CN109857134A - Unmanned plane tracking control system and method based on A*/minimum_snap algorithm - Google Patents
Unmanned plane tracking control system and method based on A*/minimum_snap algorithm Download PDFInfo
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Abstract
This application discloses a kind of unmanned plane tracking control systems and method based on A*/minimum_snap algorithm, this method comprises: obtaining the discrete coordinate of unmanned plane motion profile using A* algorithm；It is fitted using discrete coordinate of the minimum_snap algorithm to track, obtains the motion profile of planning.Present apparatus system applies singlechip technology, trajectory planning techniques, automatic control technology etc., realizes unmanned plane and independently carries out trajectory planning, in real time, efficiently control unmanned plane provides a kind of new mentality of designing and method.
Description
Technical Field
The application belongs to the field of intelligent control of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle trajectory control system and method based on an A x/minimum _ snap algorithm.
Background
With the development of automation control, computer information and network communication technologies, the unmanned aerial vehicle trajectory planning technology is gradually changed from initial manual trajectory planning to autonomous trajectory planning. The unmanned aerial vehicle autonomous trajectory planning seeks an optimal trajectory from an initial point to a terminal point in a barrier environment by taking shortest flight route and shortest flight time as criteria. Therefore, an intelligent algorithm is required to be applied to realize autonomous trajectory planning of the unmanned aerial vehicle in the obstacle environment.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle trajectory control system and method based on an A x/minimum _ snap algorithm, and the unmanned aerial vehicle trajectory control system and method can realize autonomous trajectory planning in an obstacle environment under the application background of a warehouse management unmanned aerial vehicle.
In order to achieve the purpose, the invention provides the following technical scheme:
the embodiment of the application discloses an unmanned aerial vehicle trajectory control method based on A star/minimum _ snap algorithm, which comprises the following steps:
obtaining discrete coordinate points of the unmanned aerial vehicle motion trail by adopting an A-x algorithm;
and fitting the discrete coordinate points of the track by utilizing a minimum _ snap algorithm to obtain the planned motion track.
Preferably, in the above method for controlling trajectory of an unmanned aerial vehicle based on the a × minimum _ snap algorithm, the method further includes:
and controlling the unmanned aerial vehicle to fly by adopting a fuzzy PID method by taking the attitude error of the unmanned aerial vehicle as input.
Preferably, in the above method for controlling trajectory of unmanned aerial vehicle based on a/minimum _ snap algorithm, the constraints of the positions of the points of the planned motion trajectory include at least maximum acceleration and maximum angular velocity.
Preferably, in the trajectory control method of the unmanned aerial vehicle based on the a × minimum _ snap algorithm, the unmanned aerial vehicle includes a control device, the control device imports an environment digital map containing obstacle information, and the a × algorithm performs path planning on the environment digital map and obtains discrete coordinate points of the trajectory.
Preferably, in the unmanned aerial vehicle trajectory control method based on the a × minimum _ snap algorithm, the control device uses a Stm32F1 chip.
Preferably, in the above method for controlling trajectory of unmanned aerial vehicle based on a/minimum _ snap algorithm, the minimum _ snap algorithm includes:
through the discrete coordinate points of the track, an unmanned aerial vehicle motion track point function is constructed:
wherein p is0,p1,…,pnFor the trajectory parameter, let p be [ p ]0,p1,…,pn]TObtaining:
p(t)＝[1，t，t2，…tn]p (3)
then at t0The corresponding constraint function at a time is expressed as:
position constraint
Speed constraint
Restraint of acceleration
The minimum _ snap track is the minimum jerk track, jerk is jerk, and the jerk represents the change rate of the stress; snap is jerk, which represents the change in the rate of change of the applied force,
constructing an optimization function:
adding the speed, acceleration, jerk and jerk constraints of the unmanned aerial vehicle at the moment t:
speed: v (t) ═ p' (t) ([ 0,1,2t,3 t)2,4t3,...,ntn-1]·p (8)
Acceleration: a (t) ═ p "(t) ([ 0,0,2,6t,12 t)2,...,n(n-1)tn-2]·p (9)
Acceleration:
acceleration rate of acceleration:
and solving the constructed optimization function by using the speed, acceleration, jerk and jerk constraints of the unmanned aerial vehicle at the time t to obtain the attitude information of each discrete coordinate point of the track.
