CN112596549B - Multi-unmanned aerial vehicle formation control method, device and medium based on continuous convex rule - Google Patents
Multi-unmanned aerial vehicle formation control method, device and medium based on continuous convex rule Download PDFInfo
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Abstract
The invention discloses a method, a device and a medium for controlling formation of multiple unmanned aerial vehicles based on a continuous convex rule, wherein the method comprises the following steps: determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation; converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to linearized and discretized unmanned aerial vehicle dynamics constraints; determining a local path plan of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation; and carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model. The invention can decide the corresponding formation according to the specific environment, can ensure the expansibility and stability of the formation quantity, prevents the formation deadlock, and can be widely applied to the technical field of unmanned aerial vehicle control.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a method, a device and a medium for controlling formation of multiple unmanned aerial vehicles based on a continuous convex rule.
Background
Swarm drones may be used to perform various tasks, such as monitoring, inspection, and automation plants. In these cases, drones may be required to perform fleet navigation, such as maintaining a communication network, cooperatively manipulating objects or survey areas. Mobile convoy has many advantages over conventional systems, for example, it may reduce system cost, increase system robustness and efficiency, while providing redundancy, reconfiguration capability and structural flexibility to the system. In many applications, the swarm drones are required to avoid collision with each other and obstacles in the process of arriving at a target point, and meanwhile, the required formation is kept, so that the accuracy, robustness and expansibility of the formation control method are high. In addition, the formation control method needs to take the kinematic constraints of the drones, the mutual coordination among the drones, the uncertain interference of the environment and other limiting conditions into consideration.
At present, the relatively mature and relatively common formation control algorithm has a captain-bureaucratic law, a law based on a behavior and a virtual structure.
The chanio-bureaucratic law. At least one drone plays the role of leader, the remaining drones being designated as followers. The follower tracks the leader's position with some specified offset, and the leader tracks its intended trajectory. The control strategy is characterized by being based on a preset formation structure. The bureaucratic plane is adjusted by tracking the speed, the yaw angle and the height of the longplane, so as to achieve the purpose of keeping the formation.
Based on a behavioral approach. A formation control method simulating a biological reaction type behavior mechanism. During formation flight of multiple drones, there may be 4 cases of behavioral response of each drone in the fleet to its sensor input information: collision avoidance, obstacle avoidance, target acquisition, and formation retention. The method is characterized in that the behavior response mode adopted by each unmanned aerial vehicle in the formation is determined by means of the average weight of the behavior response control.
A virtual structure method. In the virtual structure approach, the entire formation is treated as a single structure. In the virtual structure method, control is divided into three steps: firstly, the desired dynamics of the virtual structure are defined, secondly, the movements of the virtual structure are converted into the phase movements of each drone, and finally, the tracking control of each spacecraft is deduced. The virtual structure method carries out formation control by sharing the state information of the formation virtual structure, can arbitrarily set the formation form, and can realize accurate formation maintenance.
However, the longplane-bureaucratic law has no explicit feedback on formation, e.g. a longplane may move too fast for subsequent drones to track. And if a problem occurs in a long machine, the whole system is crashed. Another weakness is that the robustness is poor and is easily affected by external disturbances, since errors propagate backwards step by step and are amplified by external or other disturbances.
The main drawback of the behavior-based approach is that group behavior, referred to as "appearance", cannot be defined unambiguously, resulting in an inability to achieve accurate formation retention. Another disadvantage is that behavioral methods are difficult to analyze mathematically and characteristics of the formation (e.g., stability) are often not guaranteed.
The virtual structure method is regarded as a rigid virtual structure with the formation of unmanned aerial vehicle, and during the formation flying motion of unmanned aerial vehicles, a single unmanned aerial vehicle individual can be regarded as a fixed position fixed on the virtual structure. However, when the formation passes through a narrow area, the formation cannot pass through the area by expanding or contracting the formation or switching the formation. In addition, the virtual mechanism method requires high communication quality, and is therefore easily restricted by communication.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and a medium for controlling formation of multiple drones based on a continuous convex rule, which are high in stability, and can improve the expansibility of the formation number of drones.
One aspect of the invention provides a multi-unmanned aerial vehicle formation control method based on a continuous convex rule, which comprises the following steps:
determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation;
converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to linearized and discretized unmanned aerial vehicle dynamics constraints;
determining a local path plan of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation;
and carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model.
