CN112162569B - Method for planning and deciding path of aircraft around multiple no-fly zones - Google Patents
Method for planning and deciding path of aircraft around multiple no-fly zones Download PDFInfo
- Publication number
- CN112162569B CN112162569B CN202010941572.5A CN202010941572A CN112162569B CN 112162569 B CN112162569 B CN 112162569B CN 202010941572 A CN202010941572 A CN 202010941572A CN 112162569 B CN112162569 B CN 112162569B
- Authority
- CN
- China
- Prior art keywords
- path
- aircraft
- fly
- paths
- planning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000011156 evaluation Methods 0.000 claims abstract description 21
- 238000005265 energy consumption Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- 230000004888 barrier function Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 238000013459 approach Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 238000003491 array Methods 0.000 claims 1
- 238000010348 incorporation Methods 0.000 claims 1
- 238000005096 rolling process Methods 0.000 claims 1
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000010339 dilation Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Images
Classifications
-
- 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
- G05D1/12—Target-seeking control
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides a method for planning and deciding a path of an aircraft around a multi-no-fly zone, which comprises the following steps: firstly, establishing a directed graph model: traversing all paths from the starting point to the end point; thirdly, planning a path and designing a path evaluation index; fourthly, making a path decision scheme; through the steps, a directed graph model can be constructed based on graph theory, the path planning and decision of the aircraft around a multi-forbidden flight area are realized, the problems of heavy guidance and light planning existing at present are solved, and a plurality of feasible schemes can be provided for aircraft guidance. The method of the invention is scientific, has good manufacturability and has wide popularization and application value.
Description
Technical Field
The invention relates to a method for establishing an environment model based on graph theory aiming at a multi-flight-forbidden area environment, planning a feasible path, evaluating the requirement of the path on the maneuvering capability of an aircraft and finally making a path decision scheme, belonging to aerospace; guidance, navigation and control techniques; the field of path decision planning.
Background
Path planning is widely used in research of mobile robots initially, and with continuous expansion of the application field of unmanned equipment, for example, in practical applications such as marine science, industrial fields and military operations, related research of path planning is gradually developed and applied to unmanned vehicles, unmanned boats and various aircrafts, and becomes one of research hotspots and difficult problems in various fields. The flight of the aircraft must meet complex constraint conditions, including traditional heat flow rate, dynamic pressure, overload constraint and terminal constraint, and complex no-fly zone constraint caused by natural, military and other factors. The no-fly zones of the aircraft are path constraints, and if the number of the no-fly zones is larger or the model is more complex, the more path constraints are required for optimizing the path of the aircraft, and the more difficulty in solving the optimization is increased.
The results of the research on the flight control area defense are few nowadays, and the problems of heavy guidance and light planning exist. Most avoidance methods in documents are based on a nominal track, the processing idea of the avoidance methods is mainly considered in guidance, but the problem of overall planning is not considered when the avoidance methods stand at an upper layer, the avoidance problems are limited to avoidance of a no-fly area near the nominal track and lack of upper layer planning, so that the overall performance is limited and the expandability is poor. And the research of the aircraft path planning is developing towards the improvement and innovation of an intelligent algorithm, the combination of an autonomous obstacle avoidance and multi-sensor information fusion technology and the combined solution of various algorithms. However, the existing path planning, path searching and optimizing methods, such as the a-x algorithm, the particle swarm algorithm, and the like, are not suitable for high-speed and difficultly-controlled aircrafts, cannot reflect the magnitude of control force, can only calculate an optimal solution/local optimal solution, cannot provide alternative solutions, and are difficult to make a complete and reasonable path decision scheme.
From the above, the method takes the path decision as a starting point, establishes a mathematical model based on a graph theory method, traverses all paths, designs evaluation indexes for sequencing, and finally makes a path decision scheme.
Disclosure of Invention
Objects of the invention
The invention aims to solve the problems, provides a method for planning and deciding a path of an aircraft around a multi-no-fly-away area, and aims to establish a mathematical model method of the aircraft around the multi-no-fly-away area and obtain an effective and reasonable decision scheme of the path around the fly-away area.