The embodiment of the application also discloses an unmanned aerial vehicle trajectory control system based on A star/minimum _ snap algorithm, including:
an actuator;
the control device obtains discrete coordinate points of the motion trail of the unmanned aerial vehicle by adopting an A-x algorithm; and fitting the discrete coordinate points of the track by utilizing a minimum _ snap algorithm to obtain a planned motion track.
Preferably, in the trajectory control system of the unmanned aerial vehicle based on the a × minimum _ snap algorithm, the control device uses the attitude error of the unmanned aerial vehicle as an input and controls the actuator by using a fuzzy PID method.
Preferably, in the trajectory control system of the drone based on the a × minimum _ snap algorithm, the constraints of the positions of the points of the planned movement trajectory include at least a maximum acceleration and a maximum angular velocity.
Preferably, in the unmanned aerial vehicle trajectory control system based on the a × minimum _ snap algorithm, the control device imports an environment digital map containing obstacle information, and the a × algorithm performs path planning on the environment digital map and obtains discrete coordinate points of the trajectory.
Compared with the prior art, the invention has the advantages that:
1. after the digital map is imported into the unmanned aerial vehicle control device, the control device avoids obstacles by adopting an A-star algorithm and quickly and accurately obtains discrete track points of the optimal track of the unmanned aerial vehicle;
2. the invention utilizes a minimum _ snap algorithm to fit discrete track points obtained by processing of an A-star algorithm to obtain set values of acceleration, speed and position on each track point. And comparing the target attitude of the unmanned aerial vehicle with the current attitude of the unmanned aerial vehicle to obtain an attitude error. On the basis, the maximum acceleration and the maximum angular velocity of the unmanned aerial vehicle in each direction are added as constraints, and a more accurate track control signal of the unmanned aerial vehicle is obtained;
3. the invention inputs the attitude error of the unmanned aerial vehicle into the fuzzy PID controller for control, and realizes the control of the unmanned aerial vehicle to reach the target position.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be 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 described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic view of the overall structure of an unmanned aerial vehicle movement device according to an embodiment of the present invention;
FIG. 2 is a block diagram illustrating the control principle of the control device according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating trajectory flow control of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating the minimum _ snap algorithm according to an embodiment of the present invention;
FIG. 5 is a flow chart of fuzzy PID control according to an embodiment of the present invention.
Detailed Description
The present invention will be more fully understood from the following detailed description, which should be read in conjunction with the accompanying drawings. Detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Therefore, specific functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed embodiment.
Fig. 1 is an overall structure diagram of an unmanned aerial vehicle movement device in an embodiment of the present invention, and includes a control device 1 and an execution mechanism 2.
The control device 1 includes a Stm32F1 chip, an attitude detection sensor, and the like.
The actuator 2 includes: rotor, brushless motor, electricity accent etc.
The control device 1 can communicate with an upper computer, and the upper computer can be a handheld terminal, such as a remote controller, a mobile phone and the like, and can also be a computer.
In one embodiment, the upper computer sends the environment digital map with the processed obstacles to the control device of the unmanned aerial vehicle. When the control program of the unmanned aerial vehicle needs to be modified or updated, the program can be modified or updated through the download interface of the control device.
The host computer can realize that the motion is visual, include: and carrying out visual display on the attitude data of the unmanned aerial vehicle obtained by the measurement of the attitude sensor at the upper computer end.