Preferably, the determining the global path plan of the formation of the unmanned aerial vehicles includes:
determining a plurality of drone positions for the formation of drones, and determining an outer vertex of a center of rotation for the formation of drones;
determining the minimum distance between any pair of unmanned aerial vehicles in the unmanned aerial vehicle formation;
determining the initial configuration of the formation of the unmanned aerial vehicles through isomorphic transformation according to the positions of the unmanned aerial vehicles, the external vertexes and the minimum distance;
and determining the target configuration of the unmanned aerial vehicle formation through a global path planner according to the initial configuration of the unmanned aerial vehicle formation.
Preferably, the determining, by a global path planner, a target configuration of the formation of drones according to the initial configuration of the formation of drones includes:
establishing a polyhedron list, and including all unmanned aerial vehicles in the unmanned aerial vehicle formation in the polyhedron list;
initializing the initial configuration and the target configuration of the unmanned aerial vehicle formation;
initializing the polyhedron list through a convex area;
extracting random samples from the flight space of the formation of unmanned aerial vehicles;
judging whether the random sample is in the barrier or in the polyhedron list, if so, excluding the random sample; otherwise, the following steps are executed:
calculating an obstacle-free convex polyhedron of the random samples through an iterative algorithm;
calculating an inscribed ellipsoid of the barrier-free convex polyhedron;
and determining an action path from a starting point to an end point of the unmanned aerial vehicle formation according to the inscribed ellipsoid.
Preferably, in the step of converting collision avoidance constraints of the formation of the unmanned aerial vehicles into convex constraints according to linearized and discretized unmanned aerial vehicle dynamics constraints, the unmanned aerial vehicle dynamics constraints are:
wherein the content of the first and second substances,
a position vector representing a jth drone; u. ofjA control vector representing a jth drone; b is ═ 03×3 I3×3]T represents time;
the expression of the linearization is:
wherein the content of the first and second substances,
representing a nominal track, namely a track generated in the last iteration;
preferably, the determining a local path plan of the formation of drones according to the convex constraint includes:
generating an initial trajectory for each drone without regard to avoiding collisions;
according to the initial track, iteratively solving the optimal track of the unmanned aerial vehicle;
and determining the optimal tracks of all the unmanned aerial vehicles in the unmanned aerial vehicle formation according to the optimal track of each unmanned aerial vehicle.
The embodiment of the invention also provides a device for controlling formation of multiple unmanned aerial vehicles based on the continuous convex rule, which comprises the following components:
the global planning module is used for determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation;
the conversion module is used for converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to the linearized and discretized unmanned aerial vehicle dynamics constraints;
the local planning module is used for determining the local path planning of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation;
and the tracking control module is used for carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model.
Preferably, the global planning module comprises:
a first determination unit for determining a plurality of drone positions of the formation of drones and determining an outer vertex of a center of rotation of the formation of drones;
a second determining unit, configured to determine a minimum distance between any pair of drones in the formation of drones;
the isomorphic transformation unit is used for determining the initial configuration of the formation of the unmanned aerial vehicles through isomorphic transformation according to the positions of the unmanned aerial vehicles, the external vertexes and the minimum distance;
and the third determining unit is used for determining the target configuration of the formation of the unmanned aerial vehicles through a global path planner according to the initial configuration of the formation of the unmanned aerial vehicles.