(II) technical scheme
The invention discloses a method for planning and deciding a path of an aircraft around a multi-no-fly zone, which comprises the following specific steps:
step one, establishing a directed graph model:
firstly, combining overlapping no-fly zones; secondly, setting virtual path points above and below each no-fly zone; then, selecting the no-fly areas which can be sequentially bypassed in series to be put into a list, and forming a multi-dimensional list by all possible serial lines; upper and lower envelopes are formed between nodes above and below the barriers and a starting point and between target points in each list, the nodes above and below two adjacent barriers can cross and pass through, and all paths of each serial line are formed after feasible paths are connected; establishing a directed graph model; finally, adding weight to all the directed edges;
step two, traversing all paths from the starting point to the end point;
traversing all paths from the starting point to the end point by using a depth-first traversal method based on the directed graph model established in the first step to obtain all possibilities of the flying-around path of the aircraft;
planning a path and designing a path evaluation index;
designing an optimal guidance law for each path by using a minimum control force path point tracking guidance method to obtain a path plan capable of reflecting the requirement on the maneuvering capacity of the aircraft, and taking energy consumption as an evaluation index of the path;
step four, making a path decision scheme;
sorting the paths according to the path evaluation indexes; after sequencing, for each path, for each no-fly zone, bypassing from the upper part and marking as 0, bypassing from the lower part and marking as 1, and combining repeated paths to obtain q fly-around path schemes; assuming that n no-fly zones need to fly around, a qxn dimensional 0/1 matrix can be finally output; and finally, providing the first f schemes according to the requirements to finish the decision.
The "directed graph" in the step one refers to a graph which is composed of a node set and an edge set in a graph theory, wherein the edges have directions;
the depth-first traversal method in the step two refers to a classical traversal method for a graph and a tree in graph theory;
the minimum control force path point tracking guidance method in the third step is an analytic guidance instruction expression obtained by taking the energy consumption of minimum path point tracking as an optimization target and deducing based on a kinematic model of an aircraft;
the evaluation index in the third step is used for calculating a performance index capable of reflecting the requirement of the path on the maneuvering capability of the aircraft through path planning and evaluating the quality of the path;
the decision scheme in the fourth step is a mode of obtaining a fly-around no-fly zone by sorting according to the evaluation indexes of each path and combining;
through the steps, a directed graph model can be constructed based on graph theory, the path planning and decision of the aircraft around a multi-forbidden flight area are realized, the problems of heavy guidance and light planning existing at present are solved, and a plurality of feasible schemes can be provided for aircraft guidance.
(III) the advantages and effects of the invention
The invention has the advantages and effects that:
through the steps, a directed graph model can be constructed based on graph theory, the path planning and decision of the aircraft around a multi-forbidden flight area are realized, the problems of heavy guidance and light planning existing at present are solved, and a plurality of feasible schemes can be provided for aircraft guidance.
(1) The method is based on the graph theory, constructs a directed graph model, realizes the path planning and decision of the aircraft around multiple no-fly zones, solves the problems of heavy guidance and light planning in the prior art, realizes the path planning and decision of the aircraft around the multiple no-fly zones, establishes a directed graph model method of the aircraft around the multiple no-fly zones, can present all feasible no-fly zones around the fly paths in a mathematical modeling mode, is suitable for the obstacle avoidance and fly-around selection of various high-speed aircraft with any number and layout, and provides a plurality of feasible schemes and a plurality of feasible routes for the guidance of the aircraft;
(2) according to the method, the paths are traversed, and are sequenced according to the path evaluation indexes, so that a path decision scheme is made, the condition that only a single solution exists due to searching and optimizing can be avoided, and the method is simple and easy to operate and has strong expandability;
(3) the method of the invention is scientific, has good manufacturability and has wide popularization and application value.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a diagram of a directed graph model.
FIG. 3 is a diagram of a kinematic model of an aircraft.
Fig. 4 is a case directed graph model.
Fig. 5 is a flight path diagram of the preferred embodiment.
