CN116774735A - Unmanned aerial vehicle cluster track planning method and system based on edge calculation - Google Patents
Unmanned aerial vehicle cluster track planning method and system based on edge calculation Download PDFInfo
- Publication number
- CN116774735A CN116774735A CN202311068439.3A CN202311068439A CN116774735A CN 116774735 A CN116774735 A CN 116774735A CN 202311068439 A CN202311068439 A CN 202311068439A CN 116774735 A CN116774735 A CN 116774735A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- representing
- penalty
- constraint
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000004364 calculation method Methods 0.000 title claims abstract description 33
- 230000008878 coupling Effects 0.000 claims abstract description 24
- 238000010168 coupling process Methods 0.000 claims abstract description 24
- 238000005859 coupling reaction Methods 0.000 claims abstract description 24
- 238000005265 energy consumption Methods 0.000 claims abstract description 21
- 238000010276 construction Methods 0.000 claims description 15
- 230000007246 mechanism Effects 0.000 claims description 8
- 230000000087 stabilizing effect Effects 0.000 claims 1
- 230000006870 function Effects 0.000 description 62
- 238000005457 optimization Methods 0.000 description 13
- 238000004891 communication Methods 0.000 description 11
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000001351 cycling effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an unmanned aerial vehicle cluster track planning method and system based on edge calculation, and relates to the field of unmanned aerial vehicle path planning, wherein the method comprises the following steps: constructing a feasible region constraint of the unmanned aerial vehicle according to a kinetic equation of the unmanned aerial vehicle and an obstacle in a three-dimensional space flown by the unmanned aerial vehicle; constructing coupling collision constraint of the unmanned aerial vehicle according to the set distance kept between the unmanned aerial vehicle and other unmanned aerial vehicles in the unmanned aerial vehicle cluster; constructing an energy consumption model of the unmanned aerial vehicle from take-off time to landing time; adding two penalty function items into the energy consumption model to obtain an objective function of the unmanned aerial vehicle, wherein the first penalty function item is the collision penalty between the unmanned aerial vehicle and the obstacle, and the second penalty function item is the collision penalty of the multi-unmanned aerial vehicle collision; and (3) based on the feasible region constraint and the coupling collision constraint, utilizing the edge calculation force of each unmanned aerial vehicle to adopt a penalty function method to solve an objective function through iteration, and obtaining a track planning result of the unmanned aerial vehicle cluster. The invention reduces the energy consumption and improves the solving speed.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle cluster track planning method and system based on edge calculation.
Background
The unmanned aerial vehicle track planning refers to calculating an optimal path of the unmanned aerial vehicle flying in a 3D space under the condition of given starting points and target points so as to reach a preset task target. In unmanned aerial vehicle applications, trajectory planning is very important because it can help unmanned aerial vehicles autonomously accomplish various tasks, such as patrol, logistics, monitoring, rescue, etc.
In unmanned aerial vehicle trajectory planning, there are two key problems, single unmanned aerial vehicle trajectory planning and multiple unmanned aerial vehicle trajectory planning.
In terms of single unmanned aerial vehicle trajectory planning, common unmanned aerial vehicle trajectory planning algorithms include optimization-based, search-based, learning-based, and the like. The optimization-based approach typically converts the trajectory planning problem into a mathematical optimization problem and uses an optimization algorithm to find the optimal solution. These methods can convert the task objective into an optimization objective by defining an appropriate cost function, and then solve the optimal solution using a mathematical optimization algorithm. The method has the advantages that the globally optimal solution of the solution can be ensured, but the calculated amount is larger and the efficiency is lower. Search-based methods (e.gRRT, genetic algorithm, etc.) then typically solves the problem by finding the optimal path in the search space. Such methods typically require defining a heuristic function to guide the search process in order to find the optimal solution more quickly. The advantage of these methods is that a better solution can be found in a shorter time, but it is not guaranteed that a globally optimal solution is found. The learning-based method is to learn the trajectory planning strategy of the unmanned aerial vehicle by using a machine learning algorithm. These methods typically require a large amount of training data and training time is long. Because of neural network uncertainty, it is greatly hindered in practical applications.
In the multi-unmanned aerial vehicle trajectory planning, methods can be classified into a centralized method and a distributed method according to different calculation and control modes. The centralized method adopts a centralized controller to complete the whole planning calculation. The algorithm aims at global optimization, has high calculation complexity, and meanwhile, needs the unmanned aerial vehicle to continuously upload own state and environment information to the central node and receive a control instruction issued by the central node, so that high requirements are provided for communication bandwidth, and the unmanned aerial vehicle is not easy to expand to a large-scale unmanned aerial vehicle group. Although the calculation efficiency is low, the cooperative obstacle avoidance between the global optimal solution and the unmanned aerial vehicle can be ensured. Each unmanned aerial vehicle independently completes own planning calculation. Each unmanned aerial vehicle generates a track according to the state and the environment information of the unmanned aerial vehicle, and coordinates adjacent unmanned aerial vehicles through communication to realize cooperative flight of unmanned aerial vehicle groups. The distributed method has low computational complexity and is easy to expand to a large-scale unmanned aerial vehicle group, but because each unmanned aerial vehicle is planned only according to local information, high-quality collaborative obstacle avoidance between the global optimum and the unmanned aerial vehicles is difficult to ensure.
