CN114089780B - Urban space-oriented multi-rotor unmanned aerial vehicle path planning method - Google Patents

Urban space-oriented multi-rotor unmanned aerial vehicle path planning method Download PDF

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CN114089780B
CN114089780B CN202210073036.7A CN202210073036A CN114089780B CN 114089780 B CN114089780 B CN 114089780B CN 202210073036 A CN202210073036 A CN 202210073036A CN 114089780 B CN114089780 B CN 114089780B
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CN114089780A (en
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刘延杰
牛耕田
王超
陈忠
朱峰
韩东
刘程威
陆萍
刘佳
张原�
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CETC 28 Research Institute
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    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • 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
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Abstract

The invention discloses a multi-rotor unmanned aerial vehicle path planning method facing to urban space, which comprises the following specific steps: step 1, determining a multi-rotor unmanned aerial vehicle dynamic model; step 2, determining a multi-rotor unmanned aerial vehicle track optimization constraint condition and an optimization index; step 3, establishing a multi-rotor unmanned aerial vehicle dynamic model and barrier constraint linearization; step 4, establishing a multi-rotor unmanned aerial vehicle dynamic equation and discretization of constraint conditions; and 5, solving to complete the path planning of the multi-rotor unmanned aerial vehicle facing the urban space. Constraint condition setting is carried out towards urban environment to energy consumption is minimum to be used as the optimization index and establish many rotor unmanned aerial vehicle urban path planning equation, carries out problem conversion through linearization, discretization, carries out the problem through interior point method and solves, has reduced the problem complexity, has promoted computational efficiency. The application efficiency of many rotor unmanned aerial vehicle under urban environment has effectively been promoted.

Description

Urban space-oriented multi-rotor unmanned aerial vehicle path planning method
Technical Field
The invention relates to an unmanned aerial vehicle path planning method, in particular to a multi-rotor unmanned aerial vehicle path planning method facing to urban space.
Background
Rotor unmanned aerial vehicle is a many rotor crafts that can VTOL, independently hover, obtains extensive application in fields such as the photography of taking photo by plane, accurate agricultural, electric power are patrolled and examined, commodity circulation transportation. Under urban environment, high-rise building distributes densely to lead to many rotor unmanned aerial vehicle flight barriers many, and personnel vehicle flows and frequently leads to many rotor unmanned aerial vehicle to track the location target difficulty. In addition, multi-rotor unmanned aerial vehicles have characteristics of nonlinearity, coupling, multivariable, and the like, which all pose challenges to the accuracy and real-time performance of trajectory planning. Therefore, it is necessary to design a multi-rotor drone path planning algorithm oriented to urban space to improve the application efficiency in urban environment.
In the developed multi-rotor unmanned aerial vehicle track optimization algorithm, the obstacle avoidance constraint, the state quantity constraint and the control input quantity constraint of the multi-rotor unmanned aerial vehicle are considered in the prior art (see ZBOVOIN, Zong, Luhanchen and the like; based on the hp adaptive pseudo-spectrum method; China science: the technical science, 2017,47: 239-. The nonlinear programming problem is complex to solve, and the real-time performance of the trajectory programming is difficult to guarantee.
In the prior art (see the Optimal trajectory generation and robust flight-based tracking control of quadrotors), trajectory optimization solution is carried out based on a simplified dynamic model, and meanwhile, a B-plane method is adopted for carrying out trajectory tracking algorithm design. However, the accuracy of the optimization result is reduced based on the simplified dynamic model, and the selection of the complex optimization index further increases the calculation amount.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of providing a multi-rotor unmanned aerial vehicle path planning method facing to urban space aiming at the defects of the prior art.
In order to solve the technical problem, the invention discloses a multi-rotor unmanned aerial vehicle path planning method facing to urban space, which comprises the following steps:
step 1, determining a multi-rotor unmanned aerial vehicle dynamic model;
step 2, determining a multi-rotor unmanned aerial vehicle track optimization constraint condition and an optimization index;
step 3, establishing a multi-rotor unmanned aerial vehicle dynamic model and barrier constraint linearization;
step 4, establishing a multi-rotor unmanned aerial vehicle dynamic equation and discretization of constraint conditions;
and 5, solving to complete the path planning of the multi-rotor unmanned aerial vehicle facing the urban space.
In step 1, the multi-rotor unmanned aerial vehicle dynamic model is as follows:
Figure 555821DEST_PATH_IMAGE001
wherein x, y and z respectively represent three-axis position coordinates of the multi-rotor unmanned aerial vehicle,
Figure 650816DEST_PATH_IMAGE002
、
Figure 772356DEST_PATH_IMAGE003
and
Figure 548682DEST_PATH_IMAGE004
respectively representing the velocity components of the multi-rotor unmanned aerial vehicle along three axes,
Figure 173698DEST_PATH_IMAGE005
、
Figure 705174DEST_PATH_IMAGE006
and
Figure 508086DEST_PATH_IMAGE007
respectively representing the derivative of the three-axis position coordinates with respect to time,
Figure 150420DEST_PATH_IMAGE008
、
Figure 629943DEST_PATH_IMAGE009
and
Figure 332320DEST_PATH_IMAGE010
respectively representing second derivatives of the three-axis position coordinates with respect to time;
Figure 897293DEST_PATH_IMAGE011
、
Figure 15422DEST_PATH_IMAGE012
and
Figure 113566DEST_PATH_IMAGE013
respectively representing the pitch angle, the roll angle and the yaw angle of the multi-rotor unmanned aerial vehicle, p, q and r respectively representing the pitch angle speed, the roll angle speed and the yaw angle speed of the multi-rotor unmanned aerial vehicle,
Figure 986844DEST_PATH_IMAGE014
、
Figure 39114DEST_PATH_IMAGE015
and
Figure 960933DEST_PATH_IMAGE016
representing the first derivative of the attitude angle with respect to time,
Figure 415048DEST_PATH_IMAGE017
、
Figure 459228DEST_PATH_IMAGE018
and
Figure 700591DEST_PATH_IMAGE019
representing a second derivative of the attitude angle with respect to time;
Figure 222839DEST_PATH_IMAGE020
、
Figure 797040DEST_PATH_IMAGE021
and
Figure 746541DEST_PATH_IMAGE022
representing the rotary inertia of the multi-rotor unmanned aerial vehicle corresponding to three shafts; m represents the mass of the multi-rotor drone,
Figure 711086DEST_PATH_IMAGE023
representing the lift action point, namely the distance from the center of the propeller of the multi-rotor unmanned aerial vehicle to the center of mass;
Figure 302605DEST_PATH_IMAGE024
、
Figure 167530DEST_PATH_IMAGE025
、
Figure 287933DEST_PATH_IMAGE026
and
Figure 67670DEST_PATH_IMAGE027
represent many rotor unmanned aerial vehicle's four control input respectively, g represents acceleration of gravity.