The sequence numbers, symbols and symbols in the figures are summarized and explained as follows:
FIG. 3: u denotes an aircraft, WiRepresenting the ith path point, XOY is an inertial coordinate system, gamma is the flight path angle, V is the speed of the aircraft, and the lateral accelerations a and riAnd σiRepresenting relative distance and line of sight angle, θiRepresenting a speed lead angle;
FIG. 4: the numerical designations in the figures represent node numbers.
Detailed Description
The invention will be further explained in detail with reference to the drawings and the embodiments.
The invention discloses a method for planning and deciding a path of an aircraft around a multi-no-fly zone, which has a flow chart shown in figure 1 and comprises the following steps:
step one, establishing a directed graph model;
the purpose of this step is to perform environment modeling, that is, abstract the actual physical space into an abstract space that can be processed by the path planning method, that is, firstly perform dilation processing on the no-fly zone, and establish a coordinate system and calibrate the position of the no-fly zone. Here, the method is modeled based on graph theory.
The no-fly zones are merged first. In the actual mission environment of the aircraft, there may be an overlapping area between the no-fly zones, or the prior art cannot shuttle the aircraft because the distance between the no-fly zones is too close. At this time, if the nodes in the no-fly zone are connected, the result of path planning will be affected. The no-fly zone in which this is the case can therefore be seen as a large elliptical no-fly zone.
Next, a virtual waypoint is set. In order to simplify the model and reduce the running time, an upper node and a lower node are respectively established for each merged obstacle, coordinates are used as circle centers +/-short axis/long axis radius multiplied by an adjusting coefficient, and the coordinates are used as decision points or inflection points of a path to reflect whether the path bypasses from the upper part or the lower part of a no-fly area. The selection of the adjustment coefficient can be made according to actual conditions.
Then, a directed graph model is established. In order to avoid an increased amount of computation when determining whether an obstacle has passed between any two points, it is necessary to change the input method. Selecting the no-fly zones that can be bypassed serially places them in a list, all possible serial lines form a multi-dimensional list. The nodes above and below the obstacles in each list and the starting point form an upper envelope and a lower envelope between the target points, the nodes above and below two adjacent obstacles can cross and pass through, and all paths of each serial line are formed after the feasible paths are connected. A directed graph with nodes and directed edges can be formed after deleting the repeated edges, and the schematic diagram is shown in fig. 2, wherein the no-fly zones 1 and 2 are a serial line, and the no-fly zones 1 and 3 are the other. Thus, the calculation amount of the computer is linearly increased according to the number of the serial lines, thereby avoiding the problem of space explosion.
And finally, completing environment modeling. After the selection of the nodes and the connection between the nodes are completed, weights are added to all the directed edges, namely wij＝dijWherein d isijIs the distance between nodes i and j. A complete directed graph is formed to provide advance conditions for subsequent path planning.
Step two, traversing all paths from the starting point to the end point;
based on the directed graph generated at step one, all paths from the start point to the end point need to be traversed. Here we decide to use the depth-first principle for traversal.
The directed graph is represented by an adjacency list structure. Assume that the initial state of a given graph G is that all nodes have not been visited. Taking a node S as a starting point in the G, firstly visiting the starting point S and marking the starting point S as visited; then, each adjacent point w of S is searched from S in turn. If w has not been visited, continuing the depth-first traversal by taking w as a new starting point until all nodes with paths from the starting point S to the end point E in the graph are visited.
Depth-first traversal method idea:
A. initializing a stack, setting a starting point to be accessed, and stacking the starting point;
B. checking whether a node I at the top of the stack is in the graph, and whether the node I can be reached, is not stacked and is not visited from the node I is existed or not;
C. if yes, the found node is pushed;
D. if not, assigning the value of each element in the set of the next node accessed by the node I to be zero, and popping the node I;
E. when the stack top element is the end point, setting the end point not to be accessed, outputting the element in the stack, and popping up a stack top node;
F. B-E is repeatedly executed until the elements in the stack are empty.
By the method, all paths from the starting point to the end point in the directed graph model, namely all paths around the flight-forbidden zone, can be obtained.