In the current unmanned aerial vehicle cluster path planning algorithm, the following significant problems exist. The first one is that the consumption of computing resources is large, when the cluster scale of the unmanned aerial vehicle is large, the centralized method needs to process a large amount of unmanned aerial vehicle and environment information, the computing amount is extremely large, and the high-quality solution is difficult to solve in a limited time. Secondly, the unmanned aerial vehicle communication has high requirement on bandwidth, and no matter a centralized algorithm or a distributed algorithm, when information is frequently exchanged with a central node or other unmanned aerial vehicles, the real-time performance of information exchange needs to be ensured by depending on higher communication bandwidth. Thirdly, an optimal path is difficult to find under a complex environment, and when the environment space is complex or a large number of obstacles exist, it is very difficult to find a safe and optimal unmanned aerial vehicle cluster path, and the unmanned aerial vehicle cluster path is easy to sink into local optimum.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle cluster track planning method and system based on edge calculation, which can reduce energy consumption and improve solving speed.
In order to achieve the above object, the present invention provides the following solutions:
an unmanned aerial vehicle cluster track planning method based on edge calculation comprises the following steps:
constructing a kinetic equation of unmanned aerial vehicle motion;
constructing a feasible region constraint of the unmanned aerial vehicle according to the dynamics equation and the obstacle in the three-dimensional space of the unmanned aerial vehicle;
according to the method, the unmanned aerial vehicle and other unmanned aerial vehicles in the unmanned aerial vehicle cluster are kept at a set distance, and coupling collision constraint of the unmanned aerial vehicle is built;
discretizing a time period from the take-off time to the landing time of the unmanned aerial vehicle, and constructing an energy consumption model from the take-off time to the landing time of the unmanned aerial vehicle;
adding a first penalty function term and a second penalty function term into the energy consumption model to obtain an objective function of the unmanned aerial vehicle, wherein the first penalty function term is a collision penalty between the unmanned aerial vehicle and an obstacle, and the second penalty function term is a collision penalty for multiple unmanned aerial vehicle collisions;
based on the feasible region constraint and the coupling collision constraint, solving the objective function by using the edge calculation force of each unmanned aerial vehicle through iteration and adopting a penalty function method to obtain a track planning result of the unmanned aerial vehicle cluster; and each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through a broadcasting and sharing mechanism.
The invention also discloses an unmanned aerial vehicle cluster track planning system based on edge calculation, which comprises:
the dynamic equation construction module is used for constructing a dynamic equation of the unmanned plane motion;
the feasible region constraint construction module is used for constructing the feasible region constraint of the unmanned aerial vehicle according to the dynamics equation and the obstacle in the three-dimensional space of the unmanned aerial vehicle;
the coupling collision constraint construction module is used for constructing coupling collision constraint of the unmanned aerial vehicle according to the fact that the unmanned aerial vehicle is kept at a set distance from other unmanned aerial vehicles in the unmanned aerial vehicle cluster;
the energy consumption model construction module is used for discretizing the time period from the take-off time to the landing time of the unmanned aerial vehicle and constructing an energy consumption model from the take-off time to the landing time of the unmanned aerial vehicle;
the objective function construction module is used for adding a first penalty function item and a second penalty function item into the energy consumption model to obtain an objective function of the unmanned aerial vehicle, wherein the first penalty function item is the collision penalty between the unmanned aerial vehicle and an obstacle, and the second penalty function item is the collision penalty of the collision of multiple unmanned aerial vehicles;
the solving module is used for solving the objective function by using the edge computing force of each unmanned aerial vehicle based on the feasible region constraint and the coupling collision constraint through iteration and adopting a penalty function method to obtain a track planning result of the unmanned aerial vehicle cluster; and each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through a broadcasting and sharing mechanism.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, the trajectory optimization is performed by utilizing the edge calculation force of the unmanned aerial vehicle, so that the energy consumption can be reduced, the solving speed is improved, the first penalty function item and the second penalty function item are added into the objective function, and the solving speed and the solving success rate are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle cluster track planning method based on edge calculation according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an unmanned aerial vehicle model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an unmanned aerial vehicle cluster track planning system based on edge calculation according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an unmanned aerial vehicle cluster track planning method and system based on edge calculation, which can reduce energy consumption and improve solving speed.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Embodiment 1 provides an unmanned aerial vehicle cluster track planning method based on edge calculation.
As shown in fig. 1, an unmanned aerial vehicle cluster track planning method based on edge calculation includes the following steps.
Step 101: and constructing a kinetic equation of unmanned aerial vehicle motion.
Unmanned aerial vehicles in the unmanned aerial vehicle cluster are four rotor unmanned aerial vehicles in this embodiment.
The centre of mass of the drone is assumed to be at its geometric centre, as shown in figure 2. In order to accurately simulate the behavior of a four-axis aircraft (unmanned aerial vehicle), a kinetic equation of unmanned aerial vehicle motion is given.
；
wherein ,and the position coordinates of the unmanned aerial vehicle are expressed and used for describing the translation of the unmanned aerial vehicle, and x, y and z are x-axis, y-axis and z-axis coordinates respectively. Phi represents the roll angle of the unmanned aerial vehicle rotating around the x-axis, theta represents the pitch angle of the unmanned aerial vehicle rotating around the y-axis, phi represents the yaw angle of the unmanned aerial vehicle rotating around the z-axis, I xx ，I yy ，I zz The rotational inertia of the unmanned aerial vehicle around the x axis, the y axis and the z axis are respectively represented, u 1 Represents a first virtual control quantity, u 2 Representing a second virtual control quantity, u 3 Represents a third virtual control quantity, u 4 Represents a fourth virtual control quantity, m represents the mass of the unmanned aerial vehicle, g represents the gravitational acceleration,/->，/>，/>，/>，/>，/>The second derivatives of x, y, z, phi, theta, phi, respectively.
In FIG. 2, F 1 、F 2 、F 3 and F4 Respectively the pulling force of the four rotors.
To simplify the description, the abstract expression of the above kinetic equation is:
；
wherein ,representing the state quantity of the unmanned plane, which is a vector formed by x, y, z, phi, theta and phi->Is->Is used as a first derivative of (a),indicating the control quantity of the unmanned aerial vehicle, +.>Is u 1 、u 2 、u 3 and u4 Vectors of constitution>Representing dynamicsAbstract representation of the equation.
Step 102: and constructing the feasible region constraint of the unmanned aerial vehicle according to the dynamics equation and the obstacle in the three-dimensional space of the unmanned aerial vehicle.
For obstructions in three dimensions, several spheres are usedFitting their shape such that the spheres completely enclose the obstacle, wherein +.>。
Where r denotes the radius of the sphere surrounding the drone.
The feasible region constraint is expressed as:
。
wherein ,representing feasible region constraints, +.>Representing the position coordinates of the unmanned aerial vehicle, +.>Representing the position coordinates of the obstacle P, P representing the number of obstacles S p Representing a sphere surrounding an obstacle p, r p Represent S p Is set, and the radius of (a) is set.
Although this approach provides some reduction in the feasible region, a balance between feasible region integrity and computational effort can be achieved.
Step 103: and according to the set distance between the unmanned aerial vehicle and other unmanned aerial vehicles in the unmanned aerial vehicle cluster, constructing coupling collision constraint of the unmanned aerial vehicle.
In order to ensure safety, each quadrotor unmanned aerial vehicle needs to keep a certain safety distance (r) with other unmanned aerial vehicles in the same space-time range safe ）。
The coupled collision constraint is expressed as:
。
wherein ,representing coupled crash constraints, +.>Representing the position coordinates of the unmanned aerial vehicle, +.>Representing trajectories of other unmanned aerial vehicles, +.>Representing the position coordinates of other unmanned aerial vehicles, +.>Indicating the set distance. And j is the serial numbers of all other unmanned aerial vehicles except the unmanned aerial vehicle currently calculated in the unmanned aerial vehicle cluster.
It should be noted that for the same space, different unmanned aerial vehicles may pass at different times. That is, only when time and space are coincident, it means that the trajectory of the drone violates the constraint.
Step 104: discretizing the time period from the take-off time to the landing time of the unmanned aerial vehicle, and constructing an energy consumption model from the take-off time to the landing time of the unmanned aerial vehicle.
Efficient energy utilization in a four-axis aircraft is important to increase its flight range and endurance, which can lead to more efficient operation and reduced cost. The energy consumed by a quad-rotor drone may be defined as follows.
；
wherein ,is the take-off time of the unmanned aerial vehicle, +.>Is the landing time of the unmanned aerial vehicle, +.>Is an identity matrix.
In this embodiment, the continuous time curve is discretized into a finite time sequence using a direct point-of-placement method. The continuous time is discretized into sampling points, which means that the time discretization isDiscrete state quantity as=x[0]…x[N]。
In optimizing the above discrete problem, a given initial guess (initial trajectory value) can be written asThe optimized trajectory is marked +.>。
Under the discretization method, the optimization problem of the unmanned aerial vehicle can be expressed as follows:
；
the constraint conditions are as follows:。
wherein the input control has an upper limit of control amountAnd a lower control amount limit->. The problem has a realistic feasible domainBeam) And coupling collision constraints (+)>）。
Step 105: and adding a first penalty function item and a second penalty function item into the energy consumption model to obtain an objective function of the unmanned aerial vehicle, wherein the first penalty function item is a collision penalty between the unmanned aerial vehicle and an obstacle, and the second penalty function item is a collision penalty for multiple unmanned aerial vehicle collisions.
The second penalty function term is the collision penalty of collision between the current unmanned aerial vehicle and other unmanned aerial vehicles in the unmanned aerial vehicle cluster.
It is assumed that the trajectories of other unmanned aerial vehicles are known to be determined, i.e. the constraints can all be regarded as a space of known determinations. On this basis, the present embodiment considers the optimization space to be convex.
According to collision between unmanned aerial vehicle and obstacle, the feasible region constraint is realized) Defining a first penalty function term->。
。
wherein ,representing the distance between the drone and each obstacle, the penalty is approximately 0 when no collision occurs to the drone, and increases approximately linearly when a collision occurs, depending on the severity of the collision. The final overall penalty value is the sum of the unmanned aerial vehicle's penalty values for all obstacles. In the penalty function described above, a->For the first penalty factor, rootThe optimization can be dynamically adjusted. Contrast->An accurate penalty function that takes into account the depth of collision of the drone and ensures that the function is continuously steerable.
