.
As a preferred scheme, the current waypoint position data is detected in real time by an RTK positioning system, and the next waypoint position data is generated by a three-dimensional mapping system of the plant protection unmanned aerial vehicle, and three-dimensional elevation route data is formed.
Preferably, the three-dimensional elevation pattern data includes horizontal data and altitude data.
Preferably, the step S2 specifically includes:
s201, reading horizontal data and height data of a current waypoint position in an RTK positioning system, reading horizontal data and height data of a next waypoint position, and reading a set height change value in a parameter list;
s202, setting a displacement section between the current waypoint position and the next waypoint position as a current waypoint section;
s203, calculating whether the height difference of the current flight section exceeds a set height change value or not according to the height of the current waypoint position and the height of the next waypoint position; if yes, executing S204; if not, go to S3;
and S204, calculating the displacement value in the middle of the height change of the current flight path, calculating the corresponding horizontal position, and combining the horizontal position and the horizontal position into the position of the next flight point.
Preferably, the step S204 specifically includes:
setting P1 as the current waypoint position, P2 as the next waypoint position, and a navigation section D12 between P1 and P2 as the current navigation section; the intermediate position H1A is taken, the horizontal position and the vertical position corresponding to the intermediate position H1A are calculated, new waypoint position data is generated, and the flight plan is inserted as the next waypoint position P1A.
Preferably, the high variation value in step S3 includes a horizontal displacement L12 and a vertical displacement H12, and if the flight slope value is i, i is H12/L12 × 100%.
Preferably, the fuzzy PID controller adopts a fuzzy PID controller with gradient as a variable.
Compared with the prior art, the invention has the beneficial effects that:
the invention combines the height control algorithm and the fuzzy PID control algorithm, replans the height control waypoint when the operation height of the plant protection unmanned aerial vehicle changes, generates the optimal flight PID control parameter group by utilizing the fuzzy PID controller according to the gradient change value, optimizes the response time, overshoot and the like of the height control of the plant protection unmanned aerial vehicle, and ensures the height control precision of the plant protection unmanned aerial vehicle.
Drawings
Fig. 1 is a flowchart of a plant protection unmanned aerial vehicle elevation route control optimization method according to a first embodiment of the present invention;
fig. 2 is a flow chart of a height segmentation process of a plant protection unmanned aerial vehicle elevation route control optimization method according to a first embodiment of the present invention;
fig. 3 is a schematic view of height segmentation processing of a plant protection unmanned aerial vehicle elevation route control optimization method according to a first embodiment of the present invention;
fig. 4 is a schematic diagram of gradient calculation in the plant protection unmanned aerial vehicle elevation route control optimization method according to the first embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention, the following description will explain the embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
The first embodiment is as follows:
as shown in fig. 1 to 4, the present embodiment provides a method for optimizing control of an elevation route of a plant protection unmanned aerial vehicle, which specifically includes the following steps:
s1, reading the current navigation point position data and the next navigation point position data of the plant protection unmanned aerial vehicle;
the current waypoint position data is from a real-time RTK positioning system resolving result, and the next waypoint position data is from recorded operation waypoint data in the plant protection unmanned aerial vehicle;
the position data included in the embodiment includes horizontal GPS data and elevation altitude data corrected by differential position correction data sent by a ground reference station through RTK in a real-time RTK positioning system solution result, and relative ground altitude data calculated from the elevation altitude data and altitude data at a takeoff point;
specifically, the recorded position data in the plant protection unmanned aerial vehicle is three-dimensional elevation route data generated by a three-dimensional mapping system, and the data comprises horizontal position data and elevation position data used in the method;
s2, detecting whether the altitude change value of the next flight segment of the plant protection unmanned aerial vehicle exceeds a set altitude change value; if yes, entering a segment height segmentation program, setting an intermediate waypoint as a next waypoint, and executing a step S1; if not, go to step S3;
the method specifically comprises the following steps:
s201, reading the horizontal position and the height position of the current waypoint position in RTK positioning, reading the data of the horizontal position and the height position of a next waypoint target, and reading a set height change value in a parameter list;
the height change value set in the parameter list in the step is a parameter value written in the parameter list after the plant protection unmanned aerial vehicle leaves a factory or is debugged, and the value determines the precision of height segmentation;
s202, setting a displacement section between the current position and the next waypoint position as a current navigation section;
s203, calculating whether the altitude difference of the current flight section exceeds a set altitude change value or not according to the current altitude position and the target altitude position, and if so, executing S204; if not, go to S3;
s204, if the current position exceeds the preset height, calculating a height change middle displacement value, calculating a corresponding horizontal position, and combining the corresponding horizontal position into a next waypoint position;
specifically, as shown in fig. 3, the calculation process is: p1 is a current operation waypoint, P2 is a next waypoint, the current flight section is a flight section D12 between P1 and P2, the horizontal displacement L12 and the vertical displacement H12 between the flight sections D12 are calculated, and whether the current H12 exceeds a maximum limit height displacement change value is monitored. If yes, the intermediate height H1A is taken, the horizontal position and the height position corresponding to the intermediate height are calculated, new flight point data are generated, and the flight plan is inserted as a flight point P1A.