Planning a path and designing a path evaluation index;
a minimum control force path point tracking guidance method is used, energy consumption of path point tracking is minimized as an optimization target, an optimal guidance law is designed for each path based on a kinematics model of an aircraft, and path planning capable of reflecting requirements for maneuvering capacity of the aircraft is obtained.
First, a kinematic model is built. It is assumed that the aircraft has a high performance flight control system that provides roll, pitch, and yaw stability as well as speed tracking, heading, and altitude hold functions for the aircraft. Assuming that the aircraft needs to pass through N waypoints, the relative geometry of the aircraft and the ith waypoint is shown in fig. 3.
In FIG. 3, U denotes an aircraft, WiRepresenting the ith waypoint, XOY is the inertial coordinate system. γ represents the track angle, the aircraft speed V. The aircraft changes its direction of motion by changing its lateral acceleration a. Assuming that the aircraft is an ideal particle, the autopilot has no delay. r isiAnd σiRepresenting relative distance and line of sight angle, θiRepresenting the speed lead angle, riAnd σiThe initial value is calculated by the weight of the directed edge. Based on the kinematic model, the differential equation can be expressed as:
without loss of generality, assume that N waypoints are at their respective arrival times tf,iIncreasing the rank, i.e. tf,i<tf,i+1. As the aircraft approaches the waypoint, the arrival time may be approximated as:
wherein r isi(0) Representing the initial relative distance between the aircraft and the ith waypoint.
Next, a path evaluation index is designed. In practice, energy consumption is critical to the aircraft, as it determines the durability of the aircraft. For this purpose, the following quadratic integral control force performance index is introduced, and this performance index is used as an evaluation index of the path.
Then, to minimize J, the lead law is designed. First consider t ≦ tf,1In the case of (1), a Lagrange multiplier vector λ is defined as [ λ ═ λ [ ]1,λ2,…,λN]TAnd miss amount vector Z ═ Z1,Z2,…,ZN]T，ZiAnd i is 1,2, …, and N is off-target amount.
Let R be an element of RN×NIs a symmetric matrix such that R λ ═ Z, and:
in the same way, when t>tf,1Control instructions similar to the above equation are also available. Wherein, the simplification results in:
(1) to (7) wherein: gamma denotes the track angle, V is the aircraft speed, a is the lateral acceleration, riAnd σiRepresenting the relative distance and line of sight angle, θ, of the aircraft from the ith waypointiRepresenting the speed lead angle, the number of the path points is N, t is time, and the time when the aircraft reaches the ith path point is represented as tf,i，ri(0) Representing the initial relative distance between the aircraft and the ith waypoint, J representing the quadratic integral control force performance index, λiIs Lagrange multiplier, ZiFor off-target, i ═ 1,2, …, N, the intermediate variable R ∈ RN×NIs a symmetric matrix;
using the control instruction a, each path can be planned with the goal of minimizing energy consumption, and an evaluation index of the path can be obtained by calculating the performance index J.
Step four, making a path decision scheme;
through the above process, each fly-around path and the evaluation index can be obtained. Thus, a ranking of all paths can be obtained.
The decision scheme of the method is to provide a flying-off forbidden region flying-around mode, the path planning in the step three only provides evaluation indexes for decision making, and because aircraft dynamics are not considered, the path can be taken as a nominal path, but an actual flying track can maneuver near the nominal path according to an avoidance strategy. Therefore, for each path, for each no-fly zone, the upper bypass is marked as 0, and the lower bypass is marked as 1, so that the path can be converted into an 0/1 array according to the relative relationship between the virtual path point passed by the path and the no-fly zone. Since the different paths may be equally oriented around each no-fly zone, the merge-repeat scheme may be translated by 0/1 as described above. Assuming that n no-fly zones need to fly around, q paths after merging can finally output a qxn dimensional 0/1 matrix.
And finally, providing the first f schemes according to the requirements, finishing the decision and providing guidance for subsequent guidance.