Referring to fig. 2, the control device processes the trajectory planning data to obtain the acceleration and angular velocity information of each trajectory point. And obtaining the target attitude of the unmanned aerial vehicle through navigation calculation, comparing the target attitude with the current attitude to obtain an attitude error, and adding the maximum acceleration and the maximum angular velocity of the unmanned aerial vehicle in each direction as constraints on the basis. And the attitude error is sent to a fuzzy PID controller in a control device system, and the controller sends a control signal to an actuating mechanism, so that the high-precision track control of the unmanned aerial vehicle is realized.
As shown in fig. 3, the unmanned aerial vehicle trajectory process control includes:
s101: starting;
s102: initializing parameters;
s103: importing a digital map;
s104: a, algorithm global planning;
s105: carrying out trace fitting on a minimum _ snap algorithm;
s106: generating a control signal after information processing;
s107: judging whether the target point is reached;
s108: and (6) ending.
Specifically, a control device system receives a start instruction, firstly, parameter initialization is carried out, then, an environment digital map with processed obstacles is sent to a control device, an A-algorithm is adopted to carry out global planning on the environment digital map with processed obstacles, a minimum _ snap algorithm is adopted to fit discrete coordinate points of tracks, on the basis, the maximum acceleration and the maximum angular velocity of the unmanned aerial vehicle in each direction are added as constraints, attitude information of the discrete coordinate points of each track is obtained, the attitude information is sent to a fuzzy PID controller to obtain a control signal, and accurate control of the unmanned aerial vehicle track is achieved.
In one embodiment, the a-star algorithm (a-star algorithm) comprises:
the core of the A-algorithm is:
F＝G+H (1)
wherein G is the moving cost from the initial point to the approach point; h is the pre-cost from the waypoint to the target point. When H is 0, a is Dijkstra algorithm, and commonly used H mainly includes manhattan, diagonal and euclidean distance. A comprises the following main steps:
1. calculating the cost of all adjacent units of the current unit;
2. add them to the active list;
3. finding the cell with the minimum total cost from the effective list, making the cell become the parent cell of the next iteration, and making the cell unavailable for the next cell with the minimum total cost for comparison;
4. putting the cell with the minimum current total cost into an invalid list so that the cell cannot be accessed in the next step;
5. and circulating the operation until the cell with the minimum cost is the target cell, and ending.
In one embodiment, as shown in fig. 4, the minimum _ snap algorithm flow diagram includes:
s201: starting;
s202: initial trajectory segmentation and time allocation;
s203: constructing an equality constraint equation;
s204: constructing an optimization function;
s205: and constructing inequality constraints.
Specifically, the control device receives discrete coordinate points of the track generated by the A-x algorithm, and constructs a motion track point function of the unmanned aerial vehicle:
wherein p is0,p1,…,pnFor the trajectory parameter, let p be [ p ]0,p1,…,pn]TObtaining:
p(t)＝[1,t,t2,…tn]p (3)
time allocation: and (3) assuming that the speed between each track point section meets the requirement of constant speed or trapezoidal speed change, and distributing the total time t according to the distance of each section.
Then at t0The corresponding constraint function at a time is expressed as:
position constraint
Speed constraint
Restraint of acceleration
The minimum _ snap track is the minimum jerk track, jerk is jerk, and the jerk represents the change rate of the stress; snap is jerk, which represents the change in the rate of change of the applied force,
according to the minimum _ snap trajectory planning algorithm, the minimization objective function is:
min f(p)＝min(p(4)(t))2
constructing an optimization function from the minimized objective function:
wherein,
adding the speed, acceleration, jerk and jerk constraints of the unmanned aerial vehicle at the moment t:
speed: v (t) ═ p' (t) ([ 0,1,2t,3 t)2,4t3,...,ntn-1]·p (8)
Acceleration: a (t) ═ p "(t) ([ 0,0,2,6t,12 t)2,...,n(n-1)tn-2]·p (9)
Acceleration:
acceleration rate of acceleration:
adding position, velocity, acceleration continuous equation constraints between adjacent track segments:
and solving the constructed optimization function by using the speed, acceleration, jerk and jerk of the unmanned aerial vehicle at the time t and continuous constraints of the position, the speed and the acceleration between adjacent track segments to obtain the attitude information of each track discrete coordinate point.
In one embodiment, as shown in fig. 5, the fuzzy PID control flow block diagram includes:
s301: inputting;
s302; a fuzzy controller;
s303: a PID controller;
s304: the object is controlled.
Based on A/minimum _ snap algorithm, on the basis of avoiding obstacles, the discrete coordinate points of the planned track are quickly and accurately obtained, the motion constraints of the maximum acceleration and the maximum angular velocity of the unmanned aerial vehicle are combined, the discrete coordinate points of the track are fitted, and the planned motion track is obtained. On the basis of the planned motion trail, the control of the motion trail of the unmanned aerial vehicle is realized by adopting fuzzy PID control.
In summary, the following steps:
(1) the method ensures the feasibility of the flight of the unmanned aerial vehicle by combining the A-algorithm and the minimum-snap algorithm and adding the maximum acceleration and the maximum angular velocity of the unmanned aerial vehicle in each direction as constraints;
(2) the A-algorithm and the minimum-snap algorithm are combined, on the basis that the A-algorithm obtains discrete path coordinate points of the track, the track is segmented, time distribution is carried out according to the number of the track points, an optimization function is constructed, and the planned motion track including the acceleration, the speed and the position parameters is obtained by resolving the optimization function. The requirements of the speed and the acceleration of the starting point and the end point, and the position and the speed of the track connection position are continuous are met, and meanwhile, the problems that path points planned by an A-star algorithm are sparse and unsmooth are solved;
(3) and fuzzy PID control is adopted to realize online PID parameter adjustment. The hysteresis quantity and overshoot of the fuzzy PID control are obviously smaller than those of the classical PID control, and the fuzzy PID control has the characteristics of better rapidity, accuracy, robustness and the like.
The aspects, embodiments, features and examples of the present invention should be considered as illustrative in all respects and not intended to be limiting of the invention, the scope of which is defined only by the claims. Other embodiments, modifications, and uses will be apparent to those skilled in the art without departing from the spirit and scope of the claimed invention.
The use of headings and sections in this application is not meant to limit the invention; each section may apply to any aspect, embodiment, or feature of the disclosure.
Throughout this application, where a composition is described as having, containing, or comprising specific components or where a process is described as having, containing, or comprising specific process steps, it is contemplated that the composition of the present teachings also consist essentially of, or consist of, the recited components, and the process of the present teachings also consist essentially of, or consist of, the recited process steps.
In this application, where an element or component is referred to as being included in and/or selected from a list of recited elements or components, it is understood that the element or component can be any one of the recited elements or components and can be selected from a group consisting of two or more of the recited elements or components. Moreover, it should be understood that elements and/or features of the compositions, apparatus, or methods described herein may be combined in various ways, whether explicitly described or implicitly described herein, without departing from the spirit and scope of the present teachings.
Unless specifically stated otherwise, use of the terms "comprising", "including", "having" or "having" is generally to be understood as open-ended and not limiting.
The use of the singular herein includes the plural (and vice versa) unless specifically stated otherwise. Furthermore, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. In addition, where the term "about" is used before a quantity, the present teachings also include the particular quantity itself unless specifically stated otherwise.
It should be understood that the order of steps or the order in which particular actions are performed is not critical, so long as the teachings of the invention remain operable. Further, two or more steps or actions may be performed simultaneously.
It is to be understood that the figures and descriptions of the present invention have been simplified to illustrate elements that are relevant for a clear understanding of the present invention, while eliminating, for purposes of clarity, other elements. However, those skilled in the art will recognize that these and other elements may be desirable. However, because such elements are well known in the art, and because they do not facilitate a better understanding of the present invention, a discussion of such elements is not provided herein. It should be understood that the figures are presented for illustrative purposes and not as construction diagrams. The omission of details and modifications or alternative embodiments is within the scope of one skilled in the art.
It is to be understood that in certain aspects of the invention, a single component may be replaced by multiple components and that multiple components may be replaced by a single component to provide an element or structure or to perform a given function or functions. Except where such substitution would not operate to practice a particular embodiment of the invention, such substitution is considered within the scope of the invention.
While the invention has been described with reference to illustrative embodiments, it will be understood by those skilled in the art that various other changes, omissions and/or additions may be made and substantial equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, unless specifically stated any use of the terms first, second, etc. do not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another.
Claims (10)
1. An unmanned aerial vehicle trajectory control method based on A star/minimum _ snap algorithm is characterized by comprising the following steps:
obtaining discrete coordinate points of the unmanned aerial vehicle motion trail by adopting an A-x algorithm;
and fitting the discrete coordinate points of the track by utilizing a minimum _ snap algorithm to obtain the planned motion track.
2. The method of claim 1, further comprising:
and controlling the unmanned aerial vehicle to fly by adopting a fuzzy PID method by taking the attitude error of the unmanned aerial vehicle as input.
3. The method of claim 1, wherein the constraints on the positions of the points of the planned trajectory include at least maximum acceleration and maximum angular velocity.
4. The trajectory control method of the unmanned aerial vehicle based on the a x/minimum _ snap algorithm according to claim 1, wherein the unmanned aerial vehicle comprises a control device, the control device imports an environment digital map containing obstacle information, and the a x algorithm performs path planning on the environment digital map and obtains discrete coordinate points of the trajectory.
5. The method of claim 4, wherein the control device uses Stm32F1 chip.
6. The method of claim 1, wherein the minimum _ snap algorithm comprises:
through the discrete coordinate points of the track, an unmanned aerial vehicle motion track point function is constructed:
wherein p is0,p1,…,pnFor the trajectory parameter, let p be [ p ]0,p1,…,pn]TObtaining:
p(t)＝[1,t,t2,…tn]p (3)
then at t0The corresponding constraint function at a time is expressed as:
position constraint
Speed constraint
Restraint of acceleration
The minimum _ snap track is the minimum jerk track, jerk is jerk, and the jerk represents the change rate of the stress; snap is jerk, which represents the change in the rate of change of the applied force,
constructing an optimization function:
adding the speed, acceleration, jerk and jerk constraints of the unmanned aerial vehicle at the moment t:
speed: v (t) ═ p' (t) ([ 0,1,2t,3 t)2,4t3,...,ntn-1]·p (8)
Acceleration: a (t) ═ p "(t) ([ 0,0,2,6t,12 t)2,...,n(n-1)tn-2]·p (9)
Acceleration:
acceleration rate of acceleration:
and solving the constructed optimization function by using the speed, acceleration, jerk and jerk constraints of the unmanned aerial vehicle at the time t to obtain the attitude information of each discrete coordinate point of the track.
7. An unmanned aerial vehicle trajectory control system based on A star/minimum _ snap algorithm, comprising:
an actuator;
the control device obtains discrete coordinate points of the motion trail of the unmanned aerial vehicle by adopting an A-x algorithm; and fitting the discrete coordinate points of the track by utilizing a minimum _ snap algorithm to obtain a planned motion track.
8. The system of claim 7, wherein the trajectory control system of unmanned aerial vehicle based on A x/minimum _ snap algorithm comprises: the control device takes the attitude error of the unmanned aerial vehicle as input and adopts a fuzzy PID method to control the actuating mechanism.
9. The system of claim 7, wherein the trajectory control system of unmanned aerial vehicle based on A x/minimum _ snap algorithm comprises: the constraints on the positions of the points of the planned motion trajectory include at least a maximum acceleration and a maximum angular velocity.
10. The trajectory control system of unmanned aerial vehicle based on a x/minimum _ snap algorithm according to claim 1, wherein the control device imports an environment digital map containing obstacle information, and the a x algorithm performs path planning on the environment digital map and obtains discrete coordinate points of the trajectory.
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Application publication date: 20190607 |