S3, calculating the horizontal displacement and the height displacement of the current flight segment, and calculating the gradient change value of the flight segment;
specifically, as shown in fig. 4, the gradient i is defined as:
the segment D12 formed between the waypoint P1 and the waypoint P2, the ratio between the vertical height variation H12 and the projection length horizontal displacement L12 of the D12, namely
i＝H12/L12×100％
S4, loading the gradient into a fuzzy PID controller, acquiring a PID control value under the current gradient, and entering a flight segment operation;
specifically, the fuzzy PID controller in this step is a fuzzy PID controller using the gradient as a variable, and the control algorithm parameter is determined in the debugging by the specific parameter of the plant protection unmanned aerial vehicle of this model, and this fuzzy PID control can reduce the overshoot in the flight path control of the plant protection unmanned aerial vehicle, reduce the response time, and improve the dynamic performance of the system, compared with the conventional PID control algorithm.
The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.
Claims (7)
1. A plant protection unmanned aerial vehicle elevation route control optimization method is characterized by comprising the following steps:
s1, acquiring current navigation point position data and next navigation point position data of the plant protection unmanned aerial vehicle;
s2, detecting whether the altitude change value of the next flight segment of the plant protection unmanned aerial vehicle exceeds a set altitude change value; if yes, entering a flight segment height segmentation processing program, setting a middle flight point as a next flight point, and executing the previous step; if not, executing the next step;
s3, calculating a segment slope value according to the current waypoint position and the next waypoint position;
and S4, loading the slope value of the flight into a fuzzy PID controller, and acquiring a PID control value under the current slope so as to enter flight operation.
2. The method for optimizing control of an elevation air route of a plant protection unmanned aerial vehicle according to claim 1, wherein the current air route position data is detected in real time by an RTK positioning system; and generating the position data of the next waypoint by a three-dimensional mapping system of the plant protection unmanned aerial vehicle, and forming three-dimensional elevation route data.
3. The method of claim 2, wherein the three-dimensional elevation pattern data comprises horizontal data and elevation data.
4. The method for optimizing control over an elevation air path of a plant protection unmanned aerial vehicle according to claim 1, wherein the step S2 specifically comprises:
s201, reading horizontal data and height data of a current waypoint position in an RTK positioning system, reading horizontal data and height data of a next waypoint position, and reading a set height change value in a parameter list;
s202, setting a displacement section between the current waypoint position and the next waypoint position as a current waypoint section;
s203, calculating whether the height difference of the current flight section exceeds a set height change value or not according to the height of the current waypoint position and the height of the next waypoint position; if yes, executing S204; if not, go to S3;
and S204, calculating the middle position of the height change of the current flight path to generate the position of the next flight point.
5. The method for optimizing control over the elevation route of a plant protection unmanned aerial vehicle according to claim 4, wherein the step S204 is specifically as follows:
setting P1 as the current waypoint position, P2 as the next waypoint position, and a navigation section D12 between P1 and P2 as the current navigation section; the intermediate position H1A is taken, the horizontal position and the vertical position corresponding to the intermediate position H1A are calculated, new waypoint position data is generated, and the flight plan is inserted as the next waypoint position P1A.
6. The method as claimed in claim 1, wherein the altitude variation values in step S3 include a horizontal displacement L12 and a vertical displacement H12, and the slope value is i, i-H12/L12 × 100%.
7. The method for optimizing plant protection unmanned aerial vehicle elevation route control of claim 1, wherein the fuzzy PID controller adopts a fuzzy PID controller with gradient as a variable.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113040116A (en) * | 2021-03-12 | 2021-06-29 | 上海农林职业技术学院 | Citrus orchard insect-attack prevention system based on PID-PLPF control algorithm |
| CN113485411A (en) * | 2021-06-21 | 2021-10-08 | 安徽农业大学 | Three-dimensional route planning method for aerial accurate pesticide application |
| CN119803400A (en) * | 2024-11-28 | 2025-04-11 | 深圳联合飞机科技有限公司 | A method and device for adjusting the altitude of a drone route, and an electronic device, storage medium, and program product |
| CN119803399A (en) * | 2024-11-28 | 2025-04-11 | 深圳联合飞机科技有限公司 | A method, device and electronic device for detecting a homing route of a drone |
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