Simulation case:
the method is only used for demonstrating, is not an actual flight task, and can also be suitable for complex no-fly zone arrangement and high-speed flight tasks. The flight starting point of the aircraft is (2,4), and the target point is (40, 4). Three circular no-fly zones with numbers [1,2,3 ]]The coordinate and the radius of the circle center are respectively x1,y1＝11,2、d1＝1；x2,y2＝22,10、d2＝1；x3,y3＝34,3、d31. The unit is meter (m).
According to the first step, the set virtual waypoint coordinates are shown in table 1. Setting serial lines as no-fly zones 1 and 2 respectively; 1, 3; 2,3, obtaining a directed graph model as shown in fig. 4. The numbers in fig. 4 represent the numbers of the respective nodes of the directed graph.
TABLE 1 example node coordinates
According to the step two, the adjacency list is obtained according to the directed graph
Then all path sets P from start point 0 to end point 7 may be traversed according to the depth-first traversal algorithm and:
P＝[[0,1,2,7]；[0,1,2,3,7]；[0,1,2,6,7]；[0,1,5,7]；[0,1,5,3,7]；[0,1,5,6,7]；[0,1,3,7]；[0,1,6,7]；[0,4,2,7]；[0,4,2,3,7]；[0,4,2,6,7]；[0,4,5,7]；[0,4,5,3,7]；[0,4,5,6,7]；[0,4,3,7]；[0,4,6,7]；[0,2,7]；[0,2,3,7]；[0,2,6,7]；[0,5,7]；[0,5,3,7]；[0,5,6,7]]。
setting the average speed of the aircraft to be 2.5m/s according to the third step, obtaining the optimal path planning of the paths according to the guidance law of the transverse acceleration a, and calculating to obtain an evaluation index J of each path;
J＝[3.9520；7.3372；11.5008；2.0723；3.5438；7.0333；0.0666；0.3365；7.3153；11.8646；16.5311；5.3426；7.6628；11.7236；0.9067；0.3382；0.7458；2.3243；5.3927；0.3159；0.9306；3.4771]；
wherein, the 7 th path [0,1,3,7] is the optimal path, and the flight path diagram is shown in FIG. 5.
According to the fourth step, 0/1 transformation and merging schemes are carried out on the path sequence, and the sequence of the finally obtained decision scheme of the fly-around no-fly-off zone path is shown in table 2.
TABLE 2 decision scheme
Claims (1)
1. A method for planning and deciding a path of an aircraft around a multi-no-fly zone is characterized by comprising the following steps: the method comprises the following specific steps:
step one, establishing a directed graph model;
firstly, expanding a no-fly zone, establishing a coordinate system and calibrating the position of the no-fly zone; modeling is performed here based on graph theory;
firstly, merging the no-fly zones; regarding the no-fly zone as a large elliptic no-fly zone;
secondly, setting a virtual path point; respectively establishing an upper node and a lower node for each merged obstacle, wherein coordinates are circle centers +/-short axis/long axis radius multiplied by an adjustment coefficient, and the coordinates are used as decision points or inflection points of a path to reflect that the path bypasses from the upper part or the lower part of a no-fly area;
then, establishing a directed graph model; selecting the no-fly areas which are sequentially bypassed in serial and placing the no-fly areas into a list, so that all possible serial lines form a multi-dimensional list; upper and lower envelopes are formed between nodes above and below the barriers and a starting point and between target points in each list, the nodes above and below two adjacent barriers cross and pass through, and all paths of each serial line are formed after feasible paths are connected; deleting the repeated edges to form a directed graph with nodes and directed edges;
finally, environment modeling is completed; after the selection of the nodes and the connection between the nodes are completed, weights are added to all the directed edges, namely wij＝dijWherein d isijIs the distance between nodes i and j; forming a complete directed graph;
step two, traversing all paths from the starting point to the end point;
traversing all paths from the starting point to the end point based on the directed graph generated in the first step; traversing by using a depth-first principle;