Similarly, a second penalty function term for multiple unmanned collision is defined。
；
wherein ,and the distance j between the unmanned aerial vehicle and other unmanned aerial vehicles is calculated currently, and the value range of j is the serial number of all other unmanned aerial vehicles except the unmanned aerial vehicle in the unmanned aerial vehicle cluster.
The optimization problem of the initial unmanned aerial vehicle can be converted into the following problem segment track planning, namely, the objective function is expressed as:
；
the constraint conditions of the objective function are as follows:；
wherein , wherein ,representing energy expenditure model, +.>() Representing a first penalty function term, ">() Representing a second penalty function term, ">Representing a first penalty factor, ">Representing a second penalty factor, ">Representing the state quantity of the unmanned aerial vehicle, +.>Indicating the control quantity of the unmanned aerial vehicle, +.>=x[0]…x[N]，x[0]State quantity x [ N ] representing initial state]State quantity representing the nth time, +.>State quantity corresponding to take-off time +.>State quantity corresponding to the landing time is represented by +.>Indicating the upper limit of the control amount,/-, and>indicating a lower control amount limit.
In this embodiment, a penalty function method is used to solve the objective function, and the solving process includes:
step S1: given an initial first penalty factorAnd a second penalty factor->。
Step S2: the objective function was solved using CasADI and IPOPT, and the feasible-region constraints and coupling collision constraints were checked.
Step S3: when the constraint is not satisfied (the feasibility constraint is not satisfied or the coupling collision constraint is not satisfied), the penalty factor is enlarged (when the feasibility constraint is not satisfied, the first penalty factor is enlarged according to a first set step size, when the coupling collision constraint is not satisfied, the second penalty factor is enlarged according to a second set step size), when the constraint is satisfied (both the feasibility constraint and the coupling collision constraint are satisfied), the penalty factor is reduced (the first penalty factor and the second penalty factor are reduced according to a third set step size).
Step S4: and (2) cycling from step S2 to step S3 until the minimum penalty factor is found (the difference between the objective function value of the current iteration and the objective function value of the last iteration is smaller than the set threshold), and ending the cycling to obtain the optimal solution, wherein the optimal solution is the optimal penalty factor.
And determining the optimized track of the unmanned aerial vehicle while obtaining the optimal penalty factor.
The penalty factor and the number of iterations in a certain iteration process are changed relative to the penalty function. For constraintSince the constraint is not violated during the solution, the penalty factor continues to decrease until the minimum allowable value is reached. For constraint->No constraint is violated in the previous two iterations, and the corresponding penalty factor is reduced. However, in the third iteration, a violation of the constraint occurs and the algorithm expands the penalty factor appropriately until a viable solution is obtained in the fifth iteration. The penalty factor is considered optimal at this time.
Step 106: based on the feasible region constraint and the coupling collision constraint, solving the objective function by using the edge calculation force of each unmanned aerial vehicle through iteration and adopting a penalty function method to obtain a track planning result of the unmanned aerial vehicle cluster; and each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through a broadcasting and sharing mechanism.
In each iteration process in step 106, each unmanned aerial vehicle is based on the current track value, the edge calculation force is utilized, and the objective function corresponding to each unmanned aerial vehicle is solved by using a penalty function method, so that the optimal penalty factor and the optimized track value are obtained. And (3) obtaining a track planning result of the unmanned aerial vehicle cluster through repeated iteration until the track value of each unmanned aerial vehicle is stable. After each iteration of each unmanned aerial vehicle obtains an optimized track value, the optimized track value is shared with other unmanned aerial vehicles through a broadcasting and sharing mechanism.
During each iteration, for the nth drone: initial trajectory guess based on current iterationSolving an objective function corresponding to the nth unmanned aerial vehicle by using a penalty function method to obtain an optimized track value +.>。
Guessing values from initial trajectories of current iterationsAnd the optimized track value obtained by the current iteration +.>Determining an initial track guess value of the nth unmanned aerial vehicle in the next iteration +.>。
And (3) through multiple iterations, obtaining a track planning result of the unmanned aerial vehicle cluster until the track values of all unmanned aerial vehicles are stable.
And (3) optimizing the unmanned aerial vehicle with an unstable track by adopting an objective function every time of iteration, obtaining a track planning result of a complete unmanned aerial vehicle cluster every time of iteration, and obtaining a final track planning result of the unmanned aerial vehicle cluster after a plurality of times of iteration until the track values of all unmanned aerial vehicles are stable.
According to the initial track guess value of the current iteration and the optimized track value obtained by the current iteration, determining the initial track guess value of the n-th unmanned aerial vehicle of the next iteration, namely, an initial track guess value updating strategy specifically comprises:
according to the formulaDetermining an initial track guess value of the nth unmanned aerial vehicle in the next iteration;
wherein ,representing the initial trajectory guess value of the nth unmanned aerial vehicle of the next iteration, +.>Representing the initial trajectory guess value of the nth drone of the current iteration, +.>And representing the optimized track value of the nth unmanned aerial vehicle in the current iteration.
The unmanned aerial vehicle track stability is defined as:。
where tol is a given threshold.
Under the condition of an initial track guess value updating strategy, the solving method of the objective function comprises the following steps:
step a1:
for all drones:
step a1-1: irrespective ofAnd (5) space, and solving an objective function.
Step a1-2: inspection ofAnd (3) spatial constraint, and if the constraint is met, namely no collision occurs, ending the algorithm in advance.
Step a2:
for all drones:
step a2-1: taking the last track of other unmanned aerial vehicles asAnd (5) space, and solving an objective function.
Step a2-2: when the track is stable, the unmanned aerial vehicle is not participating in subsequent loop solving.
Step a3: and when only the last unmanned aerial vehicle with unstable track is left, optimizing the unmanned aerial vehicle once by adopting an objective function to obtain a final unmanned aerial vehicle cluster track planning result.
And each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through broadcasting and a sharing mechanism, namely, each unmanned aerial vehicle performs optimal solution sharing after obtaining an optimal solution of an objective function.
After each planning is completed, broadcasting own planning information to surrounding unmanned aerial vehicles, and for this reason, the embodiment discloses a four unmanned aerial vehicle information exchange system based on LoRaWAN:
1. the four unmanned aerial vehicle information exchange system comprises unmanned aerial vehicle clusters and a gateway station. Each unmanned aerial vehicle is provided with a LoRa communication module, and the gateway station is used for relaying information exchange among unmanned aerial vehicles.
2. And the unmanned aerial vehicles are communicated with the gateway station and the unmanned aerial vehicles through the LoRa network. The gateway station is responsible for forwarding information of each unmanned aerial vehicle and realizing full-connection communication among unmanned aerial vehicles.
3. Each drone periodically broadcasts its own ID and path information over the LoRa network. The other unmanned aerial vehicles and the gateway station receive the information and grasp the flight state of each unmanned aerial vehicle.
4. The gateway station monitors communication among the unmanned aerial vehicles, if a certain unmanned aerial vehicle is detected to lose contact with the system, the unmanned aerial vehicle is considered to be faulty, other unmanned aerial vehicles are informed of the state of the unmanned aerial vehicle through the LoRa network, and task allocation to the unmanned aerial vehicle in a loss mode is avoided.
5. After the unmanned aerial vehicle lands, the gateway station collects the flight logs of each unmanned aerial vehicle from the unmanned aerial vehicle, and the flight logs are used for evaluating the communication quality and the system performance.
The four unmanned aerial vehicle information exchange system utilizes the LoRa network to realize the simple information exchange of four unmanned aerial vehicles, adopts broadcast communication and gateway station to transmit between unmanned aerial vehicles to realize full connection. The system has small communication quantity, is suitable for unmanned aerial vehicles to exchange simple instructions and state information, and meets basic collaborative flight requirements. In general, the system provides a simple and efficient multi-drone communication scheme.
The invention constructs the unmanned aerial vehicle cluster track planning method based on edge calculation, and the method can complete information transmission between unmanned aerial vehicles and track optimization by utilizing the edge calculation force of the unmanned aerial vehicles.
The penalty function method provided by the invention can effectively improve the success rate and the solving speed of single unmanned aerial vehicle track solving, and the algorithm aims at realizing the effective solving of the track on a low-computation-force platform.
The initial value updating strategy provided by the invention can obviously improve the solving speed, and the solver can more quickly find the optimal solution by giving the optimized initial guess.
The distributed solving algorithm provided by the invention does not depend on a central server, so that the unmanned aerial vehicle cluster has better autonomous intelligence.
Embodiment 2 provides an unmanned aerial vehicle cluster track planning system based on edge calculation.
As shown in fig. 3, an unmanned aerial vehicle cluster track planning system based on edge calculation includes:
the kinetic equation construction module 201 is configured to construct a kinetic equation of the unmanned aerial vehicle motion.
The feasible region constraint construction module 202 is configured to construct a feasible region constraint of the unmanned aerial vehicle according to the dynamics equation and an obstacle in a three-dimensional space where the unmanned aerial vehicle flies.
And the coupling collision constraint construction module 203 is configured to construct coupling collision constraints of the unmanned aerial vehicle according to the set distance between the unmanned aerial vehicle and other unmanned aerial vehicles in the unmanned aerial vehicle cluster.
The energy consumption model construction module 204 is configured to discretize a time period from the take-off time to the landing time of the unmanned aerial vehicle, and construct an energy consumption model from the take-off time to the landing time of the unmanned aerial vehicle.
The objective function construction module 205 is configured to add a first penalty function term and a second penalty function term to the energy consumption model, so as to obtain an objective function of the unmanned aerial vehicle, where the first penalty function term is a collision penalty between the unmanned aerial vehicle and the obstacle, and the second penalty function term is a collision penalty for a collision of multiple unmanned aerial vehicles.
The solving module 206 is configured to solve, based on the feasible region constraint and the coupling collision constraint, the objective function by using edge computing forces of each unmanned aerial vehicle through iteration and using a penalty function method, so as to obtain a track planning result of the unmanned aerial vehicle cluster; and each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through a broadcasting and sharing mechanism.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. An unmanned aerial vehicle cluster track planning method based on edge calculation is characterized by comprising the following steps:
constructing a kinetic equation of unmanned aerial vehicle motion;
constructing a feasible region constraint of the unmanned aerial vehicle according to the dynamics equation and the obstacle in the three-dimensional space of the unmanned aerial vehicle;
according to the method, the unmanned aerial vehicle and other unmanned aerial vehicles in the unmanned aerial vehicle cluster are kept at a set distance, and coupling collision constraint of the unmanned aerial vehicle is built;
discretizing a time period from the take-off time to the landing time of the unmanned aerial vehicle, and constructing an energy consumption model from the take-off time to the landing time of the unmanned aerial vehicle;
adding a first penalty function term and a second penalty function term into the energy consumption model to obtain an objective function of the unmanned aerial vehicle, wherein the first penalty function term is a collision penalty between the unmanned aerial vehicle and an obstacle, and the second penalty function term is a collision penalty for multiple unmanned aerial vehicle collisions;
based on the feasible region constraint and the coupling collision constraint, solving the objective function by using the edge calculation force of each unmanned aerial vehicle through iteration and adopting a penalty function method to obtain a track planning result of the unmanned aerial vehicle cluster; and each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through a broadcasting and sharing mechanism.
2. The unmanned aerial vehicle cluster track planning method based on edge calculation according to claim 1, wherein the unmanned aerial vehicle cluster track planning result is obtained by solving the objective function by using a penalty function method through iteration by utilizing the edge calculation force of each unmanned aerial vehicle based on the feasible region constraint and the coupling collision constraint, and specifically comprises the following steps:
during each iteration, for the nth drone:
solving an objective function corresponding to the nth unmanned aerial vehicle by using a penalty function method based on the initial trajectory guess value of the current iteration to obtain an optimized trajectory value;
determining an initial track guess value of the n-th unmanned aerial vehicle in the next iteration according to the initial track guess value of the current iteration and the optimized track value obtained by the current iteration;
and (3) obtaining a track planning result of the unmanned aerial vehicle cluster through multiple iterations until the track values of all the unmanned aerial vehicles are stable.
3. The unmanned aerial vehicle cluster track planning method based on edge calculation according to claim 2, wherein the method is characterized in that the method comprises the following steps of determining an initial track guess value of an nth unmanned aerial vehicle in the next iteration according to the initial track guess value of the current iteration and the optimized track value obtained by the current iteration, and specifically comprises the following steps:
according to the formulaDetermining an initial track guess value of the nth unmanned aerial vehicle in the next iteration;
wherein ,representing the initial trajectory guess value of the nth unmanned aerial vehicle of the next iteration, +.>Representing the initial trajectory guess value of the nth drone of the current iteration, +.>And representing the optimized track value of the nth unmanned aerial vehicle in the current iteration.
4. The unmanned aerial vehicle cluster trajectory planning method based on edge calculation of claim 3, wherein the condition for stabilizing the unmanned aerial vehicle trajectory value is:
；
where tol is a given threshold.
5. The unmanned aerial vehicle cluster trajectory planning method based on edge computation of claim 1, wherein the objective function is expressed as:
；
the constraint conditions of the objective function are as follows:；
wherein ,representing energy expenditure model, +.>() A first penalty function term is represented,/>() Representing a second penalty function term, ">Representing a first penalty factor, ">Representing a second penalty factor, ">Representing the state quantity of the unmanned aerial vehicle, +.>Indicating the control quantity of the unmanned aerial vehicle, +.>=x[0]…x[N]，x[0]State quantity x [ N ] representing initial state]State quantity representing the nth time, +.>State quantity corresponding to take-off time +.>State quantity corresponding to the landing time is represented by +.>Indicating the upper limit of the control amount,/-, and>indicating a lower control amount limit.
6. The unmanned aerial vehicle cluster trajectory planning method based on edge computation of claim 1, wherein the feasible region constraint is expressed as:
；
wherein ,representing feasible region constraints, +.>Representing the position coordinates of the unmanned aerial vehicle, +.>Representing the position coordinates of the obstacle P, P representing the number of obstacles S p Representing a sphere surrounding an obstacle p, r p Represent S p Is set, and the radius of (a) is set.
7. The unmanned aerial vehicle cluster trajectory planning method based on edge computation of claim 1, wherein the coupling collision constraint is expressed as:
；
wherein ,representing coupled crash constraints, +.>Representing the position coordinates of the unmanned aerial vehicle, +.>Representing trajectories of other unmanned aerial vehicles, +.>Representing the position coordinates of other unmanned aerial vehicles, +.>Indicating the set distance.
8. Unmanned aerial vehicle cluster track planning system based on edge calculation, characterized by comprising:
the dynamic equation construction module is used for constructing a dynamic equation of the unmanned plane motion;
the feasible region constraint construction module is used for constructing the feasible region constraint of the unmanned aerial vehicle according to the dynamics equation and the obstacle in the three-dimensional space of the unmanned aerial vehicle;
the coupling collision constraint construction module is used for constructing coupling collision constraint of the unmanned aerial vehicle according to the fact that the unmanned aerial vehicle is kept at a set distance from other unmanned aerial vehicles in the unmanned aerial vehicle cluster;
the energy consumption model construction module is used for discretizing the time period from the take-off time to the landing time of the unmanned aerial vehicle and constructing an energy consumption model from the take-off time to the landing time of the unmanned aerial vehicle;
the objective function construction module is used for adding a first penalty function item and a second penalty function item into the energy consumption model to obtain an objective function of the unmanned aerial vehicle, wherein the first penalty function item is the collision penalty between the unmanned aerial vehicle and an obstacle, and the second penalty function item is the collision penalty of the collision of multiple unmanned aerial vehicles;
the solving module is used for solving the objective function by using the edge computing force of each unmanned aerial vehicle based on the feasible region constraint and the coupling collision constraint through iteration and adopting a penalty function method to obtain a track planning result of the unmanned aerial vehicle cluster; and each unmanned aerial vehicle in the unmanned aerial vehicle cluster performs information sharing through a broadcasting and sharing mechanism.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311068439.3A CN116774735B (en) | 2023-08-24 | 2023-08-24 | Unmanned aerial vehicle cluster track planning method and system based on edge calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311068439.3A CN116774735B (en) | 2023-08-24 | 2023-08-24 | Unmanned aerial vehicle cluster track planning method and system based on edge calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116774735A true CN116774735A (en) | 2023-09-19 |
| CN116774735B CN116774735B (en) | 2023-11-10 |
Family
ID=88012020
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311068439.3A Active CN116774735B (en) | 2023-08-24 | 2023-08-24 | Unmanned aerial vehicle cluster track planning method and system based on edge calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116774735B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117434961A (en) * | 2023-10-13 | 2024-01-23 | 南京航空航天大学 | Heterogeneous unmanned aerial vehicle cluster multitasking allocation and track planning combined optimization method |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200348696A1 (en) * | 2018-07-18 | 2020-11-05 | The Trustees Of The University Of Pennsylvania | Control of multi-drone fleets with temporal logic objectives |
| CN112379693A (en) * | 2020-11-24 | 2021-02-19 | 中国运载火箭技术研究院 | Reentry trajectory optimization method for intelligent parallel Gaussian pseudo-spectral method aircraft |
| US20210165405A1 (en) * | 2019-12-03 | 2021-06-03 | University-Industry Cooperation Group Of Kyung Hee University | Multiple unmanned aerial vehicles navigation optimization method and multiple unmanned aerial vehicles system using the same |
| KR20210069549A (en) * | 2019-12-03 | 2021-06-11 | 경희대학교 산학협력단 | Multiple unmanned aerial vehicles navigation oprimizaition method and multiple unmanned aerial vehicles system using the same |
| US20210255641A1 (en) * | 2019-09-30 | 2021-08-19 | South China University Of Technology | Method for designing three-dimensional trajectory of unmanned aerial vehicle based on wireless power transfer network |
| CN113900449A (en) * | 2021-10-26 | 2022-01-07 | 鹏城实验室 | Multi-unmanned aerial vehicle trajectory planning method and device, unmanned aerial vehicle and storage medium |
| CN114201925A (en) * | 2022-02-17 | 2022-03-18 | 佛山科学技术学院 | Unmanned aerial vehicle cluster cooperative task planning method, electronic equipment and readable storage medium |
| CN114397896A (en) * | 2022-01-10 | 2022-04-26 | 贵州大学 | Dynamic path planning method for improving particle swarm optimization |
| CN114610065A (en) * | 2022-03-15 | 2022-06-10 | 浙江大学湖州研究院 | Graph theory-based cluster unmanned aerial vehicle formation flight trajectory optimization method |
| CN115097857A (en) * | 2022-07-18 | 2022-09-23 | 浙江大学 | Real-time trajectory planning method considering appearance of rotor unmanned aerial vehicle in complex environment |
| US20220390964A1 (en) * | 2021-06-04 | 2022-12-08 | Thomas Andrew Youmans | Cloud & hybrid-cloud flight vehicle & robotic control system ai & ml enabled cloud-based software & data system method for the optimization and distribution of flight control & robotic system solutions and capabilities |
| CN116227338A (en) * | 2022-12-29 | 2023-06-06 | 中国航天空气动力技术研究院 | Variable wing aircraft track optimization method based on self-adaptive sparrow searching pseudo-spectrum method |
| CN116257082A (en) * | 2022-11-03 | 2023-06-13 | 天津大学 | Distributed active cooperative detection method for multiple unmanned aerial vehicles |
| CN116451934A (en) * | 2023-03-16 | 2023-07-18 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle edge calculation path optimization and dependent task scheduling optimization method and system |
-
2023
- 2023-08-24 CN CN202311068439.3A patent/CN116774735B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200348696A1 (en) * | 2018-07-18 | 2020-11-05 | The Trustees Of The University Of Pennsylvania | Control of multi-drone fleets with temporal logic objectives |
| US20210255641A1 (en) * | 2019-09-30 | 2021-08-19 | South China University Of Technology | Method for designing three-dimensional trajectory of unmanned aerial vehicle based on wireless power transfer network |
| US20210165405A1 (en) * | 2019-12-03 | 2021-06-03 | University-Industry Cooperation Group Of Kyung Hee University | Multiple unmanned aerial vehicles navigation optimization method and multiple unmanned aerial vehicles system using the same |
| KR20210069549A (en) * | 2019-12-03 | 2021-06-11 | 경희대학교 산학협력단 | Multiple unmanned aerial vehicles navigation oprimizaition method and multiple unmanned aerial vehicles system using the same |
| CN112379693A (en) * | 2020-11-24 | 2021-02-19 | 中国运载火箭技术研究院 | Reentry trajectory optimization method for intelligent parallel Gaussian pseudo-spectral method aircraft |
| US20220390964A1 (en) * | 2021-06-04 | 2022-12-08 | Thomas Andrew Youmans | Cloud & hybrid-cloud flight vehicle & robotic control system ai & ml enabled cloud-based software & data system method for the optimization and distribution of flight control & robotic system solutions and capabilities |
| CN113900449A (en) * | 2021-10-26 | 2022-01-07 | 鹏城实验室 | Multi-unmanned aerial vehicle trajectory planning method and device, unmanned aerial vehicle and storage medium |
| CN114397896A (en) * | 2022-01-10 | 2022-04-26 | 贵州大学 | Dynamic path planning method for improving particle swarm optimization |
| CN114201925A (en) * | 2022-02-17 | 2022-03-18 | 佛山科学技术学院 | Unmanned aerial vehicle cluster cooperative task planning method, electronic equipment and readable storage medium |
| CN114610065A (en) * | 2022-03-15 | 2022-06-10 | 浙江大学湖州研究院 | Graph theory-based cluster unmanned aerial vehicle formation flight trajectory optimization method |
| CN115097857A (en) * | 2022-07-18 | 2022-09-23 | 浙江大学 | Real-time trajectory planning method considering appearance of rotor unmanned aerial vehicle in complex environment |
| CN116257082A (en) * | 2022-11-03 | 2023-06-13 | 天津大学 | Distributed active cooperative detection method for multiple unmanned aerial vehicles |
| CN116227338A (en) * | 2022-12-29 | 2023-06-06 | 中国航天空气动力技术研究院 | Variable wing aircraft track optimization method based on self-adaptive sparrow searching pseudo-spectrum method |
| CN116451934A (en) * | 2023-03-16 | 2023-07-18 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle edge calculation path optimization and dependent task scheduling optimization method and system |
Non-Patent Citations (5)
| Title |
|---|
| HU GUI ET AL.: "Attitude Control of Spherical Unmanned Aerial Vehicle Based on Active Disturbance Rejection Control", PROCEEDINGS OF THE 34TH CHINESE CONTROL CONFERENCE, pages 1191 - 1195 * |
| 叶光 等: "能量平衡的三维传感器网络重部署算法", 兵工学报, pages 369 - 373 * |
| 吴壮: "基于移动边缘计算的无人机分布式协作", 中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑, no. 6, pages 19 - 21 * |
| 王祝 等: "基于罚函数序列凸规划的多无人机轨迹规划", 航空学报, no. 10, pages 3149 - 3158 * |
| 董超 等: "基于无人机的边缘智能计算研究综述", 智能科学与技术学报, no. 03, pages 227 - 239 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117434961A (en) * | 2023-10-13 | 2024-01-23 | 南京航空航天大学 | Heterogeneous unmanned aerial vehicle cluster multitasking allocation and track planning combined optimization method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116774735B (en) | 2023-11-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108549407B (en) | Control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance | |
| CN108459612B (en) | Unmanned aerial vehicle formation control method and device based on artificial potential field method | |
| Kuriki et al. | Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control | |
| Kreciglowa et al. | Energy efficiency of trajectory generation methods for stop-and-go aerial robot navigation | |
| CN111522258B (en) | Multi-unmanned aerial vehicle cooperative control simulation system and construction method and simulation method thereof | |
| CN110162086A (en) | A kind of cluster unmanned plane formation method based on Model Predictive Control frame | |
| CN110531786B (en) | Unmanned aerial vehicle maneuvering strategy autonomous generation method based on DQN | |
| CN116774735B (en) | Unmanned aerial vehicle cluster track planning method and system based on edge calculation | |
| Xu et al. | Two-layer distributed hybrid affine formation control of networked Euler–Lagrange systems | |
| CN106502250A (en) | The path planning algorithm of multi-robot formation in three dimensions | |
| CN117170410B (en) | Control method for unmanned aerial vehicle formation flight and related products | |
| CN112684705B (en) | Four-rotor aircraft formation tracking control method | |
| Xia et al. | Multi-UAV trajectory planning using gradient-based sequence minimal optimization | |
| CN113848982B (en) | Four-rotor unmanned aerial vehicle amphibious shut-down motion trail planning and tracking control method | |
| CN113900449B (en) | Multi-unmanned aerial vehicle track planning method and device, unmanned aerial vehicle and storage medium | |
| CN113220022A (en) | Flight formation finite time self-adaptive control method based on event trigger estimation information transmission | |
| CN112859915A (en) | High-rise fire-fighting unmanned aerial vehicle formation control method and system based on saturation threshold event triggering | |
| CN115047910A (en) | Unmanned aerial vehicle formation cruise control method based on wild goose-shaped array | |
| Zhao et al. | Four-dimensional trajectory generation for UAVs based on multi-agent Q learning | |
| CN111781945A (en) | Multi-agent formation control method with random time delay | |
| CN112882493B (en) | Cluster cooperative deployment method based on distributed optimal energy MPC | |
| CN114138022A (en) | Distributed formation control method for unmanned aerial vehicle cluster based on elite pigeon swarm intelligence | |
| CN117270572A (en) | Unmanned aerial vehicle cluster bee congestion control method based on hierarchical pinning control theory | |
| CN114415734B (en) | Four-rotor unmanned aerial vehicle formation reconfiguration control system based on ADP and obstacle avoidance mechanism | |
| CN114943168A (en) | Overwater floating bridge combination method and system |
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 |