The initial state constraints for the multi-rotor drone in step 2 are defined as follows:
Figure 931721DEST_PATH_IMAGE028
wherein,
Figure 152618DEST_PATH_IMAGE029
、
Figure 709501DEST_PATH_IMAGE030
and
Figure 710955DEST_PATH_IMAGE031
respectively represent the three-axis position coordinates of the multi-rotor unmanned aerial vehicle at the initial moment,
Figure 611653DEST_PATH_IMAGE032
、
Figure 952635DEST_PATH_IMAGE033
and
Figure 680420DEST_PATH_IMAGE034
respectively representing the velocity components of the multi-rotor drone along the x-axis, y-axis and z-axis at the initial moment,
Figure 903591DEST_PATH_IMAGE035
、
Figure 578286DEST_PATH_IMAGE036
and
Figure 836092DEST_PATH_IMAGE037
respectively represent the attitude angles of the multi-rotor unmanned aerial vehicle at the initial moment,
Figure 967734DEST_PATH_IMAGE038
、
Figure 881463DEST_PATH_IMAGE039
and
Figure 891007DEST_PATH_IMAGE040
respectively represent the attitude angular velocity of the multi-rotor unmanned aerial vehicle at the initial moment.
The terminal state constraint of the multi-rotor unmanned aerial vehicle in step 2 is defined as follows:
Figure 3320DEST_PATH_IMAGE041
wherein,
Figure 276169DEST_PATH_IMAGE042
the time of flight is represented as a function of time,
Figure 208353DEST_PATH_IMAGE043
、
Figure 785703DEST_PATH_IMAGE044
and
Figure 752522DEST_PATH_IMAGE045
respectively represent multiple rotor unmanned aerial vehicles
Figure 930694DEST_PATH_IMAGE042
The three-axis position coordinates of the time of day,
Figure 615753DEST_PATH_IMAGE046
、
Figure 232679DEST_PATH_IMAGE047
and
Figure 257267DEST_PATH_IMAGE048
respectively represent multiple rotor unmanned aerial vehicles
Figure 901612DEST_PATH_IMAGE042
The velocity components along the x-axis, y-axis and z-axis of the time,
Figure 73968DEST_PATH_IMAGE049
、
Figure 432268DEST_PATH_IMAGE050
and
Figure 373679DEST_PATH_IMAGE051
respectively represent multiple rotor unmanned aerial vehicles
Figure 955970DEST_PATH_IMAGE042
The attitude angle at the time of day,
Figure 350042DEST_PATH_IMAGE052
、
Figure 744989DEST_PATH_IMAGE053
and
Figure 806486DEST_PATH_IMAGE054
respectively represent multiple rotor unmanned aerial vehicles
Figure 559679DEST_PATH_IMAGE042
The attitude angular velocity at the moment.
The process constraints for the multi-rotor drone in step 2 are defined as follows:
Figure 378730DEST_PATH_IMAGE055
wherein,
Figure 875571DEST_PATH_IMAGE056
、
Figure 791574DEST_PATH_IMAGE057
、
Figure 417465DEST_PATH_IMAGE058
、
Figure 786130DEST_PATH_IMAGE059
、
Figure 86661DEST_PATH_IMAGE060
and
Figure 794854DEST_PATH_IMAGE061
respectively represents the minimum value of each state variable in the flight process of the multi-rotor unmanned plane,
Figure 889849DEST_PATH_IMAGE062
、
Figure 745809DEST_PATH_IMAGE063
、
Figure 584452DEST_PATH_IMAGE064
、
Figure 911266DEST_PATH_IMAGE065
、
Figure 442742DEST_PATH_IMAGE066
and
Figure 520419DEST_PATH_IMAGE067
respectively represent the maximum value of each state variable in the flight process of the multi-rotor unmanned aerial vehicle.
The control input constraints for the multi-rotor drone in step 2 are defined as follows:
Figure 100436DEST_PATH_IMAGE068
Figure 845538DEST_PATH_IMAGE069
Figure 984133DEST_PATH_IMAGE070
Figure 549107DEST_PATH_IMAGE071
wherein,
Figure 995132DEST_PATH_IMAGE072
、
Figure 798003DEST_PATH_IMAGE073
、
Figure 405702DEST_PATH_IMAGE074
and
Figure 457971DEST_PATH_IMAGE075
respectively, the maximum values of the four control input amounts.