representing the directed graph by using an adjacency list structure; setting the initial state of the given graph G as the state that all nodes have not been visited; taking a node S as a starting point in the G, firstly visiting the starting point S and marking the starting point S as visited; then, searching each adjacent point w of the S from the S in sequence; if w has not been visited, continuing the depth-first traversal by taking w as a new starting point until all nodes with paths communicated from the starting point S to the end point E in the graph are visited;
depth-first traversal method:
A. initializing a stack, setting a starting point to be accessed, and stacking the starting point;
B. checking whether a node I at the top of the stack is in the graph, and whether the node I can be reached, is not stacked and is not visited from the node I is existed or not;
C. if yes, the found node is pushed;
D. if not, assigning the value of each element in the set of the next node accessed by the node I to be zero, and popping the node I;
E. when the stack top element is the end point, setting the end point not to be accessed, outputting the element in the stack, and popping up a stack top node;
F. repeatedly executing B-E until the elements in the stack are empty;
obtaining all paths from a starting point to an end point in the directed graph model, namely all paths around the flight forbidden zone through the A-F;
planning a path and designing a path evaluation index;
the method for tracking and guiding the path point with the minimum control force is used, the energy consumption of tracking the path point is minimized as an optimization target, an optimal guidance law is designed for each path based on a kinematic model of the aircraft, and path planning capable of reflecting the requirement on the maneuvering capacity of the aircraft is obtained;
firstly, establishing a kinematic model; the aircraft is provided with a high-performance flight control system which provides the rolling, pitching and yawing stability, speed tracking, course and altitude keeping functions of the aircraft; setting N path points which are needed to be passed by the aircraft;
wherein U denotes an aircraft, WiRepresenting the ith waypoint, XOY being the inertial coordinate system; gamma denotes the track angle, the aircraft speed V; the aircraft changes the motion direction thereof by changing the lateral acceleration a thereof; the aircraft is an ideal particle, and the autopilot has no time delay; r isiAnd σiRepresenting relative distance and line of sight angle, θiRepresenting the speed lead angle, riAnd σiThe initial value is obtained by calculating the weight of the directed edge; based on the kinematic model, the differential equation is expressed as:
without loss of generality, let N waypoints be according to their respective arrival times tf,iIncreasing the rank, i.e. tf,i<tf,i+1(ii) a As the aircraft approaches the waypoint, the arrival times are:
wherein r isi(0) Representing an initial relative distance between the aircraft and the ith waypoint;
secondly, designing a path evaluation index; introducing the following quadratic integral control force performance index, and taking the performance index as an evaluation index of a path;
then, designing a guidance law; first consider t ≦ tf,1In the case of (1), a Lagrange multiplier vector λ is defined as [ λ ═ λ [ ]1,λ2,…,λN]TAnd miss amount vector Z ═ Z1,Z2,…,ZN]T，ZiI is 1,2, …, and N is off-target amount;
let R be an element of RN×NIs a symmetric matrix such that R λ ═ Z, and:
in the same way, when t>tf,1Then, obtaining the control instruction of the above formula; wherein, the simplification results in:
in formulae (1) to (7): gamma denotes the track angle, V is the aircraft speed, a is the lateral acceleration, riAnd σiRepresenting the relative distance and line of sight angle, θ, of the aircraft from the ith waypointiRepresenting the speed lead angle, the number of the path points is N, t is time, and the time when the aircraft reaches the ith path point is represented as tf,i，ri(0) Representing the initial relative distance between the aircraft and the ith waypoint, J representing the quadratic integral control force performance index, λiIs Lagrange multiplier, ZiFor off-target, i ═ 1,2, …, N, the intermediate variable R ∈ RN ×NIs a symmetric matrix;