Preferably, the third determination unit includes:
the building subunit is used for building a polyhedron list and containing all the unmanned aerial vehicles in the unmanned aerial vehicle formation in the polyhedron list;
the first initialization subunit is used for initializing the initial configuration and the target configuration of the formation of the unmanned aerial vehicles;
the second initialization subunit is used for initializing the polyhedron list through a convex region;
a random sampling subunit, configured to extract random samples in the flight space of the formation of unmanned aerial vehicles;
the judgment subunit is used for judging whether the random sample is in the barrier or in the polyhedron list, and if so, the random sample is excluded; otherwise, the following steps are executed:
a first calculation subunit, configured to calculate an obstacle-free convex polyhedron of the random samples by an iterative algorithm;
the second calculating subunit is used for calculating an inscribed ellipsoid of the barrier-free convex polyhedron;
and the determining subunit is used for determining an action path from the starting point to the end point of the formation of the unmanned aerial vehicles according to the inscribed ellipsoid.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
The embodiment of the invention firstly determines the global path planning of unmanned aerial vehicle formation; then converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to the linearized and discretized unmanned aerial vehicle dynamics constraints; then determining the local path plan of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation; and finally, carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model. The invention can decide the corresponding formation according to the specific environment, can ensure the expansibility and stability of the formation quantity, and can prevent the formation deadlock.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a method for controlling formation of multiple drones based on a continuous convex rule according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Aiming at the problems in the prior art, the invention provides an expandable and efficient multi-unmanned aerial vehicle formation control method based on continuous convex planning, which can be used for solving the problems that the formation of unmanned aerial vehicles in a group is adjusted according to a specific environment, the unmanned aerial vehicles are prevented from colliding with each other, and the unmanned aerial vehicles can move to a target position under the condition that the unmanned aerial vehicles collide with obstacles. According to the method, a group of safe intermediate formation forms from a starting point to a terminal point are calculated for the group of unmanned aerial vehicles through global path planning. Then, on the basis of the dynamic constraint of the linearized and discretized unmanned aerial vehicle and the conversion of the collision avoidance constraint into the convex constraint, local path planning is carried out through continuous convex planning, and meanwhile, the path following of the group unmanned aerial vehicle is realized in real time by combining with model prediction control. The technology belongs to the field of swarm unmanned aerial vehicle formation control, and particularly relates to a swarm unmanned aerial vehicle distributed self-adaptive formation control method based on continuous convex programming.
The method is completely based on distributed collaborative planning, has no long machine, can not cause the breakdown of the whole system when a single unmanned aerial vehicle (or the long machine) has a problem, and has stronger robustness. And the situation that the error is gradually enlarged like the Changji-Liao law can not be caused. Meanwhile, the invention has definite definition on the formation, can decide the corresponding formation according to the specific environment, can ensure the expansibility and stability of formation and prevent the formation from deadlock. The invention aims to realize the effects
As shown in fig. 1, the method of the present invention comprises the steps of:
determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation;
converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to linearized and discretized unmanned aerial vehicle dynamics constraints;
determining a local path plan of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation;
and carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model.
Preferably, the determining the global path plan of the formation of the unmanned aerial vehicles includes:
determining a plurality of drone positions for the formation of drones, and determining an outer vertex of a center of rotation for the formation of drones;
determining the minimum distance between any pair of unmanned aerial vehicles in the unmanned aerial vehicle formation;
determining the initial configuration of the formation of the unmanned aerial vehicles through isomorphic transformation according to the positions of the unmanned aerial vehicles, the external vertexes and the minimum distance;
and determining the target configuration of the unmanned aerial vehicle formation through a global path planner according to the initial configuration of the unmanned aerial vehicle formation.
Preferably, the determining, by a global path planner, a target configuration of the formation of drones according to the initial configuration of the formation of drones includes:
establishing a polyhedron list, and including all unmanned aerial vehicles in the unmanned aerial vehicle formation in the polyhedron list;
initializing the initial configuration and the target configuration of the unmanned aerial vehicle formation;
initializing the polyhedron list through a convex area;
extracting random samples from the flight space of the formation of unmanned aerial vehicles;
judging whether the random sample is in the barrier or in the polyhedron list, if so, excluding the random sample; otherwise, the following steps are executed:
calculating an obstacle-free convex polyhedron of the random samples through an iterative algorithm;
calculating an inscribed ellipsoid of the barrier-free convex polyhedron;
and determining an action path from a starting point to an end point of the unmanned aerial vehicle formation according to the inscribed ellipsoid.
Preferably, in the step of converting collision avoidance constraints of the formation of the unmanned aerial vehicles into convex constraints according to linearized and discretized unmanned aerial vehicle dynamics constraints, the unmanned aerial vehicle dynamics constraints are:
wherein the content of the first and second substances,
a position vector representing a jth drone; u. ofjA control vector representing a jth drone; b is ═ 03×3 I3×3]T represents time;
the expression of the linearization is:
wherein the content of the first and second substances,
representing a nominal track, namely a track generated in the last iteration;
preferably, the determining a local path plan of the formation of drones according to the convex constraint includes:
generating an initial trajectory for each drone without regard to avoiding collisions;
according to the initial track, iteratively solving the optimal track of the unmanned aerial vehicle;
and determining the optimal tracks of all the unmanned aerial vehicles in the unmanned aerial vehicle formation according to the optimal track of each unmanned aerial vehicle.
The following describes the implementation of the method of the present invention in detail:
the invention combines a sampling-based graph search algorithm, samples and connects convex areas in free space, combines sampling and nonlinear optimization, and finds a safe global path. The invention provides an expandable and efficient multi-unmanned aerial vehicle formation control method based on continuous convex planning, which is used for carrying out local path planning through the continuous convex planning on the basis of linear and discretized unmanned aerial vehicle dynamics constraints and converting collision avoidance constraints into convex constraints, and simultaneously realizing path following of unmanned aerial vehicles in groups in real time by combining with model prediction control. The method can be used for solving the problem that the group unmanned aerial vehicles adjust the formation according to the specific environment, avoiding mutual collision among the unmanned aerial vehicles and avoiding the unmanned aerial vehicles from going to the target position under the condition of collision with the obstacle.
The method comprises the following concrete steps:
(1) and global path planning:
the formation is first defined. Each formation f is composed of a series of (n) drone positions
And a series of outer vertices relative to the center of rotation (usually the centroid) of the formation
Given, wherein n isfRepresenting the number of outer vertices defining the formation f. These vertices represent a convex hull of the positions of the drones in the formation, thereby reducing the complexity of the formation with multiple drones. Further using dfRepresenting the minimum distance between any given pair of drones in the formation f. Then a formation is defined through isomorphic transformation, including the size s epsilon R+Translation te ∈ R3And a rotation R (q) described by the unit quaternion q ∈ SO (3), the conjugation of which is defined by
And (4) showing. With this form of definition, the drone formation may be defined entirely by the configuration z ═ t, s, q]∈R3×R+And XSO (3). Given a configuration z and a formation f, the drone position and the outer vertices of the resulting formation may be calculated by:
wherein the rotation matrix in SO (3) is given by quaternion operation as follows:
for formation f and configuration z, embodiments of the invention represent the set of external vertices with the following formula:
in the description of the method, the embodiment of the present invention forms the formation by means of this definition, but the method is general and can be applied to other definitions, and the present invention is not limited to this.
Initial configuration of a given population of drones (z)s) And target configuration (z)g) The global path planner then calculates a feasible path and an intermediate formation that connects them. This is achieved by combining a sampling-based approach with constrained non-linear optimization, the idea being to sample in a low-dimensional space (working space) and then let the optimizer calculate the remaining degrees of freedom. In particular, embodiments of the present invention create a graph in which each node is a feasible formation and contains an initial configuration and a target configuration. Two areThe feasible edges between individual nodes or formations are convex regions in free space that contain two formations. One feasible edge provides a way to transition between two nodes in the graph.
Then a global path planning algorithm is introduced. Considering the barrier-free area F, the starting position s of the formation centroid and the target position g E R3. In this algorithm, the embodiment of the present invention describes the proposed calculation method, with the goal of obtaining a solution that can make the group of drones from the initial configuration zsNavigating to a target configuration gsIs given as the configuration sequence T ═ { z ═ zs,...,zgA global path is composed. The embodiment of the invention establishes an existing list of polyhedrons P, and the group unmanned aerial vehicles can be completely contained in the polyhedrons. Configuring the initial z of the sequence with centroids s and gsAnd a target gsAnd (5) initializing the configuration. Similarly, with convex regions PsAnd PzA list of polyhedrons is initialized, both containing an initial configuration and a target configuration, respectively. The method is carried out by working in the area (for the flight space R of the unmanned plane)3) And extracting random samples. The sampling method is similar to fast search random trees (RRTs). If each random sample p ∈ R3Within an obstacle or within one of the points in list P, it is excluded. Otherwise, executing the following steps:
1. calculating a large barrier-free convex polyhedron P at a sample P by an iterative algorithm through a rapid iteration methodp。
2. Calculating the P internally tangent to the convex polyhedron through semi-definite programmingpMaximum ellipsoid e ofpIf the ellipsoid can keep a safe distance d at all unmanned planesfAnd all drones are stored while maintaining the required formation, the configuration z is then obtainedwThe configuration sequence T is stored. Where w denotes the centre of the formation, i.e. the maximum ellipsoid epThe corresponding center.
3. The sampling is continued and the above steps are repeated until the first feasible path is found, until the whole space F is explored or until a maximum time limit is reached.
4. And returning a configuration sequence T corresponding to the feasible path with the shortest (Euclidean) distance.
(2) Local path planning
If each configuration z corresponding to the sequence obtained by global path planningwGiven sequence numbers in order, i.e. T ═ z1,z2,...,zmThen the local path planning is to solve how to get from zi+1To ziTo a problem of (a). Here, a method of continuous convex programming is used. Continuous convex programming is an iterative method for solving the convex approximation of the non-convex problem, which has the following advantages:
1. continuous convex planning uses multiple iterations to ensure that the convex approximation of the non-convex constraint is accurate, resulting in a more fuel efficient trajectory.
2. A continuous convex programming algorithm may be written using free software (e.g., CVX [30,31]) to convert a convex program to a semi-definite program (SDP) or a Second Order Cone Program (SOCP).
First, the dynamics constraints of the drone are linearized and discretized. The dynamics of the drone are constrained as follows:
wherein the content of the first and second substances,
is the position vector of the jth drone, ujIs the control vector for the jth drone.
In order to rewrite the kinetic constraints as constraints that can be used for the convex programming problem, the equation must first be linearized:
the next step in converting the kinetic constraints into constraints that can be used for convex planning is converting the kinetic constraints into a limited number of algebraic constraints. To this end, the problem is discretized using a zero-order hold method, yielding:
xj[k+1]＝Aj[k]xj[k]+Bj[k]uj[k]+zj[k],k＝k0,...,T-1,j＝1,...,N
wherein x isj[k]＝xj(tk)，uj[k]＝uj(tk) And, and:
the last step in converting unmanned aerial vehicle population reconstruction into a convex planning problem is to convert collision avoidance constraints into convex constraints. The collision avoidance constraints are as follows:
wherein G ═ I3×3 03×3]，RcolIs the minimum distance allowed between two drones.
Since the current form of collision avoidance constraint is concave, the best convex approximation will be an affine constraint. Can be converted into the following formula:
the following was demonstrated:
wherein the content of the first and second substances,
and
is xi，xjThe nominal trajectory of the previous iteration, φ is the angle between the two vectors. These nominal values are assumed to be known and not variables in the optimization. Thus, the new collision avoidance constraint is affine, a form that can be used for convex planning problems.
The goal of optimal cluster reconstruction is to minimize the L1 norm of the control input. The L1 norm of the control input is equal to the total fuel used during the transfer. Thus, embodiments of the invention may define the group reconstruction as follows:
xj[k+1]＝Aj[k]xj[k]+Bj[k]uj[k]+zj[k],k＝k0,...,T-1,j＝1,...,N
||uj[k]||∞≤Umax k＝0,...,T-1,j＝1,...,N
xj[0]＝xj,0,xj[T]＝xj,f j＝1,...,N
an approximation for optimizing the trajectory to a convex program requires a nominal trajectory for each drone
In addition, the nominal trajectory should be close to the actual state trajectory to minimize the approximation error. To ensure that the nominal vector is a good estimate of the actual state vector, continuous convex programming is used. SCP is an iterative method that solves the convex approximation problem of a non-convex problem and uses the solution to convex the problem in the next iteration, i.e.
Where w is the number of iterations of the continuous convex program. This process is repeated until the trajectory sequence converges according to the following condition:
wherein epsilonscpIs a threshold value.
To enforce collision avoidance constraints, each drone communicates its own nominal trajectory to its neighbor drones.
The continuous convex programming steps are as follows. First, an initial trajectory is generated for each agent without regard to avoiding collisions. The iterative process then begins with each drone solving for its optimal trajectory. Next, each drone stores the current trajectory as the nominal trajectory for the next iteration and passes that trajectory to its neighboring drones. Finally, the iteration is repeated until the trajectory converges and the drone has no collision.
Since the convex optimization problem can be solved efficiently, the run time is now about one or two time steps, so model predictive control can be used to achieve continuous convex planning by updating future allocation and control commands based on the state that currently deviates from the optimal trajectory due to unmodeled disturbances or other errors. Model predictive control may provide some robustness to interference and allow distribution and disconnection of communication networks
In summary, compared with the prior art, the method and the device for forming the group unmanned aerial vehicle self-adaptive collaborative formation combine global path planning and local path planning to achieve self-adaptive collaborative formation of the group unmanned aerial vehicle, and the global planning and the local planning are decoupled.
The sampling of the global path planning is the sampling which rises to the formation, and a plurality of sampling points (formation) from the starting point to the end point can be quickly found out by combining the idea of quickly exploring the random tree. And each formation can be adaptively transformed according to the environment, so that different scaled formations are generated.
The local path planning constructs a group reconstruction distributed convex planning problem on the basis of linear and discretized dynamic constraints and convex collision avoidance constraints, and solves the problem through continuous convex planning. Meanwhile, the result model is used for predictive control, so that the formation process can be realized in real time, and the anti-interference performance is improved.
The embodiment of the invention also provides a device for controlling formation of multiple unmanned aerial vehicles based on the continuous convex rule, which comprises the following components:
the global planning module is used for determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation;
the conversion module is used for converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to the linearized and discretized unmanned aerial vehicle dynamics constraints;
the local planning module is used for determining the local path planning of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation;
and the tracking control module is used for carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model.
Preferably, the global planning module comprises:
a first determination unit for determining a plurality of drone positions of the formation of drones and determining an outer vertex of a center of rotation of the formation of drones;
a second determining unit, configured to determine a minimum distance between any pair of drones in the formation of drones;
the isomorphic transformation unit is used for determining the initial configuration of the formation of the unmanned aerial vehicles through isomorphic transformation according to the positions of the unmanned aerial vehicles, the external vertexes and the minimum distance;
and the third determining unit is used for determining the target configuration of the formation of the unmanned aerial vehicles through a global path planner according to the initial configuration of the formation of the unmanned aerial vehicles.
Preferably, the third determination unit includes:
the building subunit is used for building a polyhedron list and containing all the unmanned aerial vehicles in the unmanned aerial vehicle formation in the polyhedron list;
the first initialization subunit is used for initializing the initial configuration and the target configuration of the formation of the unmanned aerial vehicles;
the second initialization subunit is used for initializing the polyhedron list through a convex region;
a random sampling subunit, configured to extract random samples in the flight space of the formation of unmanned aerial vehicles;
the judgment subunit is used for judging whether the random sample is in the barrier or in the polyhedron list, and if so, the random sample is excluded; otherwise, the following steps are executed:
a first calculation subunit, configured to calculate an obstacle-free convex polyhedron of the random samples by an iterative algorithm;
the second calculating subunit is used for calculating an inscribed ellipsoid of the barrier-free convex polyhedron;
and the determining subunit is used for determining an action path from the starting point to the end point of the formation of the unmanned aerial vehicles according to the inscribed ellipsoid.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described in fig. 1.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method shown in fig. 1.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A multi-unmanned aerial vehicle formation control method based on a continuous convex rule is characterized by comprising the following steps:
determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation;
converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to linearized and discretized unmanned aerial vehicle dynamics constraints;
determining a local path plan of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation;
performing real-time path tracking control on the unmanned aerial vehicle formation through a preset model;
according to the linearized and discretized unmanned aerial vehicle dynamics constraint, converting the collision avoidance constraint of the unmanned aerial vehicle formation into a convex constraint, wherein the unmanned aerial vehicle dynamics constraint is as follows:
wherein the content of the first and second substances,
l∈Rna position vector representing a jth drone; u. ofjA control vector representing a jth drone; b is ═ 03×3 I3×3]T represents time;
the expression of the linearization is:
wherein the content of the first and second substances,
representing a nominal track, namely a track generated in the last iteration;
2. the method for controlling formation of multiple unmanned aerial vehicles based on the continuous convex rule according to claim 1, wherein the determining the global path plan of the formation of the unmanned aerial vehicles comprises:
determining a plurality of drone positions for the formation of drones, and determining an outer vertex of a center of rotation for the formation of drones;
determining the minimum distance between any pair of unmanned aerial vehicles in the unmanned aerial vehicle formation;
determining the initial configuration of the formation of the unmanned aerial vehicles through isomorphic transformation according to the positions of the unmanned aerial vehicles, the external vertexes and the minimum distance;
and determining the target configuration of the unmanned aerial vehicle formation through a global path planner according to the initial configuration of the unmanned aerial vehicle formation.
3. The method for controlling formation of multiple drones based on the continuous convex rule according to claim 2, wherein the determining, by a global path planner, the target configuration of the formation of drones according to the initial configuration of the formation of drones comprises:
establishing a polyhedron list, and including all unmanned aerial vehicles in the unmanned aerial vehicle formation in the polyhedron list;
initializing the initial configuration and the target configuration of the unmanned aerial vehicle formation;
initializing the polyhedron list through a convex area;
extracting random samples from the flight space of the formation of unmanned aerial vehicles;
judging whether the random sample is in the barrier or in the polyhedron list, if so, excluding the random sample; otherwise, the following steps are executed:
calculating an obstacle-free convex polyhedron of the random samples through an iterative algorithm;
calculating an inscribed ellipsoid of the barrier-free convex polyhedron;
and determining an action path from a starting point to an end point of the unmanned aerial vehicle formation according to the inscribed ellipsoid.
4. The method for controlling formation of multiple drones based on the continuous convex rule according to claim 1, wherein the determining the local path plan of the formation of drones according to the convex constraint comprises:
generating an initial trajectory for each drone without regard to avoiding collisions;
according to the initial track, iteratively solving the optimal track of the unmanned aerial vehicle;
and determining the optimal tracks of all the unmanned aerial vehicles in the unmanned aerial vehicle formation according to the optimal track of each unmanned aerial vehicle.
5. The utility model provides a many unmanned aerial vehicle formation controlling means based on protruding rule in succession which characterized in that includes:
the global planning module is used for determining global path planning of unmanned aerial vehicle formation; the global path planning is used for determining an action path from a starting point to an end point of the unmanned aerial vehicle formation and a formation;
the conversion module is used for converting collision avoidance constraints of the unmanned aerial vehicle formation into convex constraints according to the linearized and discretized unmanned aerial vehicle dynamics constraints;
the local planning module is used for determining the local path planning of the unmanned aerial vehicle formation according to the convex constraint; the local path plan is used for determining the action track of each unmanned aerial vehicle in the unmanned aerial vehicle formation;
the tracking control module is used for carrying out real-time path tracking control on the unmanned aerial vehicle formation through a preset model;
in the conversion module, the unmanned aerial vehicle dynamics constraint is as follows:
wherein the content of the first and second substances,
l∈Rna position vector representing a jth drone; u. ofjA control vector representing a jth drone; b is ═ 03×3 I3×3]T represents time;
the expression of the linearization is:
wherein the content of the first and second substances,
representing a nominal track, namely a track generated in the last iteration;
6. the sequential convex rule-based multi-UAV formation control device according to claim 5, wherein the global planning module comprises:
a first determination unit for determining a plurality of drone positions of the formation of drones and determining an outer vertex of a center of rotation of the formation of drones;
a second determining unit, configured to determine a minimum distance between any pair of drones in the formation of drones;
the isomorphic transformation unit is used for determining the initial configuration of the formation of the unmanned aerial vehicles through isomorphic transformation according to the positions of the unmanned aerial vehicles, the external vertexes and the minimum distance;
and the third determining unit is used for determining the target configuration of the formation of the unmanned aerial vehicles through a global path planner according to the initial configuration of the formation of the unmanned aerial vehicles.
7. The sequential convex rule-based multi-UAV formation control device according to claim 6, wherein the third determination unit comprises:
the building subunit is used for building a polyhedron list and containing all the unmanned aerial vehicles in the unmanned aerial vehicle formation in the polyhedron list;
the first initialization subunit is used for initializing the initial configuration and the target configuration of the formation of the unmanned aerial vehicles;
the second initialization subunit is used for initializing the polyhedron list through a convex region;
a random sampling subunit, configured to extract random samples in the flight space of the formation of unmanned aerial vehicles;
the judgment subunit is used for judging whether the random sample is in the barrier or in the polyhedron list, and if so, the random sample is excluded; otherwise, the following steps are executed:
a first calculation subunit, configured to calculate an obstacle-free convex polyhedron of the random samples by an iterative algorithm;
the second calculating subunit is used for calculating an inscribed ellipsoid of the barrier-free convex polyhedron;
and the determining subunit is used for determining an action path from the starting point to the end point of the formation of the unmanned aerial vehicles according to the inscribed ellipsoid.
8. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method according to any one of claims 1-4.
9. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-4.
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| CN113485418B (en) * | 2021-07-02 | 2022-07-05 | 西北工业大学 | Flexible rope system constraint multi-robot track generation method |
| CN113566826B (en) * | 2021-07-08 | 2023-11-07 | 北京航空航天大学 | Unmanned aerial vehicle path planning method based on quadratic programming |
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