In step 2, the obstacles of the multi-rotor unmanned aerial vehicle are constrained as follows:
Figure 143905DEST_PATH_IMAGE076
wherein,
Figure 863599DEST_PATH_IMAGE077
representing the center of the obstacle, a representing the distance of the edge of the obstacle from the center of the obstacle in the x-axis direction, b representing the distance of the edge of the obstacle from the center of the obstacle in the y-axis direction, z representing the distance of the edge of the obstacle from the center of the obstacle in the z-axis direction,
Figure 642200DEST_PATH_IMAGE078
a safety threshold representing a distance of the multi-rotor drone from an obstacle;
the optimization index is defined as follows:
Figure 916186DEST_PATH_IMAGE080
the multi-rotor unmanned aerial vehicle dynamic model and obstacle constraint linearization method in the step 3 is as follows:
step 3-1, defining new variables as follows:
Figure 641697DEST_PATH_IMAGE081
Figure 215898DEST_PATH_IMAGE082
；
step 3-2, the kinetic equation is written in a linearized form as follows:
Figure 165399DEST_PATH_IMAGE083
wherein, X*For any reference trajectory, X represents a state variable, and the expression is:
Figure 690796DEST_PATH_IMAGE084
u represents the control input, and the expression is:
Figure 954418DEST_PATH_IMAGE085
the expression of B is as follows:
Figure 648705DEST_PATH_IMAGE086
Figure 972370DEST_PATH_IMAGE087
the expression is as follows:
Figure 220949DEST_PATH_IMAGE088
Figure 350579DEST_PATH_IMAGE089
is composed of
Figure 132327DEST_PATH_IMAGE087
A Jacobian matrix of;
step 3-3, writing the obstacle constraints into a linearized form as follows:
Figure 626894DEST_PATH_IMAGE090
wherein,
Figure 628348DEST_PATH_IMAGE091
the expression is as follows:
Figure 764931DEST_PATH_IMAGE092
the discretization method of the dynamic equation and the constraint condition of the multi-rotor unmanned aerial vehicle in the step 4 is as follows:
step 4-1, dividing the time interval
Figure 371493DEST_PATH_IMAGE093
Dividing the obtained product into N equal parts, wherein the time step is h, and the expression is as follows:
Figure 332234DEST_PATH_IMAGE094
；
step 4-2, discretizing a kinetic equation according to an explicit fourth-order Runge Kutta formula as follows:
Figure 758667DEST_PATH_IMAGE095
wherein,
Figure 495679DEST_PATH_IMAGE096
and
Figure 691168DEST_PATH_IMAGE097
respectively indicate the state variables are in
Figure 324274DEST_PATH_IMAGE098
And a first
Figure 300321DEST_PATH_IMAGE099
The value of the node is taken as,
Figure 35100DEST_PATH_IMAGE100
、
Figure 881833DEST_PATH_IMAGE101
、
Figure 217000DEST_PATH_IMAGE102
and
Figure 149184DEST_PATH_IMAGE103
the expression is as follows
Figure 165681DEST_PATH_IMAGE104
Figure 132500DEST_PATH_IMAGE105
Figure 871524DEST_PATH_IMAGE106
Figure 494266DEST_PATH_IMAGE107
Wherein,
Figure 111192DEST_PATH_IMAGE108
and
Figure 932518DEST_PATH_IMAGE109
respectively indicate control inputs at
Figure 547170DEST_PATH_IMAGE098
And a first
Figure 453946DEST_PATH_IMAGE099
Taking a value from a node;
step 4-3, the terminal state constraint can be written as follows:
Figure 373098DEST_PATH_IMAGE110
；
step 4-4, the process constraints can be written in the form:
Figure 48930DEST_PATH_IMAGE111
wherein,
Figure 834484DEST_PATH_IMAGE112
；
and 4-5, controlling the input constraint to be written into the following form:
Figure 494135DEST_PATH_IMAGE113
Figure 390547DEST_PATH_IMAGE114
Figure 186464DEST_PATH_IMAGE115
wherein,
Figure 438192DEST_PATH_IMAGE112
；
step 4-6, the obstacle constraints are written as:
Figure 522823DEST_PATH_IMAGE116
Figure 19663DEST_PATH_IMAGE117
wherein,
Figure 935667DEST_PATH_IMAGE112
；
and 4-7, writing the optimization index into the following form:
Figure 63022DEST_PATH_IMAGE118
；
4-8, after linearization and discretization, summarizing the urban space-oriented multi-rotor unmanned aerial vehicle path planning problem into the following form:
Figure 166108DEST_PATH_IMAGE119
Figure 965174DEST_PATH_IMAGE120
wherein,
Figure 735684DEST_PATH_IMAGE112
。
solving the multi-rotor unmanned aerial vehicle path planning problem summarized in the step 4, wherein the process is as follows:
step 5-1, under the condition of the known initial state of the multi-rotor unmanned aerial vehicle, ordering
Figure 33941DEST_PATH_IMAGE121
，
Figure 889902DEST_PATH_IMAGE122
，
Figure 728545DEST_PATH_IMAGE123
，
Figure 556824DEST_PATH_IMAGE124
Obtaining an initial reference trajectory X by dynamics recursion*；
Step 5-2, the initial reference track X*The multi-rotor unmanned aerial vehicle path planning problem brought into the step 4 is solved by adopting an interior point method to obtain a new path, and the path is taken as a reference path X of the next calculation*；
And 5-3, obtaining an optimal solution after the obtained track is converged, namely obtaining the optimal track of the energy consumption of the multi-rotor unmanned aerial vehicle facing the urban space, and finishing the path planning of the multi-rotor unmanned aerial vehicle facing the urban space.
Has the advantages that:
the invention provides a multi-rotor unmanned aerial vehicle path planning method facing to urban space, which reduces the complexity of problem solving, improves the calculation efficiency of an algorithm and effectively improves the adaptability of a multi-rotor unmanned aerial vehicle to the urban environment by carrying out linearization and discretization on the original track problem.
Drawings
The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic view of a burnup optimal trajectory for a multi-rotor drone.
Fig. 3 is a schematic diagram of a burn-up optimum trajectory x-axis displacement curve for a multi-rotor drone.
Fig. 4 is a schematic view of a multi-rotor drone burnup optimum trajectory y-axis displacement curve.
Detailed Description
For a better understanding of the objects and advantages of the present invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples.
Example 1, as shown in fig. 1:
step one, determining a multi-rotor unmanned aerial vehicle dynamic model
Figure 321255DEST_PATH_IMAGE125
Wherein x, y and z respectively represent three-axis position coordinates of the multi-rotor unmanned aerial vehicle,
Figure 398933DEST_PATH_IMAGE126
、
Figure 41266DEST_PATH_IMAGE127
、
Figure 520789DEST_PATH_IMAGE128
respectively representing the velocity components of the multi-rotor unmanned aerial vehicle along three axes,
Figure 160849DEST_PATH_IMAGE129
、
Figure 725823DEST_PATH_IMAGE130
、
Figure 171848DEST_PATH_IMAGE131
respectively representing the derivative of the three-axis position coordinates with respect to time,
Figure 207674DEST_PATH_IMAGE132
、
Figure 815373DEST_PATH_IMAGE133
、
Figure 867643DEST_PATH_IMAGE134
respectively, represent the second derivative of the three-axis position coordinates with respect to time.
Figure 117359DEST_PATH_IMAGE135
、
Figure 305895DEST_PATH_IMAGE136
、
Figure 84495DEST_PATH_IMAGE137
Respectively represents the pitch angle, the roll angle and the yaw angle of the multi-rotor unmanned plane,
Figure 325858DEST_PATH_IMAGE138
、
Figure 848106DEST_PATH_IMAGE139
、
Figure 422307DEST_PATH_IMAGE140
respectively represents the pitch angle speed, the roll angle speed and the yaw angle speed of the multi-rotor unmanned aerial vehicle,
Figure 575071DEST_PATH_IMAGE141
、
Figure 601933DEST_PATH_IMAGE142
、
Figure 662292DEST_PATH_IMAGE143
representing the first derivative of the attitude angle with respect to time,
Figure 91000DEST_PATH_IMAGE144
、
Figure 975517DEST_PATH_IMAGE145
、
Figure 427358DEST_PATH_IMAGE146
representing the second derivative of the attitude angle with respect to time.
Figure 291409DEST_PATH_IMAGE147
、
Figure 574623DEST_PATH_IMAGE148
、
Figure 131506DEST_PATH_IMAGE149
Represent the inertia that many rotor unmanned aerial vehicle correspond three axles. m represents the mass of the multi-rotor drone,
Figure 867381DEST_PATH_IMAGE150
the lift action point, i.e. the distance from the center of the propeller of the multi-rotor unmanned aerial vehicle to the center of mass, is represented.
Figure 236920DEST_PATH_IMAGE151
、
Figure 374640DEST_PATH_IMAGE152
、
Figure 836846DEST_PATH_IMAGE153
、
Figure 325596DEST_PATH_IMAGE154
Represent many rotor unmanned aerial vehicle's four control input respectively, g represents acceleration of gravity.
Step two, determining a track optimization constraint condition and an optimization index
The initial state constraints are defined as follows:
Figure 531449DEST_PATH_IMAGE028
wherein,
Figure 726938DEST_PATH_IMAGE155
、
Figure 625624DEST_PATH_IMAGE156
、
Figure 100205DEST_PATH_IMAGE157
respectively represent the three-axis position coordinates of the multi-rotor unmanned aerial vehicle at the initial moment,
Figure 844171DEST_PATH_IMAGE158
、
Figure 956483DEST_PATH_IMAGE159
、
Figure 963753DEST_PATH_IMAGE160
respectively representing the velocity components of the multi-rotor unmanned aerial vehicle along the x-axis, the y-axis and the z-axis at the initial moment,
Figure 161516DEST_PATH_IMAGE161
、
Figure 974752DEST_PATH_IMAGE162
、
Figure 941571DEST_PATH_IMAGE163
respectively represent the attitude angles of the multi-rotor unmanned aerial vehicle at the initial moment,
Figure 883857DEST_PATH_IMAGE164
、
Figure 303337DEST_PATH_IMAGE165
、
Figure 920263DEST_PATH_IMAGE166
respectively represent the attitude angular velocity of the multi-rotor unmanned aerial vehicle at the initial moment.
The terminal state constraints are defined as follows:
Figure 741588DEST_PATH_IMAGE041
wherein,
Figure 356240DEST_PATH_IMAGE167
the time of flight is represented as a function of time,
Figure 263017DEST_PATH_IMAGE168
、
Figure 651010DEST_PATH_IMAGE169
、
Figure 858001DEST_PATH_IMAGE170
respectively represent multiple rotor unmanned aerial vehicles
Figure 643554DEST_PATH_IMAGE167
The three-axis position coordinates of the time of day,
Figure 37626DEST_PATH_IMAGE171
、
Figure 730776DEST_PATH_IMAGE172
、
Figure 526694DEST_PATH_IMAGE173
respectively represent multiple rotor unmanned aerial vehicles
Figure 279886DEST_PATH_IMAGE167
The velocity components along the x-axis, y-axis, and z-axis of the time,
Figure 863052DEST_PATH_IMAGE174
、
Figure 359892DEST_PATH_IMAGE175
、
Figure 275896DEST_PATH_IMAGE176
respectively represent multiple rotor unmanned aerial vehicles
Figure 934410DEST_PATH_IMAGE167
The attitude angle at the time of day,
Figure 303074DEST_PATH_IMAGE177
、
Figure 541289DEST_PATH_IMAGE178
、
Figure 75913DEST_PATH_IMAGE179
respectively represent multiple rotor unmanned aerial vehicles
Figure 905329DEST_PATH_IMAGE167
The attitude angular velocity at the moment.
The process constraints are defined as follows:
Figure 761289DEST_PATH_IMAGE180
wherein,
Figure 803195DEST_PATH_IMAGE181
、
Figure 162632DEST_PATH_IMAGE182
、
Figure 694107DEST_PATH_IMAGE183
、
Figure 771785DEST_PATH_IMAGE184
、
Figure 912654DEST_PATH_IMAGE185
、
Figure 595439DEST_PATH_IMAGE186
respectively represents the minimum value of each state variable in the flight process of the multi-rotor unmanned plane,
Figure 32237DEST_PATH_IMAGE187
、
Figure 862789DEST_PATH_IMAGE188
、
Figure 43235DEST_PATH_IMAGE189
、
Figure 580527DEST_PATH_IMAGE190
、
Figure 710198DEST_PATH_IMAGE191
、
Figure 28047DEST_PATH_IMAGE192
respectively represent the maximum value of each state variable in the flight process of the multi-rotor unmanned aerial vehicle.
The control input constraints are defined as follows:
Figure 746604DEST_PATH_IMAGE193
Figure 200720DEST_PATH_IMAGE194
Figure 182582DEST_PATH_IMAGE195
Figure 722148DEST_PATH_IMAGE196
wherein,
Figure 509975DEST_PATH_IMAGE197
、
Figure 317132DEST_PATH_IMAGE198
、
Figure 266633DEST_PATH_IMAGE199
、
Figure 231178DEST_PATH_IMAGE200
respectively, the maximum values of the four control input amounts.
The obstacles are constrained as follows:
Figure 822697DEST_PATH_IMAGE201
wherein,
Figure 985825DEST_PATH_IMAGE202
the center of the obstacle is shown, a is the distance from the center of the obstacle along the x-axis direction of the edge of the obstacle, b is the distance from the center of the obstacle along the y-axis direction of the edge of the obstacle, and z is the distance from the center of the obstacle along the z-axis direction of the edge of the obstacle.
Figure 106228DEST_PATH_IMAGE203
A safety threshold representing the distance of the multi-rotor drone from the obstacle.
The optimization index is defined as follows:
Figure 620385DEST_PATH_IMAGE079
step three, the dynamic model and the obstacle constraint linearization
The new variables are defined as follows:
Figure 186234DEST_PATH_IMAGE204
Figure 203868DEST_PATH_IMAGE205
the kinetic equation is written in linearized form as follows:
Figure 760752DEST_PATH_IMAGE206
wherein, X*Is an arbitrary reference trajectory. X represents a state variable, and the expression is as follows:
Figure 762206DEST_PATH_IMAGE207
u represents the control input, and the expression is:
Figure 429947DEST_PATH_IMAGE208
the expression of B is as follows:
Figure 505351DEST_PATH_IMAGE209
Figure 731670DEST_PATH_IMAGE210
the expression is as follows:
Figure 954841DEST_PATH_IMAGE211
Figure 160695DEST_PATH_IMAGE212
is composed of
Figure 418501DEST_PATH_IMAGE210
The jacobian matrix of.
The obstacle constraint is written in linearized form as follows:
Figure 317187DEST_PATH_IMAGE213
wherein,
Figure 965337DEST_PATH_IMAGE214
the expression is as follows:
Figure 974881DEST_PATH_IMAGE215
step four, discretizing a kinetic equation and a constraint condition
Time interval
Figure 851308DEST_PATH_IMAGE216
Dividing the obtained product into N equal parts, wherein the time step is h, and the expression is as follows:
Figure 655316DEST_PATH_IMAGE217
according to the explicit fourth-order Rungestota formula, discretizing the kinetic equation as follows:
Figure 56341DEST_PATH_IMAGE218
wherein,
Figure 603997DEST_PATH_IMAGE219
、
Figure 570816DEST_PATH_IMAGE220
respectively indicate the state variables are in
Figure 811305DEST_PATH_IMAGE221
And a first
Figure 496364DEST_PATH_IMAGE222
The value of the node is taken as,
Figure 549509DEST_PATH_IMAGE223
、
Figure 370834DEST_PATH_IMAGE224
、
Figure 47803DEST_PATH_IMAGE225
、
Figure 954579DEST_PATH_IMAGE226
the expression is as follows
Figure 47300DEST_PATH_IMAGE227
Figure 254291DEST_PATH_IMAGE228
Figure 69538DEST_PATH_IMAGE229
Figure 463610DEST_PATH_IMAGE230
Wherein,
Figure 360022DEST_PATH_IMAGE231
、
Figure 155939DEST_PATH_IMAGE232
respectively indicate control inputs at
Figure 174711DEST_PATH_IMAGE221
And a first
Figure 56079DEST_PATH_IMAGE222
And (6) taking a value of a node.
The terminal state constraint can be written as follows:
Figure 989138DEST_PATH_IMAGE110
the process constraints can be written in the form:
Figure 639562DEST_PATH_IMAGE233
wherein,
Figure 563656DEST_PATH_IMAGE234
。
the control input constraints can be written as follows:
Figure 197899DEST_PATH_IMAGE235
Figure 232852DEST_PATH_IMAGE236
Figure 737782DEST_PATH_IMAGE237
wherein,
Figure 832777DEST_PATH_IMAGE234
。
the obstacle constraint can be written as:
Figure 390535DEST_PATH_IMAGE116
Figure 963599DEST_PATH_IMAGE117
wherein,
Figure 854195DEST_PATH_IMAGE234
。
the optimization index can be written as follows:
Figure 323353DEST_PATH_IMAGE118
after linearization and discretization, the path planning problem of the multi-rotor unmanned aerial vehicle facing to the urban space can be summarized into the following form:
Figure 401031DEST_PATH_IMAGE119
Figure 308944DEST_PATH_IMAGE120
wherein,
Figure 490264DEST_PATH_IMAGE234
。
and step five, solving the problem.
Solving the multi-rotor unmanned aerial vehicle path planning problem facing the urban space in the step 4, wherein the concrete solving process is as follows:
1) under the condition of the known initial state of the multi-rotor unmanned aerial vehicle, the order
Figure 927062DEST_PATH_IMAGE238
，
Figure 492035DEST_PATH_IMAGE239
，
Figure 672481DEST_PATH_IMAGE240
，
Figure 272089DEST_PATH_IMAGE241
An initial reference trajectory X can be obtained by kinetic recursion*。
2) Mixing X*And (3) solving the path planning problem of the multi-rotor unmanned aerial vehicle brought into the fourth step by adopting an interior point method to obtain a new track, and taking the new track as a reference track X for next calculation*。
3) When the obtained track converges, an optimal solution is obtained, see fig. 2, that is, the energy consumption optimal track of the multi-rotor unmanned aerial vehicle facing the urban space, where an X-axis displacement curve is shown in fig. 3, and a Y-axis displacement curve is shown in fig. 4.
The invention provides a thought and a method for a path planning method of a multi-rotor unmanned aerial vehicle facing urban space, and a plurality of methods and ways for realizing the technical scheme are provided, the above description is only a preferred embodiment of the invention, and it should be noted that, for a person skilled in the art, a plurality of improvements and decorations can be made without departing from the principle of the invention, and the improvements and decorations should also be regarded as the protection scope of the invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (1)

1. A multi-rotor unmanned aerial vehicle path planning method facing urban space is characterized by comprising the following steps:
step 1, determining a multi-rotor unmanned aerial vehicle dynamic model;
step 2, determining a multi-rotor unmanned aerial vehicle track optimization constraint condition and an optimization index;
step 3, establishing a multi-rotor unmanned aerial vehicle dynamic model and barrier constraint linearization;
step 4, establishing a multi-rotor unmanned aerial vehicle dynamic equation and discretization of constraint conditions;
step 5, solving and completing the path planning of the multi-rotor unmanned aerial vehicle facing the urban space;
in step 1, the multi-rotor unmanned aerial vehicle dynamic model is as follows:
Figure 493741DEST_PATH_IMAGE001
wherein x, y and z respectively represent three-axis position coordinates of the multi-rotor unmanned aerial vehicle,
Figure 44546DEST_PATH_IMAGE002
、
Figure 763103DEST_PATH_IMAGE003
and
Figure 420480DEST_PATH_IMAGE004
respectively representing the velocity components of the multi-rotor unmanned aerial vehicle along three axes,
Figure 199081DEST_PATH_IMAGE005
、
Figure 4226DEST_PATH_IMAGE006
and
Figure 729736DEST_PATH_IMAGE007
respectively representing the derivative of the three-axis position coordinates with respect to time,
Figure 536893DEST_PATH_IMAGE008
、
Figure 751974DEST_PATH_IMAGE009
and
Figure 716518DEST_PATH_IMAGE010
respectively representing second derivatives of the three-axis position coordinates with respect to time;
Figure 42458DEST_PATH_IMAGE011
、
Figure 205586DEST_PATH_IMAGE012
and
Figure 591568DEST_PATH_IMAGE013
respectively representing the pitch angle, the roll angle and the yaw angle of the multi-rotor unmanned aerial vehicle, p, q and r respectively representing the pitch angle speed, the roll angle speed and the yaw angle speed of the multi-rotor unmanned aerial vehicle,
Figure 807523DEST_PATH_IMAGE014
、
Figure 671574DEST_PATH_IMAGE015
and
Figure 954788DEST_PATH_IMAGE016
representing the first derivative of the attitude angle with respect to time,
Figure 511671DEST_PATH_IMAGE017
、
Figure 450808DEST_PATH_IMAGE018
and
Figure 852971DEST_PATH_IMAGE019
representing a second derivative of the attitude angle with respect to time;
Figure 256270DEST_PATH_IMAGE020
、
Figure 685852DEST_PATH_IMAGE021
and
Figure 643444DEST_PATH_IMAGE022
representing the rotary inertia of the multi-rotor unmanned aerial vehicle corresponding to three shafts; m represents the mass of the multi-rotor drone,
Figure 380456DEST_PATH_IMAGE023
representing the lift action point, namely the distance from the center of the propeller of the multi-rotor unmanned aerial vehicle to the center of mass;
Figure 372683DEST_PATH_IMAGE024
、
Figure 209051DEST_PATH_IMAGE025
、
Figure 919519DEST_PATH_IMAGE026
and
Figure 693177DEST_PATH_IMAGE027
respectively representing four control input quantities of multi-rotor unmanned aerial vehicle, g representing gravityAcceleration;
the initial state constraints for the multi-rotor drone in step 2 are defined as follows:
Figure 539910DEST_PATH_IMAGE028
wherein,
Figure 812760DEST_PATH_IMAGE029
、
Figure 744944DEST_PATH_IMAGE030
and
Figure 823758DEST_PATH_IMAGE031
respectively represent the three-axis position coordinates of the multi-rotor unmanned aerial vehicle at the initial moment,
Figure 790577DEST_PATH_IMAGE032
、
Figure 467284DEST_PATH_IMAGE033
and
Figure 152343DEST_PATH_IMAGE034
respectively representing the velocity components of the multi-rotor drone along the x-axis, y-axis and z-axis at the initial moment,
Figure 769269DEST_PATH_IMAGE035
、
Figure 590595DEST_PATH_IMAGE036
and
Figure 939668DEST_PATH_IMAGE037
respectively represent the attitude angles of the multi-rotor unmanned aerial vehicle at the initial moment,
Figure 846444DEST_PATH_IMAGE038
、
Figure 267061DEST_PATH_IMAGE039
and
Figure 441428DEST_PATH_IMAGE040
respectively representing the attitude angular velocity of the multi-rotor unmanned aerial vehicle at the initial moment;
the terminal state constraint of the multi-rotor unmanned aerial vehicle in step 2 is defined as follows:
Figure 23719DEST_PATH_IMAGE041
wherein,
Figure 621054DEST_PATH_IMAGE042
the time of flight is represented as a function of time,
Figure 314203DEST_PATH_IMAGE043
、
Figure 110121DEST_PATH_IMAGE044
and
Figure 128893DEST_PATH_IMAGE045
respectively represent multiple rotor unmanned aerial vehicles
Figure 446479DEST_PATH_IMAGE042
The three-axis position coordinates of the time of day,
Figure 943320DEST_PATH_IMAGE046
、
Figure 859323DEST_PATH_IMAGE047
and
Figure 783417DEST_PATH_IMAGE048
respectively represent multiple rotor unmanned aerial vehicles
Figure 355343DEST_PATH_IMAGE042
The velocity components along the x-axis, y-axis and z-axis of the time,
Figure 390296DEST_PATH_IMAGE049
、
Figure 160805DEST_PATH_IMAGE050
and
Figure 754336DEST_PATH_IMAGE051
respectively represent multiple rotor unmanned aerial vehicles
Figure 344717DEST_PATH_IMAGE042
The attitude angle at the time of day,
Figure 386622DEST_PATH_IMAGE052
、
Figure 11639DEST_PATH_IMAGE053
and
Figure 277535DEST_PATH_IMAGE054
respectively represent multiple rotor unmanned aerial vehicles
Figure 620791DEST_PATH_IMAGE042
The attitude angular velocity at that moment;
the process constraints for the multi-rotor drone in step 2 are defined as follows:
Figure 997546DEST_PATH_IMAGE055
wherein,
Figure 178867DEST_PATH_IMAGE056
、
Figure 615664DEST_PATH_IMAGE057
、
Figure 180638DEST_PATH_IMAGE058
、
Figure 626662DEST_PATH_IMAGE059
、
Figure 226271DEST_PATH_IMAGE060
and
Figure 833970DEST_PATH_IMAGE061
respectively represents the minimum value of each state variable in the flight process of the multi-rotor unmanned plane,
Figure 620660DEST_PATH_IMAGE062
、
Figure 306594DEST_PATH_IMAGE063
、
Figure 760709DEST_PATH_IMAGE064
、
Figure 804889DEST_PATH_IMAGE065
、
Figure 78875DEST_PATH_IMAGE066
and
Figure 804386DEST_PATH_IMAGE067
respectively representing the maximum value of each state variable in the flight process of the multi-rotor unmanned aerial vehicle;
the control input constraints for the multi-rotor drone in step 2 are defined as follows:
Figure 113007DEST_PATH_IMAGE068
Figure 826623DEST_PATH_IMAGE069
Figure 853485DEST_PATH_IMAGE070
Figure 913845DEST_PATH_IMAGE071
wherein,
Figure 545815DEST_PATH_IMAGE072
、
Figure 666217DEST_PATH_IMAGE073
、
Figure 914796DEST_PATH_IMAGE074
and
Figure 44426DEST_PATH_IMAGE075
respectively representing the maximum values of the four control input quantities;
obstacle restraint for multi-rotor unmanned aerial vehicle in step 2
Figure 662113DEST_PATH_IMAGE076
The following were used:
Figure 156679DEST_PATH_IMAGE077
wherein,
Figure 892554DEST_PATH_IMAGE078
representing the center of the obstacle, a representing the distance of the edge of the obstacle from the center of the obstacle along the x-axis direction, b representing the distance of the edge of the obstacle from the center of the obstacle along the y-axis directionZ represents the distance of the obstacle edge from the center of the obstacle in the z-axis direction,
Figure 825875DEST_PATH_IMAGE079
a safety threshold representing a distance of the multi-rotor drone from an obstacle;
the optimization index is defined as follows:
Figure 963595DEST_PATH_IMAGE080
；
in the multi-rotor unmanned aerial vehicle dynamics model in the step 3, a dynamics equation linearization expression is as follows:
Figure 425800DEST_PATH_IMAGE081
wherein, X*For any reference trajectory, X represents a state variable, U represents a control input, B is a coefficient matrix,
the obstacle-constrained linearized form is as follows:
Figure 350769DEST_PATH_IMAGE082
wherein,
Figure 822201DEST_PATH_IMAGE083
is composed of
Figure 814428DEST_PATH_IMAGE076
A jacobian matrix of;
the discretization method of the dynamic equation and the constraint condition of the multi-rotor unmanned aerial vehicle in the step 4 is as follows:
step 4-1, dividing the time interval
Figure 713114DEST_PATH_IMAGE084
Dividing the obtained product into N equal parts, wherein the time step is h, and the expression is as follows:
Figure 423581DEST_PATH_IMAGE085
；
step 4-2, discretizing a kinetic equation according to an explicit fourth-order Runge Kutta formula as follows:
Figure 636388DEST_PATH_IMAGE086
wherein,
Figure 483121DEST_PATH_IMAGE087
and
Figure 316823DEST_PATH_IMAGE088
respectively indicate the state variables are in
Figure 249007DEST_PATH_IMAGE089
And a first
Figure 62242DEST_PATH_IMAGE090
The value of the node is taken as,
Figure 966744DEST_PATH_IMAGE091
、
Figure 207232DEST_PATH_IMAGE092
、
Figure 892292DEST_PATH_IMAGE093
and
Figure 243639DEST_PATH_IMAGE094
the expression is as follows
Figure 563499DEST_PATH_IMAGE095
Figure 178151DEST_PATH_IMAGE096
Figure 84927DEST_PATH_IMAGE097
Figure 505544DEST_PATH_IMAGE098
Wherein,
Figure 181376DEST_PATH_IMAGE099
and
Figure 763667DEST_PATH_IMAGE100
respectively indicate control inputs at
Figure 859537DEST_PATH_IMAGE089
And a first
Figure 552687DEST_PATH_IMAGE090
Taking a value from a node;
step 4-3, the terminal state constraint can be written as follows:
Figure 348604DEST_PATH_IMAGE101
；
step 4-4, the process constraints can be written in the form:
Figure 836218DEST_PATH_IMAGE102
wherein,
Figure 717586DEST_PATH_IMAGE103
；
and 4-5, controlling the input constraint to be written into the following form:
Figure 214426DEST_PATH_IMAGE104
Figure 130430DEST_PATH_IMAGE105
Figure 54523DEST_PATH_IMAGE106
wherein,
Figure 656144DEST_PATH_IMAGE103
；
step 4-6, the obstacle constraints are written as:
Figure 691096DEST_PATH_IMAGE107
Figure 664868DEST_PATH_IMAGE108
wherein,
Figure 759863DEST_PATH_IMAGE103
；
and 4-7, writing the optimization index into the following form:
Figure 350244DEST_PATH_IMAGE109
；
4-8, after linearization and discretization, summarizing the urban space-oriented multi-rotor unmanned aerial vehicle path planning problem into the following form:
Figure 454467DEST_PATH_IMAGE110
Figure 79483DEST_PATH_IMAGE111
wherein,
Figure 47177DEST_PATH_IMAGE103
；
solving the multi-rotor unmanned aerial vehicle path planning problem summarized in the step 4, wherein the process is as follows:
step 5-1, under the condition of the known initial state of the multi-rotor unmanned aerial vehicle, ordering
Figure 124854DEST_PATH_IMAGE112
，
Figure 501609DEST_PATH_IMAGE113
，
Figure 246711DEST_PATH_IMAGE114
，
Figure 683509DEST_PATH_IMAGE115
Obtaining an initial reference trajectory X by dynamics recursion*；
Step 5-2, the initial reference track X*The multi-rotor unmanned aerial vehicle path planning problem brought into the step 4 is solved by adopting an interior point method to obtain a new path, and the path is taken as a reference path X of the next calculation*；
And 5-3, obtaining an optimal solution after the obtained track is converged, namely obtaining the optimal track of the energy consumption of the multi-rotor unmanned aerial vehicle facing the urban space, and finishing the path planning of the multi-rotor unmanned aerial vehicle facing the urban space.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104165627A (en) * 2014-08-27 2014-11-26 电子科技大学 Real-time dynamic flight path planning method based on linear programming
CN106200662A (en) * 2016-08-11 2016-12-07 国网四川省电力公司检修公司 A kind of UAV Flight Control System
CN108445898A (en) * 2018-05-14 2018-08-24 南开大学 Quadrotor unmanned vehicle system motion planing method based on differential flat characteristic
CN112034887A (en) * 2020-09-10 2020-12-04 南京大学 Optimal path training method for unmanned aerial vehicle to avoid cylindrical barrier to reach target point
CN112148024A (en) * 2020-08-20 2020-12-29 中国人民解放军海军航空大学 Unmanned aerial vehicle real-time online flight path planning method based on self-adaptive pseudo-spectral method
CN113759977A (en) * 2021-09-18 2021-12-07 西北工业大学 Obstacle avoidance trajectory planning method based on optimized tether multi-unmanned aerial vehicle cooperative transportation

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104165627A (en) * 2014-08-27 2014-11-26 电子科技大学 Real-time dynamic flight path planning method based on linear programming
CN106200662A (en) * 2016-08-11 2016-12-07 国网四川省电力公司检修公司 A kind of UAV Flight Control System
CN108445898A (en) * 2018-05-14 2018-08-24 南开大学 Quadrotor unmanned vehicle system motion planing method based on differential flat characteristic
CN112148024A (en) * 2020-08-20 2020-12-29 中国人民解放军海军航空大学 Unmanned aerial vehicle real-time online flight path planning method based on self-adaptive pseudo-spectral method
CN112034887A (en) * 2020-09-10 2020-12-04 南京大学 Optimal path training method for unmanned aerial vehicle to avoid cylindrical barrier to reach target point
CN113759977A (en) * 2021-09-18 2021-12-07 西北工业大学 Obstacle avoidance trajectory planning method based on optimized tether multi-unmanned aerial vehicle cooperative transportation

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
RRT* algorithm under holonomic conditions for UAVs;Minyang Kang 等;《2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing》;20191231;全文 *
城市低空环境中多旋翼无人机在线航线规划方法;吴宇 等;《控制与决策》;20211231;第36卷(第12期);全文 *
面向城市环境的四旋翼无人机在线避障航迹规划方法;成浩浩 等;《计算机科学》;20190430;第46卷(第4期);全文 *
面向城市飞行安全的无人机离散型多路径规划方法;胡莘婷 等;《航空学报》;20210625;第42卷(第6期);全文 *

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