planning each path by using the control instruction a with the minimized energy consumption as a target, and calculating a performance index J to obtain an evaluation index of the path;
step four, making a path decision scheme;
through the steps, each fly-around path and the evaluation index are obtained; thus, the ordering of all paths is obtained;
the path planning in the third step only provides evaluation indexes for decision making, for each path, the upper bypass is marked as 0 and the lower bypass is marked as 1 for each no-fly zone, and the path can be converted into 0/1 arrays according to the relative relation between the virtual path point passed by the path and the no-fly zone; transformation by 0/1 described above and incorporation of the repeat protocol; setting n flight bypassing forbidden regions, wherein the combined paths have q paths, and finally outputting a qxn dimensional 0/1 matrix;
and finally, providing the first f schemes according to the requirements to finish the decision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010941572.5A CN112162569B (en) | 2020-09-09 | 2020-09-09 | Method for planning and deciding path of aircraft around multiple no-fly zones |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010941572.5A CN112162569B (en) | 2020-09-09 | 2020-09-09 | Method for planning and deciding path of aircraft around multiple no-fly zones |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112162569A CN112162569A (en) | 2021-01-01 |
| CN112162569B true CN112162569B (en) | 2022-02-18 |
Family
ID=73857742
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010941572.5A Active CN112162569B (en) | 2020-09-09 | 2020-09-09 | Method for planning and deciding path of aircraft around multiple no-fly zones |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112162569B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112947515B (en) * | 2021-02-02 | 2022-07-26 | 中国民用航空飞行学院 | Aircraft urban air traffic energy consumption control method based on optimized cruise altitude layer |
| CN112965532B (en) * | 2021-03-22 | 2022-03-15 | 北京航空航天大学 | Optimization method of trajectory of aircraft around multiple no-fly zones based on path optimization |
| CN113311860B (en) * | 2021-05-13 | 2022-05-10 | 北京航空航天大学 | Graph theory-based modeling method for hypersonic aircraft around multi-forbidden flight zone path |
| CN114742267A (en) * | 2022-03-07 | 2022-07-12 | 深圳市跨越新科技有限公司 | Logistics transportation route planning method, system, equipment and storage medium |
| CN116149375B (en) * | 2023-04-21 | 2023-07-07 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle search planning method and device for online decision, electronic equipment and medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107621198A (en) * | 2017-08-28 | 2018-01-23 | 北京航空航天大学 | A kind of multistep decision-making trajectory planning method around more no-fly zones |
| CN107677275A (en) * | 2017-09-15 | 2018-02-09 | 北京航空航天大学 | The heterogeneous aircraft paths planning method in one kind mixing spatial domain and device |
| CN108981715A (en) * | 2018-08-22 | 2018-12-11 | 北航（四川）西部国际创新港科技有限公司 | A kind of unmanned plane paths planning method of mountain area flight safety degree constraint |
| CN109416889A (en) * | 2016-06-13 | 2019-03-01 | 深圳市大疆创新科技有限公司 | Unmanned vehicle, delivery system, unmanned vehicle control method and control unmanned vehicle program |
| WO2019180715A1 (en) * | 2018-03-21 | 2019-09-26 | Vhive Tech Ltd. | System and method for managing multiple uas performing an adaptive mission |
| CN111399541A (en) * | 2020-03-30 | 2020-07-10 | 西北工业大学 | Unmanned aerial vehicle whole-region reconnaissance path planning method of unsupervised learning type neural network |
| US10725483B2 (en) * | 2017-09-01 | 2020-07-28 | Qualcomm Incorporated | Personal security robotic vehicle |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108897312B (en) * | 2018-05-14 | 2021-06-04 | 南开大学 | Method for planning continuous monitoring path of multiple unmanned aerial vehicles to large-scale environment |
| CN110703793B (en) * | 2019-11-13 | 2022-06-24 | 中国人民解放军海军航空大学 | Method for attacking maneuvering target by adopting aircraft integral proportion guidance of attitude angle measurement |
-
2020
- 2020-09-09 CN CN202010941572.5A patent/CN112162569B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109416889A (en) * | 2016-06-13 | 2019-03-01 | 深圳市大疆创新科技有限公司 | Unmanned vehicle, delivery system, unmanned vehicle control method and control unmanned vehicle program |
| CN107621198A (en) * | 2017-08-28 | 2018-01-23 | 北京航空航天大学 | A kind of multistep decision-making trajectory planning method around more no-fly zones |
| US10725483B2 (en) * | 2017-09-01 | 2020-07-28 | Qualcomm Incorporated | Personal security robotic vehicle |
| CN107677275A (en) * | 2017-09-15 | 2018-02-09 | 北京航空航天大学 | The heterogeneous aircraft paths planning method in one kind mixing spatial domain and device |
| WO2019180715A1 (en) * | 2018-03-21 | 2019-09-26 | Vhive Tech Ltd. | System and method for managing multiple uas performing an adaptive mission |
| CN108981715A (en) * | 2018-08-22 | 2018-12-11 | 北航（四川）西部国际创新港科技有限公司 | A kind of unmanned plane paths planning method of mountain area flight safety degree constraint |
| CN111399541A (en) * | 2020-03-30 | 2020-07-10 | 西北工业大学 | Unmanned aerial vehicle whole-region reconnaissance path planning method of unsupervised learning type neural network |
Non-Patent Citations (5)
| Title |
|---|
| 关于战场无人机航迹规划方法研究;申舟，等;《计算机仿真》;20170430;第34卷(第4期);105-109 * |
| 基于模糊多目标决策的航迹评价方法研究;中国指挥与控制学会火力与指挥控制专业委员会;《网络时代的火力与指挥控制论文集》;国防工业出版社;20131031;156-160 * |
| 基于自由空间法的航迹规划方法研究;邱杰;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20170615(第6期);C031-48 * |
| 无人机智能任务规划系统技术研究;殷永涛，等;《航空电子技术》;20180331;第49卷(第1期);13-20 * |
| 无人机航迹规划常用算法综述;王琼，等;《吉林大学学报（信息科学版）》;20190131;第37卷(第1期);58-67 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112162569A (en) | 2021-01-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112162569B (en) | Method for planning and deciding path of aircraft around multiple no-fly zones | |
| Ma et al. | Multi-objective path planning for unmanned surface vehicle with currents effects | |
| Wu | Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission | |
| Karimi et al. | Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm | |
| Neto et al. | Feasible RRT-based path planning using seventh order Bézier curves | |
| CN112230678A (en) | Three-dimensional unmanned aerial vehicle path planning method and planning system based on particle swarm optimization | |
| Shanmugavel et al. | 3D Dubins sets based coordinated path planning for swarm of UAVs | |
| Chen et al. | Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task | |
| Darbari et al. | Dynamic motion planning for aerial surveillance on a fixed-wing UAV | |
| Sun et al. | Intelligent flight task algorithm for unmanned aerial vehicle | |
| CN112947594A (en) | Unmanned aerial vehicle-oriented flight path planning method | |
| Haghighi et al. | Multi-objective cooperated path planning of multiple unmanned aerial vehicles based on revisit time | |
| CN112650306A (en) | Unmanned aerial vehicle motion planning method based on dynamics RRT | |
| Yan et al. | Two‐dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm | |
| Mok et al. | Gaussian-mixture based potential field approach for UAV collision avoidance | |
| Váňa et al. | Surveillance planning with safe emergency landing guarantee for fixed-wing aircraft | |
| CN113093789B (en) | Planning method for avoiding trajectory of aircraft no-fly zone based on path point optimization | |
| Zhang et al. | Graph-based path decision modeling for hypersonic vehicles with no-fly zone constraints | |
| Niendorf et al. | Multi-query path planning for an unmanned fixed-wing aircraft | |
| Jung et al. | Enabling operational autonomy for unmanned aerial vehicles with scalability | |
| Mujumdar et al. | Nonlinear Geometric Guidance and Di® erential Geometric Guidance of UAVs for Reactive Collision Avoidance | |
| Cai et al. | Motion planning of unmanned aerial vehicle based on rapid-exploration random tree algorithm | |
| Tang et al. | Trajectory Tracking Control for Fixed-Wing UAV Based on DDPG | |
| Li et al. | Unified path planning for composite UAVs via Fermat point-based grouping particle swarm optimization | |
| Tian et al. | An unmanned aerial vehicle path planning method based on the elastic rope algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |